International Journal of Modeling, Simulation, and Scientific Computing

We review Discrete-Event system Specification (DEVS) in the context of Model-based Systems Engineering (MBSE) and discuss an application of DEVS methodology to MBSE. We outline support for an envisioned MBSE development cycle of DEVS top-to-bottom MBSE capability and offer an example of mapping UML activity diagrams into executable activity-based DEVS models. We close with conclusions and future research directions.

Systems engineering practices are evolving to address fast-changing needs in fielding complex systems. These needs create an environment in which system needs evolve or change too quickly to be tracked or managed by humans’ natural capabilities. We propose that systems engineering must aid systems engineering managers by providing architectural alternatives and design options. Further, as systems become more complex and dynamic, there is an increased need to identify hidden risks, model emergent behavior, and expose hidden patterns in the behavior of stakeholders. Systems engineering needs to evolve to build fast-fielded, resilient, and adaptive systems that leverage positive reinforcement feedback loops with multiple experimental and real-world information sources. The very basis of systems engineering must evolve from today’s development paradigms to a future that leverages modeling, simulation, and artificial intelligence to drastically improve the capability and agility for developing new systems. This paper proposes a common way forward to enable this new form of complex adaptive systems engineering.

When Discrete Event System Specification (DEVS) is used as a modeling tool, there is a semantic gap between a DEVS model and the mathematical representation, which may result in understanding difficulties. To provide a more intuitive form of modeling, XDEVS expands the concept of states in DEVS. The continuous state is introduced in XDEVS, which enhances the ability to model hybrid systems. Based on the DEVS simulation framework, a simulation engine is developed to drive the XDEVS model safely and efficiently and avoid the wrong location of state events during the simulation of the continuous model. A hybrid model is constructed and simulated using XDEVS. A comparison between the XDEVS model and models described by DEV&DESS and GDEVS shows that XDEVS can clearly express the structure of the model and reduce the burden on modelers.

With the development of system engineering and related technology, modeling and simulation of complex systems need to include many disciplines, modeling languages, simulation environments, etc. By using unified modeling language to build models, barriers among different fields can be broken; the efficiency of model integration can be improved. Generally, a complex system contains some integrated circuit modules, which can be used for control or calculation of the system. Compared with system model, integrated circuit model is more fine-grained and lower-level and contains logic for control and algorithms. Thus, a method that modeling integrated circuit by unified modeling language is necessary. In this paper, a unified modeling language is introduced, called X language, and a modeling method of algorithm-hardware based on the X language is proposed. First of all, the hardware characteristics of the Kalman filter algorithm are extracted by analyzing the algorithm. Second, X language models are built to describe the algorithm-hardware. Then, the X language models are converted to Very-High-Speed Integrated Circuit Hardware Description Language (VHDL). Finally, the algorithm-hardware models are simulated and verified. The results show that the algorithm-hardware modeling method based on X language is feasible.

The factors that affect the performance of the equipment are numerous and complicated, which makes it difficult to establish a performance calculation model. This paper puts forward a data-driven modeling method with reverse process for this problem. Based on the partial least squares (PLS) algorithm and the gray relational analysis (GRA) method, the analysis method of the performance related factors, the extraction method of characteristic variables, and the performance modeling method are studied. The related factors of the energy consumption of an industrial steam turbine are analyzed, and an energy consumption calculation model is established, and the effectiveness of the above-mentioned modeling methods is verified with sample data, which provides a basis for the energy-saving optimization of the steam turbine.

With the development of the Internet of Things and devices continuing to scale, using cloud computing resources to process data in real-time is challenging. Edge computing technologies can improve real-time performance in processing data. By introducing the FPGA into the computing node and using the dynamic reconfigurability of the FPGA, the FPGA-based edge node can increase the edge node capability. In this paper, a task-based collaborative method for an FPGA-based edge computing system is proposed in order to meet the collaboration among FPGA-based edge nodes, edge nodes, and the cloud. The modeling of the task includes two parts, task information and task-dependent file. Task information is used to describe the running information and dependency information required for the task execution. Task-dependent file contains the configuration bit-stream of FPGA in running of the task. By analyzing the task behavior, this paper builds four basic behaviors, analyzes the critical attributes of each behavior, and summarizes the task model suitable for FPGA-based edge nodes. Tasks with specific functions can be created by modifying different attributes of model nodes. Finally, the availability of the model and the task-based collaborative method are verified by simulation experiments. The experimental results that the task model proposed in this paper can meet cloud-edge collaboration in the FPGA-based edge computing environment.

