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Two-Dimensional Thin Layer Convective Flow and Heat Transfer of a Dusty Fluid on a Nonlinear Stretching Sheet

Multiscale science and Engineering 2022年第3期4卷 111-118页

作者： Islam, Md. Robiul Ferdows, M. Misra, J. C. Murtaza, M. G. Research Group of Fluid Flow Modeling and Simulation Department of Applied Mathematics University of Dhaka Dhaka Bangladesh Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Centre for Healthcare Science and Technology Indian Institute of Engineering Science and Technology Howrah India Department of Mathematics Comilla University Cumilla Bangladesh

In this study, steady two-dimensional boundary layer flow and heat transfer of a dusty fluid over a nonlinearly stretching sheet with variable thickness, $${u}_{w} (x)={U}_{0}{(x+b)}^{m}$$ has been considered. A variable magnetic field is applied transversely to the direction of the flow. The general problem has been reduced to a system of coupled non-linear ordinary differential equations of two-dimensional boundary layer. By means of similarity transformation, the basic governing equations are transformed into a set of nonlinear ordinary differential equation and then are solved numerically by dsolve routine in MAPLE. Results of the present study have been duly validated by adopting an appropriate procedure. The influence of various parameters such as velocity power index, wall thickness parameter, magnetic parameter fluid-particle interaction parameter for velocity, fluid-particle interaction parameter for temperature, mass concentration of dust particle, Prandtl number, Eckert number has been investigated. The effects are then presented graphically both for dust phase and fluid phase. It is clear that flow field and other quantities of physical interest are significantly influenced by these parameters. Also the effects of the parameters on the skin-friction coefficients and rate of heat transfer in terms of the Nusselt number, which are of physical interest, are shown graphically/in tabular form. The study reveals that the impact of the parameters for the dusty fluid on Nusselt number is considerably higher than that for a clean fluid.

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A Biased-Randomized Simheuristic for a Hybrid Flow Shop with Stochastic Processing Times in the Semiconductor Industry 22

A Biased-Randomized Simheuristic for a Hybrid Flow Shop with...

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Proceedings of the Winter simulation Conference

作者： Majsa Ammouriova Javier Panadero Madlene Leißau Christoph Laroque Christin Schumacher Angel A. Juan Computer Science Department Universitat Oberta de Catalunya Euncet Business School Barcelona SPAIN Institute for Management and Information University of Applied Sciences Zwickau Zwickau GERMANY Informatik 4 - Modeling and Simulation Department of Computer Science TU Dortmund University Dortmund GERMANY Dept. of Applied Statistics and Operations Research Universitat Politècnica de València Alcoy SPAIN

Compared to other industries, production systems in semiconductor manufacturing have an above-average level of complexity. Developments in recent decades document increasing product diversity, smaller batch sizes, and a rapidly changing product range. At the same time, the interconnections between equipment groups increase due to rising automation, thus making production planning and control more difficult. This paper discusses a hybrid flow shop problem with realistic constraints, such as stochastic processing times and priority constraints. The primary goal of this paper is to find a solution set (permutation of jobs) that minimizes the production makespan. The proposed algorithm extends our previous work by combining biased-randomization techniques with a discrete-event simulation heuristic. This simulation-optimization approach allows us to efficiently model dependencies caused by batching and by the existence of different flow paths. As shown in a series of numerical experiments, our methodology can achieve promising results even when stochastic processing times are considered.

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Battery Management System for On-Board Data-Driven State of Health Estimation for Aviation and Space Applications

Battery Management System for On-Board Data-Driven State of ...

