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International Journal of Turbomachinery Propulsion and Power Article Component-Speciﬁc Preliminary Engine Design Taking into Account Holistic Design Aspects 1 , 1 1 2 Marco Hendler *, Michael Lockan , Dieter Bestle and Peter Flassig Department of Engineering Mechanics and Vehicle Dynamics, Brandenburg University of Technology, 03046 Cottbus, Germany; [email protected] (M.L.); [email protected] (D.B.) Rolls-Royce Deutschland Ltd. & Co KG, 15827 Blankenfelde-Mahlow, Germany; [email protected] * Correspondence: [email protected]; Tel.: +49-355-69-5148 † This paper is an extended version of our paper published in Proceedings of the 17th International Symposium on Transport Phenomena and Dynamics of Rotating Machinery (ISROMAC 2017). Received: 22 January 2018; Accepted: 23 April 2018; Published: 27 April 2018 Abstract: Efﬁcient aero engine operation requires not only optimized components like compressor, combustor, and turbine, but also an optimal balance between these components. Therefore, a holistic coupled optimization of the whole engine involving all relevant components would be advisable. Due to its high complexity and wide variety of design parameters, however, such an approach is not feasible, which is why today’s aero engine design process is typically split into different component-speciﬁc optimization sub-processes. To guarantee the ﬁnal functionality, components are coupled by ﬁxed aerodynamic and thermodynamic interface parameters predeﬁned by simpliﬁed performance calculations early in the design process and held constant for all further design steps. In order not to miss the optimization potential of variable interface parameters and the unlimited design space on higher-ﬁdelity design levels, different coupling and optimization strategies are investigated and demonstrated for a reduced compressor-combustor test case problem by use of 1D and 2D aero design tools. The new holistic design approach enables an exchange of information between components on a higher-ﬁdelity design level than just simple thermodynamic equations, as well as the persecution of global engine design objectives like efﬁciency or emissions, and provides better results than separated component design with ﬁxed interfaces. Keywords: holistic optimization; aero engines; aerodynamic design 1. Introduction Aero engine development is associated with both time and ﬁnancial effort. To minimize development costs as well as risk, engine projects are often realized as divided design processes in cooperation with other companies (joint venture) or as redesign based on established aero engines [1]. A major advantage of dividing tasks is the applicability of a modular design strategy, in which each component (fan, compressor, combustor, and turbine) is designed independently from the others [2,3]. The component-based consideration reduces the high complexity of the overall system and splits the analysis effort into more simple tasks ﬁtting to the limited computational power available [4]. For the same reasons, component design is split into different design phases with an increasing degree of ﬁdelity—starting with a simple and fast performance calculation and leading up to a time-consuming 3D unsteady ﬂow simulation [5–7]. To guarantee the ﬁnal functionality after assembling all individually designed modules, interfaces between the components have to be pre-determined in such a sequential, multi-stage design process. Typically, they are deﬁned early during the performance calculation based on rather simple thermodynamic models and remain constant in later higher-ﬁdelity design phases. Int. J. Turbomach. Propuls. Power 2018, 3, 12; doi:10.3390/ijtpp3020012 www.mdpi.com/journal/ijtpp Int. J. Turbomach. Propuls. Power 2018, 3, 12 2 of 16 Hence, a holistic analysis of the overall engine is realized only in the very ﬁrst design step. The subsequent, higher-ﬁdelity autonomous component design processes in the more detailed design phase interpret these prescribed interfaces as ﬁxed bounds and try to ﬁnd their best results within these bounds. As the performance calculation in the ﬁrst step does not consider geometric engine speciﬁcations, it may be expected that the interface state selection based on low-ﬁdelity performance models is not necessarily optimal, which ﬁnally leads to non-optimal overall engine performance. Instead, the early ﬁxing of interface states results in an unnecessary limitation of the design space for higher-ﬁdelity and more precise component design tools, which would be able to better evaluate the effect of the variations of the interface states and adapt them to ﬁnd an overall optimal engine. This raises the question of how much potential is lost in current engine design projects by keeping the interface states ﬁxed instead of allowing for modiﬁcation by higher-ﬁdelity tools during the component design process. Some beneﬁts of a holistic design strategy have been shown in [8,9], where decoupled design problems with complex interfaces are solved for aircrafts. For aero engines, holistic design is typically only performed with low-ﬁdelity strategies like scaling based on thermodynamic cycle information [10–12]. In the current paper, an alternative design approach is presented, in which components of a two-shaft aero engine are designed simultaneously by using design tools with higher ﬁdelity than just a performance calculation and in which variable interfaces are controlled by optimizing whole engine targets. For simpliﬁcation, the concept is described along a test case consisting of only two core engine modules, the high pressure compressor (C) and the combustor (B), where analysis is based on 1D Meanline and 2D Throughﬂow Rolls-Royce in-house codes for the compressor and a response surface model for the combustor. The proposed concept, however, is not restricted to this situation but may also be fully based on adaptive response surfaces as described in [13] or include computational ﬂuid dynamics (CFD) analysis. It should be noted that the design case focuses on engine design point conditions so that the main performance requirements like work level and shaft speed, as well as external boundary conditions like inlet airﬂow, overall compressor pressure ratio, and environmental parameters, are held constant. The remainder of the paper is organized as follows: In Section 2, an extended compressor part is optimized as reference solution by following the concept in [5] with ﬁxed interface conditions to the downstream combustor component. In order to enable a coupling of compressor design with the combustor feedback and allow whole engine optimization, an isolated design problem for the combustor is formulated in Section 3. For holistic design, two different approaches are described in Section 4, which are then applied to the test case and compared in Section 5. 2. Compressor Design Process The compressor module of a two-shaft aero engine consists of three main parts, as shown in Figure 1. The S-duct (SD) as ﬁrst subcomponent transfers the incoming ﬂow of the low pressure system (fan) to the faster rotating, high-pressure compressor (HPC) moving on a lower radius. Downstream of the S-duct, the bladed annulus section of the (HPC) feeds power to the ﬂuid for compression, and the annulus cross section decreases substantially. Finally, the short diffusor section (PD) reduces the ﬂow speed and directs the air mass ﬂow into the combustion chamber. To design the complete compressor section from S-duct inlet to diffusor outlet, a two-step approach is used here based on a combination of 1D Meanline and 2D Throughﬂow calculations to allow for the testing of a huge number of conﬁgurations. However, also higher-ﬁdelity calculations as 3D CFD may be incorporated depending on limitations of computational resources. The objective of the compressor design sub-process is to search for a geometry conﬁguration with best polytropic compressor efﬁciency (k1) log p h = (1) t3 log t2 Int. J. Turbomach. Propuls. Power 2018, 3, 12 3 of 16 Int. J. Turbomach. Propuls. Power 2018, 3, x FOR PEER REVIEW 3 of 16 r relating com elating compr pressor pr essor pressure r essure ratio atio p to to tota total l iinlet nlet a and nd outl outlet et temp temperatur eratures es T an and d T by by speciﬁc specific C t2 t3 heat [2]. heat k [2]. Figure Figure 1. 