Simulation Of Complex Systems Research Articles

Reducing and Calibrating for Input Model Bias in Computer Simulation

Input model bias is the bias found in the output performance measures of a simulation model caused by estimating the input distributions/processes used to drive it. When the simulation response is a nonlinear function of its inputs, as is usually the case when simulating complex systems, input modelling bias is amongst the errors that arise. In this paper, we introduce a method that recalibrates the input parameters of parametric input models to reduce the bias in the simulation output. The proposed method is based on sequential quadratic programming with a closed form analytical solution at each step. An algorithm with guidance on how to practically implement the method is presented. The method is shown to be successful in reducing input modelling bias and the total mean squared error caused by input modelling error. Summary of Contribution: This paper furthers the understanding and treatment of input modelling error in computer simulation. We provide a novel method for reducing input model bias by recalibrating the input parameters used to drive a simulation model. A sequential quadratic programming approach with an explicit solution is provided to recalibrate the input parameters. The method is therefore computationally inexpensive. An algorithm outlining our proposed procedure is provided within the paper. An evaluation of the method shows the method successfully reduces input model bias and may also reduce the mean squared error caused by input modelling in the output of a simulation model.

Error correction algorithms in non-null aspheric testing next generation sequencing data

Non-null tests are more versatile than null tests and could give fast, generic tests with sufficient performance. The non-null testing methods are able to measure aspheric surfaces with substantial deviations from a reference sphere. The ray will return along a different path and interfere with a separate reference ray in a non-null arrangement. The retrace error occurs because of the significant disparity between the aspheric and the reference. To correct retracing errors and replicate aspheric fabrication imperfections, complex systems modelling and simulations are required. We propose a ray-tracing-based technique for simulating the non-null interferometer that estimates the optical path differences by tracking lasers across the reference and testing paths. An error correction method is used by ray trace analysis to correct the retracing errors in non-null tests of aspheric surfaces. Experiments were conducted to demonstrate the effectiveness of this strategy. The result of the study shows that the proposed system successfully and effectively rectifies the retracing error compared to other practical findings and computer simulation methods. It is more versatile, faster, and provides adequate aspheric assessment performance.

Metamodel optimization of a complex, rural–urban emergency medical services system

Complex simulation systems, such as those involving emergency medical services (EMS), are often too computationally demanding to be used in optimization problems. Metamodeling is an attractive alternative, in which a sample of system configurations is evaluated using simulation, and a fast predictive model is developed as a surrogate for the slow simulator. Though the metamodeling literature is extensive, there has been little exploration of how much data is required to construct metamodels that can be used to solve optimization problems effectively, particularly in the context of a complicated rural–urban EMS system environment. In this work, the EMS system in northern St. Louis County, Minnesota is studied, with the goal of discovering station configurations with improved response times. The underlying physical system is complex, with 12 stations spread across both rural and urban areas and a fairly large geographic footprint. A decade of call data is used to develop and validate a stochastic discrete event simulator (DES) for this system, and then the simulator and raw data are used to select realistic station configurations to train the metamodel. Results are first given for just a single station within the system, and then increasingly complex settings are examined culminating with consideration of all 12 stations. Overall, though the metamodeling approach was effective for simpler cases, it requires a tremendous amount of data for larger settings. Specifically for the St. Louis County example, improved configurations were found for the one- and two-station cases, but the amount of data required to produce effective metamodels for the five- and twelve-station versions of the system was computationally infeasible given current DES and optimization heuristic implementations.

Digital Twin Technology Challenges and Applications: A Comprehensive Review

A digital twin is a virtual representation of a physical object or process capable of collecting information from the real environment to represent, validate and simulate the physical twin’s present and future behavior. It is a key enabler of data-driven decision making, complex systems monitoring, product validation and simulation and object lifecycle management. As an emergent technology, its widespread implementation is increasing in several domains such as industrial, automotive, medicine, smart cities, etc. The objective of this systematic literature review is to present a comprehensive view on the DT technology and its implementation challenges and limits in the most relevant domains and applications in engineering and beyond.