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IEEE Space Computing Conference (SCC)

作者： Steffen Bockrath Johannes Wachtler Martin Wenger Radu Schwarz Marco Pruckner Vincent R. H. Lorentz Fraunhofer IISB Institute for Integrated Systems and Device Technology Schottkystraße 10 Erlangen Germany Modeling and Simulation Institute of Computer Science University of Würzburg Am Hubland Würzburg Germany Chair of Electronics for Electrical Energy Storage Bavarian Center for Battery Technology (BayBatt) University of Bayreuth Weiherstrasse 26 Bayreuth Germany

To ensure a safe and economically valuable operation of a battery system over the whole lifetime, a battery management system is used for measuring and monitoring battery parameters and controlling the battery system. Since the battery performance decreases over its lifetime, a precise on-board aging estimation is needed to identify significant capacity degradation endangering the functionality and safety of a battery system. Especially for aviation and space applications, this can result in catastrophic scenarios. Therefore, in this work, a generic battery management system approach is presented considering aerospace application requirements. The modular hardware and software architecture and its components are described. Moreover, it is shown that the developed battery management system supports the execution of data-driven state of health estimation algorithms. For this purpose, aging estimation models are developed that only receive eight high-level parameters of partial charging profiles as input without executing further feature extraction steps and can thus be easily provided by a battery management system. Three different neural network architectures are implemented and evaluated: a fully connected neural network, a 1D convolutional neural network and a long short-term memory network. It is shown that all three aging models provide a precise state of health estimation by only using the obtained high-level parameters. The achieved fully connected neural network provides the best tradeoff between required memory resources and accuracy with an overall mean absolute percentage error of 0.41%.

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A General Model for Large-Scale Paddy Rice Mapping by Combining Biological Characteristics, Deep Learning, and Multisource Remote Sensing Data

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 14705-14717页

作者： Zhenjie Liu Jialin Liu Yingyue Su Xiangming Xiao Jingwei Dong Luo Liu Hubei Key Laboratory of Intelligent Geo-Information Processing School of Computer Science China University of Geosciences Wuhan China Guangdong Province Key Laboratory for Agricultural Resources Utilization South China Agricultural University Guangzhou China Department of Microbiology and Plant Biology Center for Earth Observation and Modeling University of Oklahoma Norman OK USA Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

Due to the influences combined with global climate change and human activity, paddy rice area and distribution have undergone dramatic changes. Currently, many approaches for paddy rice mapping rely on the prior knowledge of paddy rice phenology or require widely distributed ground samples of paddy rice, which are limited for large-scale applications. In this work, we propose a general paddy rice mapping (GPRM) model by combining biological characteristics, deep learning, and multisource remote sensing data. The proposed GPRM first utilizes the normalized difference vegetation index and land surface water index to acquire large-scale remote sensing dataset in key phenology periods of paddy rice, such as the transplanting period and peak vegetative growth period. Then, a general model using object-based deep neural networks is developed and trained by the remote sensing dataset and the ground reference data collected in one region (e.g., Guangdong Province), which can be directly applied for generating 10-m paddy rice maps in other regions with different climate conditions and complex cropping systems (e.g., Jiangxi Province and Heilongjiang Province). The results demonstrate that the GPRM can realize remarkable performance of paddy rice mapping in China. The overall accuracies are over 99%, and the user accuracy, producer accuracy, and Kappa coefficient vary from 0.77 to 0.93, 0.94 to 0.97, 0.9 to 0.95, respectively. Overall, the GPRM is has significant promise for large-scale paddy rice mapping with complex cropping systems, thus supporting global agricultural development strategies and food security.

关键词： Deep learning Climate change Earth Observing System Phenology Crops Human activity recognition Terrain mapping

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Securing the light escaping in a Li-Fi network environment

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Procedia computer science 2022年 201卷 684-689页

作者： D.D. Diambeki R.E. Mandiya K. Kyamakya S.K. Kasereka University of Kinshasa Mathematics and Computer Science Department Kinshasa DR Congo Artificial intelligence Big data and modeLing simulation research center (ABIL) Kinshasa Democratic Republic of the Congo Alpen-Adria-Universitaet Klagenfurt Institute of Smart Systems Technologies Department of Mathematical Sciences Klagenfurt Austria

