SCS Editor Pick Papers
The Editor Pick Papers are chosen each month by the Editor in Chief of Simulation: Transactions of the Society for Modeling and Simulation International (SCS) and honors some of the most outstanding papers from our monthly journal.
February 5, 2024 (Open Access) 2023 Editor Pick Papers
Graph machine learning classification using architectural 3D topological models Authors: Abdulrahman Alymani, Wassim Jabi, and Padraig Corcoran
Abstract: Some architects struggle to choose the best form of how the building meets the ground and may benefit from a suggestion based on precedents. This paper presents a novel proof of concept workflow that enables machine learning (ML) to automatically classify three-dimensional (3D) prototypes with respect to formulating the most appropriate building/ground relationship. Here, ML, a branch of artificial intelligence (AI), can ascertain the most appropriate relationship from a set of examples provided by trained architects. Moreover, the system classifies 3D prototypes of architectural precedent models based on a topological graph instead of 2D images. The system takes advantage of two primary technologies. The first is a software library that enhances the representation of 3D models through non-manifold topology (Topologic). The second is an end-to-end deep graph convolutional neural network (DGCNN). The experimental workflow in this paper consists of two stages. First, a generative simulation system for a 3D prototype of architectural precedents created a large synthetic database of building/ground relationships with numerous topological variations. This geometrical model then underwent conversion into semantically rich topological dual graphs. Second, the prototype architectural graphs were imported to the DGCNN model for graph classification. While using a unique data set prevents direct comparison, our experiments have shown that the proposed workflow achieves highly accurate results that align with DGCNN’s performance on benchmark graphs. This research demonstrates the potential of AI to help designers identify the topology of architectural solutions and place them within the most relevant architectural canons.
February 5, 2023 (Applications)
Simulating invisible light: a model for exploring radiant cooling’s impact on the human body using ray tracing Authors: Dorit Aviv, Miaomiao Hou, Eric Teitelbaum, and Forrest Meggers
Abstract: Radiant systems are an energy-efficient method for providing cooling to building occupants through active surfaces. To assess the impact of the radiant environment on occupants in space, we develop a ray-tracing simulation, which accounts for longwave radiation. Thermal radiation shares many characteristics with visible light, and thus is highly dependent on surface geometry. Much research effort has been dedicated to characterizing the behavior of visible light in the built environment and its impact on the human experience of space. However, longwave infrared radiation’s effect on the human perception of heat is still not well characterized or understood within the design community. In order to make the embodied effect of radiant surfaces’ geometry and configuration legible, we have developed a Mean Radiant Temperature (MRT) simulation method, which is based on a ray-tracing technique. It accounts for the detailed geometry of the human body and its surrounding environment. We use a case study of a pavilion built with an envelope consisting of active cooling panels in Singapore. Using measured data for the surrounding surface temperatures in the pavilion, we explore the impact of both the active panels and the surrounding passive elements and thermal environment on a person’s radiant heat exchange in different postures. The reflectivity and emissivity values of different surfaces are taken into account, and the ray-tracing process allows for multiple-bounce simulation. The model accounts for both longwave and shortwave radiation, and the simulation results are compared with field measurements for validation. The results are expressed both numerically and as spatial radiant-heat-maps. These show a variation of up to 11°C in MRT across the space studied. Furthermore, a digital manikin is used to assess the impact of the radiant cooling panels across the human body. The results show a 10°C variation in radiant temperature perceived by different regions of the body in one position. The findings reveal a significant heterogeneity of radiant heat transfer that current analysis methods typically overlook for both architectural space and the geometry of the human body.