Visualization Techniques for Simulation-Based Dependent Failure Analysis

<div class="section abstract"><div class="htmlview paragraph">Failure analysis of engineering systems typically emphasizes identification and mitigation under an independent failure assumption with dependent failures treated as the exception rather than the rule. Some frameworks for addressing dependent failures through analysis appear in standards including NUREG 0492, ISO 26262, MIL-1629-A, and ARP4761 amongst others. The purpose of identifying these dependencies is to allow system analysts to determine and quantify the factors that influence dependent fault probabilities. Once identified, failure relationships can be incorporated into a Discrete Event Simulation (DES) of the system, providing a mathematically rigorous estimate of system utility (e.g., availability, reliability). The output of a simulation can provide an expected value of performance but additionally, can also allow the analyst to identify the downstream impact of probabilistic dependencies between system elements. Detailed simulations of complex systems have the potential to generate rich data sets, which once analyzed may aid in the formulation of corrective actions to mitigate any detrimental effects identified from simulated failure dependencies. However, human factors research has demonstrated that effective decision making requires that only the key information is provided to the user, which in turn should be supported through effective data visualization techniques. In this paper, case studies are conducted using automated failure dependency mappings generated using a model-based RAMS software and then simulated using a DES framework. Key mappings and transformations of temporal data from the simulation are then presented along with various supporting visualization techniques that together aim to elevate the situational awareness of analysts when performing complex system evaluation.</div></div>

1D and Map-Based Modeling Approaches for Railway Compression Ignition Engine in NRTC Application

Internal combustion engine-driven railway vehicles play an important role in the sector even today, due to the incomplete electrification of railway routes. However, stringent COP26 environmental rules are driving manufacturers and the scientific community to study more complex or alternative propulsion systems. Therefore, the design of new powertrains is becoming more challenging. Affordable, along with robust, development tools are fundamental for their development and optimization. In this framework, numerical simulation can represent an effective instrument to face these requirements. The proposed study assesses the accuracy of different modelling approaches for the same engine. In particular, a detailed 1D model, a simplified 1D model and a map-based model are compared. Although studies on engine simulation are available in the technical literature, the novelty introduced with this work is the assessment of accuracy and computational times of the engine models, considered by performing the new emission standard Non-Road Transient Cycle (NRTC), which is applied to a specific field such as Heavy Duty (HD) Compression Ignition (CI) engines for railway applications. This study provides new and quantitative results rarely available in the specific literature. The results show that the simplest model, despite its lower accuracy, maintains good predictive results in terms of cumulative fuel consumption and cumulative nitric oxide (NOx) emissions over the cycle considered. In particular, the difference in terms of fuel consumption for the map-based model is within 5% compared with the more detailed models. Moreover, the computational effort required by the simplest model is three orders of magnitude lower compared with the more detailed model. Therefore, as the simulation run-time is the priority, the simplest modeling approach is suitable for the evaluation of the global performance, in view of a more complex systems simulation, such as a hybrid powertrain.

Investigation into different 1D/3D co-simulation methodologies applied to a natural circulation loop

1D/3D co-simulation is a promising approach for the modelling and simulation of complex systems. These systems are typically computationally intensive. By combining the desired features of a one-dimensional (1D) systems code and a three-dimensional (3D) Computational Fluid Dynamics (CFD) code, the required resources are optimized whilst still retaining an acceptable level of accuracy. Co-simulation has been applied in various domains and scenarios, often without a comprehensive study on the impact to simulation- stability, -time, and results. The purpose of this study is to explore the behaviour of some of the strategies by applying them to the simulation of the natural circulation occurring in a simplified Reactor Cavity Cooling System (RCCS). Four different co-simulation approaches were explored to investigate the radiative- and convective- heat transfer as well as the natural circulation phenomena occurring in a RCCS facility. Typically, in these four modelling approaches a 3D methodology was employed to model the radiation and convection heat transfer in the cavity between the heater wall and the outer wall of the riser. The 1D methodology was employed to model the remainder of the RCCS system. The 1D systems code, Flownex, and the 3D CFD code Ansys Fluent were used to perform the 1D/3D co-simulations. Each of the codes modelled a relevant part of the RCCS facility and the models were coupled by explicitly exchanging boundary conditions or relevant data between the 1D and 3D regions. The validity of the coupled 1D/3D approaches was investigated by comparing them with a full 1D simulation.