1. Com Compr pressor annu essor annulus lus param parametrization etrization strateg strategy y based on pertu based on perturbation rbation spline splinessd r ((xe )) H PC H PC r resu esulting lting in m in modiﬁcation odification of th of the e hu hub b contou contour r spline (red spline (red line) line) for S-du for S-duct ct (S (SD), D), blad bladed ed annu annulus lus secti section on (HPC) and diffusor. (HPC) and diffusor. In the first design step, the optimizer tries to find a valid compressor part from inlet guide vane In the ﬁrst design step, the optimizer tries to ﬁnd a valid compressor part from inlet guide vane (IGV) to outlet guide vane (OGV), i.e., in the HPC section, with the help of the 1D Meanline code (IGV) to outlet guide vane (OGV), i.e., in the HPC section, with the help of the 1D Meanline code only. Therefore, different design parameters summarized in design vector concerning annulus only. Therefore, different design parameters summarized in design vector p concerning annulus shape, stage pressure ratio, exit flow angles, and axial chord lengths are modified. The flow shape, stage pressure ratio, exit ﬂow angles, and axial chord lengths are modiﬁed. The ﬂow evaluation evaluation takes place only at the annulus midline and ignores adjoining upstream and downstream takes place only at the annulus midline and ignores adjoining upstream and downstream compressor compressor components; compare [5]. components; compare [5]. To receive more detailed information, a 2D streamline curvature analysis called Throughflow is To receive more detailed information, a 2D streamline curvature analysis called Throughﬂow is executed for each valid Meanline compressor design. This analysis starts from the flow information executed for each valid Meanline compressor design. This analysis starts from the ﬂow information of the preceding 1D calculation and is able to evaluate the complete compressor geometry including of the preceding 1D calculation and is able to evaluate the complete compressor geometry including S-duct and diffusor as shown in Figure 1. Here, flow information is determined in radial direction on S-duct and diffusor as shown in Figure 1. Here, ﬂow information is determined in radial direction different streamlines. The final information of the Throughflow calculation could then be used to on different streamlines. The ﬁnal information of the Throughﬂow calculation could then be used to generate aerofoils [14] and to build a 3D compressor geometry model for executing stress analyses or generate aerofoils [14] and to build a 3D compressor geometry model for executing stress analyses or a a multistage 3D-CFD. multistage 3D-CFD. Only with such an embedded compressor calculation taking S-duct and diffusor into account, Only with such an embedded compressor calculation taking S-duct and diffusor into account, the relevant compressor interface values at the diffusor exit position can be determined properly. the relevant compressor interface values at the diffusor exit position can be determined properly. Relevant interface parameters between compressor and the downstream combustor component are Relevant interface parameters between compressor and the downstream combustor component are exit exit flow angle , exit flow Mach number , exit temperature , and exit pressure ﬂow angle a , exit ﬂow Mach number Ma , exit temperature T , and exit pressure p summarized in ex 30 30 30 summarized in coupling vector = , 4, , ∈ℝ ; see also Figure 2. They result from coupling vector y = a , Ma , T , p 2 R ; see also Figure 2. They result from the compressor [ ] ex 30 30 30 CjB the compressor flow analysis, which is why their compliance with combustor specifications cannot ﬂow analysis, which is why their compliance with combustor speciﬁcations cannot be guaranteed be guaranteed in advance, but must be enforced during the optimization process. This can be in advance, but must be enforced during the optimization process. This can be achieved either in a achieved either in a classical way by equating them with prescribed values from the performance classical way by equating them with prescribed values from the performance calculation, or by using calculation, or by using them as flexible interface variables in a global engine optimization problem them as ﬂexible interface variables in a global engine optimization problem as proposed in Section 4. as proposed in Section 4. The same applies to the geometric parameters = , ∈ℝ in The same applies to the geometric parameters p = [r , h ] 2 R in Figure 2, which have to be CjB PD PD Figure 2, which have to be identical for both components (i.e., compressor exit and combustor inlet). identical for both components (i.e., compressor exit and combustor inlet). To ensure a valid compressor design, various constraints limiting diffusion factors, de Haller To ensure a valid compressor design, various constraints limiting diffusion factors, de Haller numbers, stage works, inlet Mach numbers, or surge margin according to [5] have to be considered. numbers, stage works, inlet Mach numbers, or surge margin according to [5] have to be considered. All these compressor-specific limits are summarized in inequality constraints ≤ ∈ℝ , which leads to the single objective compressor optimization problem | ( ) max .. = ∈ ℝ ≤, ≤ ≤ (2) Int. J. Turbomach. Propuls. Power 2018, 3, 12 4 of 16 All these compressor-speciﬁc limits are summarized in inequality constraints h 0 2 R , which leads to the single objective compressor optimization problem n o 47 l u Int. J. Turbomach. Propuls. Power 2018, 3, x FOR PEER REVIEW 4 of 16 max h s.t. P = p 2 R h p 0, p p p (2) ( ) C C C C C C C C p 2P C C with lower and upper bounds and on the design vector defined in the following. l u with lower and upper bounds p and p on the design vector p deﬁned in the following. C C The biggest influence on finding a valid compressor geometry can be attributed to design The biggest inﬂuence on ﬁnding a valid compressor geometry can be attributed to design parameters effecting the inner and outer annulus contour represented by two B-spline curves. They parameters effecting the inner and outer annulus contour represented by two B-spline curves. They are are based on a reference compressor contour, which, in a first design step, is scaled to the inlet and based on a reference compressor contour, which, in a ﬁrst design step, is scaled to the inlet and outlet of the actual compressor, and then the scaled control points are additionally modified within outlet of the actual compressor, and then the scaled control points are additionally modiﬁed within user-defined margins by the optimizer, see, e.g., lower annulus contour in Figure 1. To ensure the user-deﬁned margins by the optimizer, see, e.g., lower annulus contour in Figure 1. To ensure the highest possible level of flexibility independently of the number of control points used for the highest possible level of ﬂexibility independently of the number of control points used for the contour contour lines, their variation is controlled by separate perturbation splines for hub ( ) and tip ( ) lines, their variation is controlled by separate perturbation splines for hub (h) and tip (t) with a constant with a constant number of control points being lower than those of the reference contour. An number of control points being lower than those of the reference contour. An example for such an HPC example for such an HPC hub section perturbation spline is given in the lower part of Figure 1. hub section perturbation spline is given in the lower part of Figure 1. Control points of the annulus Control points of the annulus splines are not modified directly by the optimizer, but rather by splines are not modiﬁed directly by the optimizer, but rather by changes of the perturbation splines changes of the perturbation splines whose control points are actively modified as part of the design whose control points are actively modiﬁed as part of the design vector p in Equation (2). In order to vector in Equation (2). In order to reduce the dimension of the design space, perturbation reduce the dimension of the design space, perturbation information is only applied in radial direction information is only applied in radial direction by of the hub control points, where axial by dr of the hub control points, where axial variations may be applied analogously if more general variations may be applied analogously if more general modifications of the compressor geometry modiﬁcations of the compressor geometry are desired. are desired. Figure 2. Geometric and aerodynamic compressor and combustor interface parameters: with exit Figure 2. Geometric and aerodynamic compressor and combustor interface parameters: with exit ﬂow flow angle , exit flow Mach number , exit temperature , exit pressure , diffusor exit angle a , exit ﬂow Mach number Ma , exit temperature T , exit pressure p , diffusor exit height ex 30 30 30 height and diffusor exit mid-height radius . h and diffusor exit mid-height radius r . PD PD Similar procedures are applied to S-duct and diffusor, where the three parts of the contour line Similar procedures are applied to S-duct and diffusor, where the three parts of the contour line are treated separately to allow for more individual design flexibility [15]. To guarantee an overall are treated separately to allow for more individual design ﬂexibility [15]. To guarantee an overall continuous perturbation spline, the transition control points to neighboring compressor parts are continuous perturbation spline, the transition control points to neighboring compressor parts are identical on both sides, respectively. The outer perturbation control points of the diffusor identical on both sides, respectively. The outer perturbation control points of the diffusor perturbation perturbation section are set to meet the predefined coordinates, which are either fixed or set by the section are set to meet the predeﬁned coordinates, which are either ﬁxed or set by the interface