February 5, 2023 (Methodology)
Combining PDEVS and Modelica for describing agent-based models Authors: Victorino Sanz and Alfonso Urquia
Abstract: Modelica is a general-purpose modeling language mainly designed to facilitate the development, reusability and exchange of models. It represents the state-of-the-art in equation-based modeling of continuous-time systems. Modelica libraries facilitate the description of multi-formalism and multi-domain models. However, the description of agent-based models (ABMs) in Modelica is not currently supported, mainly due to the characteristics of the language and its simulation algorithm. The combination of ABMs with continuous-time equations provides a powerful tool for describing and analyzing complex systems. An approach for describing ABMs using the Modelica language is presented in this manuscript, with the objective of facilitating the combination of ABMs with the rest of Modelica functionality. Agent behavior is described using a process-oriented modeling approach. Agents are described as individual entities that move across a flowchart diagram, that represents the processes that agents undergo. Processes are formally described using the Parallel DEVS formalism, extended to describe the interface with other Modelica models. The environment where agents interact is described as a cellular automaton. This approach has been implemented in a free Modelica library, named ABMLib. Three case studies are discussed to illustrate the modeling functionality of the library and its combination with other models: a basic traffic model, a sheep–wolves predator–prey model, and a consumer market model.
February 5, 2024 (Medical)
Combining DEVS simulation and ontological modeling for hierarchical analysis of the SARS-CoV-2 replication Authors: Ali Ayadi, Claudia Frydman, Wissame Laddada, Isabelle Imbert, Cecilia Zanni-Merk, and Lina F Soualmia
Abstract: This article presents an hybrid and hierarchical model in which two modeling and simulation approaches, discrete event system specification simulation (DEVS) and semantic technologies, were used together in order to help in the analysis of a major healthcare problem, the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2). Indeed, the complexity of the SARS-CoV-2 replication process, and the range of hierarchical scales over which it interacts with cellular components (extending from genomic and transcriptomic to proteomic and metabolomic scales), and the intricate way in which they are interwoven, make its understanding very challenging. It is therefore crucial to model the different scales of the replication process, by taking into account all interactions with the infected cell. By combining the advantages of both DEVS simulation and ontological modeling, we propose a hierarchical ontology-based DEVS simulation model of the SARS-CoV-2 viral replication at both the micro-molecular (proteomic and metabolomic) and macro-molecular (genomic and transcriptomic) scales. First, we demonstrate the usefulness of combining DEVS simulation and semantic technologies in a common modeling framework to face the complexity of the SARS-CoV-2 viral replication at different scales. Second, the modeling and simulation of the SARS-CoV-2 replication process on different levels provide valuable information on the different stages of the virus’s life cycle and lays the foundation for a system to anticipate future mutations selected by the virus.
October 19, 2023 2022 Editor Pick Papers
Linear Actuator–Based Knee Exoskeleton for Stand–Sit–Stand motions: A Bond Graph Approach Authors: Prakhar Jain, Tarun Kumar Bera, Ashish Singla, and Magnus Isaksson
Abstract: People with knee disorders often find it difficult to perform common mobility tasks, such as stand–sit–stand motions. High knee torque is required to complete such transitions, as the chances of toppling increase during these motions. Most of the existing conventional approaches, such as wheelchairs and crutches, have failed to provide complete independence to the users. Conversely, contemporary systems like lower body exoskeletons which are bulky, complex, and expensive do not specifically target the knee joint instead of assisting other joints. Hence, there is a need to aid the knee joint using a robotic knee exoskeleton capable of accurately providing the desired knee torque. In the present work, to assist the user in performing the stand–sit–stand motions, an electromyography sensor-based four-bar knee exoskeleton actuated by a linear actuator is proposed. The modeling of the complete exoskeleton is developed using bond graph technique, as the components exist in different energy domains and it is possible to frame a dynamic bond graph model using only kinematic equations. The prototype is fabricated, and experiments are carried out on an artificial limb to prove the efficacy of the design of the current knee exoskeleton. The assistive torque developed by the actuator at the knee joint of the exoskeleton is found to be suitable to assist the wearer. As a result, little effort is required by the wearer for performing the stand–sit–stand motions. The rotation of the thigh link of the developed exoskeleton was found to be suitable for performing the stand–sit–stand activity.