A novel framework for verification and validation of simulations of autonomous robots

Modeling and simulation (M&S) plays a critical role in both engineering and basic research processes. However, M&S is only truly useful if the model and simulation outputs are accurate. As such, significant research has been undertaken to establish what “accurate” means for simulations and what subsequent level of “trust” should be given to M&S outputs. Trust in M&S outputs is established by verifying and validating the models and simulations. While a wealth of research can be found to define verification and validation (V&V) for traditional M&S, little research has been done to define a methodology for the V&V of simulations of complex, intelligent, and autonomous systems. Specifically, no methodology for V&V of simulations of autonomous robots has been developed to date. This paper presents a brief overview of the current V&V methods in use for traditional simulations. In light of this review, a novel framework for the V&V of simulations for predicting the behaviors of autonomous robots is developed, and this framework is presented in detail. This V&V framework is then applied to the use-case of an autonomous unmanned ground vehicle’s (UGV’s) navigation task. The framework is applied for model validation of Global Positioning System (GPS), inertial measurement unit (IMU), and RGB camera sensor models. The framework is further applied to validate these sensor models for an example camera-based autonomous navigation algorithm, stop sign detection.

Framework of Experimental Design and Data Mining in Multi-agent Simulation

Multi-agent based simulation (MABS) is an important approach for studying complex systems. The Agent-based model often contains many parameters, these parameters are usually not independent, with differences in their range, and may be subjected to constraints. How to use MABS investigating complex systems effectively is still a challenge. The common tasks of MABS include: summarizing the macroscopic patterns of the system, identifying key factors, establishing a meta-model, and optimization. We proposed a framework of experimental design and data mining for MABS. In the framework, method of experimental design is used to generate experiment points in the parameter space, then generate simulation data, and finally using data mining techniques to analyze data. With this framework, we could explore and analyze complex system iteratively. Using central composite discrepancy (CCD) as measure of uniformity, we designed an algorithm of experimental design in which parameters could meet any constraints. We discussed the relationship between tasks of complex system simulation and data mining, such as using cluster analysis to classify the macro patterns of the system, and using CART, PCA, ICA and other dimensionality reduction methods to identify key factors, using linear regression, stepwise regression, SVM, neural network, etc. to build the meta-model of the system. This framework integrates MABS with experimental design and data mining to provide a reference for complex system exploration and analysis.

PROBLEMS OF RELIABILITY ASSESSMENT OF COMPLEX TECHNICAL SYSTEMS

The article discusses the problems and factors that prevent an accurate assessment of the reliability and reliability of the functioning of complex technical systems. The issues of modeling and simulation of complex structural objects and systems are raised.

Agents.jl: a performant and feature-full agent-based modeling software of minimal code complexity

Agent-based modeling is a simulation method in which autonomous agents interact with their environment and one another, given a predefined set of rules. It is an integral method for modeling and simulating complex systems, such as socio-economic problems. Since agent-based models are not described by simple and concise mathematical equations, the code that generates them is typically complicated, large, and slow. Here we present Agents.jl, a Julia-based software that provides an ABM analysis platform with minimal code complexity. We compare our software with some of the most popular ABM software in other programming languages. We find that Agents.jl is not only the most performant but also the least complicated software, providing the same (and sometimes more) features as the competitors with less input required from the user. Agents.jl also integrates excellently with the entire Julia ecosystem, including interactive applications, differential equations, parameter optimization, and so on. This removes any “extensions library” requirement from Agents.jl, which is paramount in many other tools.

Predicting Molecular Photochemistry Using Machine-Learning-Enhanced Quantum Dynamics Simulations.