variables. interface variables. Special attention is payed to the properties of the diffusor part: it acts as adapter between Special attention is payed to the properties of the diffusor part: it acts as adapter between compressor and combustor component with the task of converting dynamic pressure into static compressor and combustor component with the task of converting dynamic pressure into static pressure and ensuring a low-loss inﬂow into the combustor section [16]. The exit ﬂow of this component pressure and ensuring a low-loss inflow into the combustor section [16]. The exit flow of this has signiﬁcant inﬂuence on the ﬂow of the combustor dome and on the air distribution inside the component has significant influence on the flow of the combustor dome and on the air distribution combustor. The large number of free contour variables in this section allows for the adjustment of inside the combustor. The large number of free contour variables in this section allows for the different exit ﬂow angles, even for constant diffusor exit coordinates. adjustment of different exit flow angles, even for constant diffusor exit coordinates. The presented parametrization strategy allows for the introduction of an additional design The presented parametrization strategy allows for the introduction of an additional design parameter, which varies the inﬂuential length ratio l /l between the bladed compressor part HPC PD parameter, which varies the influential length ratio / between the bladed compressor part l = x x and the diffusor l = l x , where the overall compressor length l is HPC HPC SD PD C HPC C = − and the diffusor = − , where the overall compressor length is fixed, as well as the swan neck duct length . This complicates the optimization effort on the one hand, but increases the design space on the other. The additional parameter may help to find a valid compressor design faster or to find more efficient design configurations. Some relevant geometric changes caused by perturbation of some design parameters in vector are demonstrated in Figure 3, causing geometry modifications of annulus outline, diffusor slope, length ratio, radial exit height, or axial chord lengths. Int. J. Turbomach. Propuls. Power 2018, 3, x FOR PEER REVIEW 5 of 16 For fixed coupling conditions , , as this would be the case in classical isolated | | component design processes, the extended compressor design process creates more realistic flow results than optimization of the bladed part only. Figure 4 displays a 2D flow field of an optimized Int. J. Turbomach. Propuls. Power 2018, 3, 12 5 of 16 compressor geometry, in which the absolute Mach number in the S-duct geometry is distributed rather homogenously with no local peaks; the bladed section shows the typically desired intensive ﬁxed, as well as the swan neck duct length x . This complicates the optimization effort on the one SD stressing of the hub profiles of the first rotor stations. The consideration of 36 compressor specific hand, but increases the design space on the other. The additional parameter may help to ﬁnd a valid constraints in ≤ ensures the aspired, radially uniform outflow at the last high pressure compressor design faster or to ﬁnd more efﬁcient design conﬁgurations. Some relevant geometric compressor stator (i.e., OGV), a minimum annulus height for minimal flow blockage, and a diffusor changes caused by perturbation of some design parameters in vector p are demonstrated in Figure 3, geometry preventing flow separation. The last mentioned property is evaluated by a quality factor causing geometry modiﬁcations of annulus outline, diffusor slope, length ratio, radial exit height, checking the inlet to outlet area ratio of the diffusor and comparing it to empirically determined Int. J. Turbomach. Propuls. Power 2018, 3, x FOR PEER REVIEW 5 of 16 or axial chord lengths. limits. For fixed coupling conditions , , as this would be the case in classical isolated | | component design processes, the extended compressor design process creates more realistic flow results than optimization of the bladed part only. Figure 4 displays a 2D flow field of an optimized compressor geometry, in which the absolute Mach number in the S-duct geometry is distributed rather homogenously with no local peaks; the bladed section shows the typically desired intensive stressing of the hub profiles of the first rotor stations. The consideration of 36 compressor specific constraints in ≤ ensures the aspired, radially uniform outflow at the last high pressure compressor stator (i.e., OGV), a minimum annulus height for minimal flow blockage, and a diffusor geometry preventing flow separation. The last mentioned property is evaluated by a quality factor checking the inlet to outlet area ratio of the diffusor and comparing it to empirically determined limits. Figure 3. Possible annulus and aero block variations for fixed interface parameters and Figure 3. Possible annulus and aero block variations for ﬁxed interface parameters p and comparison CjB comparison with a reference design (dashed line). with a reference design (dashed line). For ﬁxed coupling conditions y , p , as this would be the case in classical isolated CjB CjB component design processes, the extended compressor design process creates more realistic ﬂow results than optimization of the bladed part only. Figure 4 displays a 2D ﬂow ﬁeld of an optimized compressor geometry, in which the absolute Mach number in the S-duct geometry is distributed rather homogenously with no local peaks; the bladed section shows the typically desired intensive stressing of the hub proﬁles of the ﬁrst rotor stations. The consideration of 36 compressor speciﬁc constraints in h 0 ensures the aspired, radially uniform outﬂow at the last high pressure compressor stator (i.e., OGV), a minimum annulus height for minimal ﬂow blockage, and a diffusor geometry preventing Figure 3. Possible annulus and aero block variations for fixed interface parameters and ﬂow separation. The last mentioned property is evaluated by a quality factor checking the | inlet to comparison with a reference design (dashed line). outlet area ratio of the diffusor and comparing it to empirically determined limits. Figure 4. Axisymmetric meridional flow field of an optimized compressor geometry predicted by Throughflow: (a) absolute Mach number contour plot = (,) and (b) de Haller criterion = ( ̃, ̃) for the bladed part with respect to normalized axial and radial coordinates. 3. Combustor Design Problem An isolated component optimization such as the example presented above cannot be guaranteed to receive best overall engine performance, because the optimization potential there lies in the modification of local design parameters only. To expand the design diversity and to find a globally optimal aero engine configuration, it is necessary to take into account the requirements of neighboring components. For the current test case, combustor design aspects will be incorporated into the compressor design process. Figure 4. Axisymmetric meridional ﬂow ﬁeld of an optimized compressor geometry predicted by Figure 4. Axisymmetric meridional flow field of an optimized compressor geometry predicted by Throughﬂow: (a) absolute Mach number contour plot Ma = Ma x, r and (b) de Haller criterion ( ) Throughflow: (a) absolute Mach number contour plot = (,) and (b) de Haller criterion DH = DH(xe,er) for the bladed part with respect to normalized axial and radial coordinates. = ( ̃, ̃) for the bladed part with respect to normalized axial and radial coordinates. 3. Combustor Design Problem An isolated component optimization such as the example presented above cannot be guaranteed to receive best overall engine performance, because the optimization potential there lies in the modification of local design parameters only. To expand the design diversity and to find a globally optimal aero engine configuration, it is necessary to take into account the requirements of neighboring components. For the current test case, combustor design aspects will be incorporated into the compressor design process. Int. J. Turbomach. Propuls. Power 2018, 3, 12 6 of 16 3. Combustor Design Problem An isolated component optimization such as the example presented above cannot be guaranteed to receive best overall engine performance, because the optimization potential there lies in the modiﬁcation of local design parameters only. To expand the design diversity and to ﬁnd a globally optimal aero engine conﬁguration, it is necessary to take into account the requirements of neighboring components. For the current test case, combustor design aspects will be incorporated into the compressor design process. A simple process extension by neighboring component analyses will not be sufﬁcient because of the ﬁxed interfaces. For example, if the compressor parameters are modiﬁed and the combustor module is only analyzed without changing its local design parameters, the combustor will always contribute in the same way to the overall engine behavior. Therefore, interface values have to be considered as ﬂexible and adaptable for higher-ﬁdelity design processes, as this allows for a direct estimation of the compressor