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Design and Numerical simulation of a Ball Cutting Type Energy Absorber Device 7

Design and Numerical Simulation of a Ball Cutting Type Energ...

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2020 7th international conference on computer-Aided Design, Manufacturing, modeling and simulation, CDMMS 2020

作者： Hu, Zhiqing Guo, Liming Zhang, Yuheng College of Materials Engineering Jilin University Changchun130025 China Roll Forging Research Institute Jilin University Changchun130025 China

This paper is verified by numerical simulation and experiment. Based on the principle of energy absorption in metal cutting process, this paper presents a new type of energy absorption structure, which can improve the collision performance of urban rail vehicles. The structure is composed of solid energy absorption tube, hard ball, anti-crawling board and mounting board. Based on the nonlinear dynamic simulation theory and the explicit algorithm of finite element software ABAQUS, the finite element model of the structure of the cutting type energy absorber was established, and the energy absorption process was simulated. Based on the verified simulation model, the influences of the design parameters such as the cutting depth and the number of balls on the energy absorption process are analyzed. It is found that the design parameters affect the cutting force and energy absorption, and the structure has a good energy absorption effect during the collision. © Published under licence by IOP Publishing Ltd.

关键词： Numerical models

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Energy-efficient optimization strategy for simulation task scheduling based on supercomputing platform

Energy-efficient optimization strategy for simulation task s...

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computer Science and Management Technology (ICCSMT), international conference on

作者： Rundong Gan Xun Li Yujiang Long Haibin Su Bin Liu Information Center of Guizhou Power Grid Co. Ltd Guiyang China Fangxin Technology Co. Ltd Changsha China

To address the problems of high energy consumption and underutilization of resources in the supercomputing platform of the grid center, a regression analysis is used to deal with the relationship between resource usage data and energy consumption, and an energy consumption evaluation model is created. The proposed energy consumption assessment model is combined with the existing simulation task scheduling strategy to propose an energy consumption-aware load balancing strategy to improve the overall resource utilization rate from the perspective of improving the load balancing of computing nodes on the platform; a node adjustment algorithm based on real-time CPU utilization is proposed to dynamically adjust the number of nodes involved in computing. The task-node mapping relationship is scheduled according to the load of computing nodes to reduce the energy consumption of the platform without significant impact on the execution time of existing power simulation tasks. The energy consumption modeling and performance testing of the scheduling scheme are carried out under Linux system and through simulated simulation environment such as CloudSim. The experimental results show that the energy consumption evaluation model based on the system resource utilization rate can achieve about 95% accuracy; the total energy consumption of the two scheduling strategies is basically the same, and there is about 16% energy saving compared with the basic scheduling strategy and about 11% energy saving compared with the Max-Min scheduling strategy.

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An Enhanced Routing Protocol Design to Perform Cost Efficient Data Communication over Wireless Sensor Networks

An Enhanced Routing Protocol Design to Perform Cost Efficien...

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Applied Artificial Intelligence and Computing (ICAAIC), international conference on

作者： Dattatraya Arun Jadhav Sajja Suneel M. Amina Begum Meghana Harsh Ghogare Madhan Sekhar Murri Parashuram Shankar Vadar Electronic and telecommunication department Keystone school of Engineering pune Department of CSE(Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad Telangana India Department of ECE Oxford College of Engineering Venmani Tamilnadu India byteXL-Department of Computer Science and Engineering Parul University Vadodara Department of Management Studies Vignan’s Institute of Information Technology(A) AP India Yashwantrao Chavan School of Rural Development Shivaji University Kolhapur Maharashtra India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

This research introduces a groundbreaking approach to address the challenges of cost-efficient data communication in Wireless Sensor Networks (WSNs) through the development of a novel routing protocol named Cost-Efficient Enhanced Routing Protocol (CEERP). WSNs, characterized by resource-constrained sensor nodes and dynamic environmental conditions, necessitate innovative routing strategies to optimize energy consumption and enhance overall network performance. CEERP leverages a sophisticated methodology that integrates Energy Consumption modeling, Adaptive Routing Decisions, alongside Optimization and Fine-Tuning. The Energy Consumption modeling method systematically analyzes various factors influencing energy dynamics in sensor nodes, facilitating the creation of accurate mathematical models. These models inform CEERP’s routing algorithm, enabling intelligent decisions based on predicted energy consumption patterns. The protocol adapts dynamically to real-time variations in network conditions, ensuring responsiveness to changing environmental factors. Implementation and simulation validate CEERP’s performance in diverse scenarios, while Optimization and Fine-Tuning finetune parameters for optimal efficiency. Security and reliability measures are integrated to safeguard data integrity. The research concludes with a comprehensive evaluation of CEERP’s effectiveness, positioning it as a promising solution for achieving cost-efficient and reliable data communication in WSNs. The proposed methodology and CEERP contribute to the advancement of routing protocols for WSNs, offering a holistic and adaptive approach to address the unique challenges posed by resource-constrained environments.