The processes which occur after molecules absorb light underpin an enormous range of fundamental technologies and applications, including photocatalysis to enable new chemical transformations, sunscreens to protect against the harmful effects of UV overexposure, efficient photovoltaics for energy generation from sunlight, and fluorescent probes to image the intricate details of complex biomolecular structures. Reflecting this broad range of applications, an enormously versatile set of experiments are now regularly used to interrogate light-driven chemical dynamics, ranging from the typical ultrafast transient absorption spectroscopy used in many university laboratories to the inspiring central facilities around the world, such as the next-generation of X-ray free-electron lasers.Computer simulations of light-driven molecular and material dynamics are an essential route to analyzing the enormous amount of transient electronic and structural data produced by these experimental sources. However, to date, the direct simulation of molecular photochemistry remains a frontier challenge in computational chemical science, simultaneously demanding the accurate treatment of molecular electronic structure, nuclear dynamics, and the impact of nonadiabatic couplings.To address these important challenges and to enable new computational methods which can be integrated with state-of-the-art experimental capabilities, the past few years have seen a burst of activity in the development of "direct" quantum dynamics methods, merging the machine learning of potential energy surfaces (PESs) and nonadiabatic couplings with accurate quantum propagation schemes such as the multiconfiguration time-dependent Hartree (MCTDH) method. The result of this approach is a new generation of direct quantum dynamics tools in which PESs are generated in tandem with wave function propagation, enabling accurate "on-the-fly" simulations of molecular photochemistry. These simulations offer an alternative route toward gaining quantum dynamics insights, circumventing the challenge of generating ab initio electronic structure data for PES fitting by instead only demanding expensive energy evaluations as and when they are needed.In this Account, we describe the chronological evolution of our own contributions to this field, focusing on describing the algorithmic developments that enable direct MCTDH simulations for complex molecular systems moving on multiple coupled electronic states. Specifically, we highlight active learning strategies for generating PESs during grid-based quantum chemical dynamics simulations, and we discuss the development and impact of novel diabatization schemes to enable direct grid-based simulations of photochemical dynamics; these developments are highlighted in a series of benchmark molecular simulations of systems containing multiple nuclear degrees of freedom moving on multiple coupled electronic states. We hope that the ongoing developments reported here represent a major step forward in tools for modeling excited-state chemistry such as photodissociation, proton and electron transfer, and ultrafast energy dissipation in complex molecular systems.

Unraveling membrane properties at the organelle-level with LipidDyn

Cellular membranes are formed from different lipids in various amounts and proportions depending on the subcellular localization. The lipid composition of membranes is sensitive to changes in the cellular environment, and its alterations are linked to several diseases. Lipids not only form lipid-lipid interactions but also interact with other biomolecules, including proteins.Molecular dynamics (MD) simulations are a powerful tool to study the properties of cellular membranes and membrane-protein interactions on different timescales and resolutions. Over the last few years, software and hardware for biomolecular simulations have been optimized to routinely run long simulations of large and complex biological systems. On the other hand, high-throughput techniques based on lipidomics provide accurate estimates of the composition of cellular membranes at the level of subcellular compartments. Lipidomic data can be analyzed to design biologically relevant models of membranes for MD simulations. Similar applications easily result in a massive amount of simulation data where the bottleneck becomes the analysis of the data. In this context, we developed LipidDyn, a Python-based pipeline to streamline the analyses of MD simulations of membranes of different compositions. Once the simulations are collected, LipidDyn provides average properties and time series for several membrane properties such as area per lipid, thickness, order parameters, diffusion motions, lipid density, and lipid enrichment/depletion. The calculations exploit parallelization, and the pipeline includes graphical outputs in a publication-ready form. We applied LipidDyn to different case studies to illustrate its potential, including membranes from cellular compartments and transmembrane protein domains. LipidDyn is available free of charge under the GNU General Public License from https://github.com/ELELAB/LipidDyn.

Multi-method simulation modelling of circular manufacturing systems for enhanced decision-making

Circular manufacturing systems (CMS) constitute complex value networks comprising a large and diverse set of stakeholders that collaborate to close the loop of products through multiple lifecycles. Complex systems modelling and simulation play a crucial role in providing quantitative and qualitative insights into the behaviour of such systems. In particular, multi-method simulation modelling that combines agent-based, discrete-event, and system dynamics simulation methods is considered more suitable to model and simulate CMS as it allows to capture their complex and dynamic nature. This paper provides a step-by-step approach on how to build a CMS multi-method simulation model in order to assess their economic, environmental, and technical performance for enhanced decision-making. To model and simulate CMS three main elements need to be considered:•A multi-method model architecture where the CMS stakeholders with heterogeneous characteristics are modelled individually as autonomous agents using agent-based, discrete-event, and system dynamics.•An agent environment defined by a Geographic Information System (GIS) to establish connections based on agents’ geographic location.•The product journey resulting from the product's interaction with various CMS stakeholders in the circular value network is traced throughout its multiple lifecycles.