effects on the combustor module and ﬁnally on the overall design objectives. In contrast, for the current test case without turbine, the combustor exit station has to be considered as a global system boundary where aerodynamic and geometric parameters are regarded as constraints. The geometric information at the combustor-turbine interface is scaled from a valid reference design. Other important combustor operating parameters (e.g., fuel mass ﬂow and relight sizing) are also given by the performance calculation and held constant. Because of the signiﬁcant inﬂuence of the compressor-combustor interface parameters on the combustor performance, a parametrized combustor design process is needed that is able to recalculate the combustor geometry and internal ﬂow in every iteration loop. A fast state-of-the-art preliminary Rolls-Royce inhouse design tool is used that fulﬁlls all necessary requirements regarding accuracy and ﬂexibility. The tool uses internal design rules and correlations to provide all relevant aerodynamic ﬂow parameters, as well as a 2D combustor geometry, as shown in Figure 5. Key element of the tool is an internal ﬂow solver adjusting the main geometric components within the given boundary conditions until, e.g., combustion stability, relight sizing, and desired Mach number distributions are fulﬁlled. Main objectives for the combustor design are to minimize the emissions and to maximize its efﬁciency h based on empirical correlations in which different aerodynamic and geometric combustor parameters are taken into account. The main emissions of carbon monoxide m , nitrogen oxides CO m , and unburned hydrocarbons m are summarized in the emission index NOx UHC m + m + m CO NO UHC E = (3) Fuel which equals the sum of the single emission values divided by the mass of injected fuel [17]. In order to inﬂuence the combustor outline and to optimize the efﬁciency and emission index, ﬂow settings concerning the internal air distribution will be varied. Relevant design parameters are the air-to-fuel-ratios at injector r and primary zone exit r , length of primary and secondary a f ,in a f ,pz combustion zones represented by the relative position of the related mixing ports (g , g ), and the pp sp style of the primary and secondary mixing ports (s , s ) similar to [18]. The air-to-fuel ratios are pp sp related to the local mass ﬂows at the injector, as well as primary zone exit, and are normalized by the fuel mass ﬂow. They are the driving parameters for the inner combustor air distribution. For a constant fuel mass ﬂow, the air-to-fuel ratios affect the air ﬂow distribution between mass ﬂow passing the injector and the one ﬂowing through the outer and inner annulus, see Figure 5. As already indicated, the position of the mixing ports is related to the length of the combustion zones. The length and volume of each combustion zone has a signiﬁcant inﬂuence on the residence time of the air-fuel-mixture with a direct effect on the resulting emissions. For example, if the ﬁrst mixing port is located further downstream, the hot primary zone is very distinctive, and the residence time is high. This favors the relight ability and the fuel burn-out, and reduces CO generation. But for lower NO emissions it would be better to have only a small primary section and to leave the hot zone as quickly Int. J. Turbomach. Propuls. Power 2018, 3, x FOR PEER REVIEW 6 of 16 A simple process extension by neighboring component analyses will not be sufficient because of the fixed interfaces. For example, if the compressor parameters are modified and the combustor module is only analyzed without changing its local design parameters, the combustor will always contribute in the same way to the overall engine behavior. Therefore, interface values have to be considered as flexible and adaptable for higher-fidelity design processes, as this allows for a direct estimation of the compressor effects on the combustor module and finally on the overall design objectives. In contrast, for the current test case without turbine, the combustor exit station has to be considered as a global system boundary where aerodynamic and geometric parameters are regarded as constraints. The geometric information at the combustor-turbine interface is scaled from a valid reference design. Other important combustor operating parameters (e.g., fuel mass flow and relight sizing) are also given by the performance calculation and held constant. Because of the significant influence of the compressor-combustor interface parameters on the combustor performance, a parametrized combustor design process is needed that is able to recalculate the combustor geometry and internal flow in every iteration loop. A fast state-of-the-art preliminary Rolls-Royce inhouse design tool is used that fulfills all necessary requirements regarding accuracy and flexibility. The tool uses internal design rules and correlations to provide all relevant aerodynamic flow parameters, as well as a 2D combustor geometry, as shown in Figure 5. Key element of the tool is an internal flow solver adjusting the main geometric components within the given boundary conditions until, e.g., combustion stability, relight sizing, and desired Mach number distributions are fulfilled. Main objectives for the combustor design are to minimize the emissions and to maximize its efficiency based on empirical correlations in which different aerodynamic and geometric combustor parameters are taken into account. The main emissions of carbon monoxide , nitrogen oxides , and unburned hydrocarbons are summarized in the emission index (3) which equals the sum of the single emission values divided by the mass of injected fuel [17]. In order to influence the combustor outline and to optimize the efficiency and emission index, flow settings concerning the internal air distribution will be varied. Relevant design parameters are the air-to-fuel-ratios at injector and primary zone exit , length of primary and secondary , , Int. J. Turbomach. Propuls. Power 2018, 3, 12 7 of 16 combustion zones represented by the relative position of the related mixing ports ( , ), and the style of the primary and secondary mixing ports ( , ) similar to [18]. The air-to-fuel ratios are related to the local mass flows at the injector, as well as primary zone exit, and are normalized by the as possible. Hence, with the variation of the mixing port position, the location can be deﬁned where fuel mass flow. They are the driving parameters for the inner combustor air distribution. cooling air is fed, and also the kind of emissions that are generated can be deﬁned. Figure 5. Combustor model with overlapping interface area to the compressor and following design Figure 5. Combustor model with overlapping interface area to the compressor and following design parameters: air-to-fuel-ratios at parameters: air-to-fuel-ratios at injector injector r and primary and primary zone exit zone exit r , relative , relative position posit of primary ion of a f ,in , a f ,pz , mixing port g and secondary mixing port g , and style of primary mixing port s and secondary primary mixing pp port and secondary mixing sp port , and style of primary mixing port pp and mixing port s . secondary mixsip ng port . The style of the mixing port deﬁnes the jet inclination angle and thus inﬂuences the jet penetration. Here, two different port styles can be selected, which are represented by logical parameters s , pp s 2 f0, 1g. To get a plain mixing port style, which implicates a lower jet inclination angle, s = 0 sp must be selected. In contrast, s = 1 leads to a higher jet inclination angle. The ﬁnal design vector results in h i p = r , r , g , g , s , s . (4) pp sp pp sp B a f ,in a f ,pz Because of the high inﬂuence of the zonal combustor volumes on emissions and efﬁciency, they are taken into account by three inequality constraints h (p ) 0 during the design process B B based on internal design rules. The given constraints guarantee a combustor conﬁguration which fulﬁlls all International Civil Aviation Organization (ICAO) admission requirements. Thus, the isolated combustor optimization problem reads as Due to the mixing port parameters in design vector p , Equation (5) is a mixed-variable optimization problem that requires special strategies. However, this may also be handled by a bound-and-cut type strategy, in which the optimizer works with continuous variables h i s , s 2 [0, 1] R instead of discrete variables s and s . For analyses, these real values pp sp pp sp are rounded as 0 f or 0.0 s 0.5 s = (6) 1 f or 0.5 < s 1.0 to select the respective mixing port styles. 