Modeling and Simulation of Cyber-Physical Systems (MSCPS) is demanding in terms of immediate response to dynamic and complex changes of CPS. Simulation-oriented model reuse can be used to build a whole CPS model by reusing developed models in a new simulation application, which avoid repeated modeling and thus reduce the redevelopment of submodels. Model composition, one of the important methods, enables model reuse by selecting and adopting diversified integration solutions of simulation components to meet the requirements of simulation application systems. In this paper, a real-time model integration approach for global CPS modeling is proposed, which reuses developed submodels by compositing submodel nodes. Specifically, a constrained directed graph of submodels for the whole system which can meet the simulation requirements is constructed by reverse matching. Submodel properties, including co-simulation distance between submodel nodes, reuse benefit and simulation performance of model nodes, are quantified. Based on the properties, the model-integrated solution for the whole CPS simulation is retrieved throughout the model constrained digraph by the Genetic Algorithm (GA). In the experiment, the proposed method is applied to a typical model integrated computing scenario containing multiple model-integration solutions, among which the Pareto optimal solutions are retrieved. Results show that the effectiveness of the model integration method proposed in this paper is verified.

Deep neural network (DNN) models have been widely used in e-commerce, games, automobiles, manufacturing, and so on. Improper structure, parameters, activation function, or incorrect loss function of the DNN models may cause defects in performance or security. As a result, there are some researches that focus on repairing DNN such as MODE and Apricot. However, the cost of repairing is high or the repair may lead to overfitting. In order to solve this problem, we propose GenMuNN, which is a Mutation-Based Approach to Repair Deep Neural Network Models. First, it analyzes the importance of the weights of the neurons in each layer of the DNN model to the correctness of the final prediction results, and ranks the weights according to the influence on the prediction results of the DNN model. Second, mutation is performed to generate mutants based on the rank of weights, and genetic algorithms are used to select mutants for the next round of mutation until the stop condition is touched. Experiments are carried on a set of DNN models which are trained with the MNIST dataset. The experimental results show that GenMuNN can improve the accuracy of the DNN models.

Online prediction as well as online simulation of surface temperature will play a significant role in flight safety of future near space hypersonic vehicles (HVs). But it still remains a classical scientific problem both in thermodynamics and aerospace science. In view of the complex HV structure and complex heat conduction procedure, three-dimensional numerical simulation is too inefficient for online prediction, while current rapid computation methods cannot meet the requirement of accuracy. Therefore, a hybrid intelligent dynamic modeling approach is proposed to estimate the surface temperature of HV with the combination of mechanism equations, test data and intelligent modeling technology. A simplified model based on a mechanism equation and experimental formulas is presented for predicting or simulating transient heat conduction procedure efficiently, while a case-based reasoning (CBR) algorithm is developed to estimate two uncertain coefficients in the simplified model. Furthermore, a support vector regression (SVR)-based model is developed to compensate the modeling error. With the data both from high-precision finite element computation and from real-world HV thermal protection experiments, a number of comparative simulations demonstrate the effectiveness of the proposed hybrid intelligent modeling approach.

As the system becomes more intelligent and embedded, the operating environment is gradually changed from closed, static, and controllable to more open, dynamic, and difficult to control. As a result, the design of complex software systems is faced with many challenges arising from the uncertainty of the environment (UoE). On the one hand, ignoring the UoE to manually describe requirements is not only a tough job, but it can also hinder the discovery of potential requirements; on the other hand, it is a challenge to integrate the representation of and reasoning of UoE into the process of modeling complex systems. Based on the analysis of the characteristics of complex systems engineering, this paper takes solving the UoE caused by stakeholders preferences and complex environment context as the entry point and designs a fuzzy control decision-making framework. Specifically, the framework contributes to the spiral of complex systems while solving the UoE by constructing a closed-loop intelligent system based on automatic data flow between information space and physical space for environment sensing, uncertainty analysis, requirements mining, fuzzy reasoning, decision execution as well as feedback optimization. Finally, the framework is validated with a concrete example of an adaptive treadmill system based on the support tools developed.