Preface

Great honor to have your supports to 2021 2nd International Conference on Modeling, Big Data Analytics and Simulation (MBDAS2021) and participation in this academic discussion mainly direct at modelling and simulation, simulation optimization, mathematical modeling, time series analysis, complex system modeling and simulation, big data analytics and metrics, big data quality and provenance control, intelligent computing, quantum computing and related areas.Due to the severe impact of Covid-19 and the travel restrictions, most of the authors can’t attend the conference physically. The organizing committee of MBDAS2021 actively response to the call for epidemic prevention and control and adopt virtual form to conduct this academic conference. MBDAS2021 is rescheduled to be held via Tencent Meeting, November 15, 2021. The conference staff send a formal email notification to all the authors and participants before the meeting, try our best to co-ordinate the meeting and possibly make sure that all the details are in place. We set test room for our experts and authors to have a test on their video, audio and screen sharing, etc. in order to avoid the network meeting uncertainty. This virtual mainly conduct in the forms of Keynote Speech, Oral presentations, Posters, Free discussion.It is great honor to invite five senior experts at here to share their keynote speeches to us. The first expert is Prof. Hui-Ming Wee from College of Electrical Engineering & Computer Science, Chung Yuan Christian University, Taiwan. The second expert is Prof. Christophe Claramunt from Naval Academy Research Institute & Arts & Métiers Institute of Technology, France. The third expert is Prof. Cheng Siong Chin from Newcastle University in Singapore, Singapore. The fourth expert is Prof. Stefano Zedda from Department of Business and Economics, University of Cagliari, Italy. The fifth expert is Prof. T. Velmurugan from Dwaraka Doss Goverdhan Doss Vaishnav College, India. They each make keynote speech for 50 minutes. And then the authors make 10mins oral presentations separately. All of them has contribute to a huge success of MBDAS2021 online conference.List of MBDAS2021 Organization Committees, Keynote Speakers, Technical Program Committees are avilable in this pdf.

Data-driven control-oriented reduced order modeling for open channel flows

Model order reduction can be used for efficient simulation of complex systems. Data-based system identification approaches using neuronal networks or Dynamic Mode Decomposition enable us to extract characteristic properties of the system dynamics in order to reduce them to a low-dimensional space. There the temporal propagation can be described with significantly less computational effort. Both approaches are applied to the boundary actuated St. Venant equations, to obtain control-oriented reduced order models, which try to capture the dynamics of open channel flows for a wide range of input signals. It is investigated whether these models can be used for efficient simulation and how accurately they reconstruct the dynamic behavior of the water depth and velocity of an open channel.

Retracted] Realization of a Noncontact IC Chip with Embedded Ferroelectric Memory in an Auxiliary Timing Device for Sports Games

The research direction of the new generation of embedded memory can be summarized into two types of embedded nonvolatile memory and embedded volatile memory; the research on online testing of embedded memory started in the past ten years, and there are few research results. This article analyzes the feasibility of the noncontact IC chip in the embedded ferroelectric memory of the sports game auxiliary timing device and is aimed at obtaining an optimized embedded ferroelectric memory by analyzing the relevant data to achieve the update and update of the sports game timing device system. Early sports event timing methods generally use manual timing (stopwatch) or camera shooting timing; this method is inefficient, poor real‐time, huge workload, and prone to errors. This research mainly focuses on the analysis and discussion of the material structure and performance of the embedded ferroelectric memory and the process of noncontact IC chip. This article uses custom welding circuit technology to prepare the best ferroelectric filter in the test part and verifies the influence of temperature on the material; in order to understand the properties of ferroelectric materials at the electronic and atomic level, a first‐order statistical method is obtained. The numerical calculation results of the experiment verify that the evaluation value of the serial port synchronization module as a whole exceeds the pulse synchronization; the network synchronization as a whole exceeds the code synchronization, and the result of the network time service module is the opposite, but as a whole, each module of the noncontact IC chip has strong performance adaptability; in the application of auxiliary timing, the maintainability of noncontact IC chip is quite outstanding, and the maximum value is 7.97; a large number of complex simulation system tasks can be completed by simple and direct tasks.

