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This handbook describes a framework for development, validation, and integration of multipurpose simulation models. The presented methodology enables reuse of models in different applications with different purposes. The scope is simulation models representing physical environment, physical aircraft systems or subsystems, avionics equipment, and electronic hardware. The methodology has been developed by a small interdisciplinary team, with experience from Modeling and Simulation (M&S) of vehicle systems as well as development of simulators for verification and training. Special care has been taken to ensure usability of the workflow and method descriptions, mainly by means of 1) a user friendly format, easy to overview and update, 2) keeping the amount of text on an appropriate level, and 3) providing relevant examples, templates, and checklists. A simulation model of an aircraft Environmental Control System (ECS) is used as an example to guide the reader through the workflow of developing and validating multipurpose simulation models

A Framework for Executable Systems Modeling

Systems Modeling Language (SysML), like its parent language, the Unified Modeling Language (UML), consists of a number of independently derived model languages (i.e. state charts, activity models etc.) which have been co-opted into a single modeling framework. This, together with the lack of an overarching meta-model that supports uniform semantics across the various diagram types, has resulted in a large unwieldy and informal language schema. Additionally, SysML does not offer a built in framework for managing time and the scheduling of time based events in a simulation. In response to these challenges, a number of auxiliary standards have been offered by the Object Management Group (OMG); most pertinent here are the foundational UML subset (fUML), Action language for fUML (Alf), and the UML profile for Modeling and Analysis of Real Time and Embedded Systems (MARTE). However, there remains a lack of a similar treatment of SysML tailored towards precise and formal modeling in the systems engineering domain. This work addresses this gap by offering refined semantics for SysML akin to fUML and MARTE standards, aimed at primarily supporting the development of time based simulation models typically applied for model verification and validation in systems engineering. The result of this work offers an Executable Systems Modeling Language (ESysML) and a prototype modeling tool that serves as an implementation test bed for the ESysML language. Additionally a model development process is offered to guide user appropriation of the provided framework for model building

Synthesizing Executable Simulations from Structural Models of Component-Based Systems

Experts in robotics systems have developed substantial software tools for simulation, execution, and hardware-in-the-loop testing. Unfortunately, many of these robotics-domain software infrastructures pose challenges for a robotics expert to use, unless that robotics expert is also familiar with middleware programming, and the integration of heterogeneous simulation tools. In this paper, we describe a novel modeling language designed to bridge these two domains in an intuitive visual representation. Using this metamodel-defined modeling language, we can design and build structural models of robotics systems, and synthesize experiments from these constructed models. The restrictions implicit (and explicit) in the visual language guide modelers to build only models that can be synthesized, a "correct by construction" approach. We discuss the impact of this language with a running example of an autonomous ground vehicle, and the hundreds of configuration parameters and several simulation tools that are necessary in order to simulate this complex example

Structural phase transformations in metallic grain boundaries

Structural transformations at interfaces are of profound fundamental interest as complex examples of phase transitions in low-dimensional systems. Despite decades of extensive research, no compelling evidence exists for structural transformations in high-angle grain boundaries in elemental systems. Here we show that the critical impediment to observations of such phase transformations in atomistic modeling has been rooted in inadequate simulation methodology. The proposed new methodology allows variations in atomic density inside the grain boundary and reveals multiple grain boundary phases with different atomic structures. Reversible first-order transformations between such phases are observed by varying temperature or injecting point defects into the boundary region. Due to the presence of multiple metastable phases, grain boundaries can absorb significant amounts of point defects created inside the material by processes such as irradiation. We propose a novel mechanism of radiation damage healing in metals which may guide further improvements in radiation resistance of metallic materials through grain boundary engineering.Comment: 25 pages, 11 figure