4. Holistic Design Strategy The consideration of several components in a coupled design process implicates a number of advantages, but speciﬁc challenges as well. For instance, impacts on downstream components by local geometry variation in upstream components can be evaluated directly, and the ﬁndings gained can be applied in subsequent iteration loops. With regard to the present compressor-combustor test problem, not only the interface parameters y will be exchanged, but also the complete CjB diffusor annulus geometry is handed over from the compressor to the combustor. The diffusor geometry is considered in both design processes, because it is an integral part of both underlying sub-processes, i.e., the Throughﬂow solver for compressor analyses and the combustor design tool. Thus, the diffusor geometry is treated as an overlapping interface and exchanged to guarantee a Int. J. Turbomach. Propuls. Power 2018, 3, 12 8 of 16 consistent gas path geometry; otherwise the isolated compressor and combustor design processes would develop different diffusor interface sections independently from each other. The exchange of geometry information extends the design space and enables consideration of more design parameters like lengths modiﬁcations of the compressor subcomponents or unblocking of the radial interface coordinates by r and h . For the holistic design, the last hub and tip control points of the compressor PD PD perturbation spline are added as design variables to p , and the previously used parameters r and PD h in p at a speciﬁc transition point are replaced by the coordinates of the 2D hub and tip diffusor PD CjB annulus contour. The transferred aerodynamic and geometric diffusor design information is used as pure input in the combustor component design process. The geometry exchange between the two design processes impacts the performance prediction of both compressor and combustor. This would lead to a double-counting of the diffusor losses corrupting the overall efﬁciency. In order to avoid this corruption of an overall design criterion, the compressor efﬁciency value is read out directly behind the bladed part, i.e., at OGV exit, whereby the diffusor ﬂow is not recognized in the compressor efﬁciency value but still for the compressor constraint calculation. However, through the extension the requirements and interests of different Int. J. Turbomach. Propuls. Power 2018, 3, x FOR PEER REVIEW 8 of 16 components must be combined. This leads to a multi-criterion optimization problem and a higher The geometry exchange between the two design processes impacts the performance prediction number of design parameters, resulting in a more complex problem deﬁnition and design task. of both compressor and combustor. This would lead to a double-counting of the diffusor losses Two different solution strategies for coupling the two engine components will be investigated. corrupting the overall efficiency. In order to avoid this corruption of an overall design criterion, the The ﬁrst design approach is an all-at-once strategy, in which both component design processes are compressor efficiency value is read out directly behind the bladed part, i.e., at OGV exit, whereby integrated into a single optimization process and all design parameters are managed simultaneously the diffusor flow is not recognized in the compressor efficiency value but still for the compressor constraint calculation. However, through the extension the requirements and interests of different by the overall optimizer, Figure 6a. The sub-processes are executed sequentially, where the upstream components must be combined. This leads to a multi-criterion optimization problem and a higher compressor component is executed ﬁrst to determine the input parameters y and p for the CjB CjB number of design parameters, resulting in a more complex problem definition and design task. subsequent combustor component. Due to prediction limitations of the involved design tools, upstream Two different solution strategies for coupling the two engine components will be investigated. coupling effects from the combustor onto the compressor, such as the pressure increase in the combustor The first design approach is an all-at-once strategy, in which both component design processes are integrated into a single optimization process and all design parameters are managed simultaneously dump region, are neglected here. However, if the design process is extended by higher-ﬁdelity design by the overall optimizer, Figure 6a. The sub-processes are executed sequentially, where the upstream tools, upstream ﬂow information should be considered and evaluated as well. After execution of both compressor component is executed first to determine the input parameters and for the | | analyses, the results are returned to the optimizer. The global design problem formulation combines subsequent combustor component. Due to prediction limitations of the involved design tools, Equations (2) and (5) and represents a multi-criterion optimization problem [19] with design parameters upstream coupling effects from the combustor onto the compressor, such as the pressure increase in T T T T T T the combustor dump region, are neglected here. However, if the design process is extended by p = p , p and constraint functions h = h , h . However, to reduce the number of global B B C C higher-fidelity design tools, upstream flow information should be considered and evaluated as well. objectives, the component efﬁciencies are combined. For the present chained compressor-combustor After execution of both analyses, the results are returned to the optimizer. The global design system, the efﬁciency values may be multiplied to receive the overall efﬁciency h = h h . This leads C B problem formulation combines Equations (2) and (5) and represents a multi-criterion optimization to the bi-criterion problem [1 optimization 9] with design pa prra oblem meters = , and constraint functions = , . However, to reduce the number of global objectives, the component efficiencies are combined. For the present " # ( " # " # ) chained compressor-combustor system, the efficiency values may be multiplied to receive the h h p h C B C C 54 2 l u l u min s.t. P = p = 2 R Z h = 0, y y y , p p p (7) overall efficiency = . This leads to the bi-criterion optimization problem CjB CjB CjB p2P E p h I B B .. = = ∈ ℝ ×ℤ = ≤, ≤ ≤ , ≤≤ (7) | | | with interface quantities y determined by compressor analysis and used for combustor analysis. CjB with interface quantities determined by compressor analysis and used for combustor analysis. Lower and upper bounds for y are determined with regard to empirical knowledge to ensure CjB Lower and upper bounds for are determined with regard to empirical knowledge to ensure feasible designs. feasible designs. Figure 6. Investigated holistic design concepts: (a) all-at-once-approach and (b) decoupled Figure 6. Investigated holistic design concepts: (a) all-at-once-approach and (b) decoupled combustor combustor optimization with design parameters , , design criteria , , and interface parameters , . optimization with design parameters p , p , design criteria h , h , E and interface parameters y , p . | | C B C B I CjB CjB In order to reduce the number of design variables in the overall optimizer, a second design process is proposed with a separated combustor optimization, Figure 6b. In contrast to the all-at-once approach, the global optimizer concentrates on the compressor optimization and varies the compressor design variables only. The combustor design process is not executed for every converged compressor optimization. Only if a compressor configuration fulfills all compressor constraints ≤ and lies within prescribed interface bounds , a subsequent combustor optimization is performed, which searches for an optimal combustor geometry for the given set of interface parameters and by varying local design parameters . | | Int. J. Turbomach. Propuls. Power 2018, 3, 12 9 of 16 In order to reduce the number of design variables in the overall optimizer, a second design process is proposed with a separated combustor optimization, Figure 6b. In contrast to the all-at-once approach, the global optimizer concentrates on the compressor optimization and varies the compressor design variables only. The combustor design process is not executed for every converged compressor optimization. Only if a compressor conﬁguration fulﬁlls all compressor constraints h 0 and lies l,u within prescribed interface bounds y , a subsequent combustor optimization is performed, which CjB searches for an optimal combustor geometry for the given set of interface parameters y and p by CjB CjB varying local design parameters p . To reduce the computational time of the combustor optimization according to problem (5), only a single-criterion optimization is executed. Since the combustor efﬁciency for the considered “cruise” ﬂight cycle is very high and almost invariant, it is not considered as an objective anymore, but as a constraint with a lower bound h . All in all, this leads to the modiﬁed combustor optimization problem 4 2 min E p , y , p s.t. P = p 2 R Z h p , y , p 0, I B B B B CjB CjB B CjB CjB p 2P o (8) l u l p p p , h h p , y , p . B B B CjB CjB B B B However, the higher-level optimizer will receive both values, i.e., the optimal emission E = minE and the associated combustor efﬁciency h , to calculate the overall efﬁciency and to evaluate the overall engine performance similar to the all-at-once approach. If no valid compressor design exists P P for a given set of parameters, penalty values E = E and h = h for the combustor objectives are I B I B returned. In summary, the system design problem " # ( " # ) h h h C C 50 l u l u min s.t. P = p 2 R 0, y y y , p p p (9) C C C CjB C C CjB CjB p 2P E h C B C I needs to be solved to obtain an overall optimal compressor with the decoupled optimization approach. 