Nowadays, everyone knows Wi-Fi. The technology that ensures connection of computer equipment. It is now positioned as the easiest way to interconnect multiple end users because It allows user mobility in workspaces, avoids cable clutter within the network coverage, such as offices, homes and so on. Despite these advantages, several scientific studies have shown that this technology can raise security and safety issues and is dangerous to human health, and therefore not suitable for hospital networks. In an attempt to overcome these difficulties, Li-Fi technology based on visible light is beginning to interest researchers. This technology is considered more secure than Wi-Fi because the light can be contained in a physical space and waves can’t pass through walls and other objects. Unfortunately, when the physical environment contains spaces that can escape light to the outdoors, this can be a security risk for Li-Fi because it uses light sources to transmit data. That why, in this paper, the main differences between Wi-Fi and Li-Fi technologies are explained, and a Li-Fi architecture provided with an authentification captive portal and a data transfer security mechanism between end users based on the AES algorithm is proposed. The developed prototype and the architecture proposed guarantee the security of the Li-Fi network even if the physical environment is provided with multiple light escape spaces.

关键词： Li-Fi (Light-Fidelity) VLC (Visible Light Communication) LED (Light Emitting Diode) Photodetector LDR (Light Dependent Resistor) Arduino AES algorithm Captive portal

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modeling and simulation to Improve Real Electric Vehicles Charging Processes by Integration of Renewable Energies and Buffer Storage

Modeling and Simulation to Improve Real Electric Vehicles Ch...

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simulation Winter Conference

作者： Konstantin Sing Pierre Mertiny Marco Pruckner Department of Mechanical Engineering University of Erlangen-Nürnberg Erlangen GERMANY Department of Mechanical Engineering University of Alberta Edmonton Alberta CANADA Modeling and Simulation Institute of Computer Science University of Würzburg Würzburg GERMANY

ISBN: (纸本)9781665476621

The present study explores a simulation model combining system dynamics and discrete-event simulation for an electric vehicle charging system. For the representation of the charging demand the model employs data from an actual facility for vehicle charging. While being connected to the electrical grid, the system is augmented by a solar photovoltaic installation and stationary battery energy storage. Multiple simulation runs were performed to analyze the considered energy system over a 1-year period and compare relevant output parameters for different system configurations and system locations. Results show that a solar photovoltaic installation can be effectively integrated. For the degree of self-sufficiency, high values of 87 % can be achieved with combined solar photovoltaic and battery energy storage systems.

关键词： Photovoltaic systems Renewable energy sources System dynamics Buffer storage Carbon dioxide Electric vehicle charging Data models

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Design of an Incremental Conductance Maximum power point tracking Controller based on Automation PLC for PV Applications 3

Design of an Incremental Conductance Maximum power point tra...

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3rd International Conference on Electrical, Communication and computer Engineering, ICECCE 2021

作者： Yatlml, Hanane Ouberri, Youness Marah, Rim Aroudam, Elhassan Chahid, Sara Essaadi University Modeling and Simulation of Mechanical Systems Team Faculty of Sciences Physics Department Tetouan Morocco Essaadi University Computer Science and Systems Engineering Laboratory Faculty of Sciences Abdelmalek Tetouan Morocco Chapman University Advanced Physics Laboratory Institute for Quantum Studies BurtonsvilleMD United States

ISBN: (纸本)9781665438971

One of the most important concerns about the control of the PV system is the maximum power point tracking algorithms, their implementation in various electronic devices for multiple technologies, and then the improvement of the quality and the accuracy of the outcomes using different techniques. This paper presents a programmable logic controller software design for a standalone photovoltaic system, and a comparison analysis between the incremental conductance and the perturb and observe MPPT algorithms based on an automation approach. The simulation using an automation software (Siemens TIA portal) and the experimental implementation using an automation PLC (S7-1200 PLC) reveals that the later presents a good MPP tracker for both algorithms;the only difference was that the IC algorithm shows better performance and lower oscillation around the maximum power point. The implementation of the incremental conductance on the PLC program has many advantages compared to the perturb and observe algorithm: mended tracking of the maximum power point despite the various and the fast-changing on weather conditions, fewer oscillations around the maximum power point, fast response to the change on temperature and irradiation levels, better stability for battery charging signals. The entire PV system is simulating in Siemens total integration automation interface (TIA portal) and the results are plotting using Siemens Human Machine Interface (WinCC). © 2021 IEEE.

