SIMULATION: Transactions of The Society for Modeling and Simulation International

Liao, Xiangyun; Yuan, Zhiyong; Hu, Pengfei; Lai, Qianfeng

doi: 10.1177/0037549714552708pmid: N/A

Soft tissue deformation simulation is a key technology of virtual surgical simulation. In this work, we present a graphics processing unit (GPU)-assisted energy asynchronous diffusion parallel computing model which is stable and fast in processing complex models, especially concave surface models. We adopt hexahedral voxels to represent the physical model of soft tissue to improve the visual realistic quality and computing efficiency of deformation simulation. We also adopt the concept of free boundary to simulate soft tissue geometric characteristics more precisely during the deformation process and introduce asynchronous diffusion by using the mechanical energy of mass points to achieve realistic soft tissue deformation effects. In order to meet the requirement of real-time surgery simulation, we accelerate the soft tissue deformation by using OpenCL (Open Computing Language) and optimize the parallel computing process in several means. Experimental results have shown that the GPU-assisted energy asynchronous diffusion parallel computing model for soft tissue deformation simulation implements satisfactory effects on deformation in visual realistic and real-time quality.

Doğan, Atakan; Atanak, Mustafa Müjdat; Tandoğan, Safai; Altuğ, Reha Oğuz; Şenel, Hakan Güray

doi: 10.1177/0037549714545415pmid: N/A

Data grid systems are utilized to share, manage, and process large data sets. On the other hand, an increasing number of applications with real-time constraints arise in several disciplines of science and engineering. The performance of a data grid system for real-time applications is highly dependent on the underlying job scheduling, data scheduling, and data replication algorithms and advance reservation mechanism. Thus, in the literature, there are numerous studies that propose solutions to the job/data scheduling, data replication, and advance reservation problems. In these studies, a number of simulators, emulators, or test beds have been used to evaluate the proposed algorithms. Furthermore, these simulators/emulators usually adopt fixed-grid models, which in turn dictate specific job/data scheduling and data replication mechanisms. In the literature, there is no unified framework for modeling grid systems with different architectures, which can allow researchers to develop new grid system models and evaluate them in a flexible manner. This paper presents a unique framework for modeling real-time data grid systems that attempts to unify a large class of job scheduling, data scheduling, and data replication algorithms based on several system services. Then, in order to enable the development of these algorithms under different system models, DGridSim is realized to be a multi-model discrete-event simulator, and its capabilities are exemplified by means of a set of simulation results. The main contribution of the research is DGridSim, which can model and simulate a variety of different data grid system models by means of several system services and their interactions.

Andrejević Stošović, Miona; Litovski, Ivan; Lukač, Duško; Dimitrijević, Marko; Litovski, Vančo

doi: 10.1177/0037549714551290pmid: N/A

Starting with the experience that the output voltage and the output current of a photovoltaic panel are not pure direct current constants due to the inevitable connection to a converter (or inverter) that is working as a switching system, we came to the conclusion that interest exists for the behavior of the solar cell at the frequencies of the harmonics of the converter’s switching frequency, which is subject to change according to the maximum power-point tracking. In other words, a need exists for frequency domain characterization of the solar cell, for which a linear small-signal model is necessary. To enable simulation for small signals, development of a linear reactive model was considered. Since a one-diode large-signal model already exists, it was used as a basis for the extraction of the parameters of the small-signal model. The new model was represented in the form of a parallel RC two-terminal circuit, the R and C being functions of the photocurrent (acting as a map of the illumination) and the diode voltage. Since the R and C of the model are quiescent-point dependent, their values as a function of the illumination and the diode voltages were approximated by artificial neural networks (ANNs). Separate ANNs were created for modeling R and C. To verify the model, two small-signal simulations were performed. The first one was done with the existing nonlinear model, while the second was done with the new linear model (running the ANNs). Excellent agreement was obtained.

Bae, Jang Won; Lee, SeHoon; Hong, Jeong Hee; Moon, Il-Chul

doi: 10.1177/0037549714551291pmid: N/A

The bombardment of a metropolis is considered a nightmare scenario. To reduce losses from such an assault, big cities have developed evacuation policies in case of bombardment. However, to build efficient evacuation policies, much footing data is required that considers both military and civilian views. Agent-based modeling and simulation could be utilized as a method to obtain the footing data. In this paper, we develop an evacuation agent-based model that describes a massive evacuation through the road network of a metropolis during a bombardment. In particular, our model took account of bombing strategies (i.e. the military view) as well as the characteristics of roads and evacuation agents (i.e. the civilian view) in order to analyze evacuations from both military and civilian perspectives. Moreover, we applied real data from a target region to calibrate parameters and initial conditions of the evacuation agent-based models, which increased the reliability of simulation results. Using the evacuation agent-based model, we designed and performed virtual experiments with varying military and civilian factors. Through the various analyses on the experiment results, we showed that our model could be a framework that provides footing data to develop efficient evacuation policies and preparations.

Jalali, Leila; Mehrotra, Sharad; Venkatasubramanian, Nalini

doi: 10.1177/0037549714553151pmid: N/A

This paper considers the challenge of designing a framework that supports the integration of multiple existing simulation models into an integrated simulation environment (multisimulation). We aim to facilitate the process of fusing together the independently created simulators into an integrated simulation environment wherein we can model and execute complex scenarios involving multiple simulators. In this paper, we focus on solutions for the synchronization problem in multisimulation to orchestrate consistent information flow through multiple simulators. In particular, we provide: (1) a transaction-based approach to modeling the synchronization problem in multisimulations by mapping it to a problem similar to multidatabase concurrency; we express multisimulation synchronization as a scheduling problem where the goal is to generate “correct schedules” for time advancement and data exchange across simulators that meets the dependencies without loss of concurrency; (2) a hybrid scheduling strategy which adapts itself to the “right” level of pessimism/optimism based on the state of the execution and underlying dependencies. We also develop two key optimizations: (a) efficient checkpointing/rollback techniques; and (b) relaxation model for dependencies which guarantees bounded violation of consistency to support higher levels of concurrency. We evaluate our proposed techniques via a detailed case study from the emergency response domain by integrating three disparate simulators: a fire simulator (CFAST), an evacuation simulator (Drillsim) and a communication simulator (LTEsim).

doi: 10.1177/0037549714558561pmid: N/A