SIMULATION: Transactions of The Society for Modeling and Simulation International

Khalil, Hoda; Wainer, Gabriel

doi: 10.1177/00375497231212198pmid: N/A

Carbon dioxide concentration in enclosed spaces is an air quality indicator that affects occupants’ well-being. To maintain healthy carbon dioxide levels indoors, enclosed space settings must be adjusted to maximize air quality while minimizing energy consumption. Studying the effect of these settings on carbon dioxide concentration levels is not feasible through physical experimentation and data collection. This problem can be solved by using validated simulation models, generating indoor settings scenarios, simulating those scenarios, and studying results. In previous work, we presented a formal Cellular Discrete Event System Specifications simulation model for studying carbon dioxide dispersion in rooms with various settings. However, designers may need to predict the results of altering large combinations of settings on air quality. Generating and simulating multiple scenarios with different combinations of space settings to test their effect on indoor air quality is time-consuming. In this research, we solve the two problems of the lack of ground truth data and the inefficiency of producing and studying simulation results for many combinations of settings by proposing a novel framework. The framework utilizes a Cellular Discrete Event System Specifications model, simulates different scenarios of enclosed spaces with various settings, and collects simulation results to form a data set to train a deep neural network. Without needing to generate all possible scenarios, the trained deep neural network is used to predict unknown settings of the closed space when other settings are altered. The framework facilitates configuring enclosed spaces to enhance air quality. We illustrate the framework uses through a case study.

Ma, Jun; Xiao, Chunbei; Hu, Jun

doi: 10.1177/00375497231216473pmid: N/A

Many enclosed public spaces with multiple destinations often feature obstacles, such as columns within the building space and dense crowds, which adversely affect the recognition and guidance of pedestrian sign. To mitigate this issue, enhancing traffic efficiency necessitates the implementation of auxiliary guidance signs. However, experience-based schemes for positioning these signs often require iterative adjustments to achieve the desired effect. Thus, this paper endeavors to address this challenge by analyzing the relationship between obstacles and pedestrians’ visual blind areas. It establishes a mathematical model to describe pedestrian visual perception and its correlation with crowd density and individual visual field function. The model employs the effective aggregation area of the population exceeding a certain density threshold as its primary variable and enhances this variable’s function. Subsequently, it formulates an effect function that yields a substantial improvement in various combinations and permutations of auxiliary guidance signs. To exemplify this research, a large subway transfer hall with multiple destinations serves as a simulation model. The study calculates the optimal combination of auxiliary guidance sign placements that align with the enhancement effect function. Based on this combination, an optimized layout plan is derived, resulting in a significant improvement. The results demonstrate that the auxiliary guidance sign layout scheme proposed in this study markedly enhances traffic efficiency in enclosed public spaces. This research serves as a valuable reference for decision-makers seeking to optimize traffic flow in such spaces.

Carensac, Samuel; Pronost, Nicolas; Bouakaz, Saida

doi: 10.1177/00375497231216477pmid: N/A

Fluid simulation is an essential tool to produce realistic looking animations. In particular, Lagrangian simulations offer interactive computation times with an easy integration of the two-way interaction with rigid bodies. However, the interactivity is lost for larger scenes even if only the areas around the bodies have any visual interest. In this paper, we present a novel approach to quickly initialize additional fluid in a rest state in simulations with any 3D boundary shape and able to preserve any already existing fluid. Our approach only uses the density property of the particles to allow compatibility with any smoothed-particle hydrodynamics (SPH) simulation scheme and any boundaries model. This initialization method is fast enough to allow the initialization of new fluid volumes interactively while the simulation is running. We showcase our approach by proposing a method to create a dynamic simulation window, allowing the restriction of simulating the fluid only around moving objects. We propose multiple experiments to demonstrate the capabilities and performance of our approach.

Eremeev, Pavel; Cock, Alexander De; Devriendt, Hendrik; Naets, Frank

doi: 10.1177/00375497231217301pmid: N/A

This paper proposes a method for simultaneous evaluation of the assembly process complexity together with the performance of the future product. It allows for product design optimization, considering different aspects of the future design at the early stage of the development process. The proposed method, embodied in a fully automated framework, substitutes the traditional sequential development process with a more efficient and rapid combined procedure, which addresses multiple design aspects simultaneously. Design for assembly (DFA) rules, used as quantitative metrics of the ease-of-assembly of the whole product and individual assembly operations, are automatically evaluated together with performance metrics, estimated based on finite element (FE) simulations. The direct solution to this optimization problem might be inefficient or impossible since it requires the recurrent evaluation of computationally expensive discrete and continuous functions with unknown behavior that represent the optimization objectives and constraints. For that reason, the proposed framework employs regression models based on the Gaussian process and artificial neural networks, thus achieving the optimal design of a product as a result of metamodel-based design optimization (MBDO). The suggested approach is demonstrated in the optimization of a gearbox assembly, considering its mechanical performance and assembly process. Comparing the results of the metamodel-based and direct design optimization shows that MBDO allows finding a better solution using a three times smaller computational budget. In addition, analysis of the results obtained using stationary sampling data sets of different sizes highlighted the limitations of the employed sampling procedure.

Irani, Hamid Reza; Karimi, Tooraj; Shafiei, Samira

doi: 10.1177/00375497231223519pmid: N/A

In E-commerce, one of the most essential reasons of company failures is conveying the right content of their product to the customers at the wrong time. This could include the company inability to adapt to their environment dynamically. With the existence of special circumstances like COVID-19 pandemic, companies require launching new techs, if they are to grow and stay competitive, and since the technology and related factors have a major effect on marketing success, “Systems Dynamic” sounds like a suitable tool in this regard. This article discusses augmented reality (AR) roles in contextual marketing (CM) using a system dynamics approach. After the identification of the key variables in both augmented technology and CM fields, casual relationships are defined. Then, the mathematical functions between these variables are extracted and the behavior of each factor is analyzed. Finally, based on this behavioral model, various scenarios are simulated to demonstrate how augmented reality technology can enhance the CM.