关键词： Maximum power point trackers

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An Auto-Parallel Method for Deep Learning Models Based on Genetic Algorithm

An Auto-Parallel Method for Deep Learning Models Based on Ge...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yan Zeng ChengChuang Huang YiJie Ni ChunBao Zhou JiLin Zhang Jue Wang MingYao Zhou MeiTing Xue YunQuan Zhang School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory for Modeling and Simulation of Complex Systems Ministry of Education Hangzhou China Data Security Governance Zhejiang Engineering Research Center Hangzhou China School of ITMO Joint Institute Hangzhou Dianzi University Hangzhou China Institute of Computer Network Information Center of the Chinese Academy of Sciences Beijing China HuaWei State Key Laboratory of Computer Architecture Institute of Computing Technology of the Chinese Academy of Sciences Beijing China

As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability. This paper proposes an automatic parallel method based on a dual-population genetic algorithm, TGA, which transforms model partitioning and placement into an integer linear programming problem and constructs a cost model to evaluate the solution. The solution space is built using the neural network’s dataflow graph and device cluster’s topology, and the dual-population genetic algorithm is used to search for the optimal model parallel strategy. Experiments with various models show that the proposed method can improve single-step execution time by up to 42% compared to the Baechi method and up to 37.7% compared to the Hierarchical method.

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Efficient time stepping for numerical integration using reinforcement learning

arXiv

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arXiv 2021年

作者： Dellnitz, Michael Hüllermeier, Eyke Lücke, Marvin Ober-Blöbaum, Sina Offen, Christian Peitz, Sebastian Pfannschmidt, Karlson Department of Mathematics Paderborn University Germany Department of Computer Science LMU Munich Germany Modeling and Simulation of Complex Processes Zuse Institute Berlin Germany Department of Computer Science Paderborn University Germany

Many problems in science and engineering require an efficient numerical approximation of integrals or solutions to differential equations. For systems with rapidly changing dynamics, an equidistant discretization is often inadvisable as it either results in prohibitively large errors or computational effort. To this end, adaptive schemes, such as solvers based on Runge-Kutta pairs, have been developed which adapt the step size based on local error estimations at each step. While the classical schemes apply very generally and are highly efficient on regular systems, they can behave sub-optimal when an inefficient step rejection mechanism is triggered by structurally complex systems such as chaotic systems. To overcome these issues, we propose a method to tailor numerical schemes to the problem class at hand. This is achieved by combining simple, classical quadrature rules or ODE solvers with data-driven time-stepping controllers. Compared with learning solution operators to ODEs directly, it generalises better to unseen initial data as our approach employs classical numerical schemes as base methods. At the same time it can make use of identified structures of a problem class and, therefore, outperforms state-of-the-art adaptive schemes. Several examples demonstrate superior efficiency. Source code is available at https://***/lueckem/quadrature-ML. © 2021, CC BY-NC-SA.

关键词： Ordinary differential equations

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Chances and Challenges of ChatGPT and Similar Models for Education in M&S

Chances and Challenges of ChatGPT and Similar Models for Edu...

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simulation Winter Conference

作者： Andreas Tolk Philip Barry Margaret L. Loper Ghaith Rabadi William T. Scherer Levent Yilmaz Modeling and Analysis Innovation Center The MITRE Corporation Charlottesville VA USA Mission Analysis Group L3Harris Corporation Herndon VA USA Georgia Tech Research Institute Georgia Institute of Technology Atlanta GA USA University of Central Florida School of Modeling Simulation and Training Orlando FL USA Department of Systems and Information Engineering University of Virgina Charlottesville VA USA Samuel Ginn College of Engineering Computer Science and Software Engineering Auburn University Auburn AL USA

This position paper summarizes the inputs of a group of experts from academia and industry presenting their view on chances and challenges of using ChatGPT within modeling and simulation education. The experts also address the need to evaluate continuous education as well as education of faculty members to address scholastic challenges and opportunities while meeting the expectation of industry. Generally, the use of ChatGPT is encouraged, but it needs to be embedded into an updated curriculum with more emphasis on validity constraints, systems thinking, and ethics.

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