5. Results To demonstrate the beneﬁt of a holistic compressor-combustor design process and to investigate differences between the two proposed coupling strategies, three optimizations have been performed. The ﬁrst one is a classical isolated compressor design according to Section 2 with ﬁxed values for interface quantities y , p and a subsequent compressor analysis to ﬁnally obtain reference values CjB CjB for overall efﬁciency h and emission E . The problem (2) is solved with the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) [20], and the obtained result is denoted as reference design R later on. Subsequently, holistic design formulations (7) and (9) are solved, where, in contrast to reference design R, the interface parameters y and p are not ﬁxed but kept variable within deﬁned ranges. CjB CjB This leads to an increased design space and offers completely new possibilities in the conﬁguration and design of the individual components, as well as the interface section. The larger the range of the variables, the higher is the degree of variation. For solving the all-at-once problem (7) represented in Figure 6a, the multi-objective genetic AMGA (Archive-based Micro Genetic Algorithm) [21] with the settings in Table 1 is used. In total, 10,000 designs are evaluated. For the decoupled compressor-combustor design problem, also the AMGA is used with similar settings for the problem (9) represented by the upper box in Figure 6b. In addition, the underlying combustor design problem (8) is solved with CMA-ES. The number of function evaluations for each CMA-ES search, which is executed only for valid compressor conﬁgurations, is set to 160 or 20 generations with a population size of 8 only. With these settings, sufﬁcient convergence accuracy can be obtained with the help of the used tools. Int. J. Turbomach. Propuls. Power 2018, 3, 12 10 of 16 Table 1. Archive-based Micro Genetic Algorithm (AMGA) optimizer settings. Optimizer Settings Value Initial population size 500 Population size 40 Function evaluations 10,000 Archive size limit 1000 Pareto size limit 100 In direct comparison of compressor and combustor design processes, a combustor analysis takes 20 times longer than the used compressor design evaluation. To reduce this execution time discrepancy for utilizing both design strategies in an efﬁcient manner and to make them more attractive for industrial application, the time intensive combustor performance prediction is replaced by a response surface. A polynomial regression model of degree two is used to emulate effects of relevant combustor parameters, in which port style parameters are treated as continuous variables, but training of the response surface is performed with discrete values (6) only. The surrogate model is based on an initial Latin hypercube sampling [22] with 300 design evaluations, which are generated before execution of the optimization. The use of this surrogate model leads to a signiﬁcant reduction of the combustor evaluation response time by a factor of 400, see Table 2. Table 2. Process settings and runtimes. All-at-Once Strategy Decoupled Strategy Number of design variables (global/local) 56/0 50/6 Number of objectives (global/local) 2/0 2/1 Number of constraints (global/local) 40/0 37/3 Runtime compressor evaluation ~10.0 s ~10.0 s Runtime combustor evaluation ~0.5 s/~200.0 s ~80.0 s/~9.0 h (with/without surrogate model) Overall runtime 14.5 h 35.9 h (optimization with surrogate model) Number of valid designs 95 295 First valid design found (iterations/time) 2037/6.2 h 1298/1.1 h As can be seen in Figure 7, both holistic optimization strategies yield non-dominated solutions (represented by black triangles and circles), which are better in both criteria compared to the reference design R obtained from isolated compressor optimization. However, also differences between the all-at-once approach and the decoupled strategy are visible: Although shown results are not representative and may change for another search due to the random nature of evolutionary algorithms, it is interesting that the all-at-once optimization is able to identify the disconnected Pareto-front. The discontinuities, in particular for the emission index, are due to the binary character of the discrete port style parameters s and s . If the ﬁrst mixing port style, i.e., for primary zone, is set to plain pp sp and the secondary to chuted, lowest emissions are obtained. Compared to this, the two optimal conﬁgurations of the all-at-once optimization with high emissions do have chuted port styles only. Reasons why the decoupled strategy converged into the feasible design space with plain mixing ports only are (i) low number of function evaluations for each individual combustor optimization, (ii) better global search properties of the AMGA compared to the local CMA-ES based combustor optimization, and (iii) an early convergence of the top level optimization since combustor optimizations are only executed for feasible compressor designs. The results of Figure 7 have been conﬁrmed in several runs, whereby the obtained representations of the Pareto-fronts are not exactly identical due to the utilization of evolutionary optimization algorithms. Further investigations are required to gain more conﬁdence, and a multi-objective treatment of discrete variables as proposed in [5] may be applied to prevent premature convergence. Int. J. Turbomach. Propuls. Power 2018, 3, x FOR PEER REVIEW 10 of 16 discrepancy for utilizing both design strategies in an efficient manner and to make them more attractive for industrial application, the time intensive combustor performance prediction is replaced by a response surface. A polynomial regression model of degree two is used to emulate effects of relevant combustor parameters, in which port style parameters are treated as continuous variables, but training of the response surface is performed with discrete values (6) only. The surrogate model is based on an initial Latin hypercube sampling [22] with 300 design evaluations, which are generated before execution of the optimization. The use of this surrogate model leads to a significant reduction of the combustor evaluation response time by a factor of 400, see Table 2. As can be seen in Figure 7, both holistic optimization strategies yield non-dominated solutions (represented by black triangles and circles), which are better in both criteria compared to the reference design ℛ obtained from isolated compressor optimization. However, also differences between the all-at-once approach and the decoupled strategy are visible: Although shown results are not representative and may change for another search due to the random nature of evolutionary algorithms, it is interesting that the all-at-once optimization is able to identify the disconnected Pareto-front. The discontinuities, in particular for the emission index, are due to the binary character of the discrete port style parameters s and . If the first mixing port style, i.e., for primary zone, is set to plain and the secondary to chuted, lowest emissions are obtained. Compared to this, the two optimal configurations of the all-at-once optimization with high emissions do have chuted port styles only. Reasons why the decoupled strategy converged into the feasible design space with plain mixing ports only are (i) low number of function evaluations for each individual combustor optimization, (ii) better global search properties of the AMGA compared to the local CMA-ES based combustor optimization, and (iii) an early convergence of the top level optimization since combustor optimizations are only executed for feasible compressor designs. The results of Figure 7 have been confirmed in several runs, whereby the obtained representations of the Pareto-fronts are not exactly identical due to the utilization of evolutionary optimization algorithms. Further investigations are required to gain more confidence, and a multi-objective treatment of discrete variables as proposed Int. J. Turbomach. Propuls. Power 2018, 3, 12 11 of 16 in [5] may be applied to prevent premature convergence. Figure 7. Visualization of feasible optimization results in the bi-criterion space with reference design Figure 7. Visualization of feasible optimization results in the bi-criterion space with reference design (ℛ), all-at-once optimization results (triangles), and decoupled compressor-combustor optimization (R), all-at-once optimization results (triangles), and decoupled compressor-combustor optimization results (circles). results (circles). From an automated search perspective, an all-at-once approach is always the preferable option, because only one optimization needs to be performed and convergence into local minima or local sets of non-dominated solutions is less likely. Additionally, the process architecture is rather clear and thus more user friendly. On the other side, an all-at-once strategy includes the highest number of design parameters (here 56), which makes the optimizer inefﬁcient due to longer time for initializing the starting