From visualization to discovery, a transdisciplinary framework to re-engineer engineering practices

One way business students apply theoretical knowledge and enhance their decision-making skills is by using digital simulators and seldomly in real life due to the strategic and economic nature of the consequences involved. Besides, engineering students practice their knowledge and skills in laboratories and or actual companies, in many cases, due to the economic and tactical nature of the decisions involved. Due to the COVID pandemic starting in 2020, many engineering schools closed their laboratory access while companies also paused their students' involvement. Hence, many engineering faculties searched for new ways to keep educational schedules and standards. The contribution of this work supplies a transdisciplinary framework (involving faculty, students, and practitioners) to redesign engineering practices to achieve and improve pre-pandemic learning levels. A Six Sigma experience using a web-based Virtual Reality production facility illustrates the framework through three iterations: one-way information flow, two-way interaction with limited capabilities, and complex systems simulations to explore sophisticated challenges. Results show that students experience a more substantial engagement in these novel practices where they expose themselves to challenges that, in many cases, could not be possible in a real-life experience due to safety and economic consequences.

State-of-the-Art Development of Complex Systems and Their Simulation Methods

The research on complex systems is different from that on general systems because the former must consider self-organization, emergence, uncertainty, predetermination, and evolution. As an important method to transform the world, a simulation is one of the most important skills to discover complex systems. In this study, we provide a survey on complex systems and their simulation methods. Initially, the development history of complex system research is summarized from two main lines. Then, the eight common characteristics of the most complex systems are presented. Furthermore, the simulation methods of complex systems are introduced in detail from four aspects, namely, meta-synthesis methods, complex networks, intelligent technologies, and other methods. From the overall point of view, intelligent technologies are the driving force, and complex networks are the advanced structure. Meta-synthesis methods are the integration strategy, and other methods are the supplements. In addition, we show three complex system simulation examples: digital reactor simulation, simulation of a logistics system in the industrial site, and crowd evacuation simulation. The examples show that a simulation is a useful means and an important method in complex system research. Finally, the future development prospects for complex systems and their simulation methods are suggested.

Extensive numerical tests of leapfrog integrator in middle thermostat scheme in molecular simulations

Accurate and efficient integration of the equations of motion is indispensable for molecular dynamics (MD) simulations. Despite the massive use of the conventional leapfrog (LF) integrator in modern computational tools within the framework of MD propagation, further development for better performance is still possible. The alternative version of LF in the middle thermostat scheme (LF-middle) achieves a higher order of accuracy and efficiency and maintains stable dynamics even with the integration time stepsize extended by several folds. In this work, we perform a benchmark test of the two integrators (LF and LF-middle) in extensive conventional and enhanced sampling simulations, aiming at quantifying the time-stepsize-induced variations of global properties (e.g., detailed potential energy terms) as well as of local observables (e.g., free energy changes or bondlengths) in practical simulations of complex systems. The test set is composed of six chemically and biologically relevant systems, including the conformational change of dihedral flipping in the N-methylacetamide and an AT (Adenine-Thymine) tract, the intra-molecular proton transfer inside malonaldehyde, the binding free energy calculations of benzene and phenol targeting T4 lysozyme L99A, the hydroxyl bond variations in ethaline deep eutectic solvent, and the potential energy of the blue-light using flavin photoreceptor. It is observed that the time-step-induced error is smaller for the LF-middle scheme. The outperformance of LF-middle over the conventional LF integrator is much more significant for global properties than local observables. Overall, the current work demonstrates that the LF-middle scheme should be preferably applied to obtain accurate thermodynamics in the simulation of practical chemical and biological systems.