Scientific modeling provides mathematical abstractions of real-world systems and builds software as implementations of these mathematical abstractions. Ocean science is a multidisciplinary discipline developing scientific models and simulations as ocean system models that are an essential research asset. In software engineering and information systems research, modeling is also an essential activity. In particular, business process modeling for business process management and systems engineering is the activity of representing processes of an enterprise, so that the current process may be analyzed, improved and automated. In this paper, we employ process modeling for analyzing scientific software development in ocean science to advance the state in engineering of ocean system models and to better understand how ocean system models are developed and maintained in ocean science. We interviewed domain experts in semi-structured interviews, analyzed the results via thematic analysis, and modeled the results via the Business Process Modeling Notation (BPMN). The processes modeled as a result describe an aspired state of software development in the domain, which are often not (yet) implemented. This enables existing processes in simulation-based system engineering to be improved with the help of these process models.

In order to realize the agility, collaboration and visualization of alloy material development process, a product development platform based on simulation and modeling technologies is established in this study. In this platform, the whole-process simulation module builds multi-level simulation models based on metallurgical mechanisms from the production line level, the thermo-mechanical coupling field level and the microstructure evolution level. The design knowledge management module represents the multi-source heterogeneous material design knowledge through ontology model, including customers’ requirement knowledge, material component knowledge, process design knowledge and quality inspection knowledge, and utilizes the case-based reasoning approach to reuse the knowledge. The data-driven modeling module applies machine learning algorithms to mine the relationships between product mechanical properties, material components, and process parameters from historical samples, and utilizes multi-objective optimization algorithms to find the optimal combination of process parameters. Application of the developed platform in actual steel mills shows that the proposed method helps to improve the efficiency of product design process.

In order to analyze the proposed method, this paper takes the cockpit as the complex system, since the traditional cockpit design evaluation method can set up an actual engineering prototype. Although this method can realize the accurate evaluation of the internal structure layout, control devices, and interface display of the cockpit, it is difficult to make modifications once the production is completed, which is not suitable for multi-scheme comparison and rapid iteration in the early stage of civil aircraft design. In this regard, this paper establishes the civil aircraft cockpit physical environment and the corresponding virtual cockpit environment based on the virtual-real matching technology, then proposes a mixed reality simulation evaluation method so as to realize the real-time ergonomic evaluation of the digital human body followed by a real person. Experimental results show that this mixed reality simulation evaluation method can effectively improve the evaluation accuracy of cockpit design, ensure the efficiency of multi-scheme comparison and optimization iteration in the early stage of cockpit design.

In order to obtain stable and high-precision motion tracking in mixed reality (MR) simulation of the complex system, an optical-inertial hybrid tracking method is proposed, which combines the accuracy of optical motion capture equipment and the robustness of inertial motion capture equipment, and can reduce the occlusion problem of entity scene on the premise of ensuring the localization accuracy. The method has been applied to the ergonomic evaluation of large civil aircraft development processes, and the effectiveness and usability of the method have been verified.

Modeling and Simulation Technology has become an important means to study various complex systems with its extensive application. Thus, the accuracy of the simulation models becomes a critical problem and needs to be assessed by employing an appropriate model validation method. The simulation models often have multivariate dynamic responses with uncertainty, while most of the existing validation methods concentrate on the validation of the static responses. Hence, a new validation method is proposed in this paper to validate the dynamic responses of the simulation models over the time domain at a single validation site and multiple validation sites through introducing the discrete Chebyshev polynomials and area metric. For each time series, the orthogonal expansion coefficients are extracted primarily by representing the time series with the discrete orthogonal polynomials. Then, the area metric and the $u$-pooling metric are employed to validate all the uncorrelated coefficients at a single validation site and multiple validation sites, respectively, and the final validation result is obtained by summarizing the metric values. The feasibility and effectiveness of the proposed model validation method are illustrated through the example of the terminal guidance stage of the flight vehicle.