population and for achieving convergence. Furthermore, the immediate combustor analysis for each converged but not necessarily valid compressor design conﬁguration increases the calculation time as well. In contrast, the separated compressor and combustor optimization of the decoupled design strategy ﬁnds a valid engine design within a shorter period of time after starting the process, see Table 2. The reason may be that ﬁrstly the fast 1D and 2D compressor design tools are able to optimize the compressor geometry without disturbance until a valid design is obtained. The combustor optimization is then performed only for valid designs in order to ﬁnd a matching combustor geometry fulﬁlling all design rules. With further design evaluations, the compressor design process will propose more and more valid designs. For each of these designs, a complete combustor subsystem optimization with 160 function evaluations is performed. From that point on, the overall computational time would substantially increase without the use of a surrogate model. Figure 8 representatively shows the behavior of both holistic design strategies for the ﬁrst 5000 iteration steps while searching for an optimal solution for problems (7) and (9). Obviously, the decoupled optimization strategy (crosses) is able to ﬁnd a ﬁrst valid design four to ﬁve times faster than the all-at-once approach (diamonds) due to the reasons named above. Especially, the consideration of a higher number of constraints, as well as the execution of both the compressor and the combustor analysis tool for every single iteration, lead to a less efﬁcient convergence of the all-at-once approach at the beginning of the design process. The extended runtime in this phase, however, enables the optimizer to ﬁnd a more efﬁcient ﬁrst valid design compared to the decoupled approach. With regard to the overall runtime, the all-at-once approach is up to three times faster for the same number of iteration steps because of the single combustor performance analysis for each compressor design proposal instead of executing a complete subsystem optimization. As can also be seen in Figure 8, with the decoupled approach a higher number of valid designs is identiﬁed, see also Table 2, although the ﬁrst designs are underperforming in comparison to R, while all valid designs of the all-at-once approach are instantly more efﬁcient than R. Nonetheless, the ﬁnal best efﬁciency values are nearly the same for both processes. Int. J. Turbomach. Propuls. Power 2018, 3, 12 12 of 16 Int. J. Turbomach. Propuls. Power 2018, 3, x FOR PEER REVIEW 12 of 16 Figure Figure 8. 8. Feasible Feasible designs designs f for all-at-once or all-at-on optimization ce optimization ( (diamonds) diamonds) and decoupled and deoptim coupled ization optimization (crosses) in(crosse comparison s) in co to mrp efer arison to re ence design ference de (R) for sig ﬁrst n ( 5000 ℛ) for first runs for 500 (a) 0 runs for ( efﬁciency vs. a)number efficiency vs. number o of iterations and f (b iterations ) efﬁciency and ( vs.b CPU ) effitime. ciency vs. CPU time. In summary, the selection of the design strategy depends on the intentions or requirements of In summary, the selection of the design strategy depends on the intentions or requirements of the user: If an optimal design has to be found within a restricted time frame, the all-at-once approach the user: If an optimal design has to be found within a restricted time frame, the all-at-once approach should be applied. However, if a target value must be met as quickly as possible, the decoupled should be applied. However, if a target value must be met as quickly as possible, the decoupled strategy strategy would be the preferred option. Nevertheless, direct consideration of all component-specific would be the preferred option. Nevertheless, direct consideration of all component-speciﬁc design design trends in every design step of the all-at-once approach allows an early consideration of trends in every design step of the all-at-once approach allows an early consideration of individual individual design requirements and the creation of a widely dispersed holistic engine population. design requirements and the creation of a widely dispersed holistic engine population. This ﬁnally This finally leads to slightly better results when comparing both methods in Figure 7. leads to slightly better results when comparing both methods in Figure 7. Both all-at-once and decoupled strategy may be seen as suitable here. With respect to Both all-at-once and decoupled strategy may be seen as suitable here. With respect to intellectual intellectual property rights (IPR) problems in case of cooperating companies and departments, an property rights (IPR) problems in case of cooperating companies and departments, an all-at-once all-at-once approach requiring full software access to all component analysis tools is not feasible. approach requiring full software access to all component analysis tools is not feasible. Instead, the Instead, the decoupled approach should be preferred, in which sharing of local design parameters decoupled approach should be preferred, in which sharing of local design parameters and constraints and constraints (here and ), as well as specific parametrization strategies, is not required. The (here p and h ), as well as speciﬁc parametrization strategies, is not required. The combustor design B B combustor design process of the decoupled approach is similar to a black box, where only the process of the decoupled approach is similar to a black box, where only the interface parameters are interface parameters are shared and the result is fed back to the overall design process. shared and the result is fed back to the overall design process. However, it must be kept in mind that the decoupled strategy is an optimization double-loop. However, it must be kept in mind that the decoupled strategy is an optimization double-loop. Fully converged, i.e., optimal combustor designs cannot be guaranteed, because noisy combustor Fully converged, i.e., optimal combustor designs cannot be guaranteed, because noisy combustor results are fed back to the global search. This is due to the fact that the evolutionary optimization results are fed back to the global search. This is due to the fact that the evolutionary optimization algorithm CMA-ES is used for the individual combustor search according to Equation (8). Future algorithm CMA-ES is used for the individual combustor search according to Equation (8). studies with an increased number of design evaluations to investigate the level of uncertainties are Future studies with an increased number of design evaluations to investigate the level of uncertainties being conducted at the moment. Nonetheless, in comparison to the isolated optimized design ℛ, are being conducted at the moment. Nonetheless, in comparison to the isolated optimized design R, better results are obtained independently of the choice of the holistic design approach. better results are obtained independently of the choice of the holistic design approach. In Figure 7, annulus geometries for design representing a non-dominated solution with low In Figure 7, annulus geometries for design A representing a non-dominated solution with emissions from the all-at-once strategy, and tradeoff design from the decoupled approach low emissions from the all-at-once strategy, and tradeoff design D from the decoupled approach corresponding to designs with low emission and maximal efficiency, are shown and compared to corresponding to designs with low emission and maximal efﬁciency, are shown and compared to the the reference design ℛ. For all three configurations (ℛ, , ), similar overall design requirements reference design R. For all three conﬁgurations (R, A, D), similar overall design requirements like like overall compressor pressure ratio, shaft speed, environmental conditions, etc., have been used. overall compressor pressure ratio, shaft speed, environmental conditions, etc., have been used. As can As can be seen, the annulus outline for both designs and deviates from the reference design be seen, the annulus outline for both designsA andD deviates from the reference designR. While the ℛ. While the cross section area of design increases, especially in the S-duct section and the first cross section area of design A increases, especially in the S-duct section and the ﬁrst stages of the stages of the bladed part, the annulus contour of design is located on a lower mean radius with bladed part, the annulus contour of design D is located on a lower mean radius with similar cross similar cross section areas. section areas. The performance benefits can be explained by the geometry adaption and the consequently The performance beneﬁts can be explained by the geometry adaption and the consequently changed aerodynamic flow conditions. For both compressor configurations and , the inflow to changed aerodynamic ﬂow conditions. For both compressor conﬁgurations A and D, the inﬂow to the the bladed part was optimized. Figure 9b,d shows a more homogeneous velocity profile in radial bladed part was optimized. Figure 9b,d shows a more homogeneous velocity proﬁle in