AGENT-BASED DISCRETE EVENT SIMULATION MODELING AND EVOLUTIONARY REAL-TIME DECISION MAKING FOR LARGE-SCALE SYSTEMS

Computer simulations are routines programmed to imitate detailed system operations. They are utilized to evaluate system performance and/or predict future behaviors under certain settings. In complex cases where system operations cannot be formulated explicitly by analytical models, simulations become the dominant mode of analysis as they can model systems without relying on unrealistic or limiting assumptions and represent actual systems more faithfully. Two main streams exist in current simulation research and practice: discrete event simulation and agent-based simulation. This dissertation facilitates the marriage of the two. By integrating the agent-based modeling concepts into the discrete event simulation framework, we can take advantage of and eliminate the disadvantages of both methods.Although simulation can represent complex systems realistically, it is a descriptive tool without the capability of making decisions. However, it can be complemented by incorporating optimization routines. The most challenging problem is that large-scale simulation models normally take a considerable amount of computer time to execute so that the number of solution evaluations needed by most optimization algorithms is not feasible within a reasonable time frame. This research develops a highly efficient evolutionary simulation-based decision making procedure which can be applied in real-time management situations. It basically divides the entire process time horizon into a series of small time intervals and operates simulation optimization algorithms for those small intervals separately and iteratively. This method improves computational tractability by decomposing long simulation runs; it also enhances system dynamics by incorporating changing information/data as the event unfolds. With respect to simulation optimization, this procedure solves efficient analytical models which can approximate the simulation and guide the search procedure to approach near optimality quickly.The methods of agent-based discrete event simulation modeling and evolutionary simulation-based decision making developed in this dissertation are implemented to solve a set of disaster response planning problems. This research also investigates a unique approach to validating low-probability, high-impact simulation systems based on a concrete example problem. The experimental results demonstrate the feasibility and effectiveness of our model compared to other existing systems

Final Report of the ModSysC2020 Working Group - Data, Models and Theories for Complex Systems: new challenges and opportunities

Final Report of the ModSysC2020 Working Group at University Montpellier 2At University Montpellier 2, the modeling and simulation of complex systems has been identified as a major scientific challenge and one of the priority axes in interdisciplinary research, with major potential impact on training, economy and society. Many research groups and laboratories in Montpellier are already working in that direction, but typically in isolation within their own scientific discipline. Several local actions have been initiated in order to structure the scientific community with interdisciplinary projects, but with little coordination among the actions. The goal of the ModSysC2020 (modeling and simulation of complex systems in 2020) working group was to analyze the local situation (forces and weaknesses, current projects), identify the critical research directions and propose concrete actions in terms of research projects, equipment facilities, human resources and training to be encouraged. To guide this perspective, we decomposed the scientific challenge into four main themes, for which there is strong background in Montpellier: (1) modeling and simulation of complex systems; (2) algorithms and computing; (3) scientific data management; (4) production, storage and archiving of data from the observation of the natural and biological media. In this report, for each theme, we introduce the context and motivations, analyze the situation in Montpellier, identify research directions and propose specific actions in terms of interdisciplinary research projects and training. We also provide an analysis of the socio-economical aspects of modeling and simulation through use cases in various domains such as life science and healthcare, environmental science and energy. Finally, we discuss the importance of revisiting students training in fundamental domains such as modeling, computer programming and database which are typically taught too late, in specialized masters

Modeling, Simulating, and Parameter Fitting of Biochemical Kinetic Experiments

In many chemical and biological applications, systems of differential equations containing unknown parameters are used to explain empirical observations and experimental data. The DEs are typically nonlinear and difficult to analyze, requiring numerical methods to approximate the solutions. Compounding this difficulty are the unknown parameters in the DE system, which must be given specific numerical values in order for simulations to be run. Estrogen receptor protein dimerization is used as an example to demonstrate model construction, reduction, simulation, and parameter estimation. Mathematical, computational, and statistical methods are applied to empirical data to deduce kinetic parameter estimates and guide decisions regarding future experiments and modeling. The process demonstrated serves as a pedagogical example of quantitative methods being used to extract parameter values from biochemical data models.Comment: 23 pages, 9 figures, to be published in SIAM Revie

Agent-Based Simulations of Blockchain protocols illustrated via Kadena's Chainweb

While many distributed consensus protocols provide robust liveness and consistency guarantees under the presence of malicious actors, quantitative estimates of how economic incentives affect security are few and far between. In this paper, we describe a system for simulating how adversarial agents, both economically rational and Byzantine, interact with a blockchain protocol. This system provides statistical estimates for the economic difficulty of an attack and how the presence of certain actors influences protocol-level statistics, such as the expected time to regain liveness. This simulation system is influenced by the design of algorithmic trading and reinforcement learning systems that use explicit modeling of an agent's reward mechanism to evaluate and optimize a fully autonomous agent. We implement and apply this simulation framework to Kadena's Chainweb, a parallelized Proof-of-Work system, that contains complexity in how miner incentive compliance affects security and censorship resistance. We provide the first formal description of Chainweb that is in the literature and use this formal description to motivate our simulation design. Our simulation results include a phase transition in block height growth rate as a function of shard connectivity and empirical evidence that censorship in Chainweb is too costly for rational miners to engage in. We conclude with an outlook on how simulation can guide and optimize protocol development in a variety of contexts, including Proof-of-Stake parameter optimization and peer-to-peer networking design.Comment: 10 pages, 7 figures, accepted to the IEEE S&B 2019 conferenc