October 19, 2023 (Open Access)
Numerically Robust Co-Simulation Using Transmission Line Modeling and the Functional Mock-Up Interface-Editor Pick Paper Authors: Robert Braun and Dag Fritzson
Abstract: Modeling and simulation are important tools for efficient product development. There is a growing need for collaboration, interdisciplinary simulation, and re-usability of simulation models. This usually requires simulation tools to be coupled together for co-simulation. However, the usefulness of co-simulation is often limited by poor performance and numerical instability. Achieving stability is especially hard for stiff mechanical couplings. A suitable method is to use transmission line modeling (TLM), which separates submodels using physically motivated time delays. The most established standard for tool coupling today is the Functional Mock-up Interface (FMI). Two example models in one dimension and three dimensions are used to demonstrate how the next version of FMI for co-simulation can be used in conjunction with TLM. The stability properties of TLM are also proven by numerical analysis. Results show that numerical stability can be ensured without compromising on performance. With the current FMI standard, this requires tailor-made models and custom solutions for the interpolation of input variables. Without using custom solutions, variables must be exchanged using sampled communication and extrapolation. In this case, stability properties can be improved by reducing communication step size. However, it is shown that stability cannot be achieved even when using unacceptably small communication steps. This motivates the need for the next version of FMI to include an intermediate update mode, where variables can be interchanged in between communication points. It is suggested that the FMI standard should be extended with optional callback functions for providing intermediate output variables and requesting intermediate input variables.
October 19, 2023
Quantifying Means-End Reasoning Skills in Simulation-Based Training: A Logic-Based Approach Authors: Audun Stolpe and Jo Erskine Hannay
Abstract: We develop a logic-based approach for designing simulation-based training scenarios. Our methodology embodies a concise definition of the scenario concept and integrates the notions of training goals, acceptable versus unacceptable actions and performance scoring. The approach applies classical artificial intelligence (AI) planning to extract coherent plays from a causal description of the training domain. The domain- and task-specific parts are defined in a high-level action description language AL. Generic causal and temporal logic is added when the causal theory is compiled into the underlying Answer Set Programming (ASP) language. The ASP representation is used to derive a scoring function that reflects the quality of a play or training session, based on a distinction of states and actions into green (acceptable) and red (unacceptable) ones. To that end, we add to the casual theory a set of norms that specify an initial assignment of colors. The ASP engine uses these norms as axioms and propagates colors by consulting the causal theory. We prove that any set of such norms constitutes a conservative extension of the underlying causal theory. With this work, we hope to lay the foundation for the development of design and analysis tools for exercise managers. We envision a software system that lets an exercise manager view all plays of a tentative scenario design, with expediency information and scores for each possible play. Our approach is applicable to any domain in which means-ends reasoning is pertinent. We illustrate the approach in the domain of crisis response and management.
October 19, 2023
A Kinetic Theory Model of Human Crowds Accounting for Visual Attention-Editor Pick Paper Authors: Jun Ma, Meiling Wang, and Linze Li
Abstract: The visual attention of pedestrians has been rarely considered in studies of congestion prevention in long-distance passages. This paper proposes a kinetic theory model of human crowds accounting for visual attention to study congestion in long-distance passages. The population is divided into visual attention-shifting pedestrians (VAS pedestrians) and nonvisual attention-shifting pedestrians (non-VAS pedestrians). First, the movement characteristics of all pedestrians are analyzed based on observations and measurements obtained through controlled experiments. Moreover, a pedestrian flow model accounting for visual attention is built to transform the characteristics of pedestrian movement into a mathematical model. Finally, validation is done, and the density and the proportion of VAS pedestrians are selected as congestion warning parameters. Simulations are performed for a subway passage connected to stairs, and the effect of visual attention, the critical thresholds of congestion warning parameters, and the effects of implementing mitigation measures immediately after congestion occurs are assessed. The experimental results show that the movement characteristics of VAS pedestrians and non-VAS pedestrians are different. Simulation results show that the model is effective. Notably, visual attention has an impact on pedestrian movement, and using the density and the proportion of VAS pedestrians as early warning indicators is effective for preventing the occurrence of congestion, as demonstrated by the negative correlation between the two critical thresholds. This description of human groups provides quantitative guidelines for crowd management.