radial direction direction at the first rotor and stator positions. Additionally, lower relative tip Mach numbers, especially at inlet of rotor one and two, and decreased inlet Mach numbers at the front stator Int. J. Turbomach. Propuls. Power 2018, 3, x FOR PEER REVIEW 13 of 16 compared to the reference geometry are clearly recognizable. The lower velocity results in a decreased loading in the front part of the compressor, which is indicated by lower de Haller numbers and lower stage pressure ratios shown in Figure 9a,c. This leads to lower flow loss around the blades, and thus higher efficiency. The loading decrease of design compared to ℛ is higher than the reduction between and ℛ. This can be traced back to the increased cross section area for the same mass flow rate. Furthermore, the axial chord lengths, as well as the space-to-chord ratios, have been changed. The increased space-to-chord ratios for design and lead to a reduced number of blades, which also results in lower losses due to fewer wall interactions and, finally, to increased efficiency. However, the geometric changes result in a minor reduction of the surge margin, which is still acceptable and uncritical as the surge margin constraint is fulfilled. Several interface values initially set to the fixed interface parameters of design ℛ have changed during the holistic optimization. Compared to the reference design, the temperature was reduced by six degrees in design and with a constant overall compressor pressure ratio. This finally leads to a higher cooling performance of the air flow in the combustion chamber supporting the cool down of the hot combustion gas and stopping the NOx production. Moreover, the materials are not loaded so heavily. The diffusor geometry has changed towards a lower diffusor exit height, a lower exit mean Int. J. Turbomach. Propuls. Power 2018, 3, 12 13 of 16 radius, and a longer compressor bladed part in configuration , in which the latter leads to a shorter diffusor part. Design has a diffusor exit geometry similar to ℛ, but a shorter bladed part resulting in a longer diffusor and lower exit flow angle. at the ﬁrst rotor and stator positions. Additionally, lower relative tip Mach numbers, especially at inlet The present geometry changes have to be discussed in the context of mass distribution, as it is of rotor one and two, and decreased inlet Mach numbers at the front stator compared to the reference an important criterion for overall engine design. This step has been neglected in the present paper geometry are clearly recognizable. The lower velocity results in a decreased loading in the front part of because of the focus on aerodynamic gas path design. The release of the interface parameters the compressor, which is indicated by lower de Haller numbers and lower stage pressure ratios shown supports the design flexibility, as it is now possible to meet a range of parameters rather than having in Figure 9a,c. This leads to lower ﬂow loss around the blades, and thus higher efﬁciency. The loading to meet specific values that are often based on experience or defined too early in the design process decrease of designA compared toR is higher than the reduction betweenD andR. This can be traced by limited tools. back to the increased cross section area for the same mass ﬂow rate. Figure 9. Optimization results of designs and compared to reference design ℛ: (a) rotor de Figure 9. Optimization results of designs A and D compared to reference design R : (a) rotor de Haller number, (b) rotor relative inlet Mach number, (c) rotor static pressure rise, and (d) stator inlet Haller number, (b) rotor relative inlet Mach number, (c) rotor static pressure rise, and (d) stator inlet Mach number. Mach number. Furthermore, the axial chord lengths, as well as the space-to-chord ratios, have been changed. The increased space-to-chord ratios for design A and D lead to a reduced number of blades, which also results in lower losses due to fewer wall interactions and, ﬁnally, to increased efﬁciency. However, the geometric changes result in a minor reduction of the surge margin, which is still acceptable and uncritical as the surge margin constraint is fulﬁlled. Several interface values initially set to the ﬁxed interface parameters of design R have changed during the holistic optimization. Compared to the reference design, the temperature T was reduced by six degrees in design A and D with a constant overall compressor pressure ratio. This ﬁnally leads to a higher cooling performance of the air ﬂow in the combustion chamber supporting the cool down of the hot combustion gas and stopping the NO production. Moreover, the materials are not loaded so heavily. The diffusor geometry has changed towards a lower diffusor exit height, a lower exit mean radius, and a longer compressor bladed part in conﬁguration D, in which the latter leads to a shorter diffusor part. DesignA has a diffusor exit geometry similar toR, but a shorter bladed part resulting in a longer diffusor and lower exit ﬂow angle. The present geometry changes have to be discussed in the context of mass distribution, as it is an important criterion for overall engine design. This step has been neglected in the present paper because of the focus on aerodynamic gas path design. The release of the interface parameters supports the Int. J. Turbomach. Propuls. Power 2018, 3, 12 14 of 16 design ﬂexibility, as it is now possible to meet a range of parameters rather than having to meet speciﬁc values that are often based on experience or deﬁned too early in the design process by limited tools. 6. Conclusions The present paper examines two different approaches to design components of aero engines from a holistic point of view. The test case couples a compressor with a combustor to optimize the overall system w.r.t. global objectives, such as overall efﬁciency and emission. A comparison with the state-of-the-art design strategy of isolated component optimization with ﬁxed interfaces between the engine components reveals that the holistic design approach results in better designs. Both the presented all-at-once approach and compressor optimization incorporating a decoupled combustor optimization deliver comparable results, and both holistic design strategies have advantages and disadvantages. Limitations with respect to IPR may prohibit usage of the all-at-once approach, although it delivers better results in a smaller amount of time. The decoupled approach also gains signiﬁcance when handling a large number of design parameters, for example, if more than two engine components have to interact during the design process. The gained knowledge about holistic optimization strategies for complex coupled problems can be transferred also to other engineering ﬁelds like aircraft or vehicle development. The proposed holistic design strategy offers new opportunities for ﬁnding better results, but also involves new challenges for handling the increased number of design parameters. The shown design strategies allow only for downstream information transfer. Therefore, in future work other coupling methods [23] must be examined with regard to upstream information transfer as well. Furthermore, the signiﬁcance and quality of the process should be increased and the runtime decreased. For example, a parameter reduction would simplify the optimization problem and thus favor process acceleration, where a parameter sensitivity analysis may help to identify relevant design parameters for the optimization instead of an experience-based selection as applied here. To optimize the prediction accuracy of the combustor tool, higher ﬁdelity surrogate models as radial basis functions need to be investigated. To realize the holistic design philosophy, compressor conﬁgurations shall not be determined only by combustor criteria, but also by other components such as turbine or fan in order to make more precise statements about optimal compressor properties in the future. Additionally to the component-speciﬁc process, ﬁdelity may be increased by the use of 3D analyzing tools. Author Contributions: Conceptualization, M.H., M.L. and P.F.; Methodology, M.H. and M.L.; Software, M.H.; Validation, M.H. and P.F.; Formal Analysis, M.H. and D.B.; Investigation, M.H.; Resources, M.H.; Data Curation, M.H.; Writing-Original Draft Preparation, M.H. and D.B.; Writing-Review & Editing, M.H., D.B. and P.F.; Visualization, M.H.; Supervision, D.B. and P.F.; Project Administration, D.B.; Funding Acquisition, D.B. Acknowledgments: This work has been carried out in collaboration with Rolls-Royce Deutschland as part of the research project VITIV (Virtual Turbomachinery with Integrative Strategies, Proj.-No. 80164702) funded by the State of Brandenburg, the European Regional Development Fund, and Rolls-Royce Deutschland. Rolls-Royce Deutschland’s permission to publish this work is greatly acknowledged. Conﬂicts of Interest: The authors declare no conﬂict of interest. 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International Journal of Turbomachinery, Propulsion and Power – Multidisciplinary Digital Publishing Institute
Published: Apr 27, 2018
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