关键词： Wireless sensor networks Energy consumption Adaptation models Routing Routing protocols Mathematical models Data communication

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Research on production decision problem based on dynamic programming and genetic algorithm 24

Research on production decision problem based on dynamic pro...

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Proceedings of the 2024 4th international conference on Computational modeling, simulation and Data Analysis

作者： Jiasen Niu College of Computer and Information Engineering Henan Normal University Xinxiang Henan China

ISBN: (纸本)9798400711831

With the rapid development of the manufacturing industry and the increasingly fierce market competition, enterprises face numerous complex decision-making challenges while pursuing efficient and high-quality production. This paper systematically analyzes the optimal strategies under different production scenarios using methods such as dynamic programming and genetic algorithms, aiming to help enterprises achieve optimal resource allocation, thereby maximizing production benefits and competitiveness. The production process of enterprises involves multiple components and multiple processes. To optimize the multi-stage decision-making process in the production of a certain product, this paper first adopts a dynamic programming model, expanding the decision-making stages to five stages: component inspection, semi-finished product inspection, disassembly of defective semi-finished products, finished product inspection, and handling of defective finished products. Through the method of backward analysis, a decision-making model is constructed that can capture the chain reactions between decisions at each stage and the long-term optimal solution, ensuring the efficiency and optimization of the entire production process. Finally, based on dynamic programming, a genetic algorithm is used to solve the problem, selecting the optimal production plan with the lowest cost and highest efficiency.

关键词： Backward Solution Method

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Comparisons of Subgrid-Scale Models for OpenFoam Large- Eddy simulation 7

Comparisons of Subgrid-Scale Models for OpenFoam Large- Eddy...

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2020 7th international conference on computer-Aided Design, Manufacturing, modeling and simulation, CDMMS 2020

作者： Feng, Zhipeng Qi, Huanhuan Huang, Xuan Liu, Shuai Liu, Jian Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu Sichuan China

OpenFOAM is a free, open-source software package that can be used for the solutions of computational fluid dynamics and simulation of various fluid flow processes. Nevertheless, OpenFOAM still lacks default settings and a large number of different numerical schemes and turbulent models should be validated. In this paper, the unsteady flow around a cylinder (Re=3900) is calculated by the large eddy simulation of OpenFOAM. The predictions include the drag and lift coefficient, the pressure distribution around the cylinder, the velocity distribution and Reynolds stress distribution in the wake region, as well as the prediction of the recirculation length and separation angle. Thanks to several simulations, these five subgrid-scale (SGS) models are compared and studied: The Smagorinsky SGS model, wall adaptive local eddy visibility SGS model, dynamic SGS model with Lagrangian averaging, dynamic one equation eddy visibility model, one equation eddy visibility model. The numerical results are verified with the published experimental data. © Published under licence by IOP Publishing Ltd.

关键词： Large eddy simulation

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Bias Correction in Urban Building Energy modeling for Chicago Using Machine Learning

Bias Correction in Urban Building Energy Modeling for Chicag...

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Intelligent Data Science Technologies and Applications (IDSTA), international conference on

作者： Shovan Chowdhury Fengqi Li Avery Stubbings Joshua New Ankur Garg Santiago Correa Kevin Bacabac The Bredesen Center for interdisciplinary Research The University of Tennessee Knoxville Tennessee USA Electrification and Energy Infrastructures Division Oak Ridge National Laboratory Oak Ridge Tennessee USA Department of Electrical and Computer Engineering Illinois Institute of Technology Chicago IL USA BlocPower Brooklyn NY USA

Urban-scale building energy modeling (UBEM) holds promise for optimizing energy usage across extensive geographic regions. However, there is a recognized bias between simulated energy consumption and actual measured data. This study, based on building data from Chicago, delved into bias correction techniques for enhancing the accuracy of UBEM energy consumption estimates. Initially, the AutoBEM simulation yielded a normalized mean bias error (NMBE) of 1.1% and 51% of Coefficient of the Variation of the Root Mean Square Error (CVRMSE) after outlier exclusion. To address this, three bias correction methods were deployed: Average Mean Bias Error based bias correction, Quantile mapping bias correction, and Machine learning-based bias correction using Linear Regression and Random Forest models. Post-correction results exhibited marked improvement. The NMBE values were diminished to 0 for Average MBE-based, 0.36 for Quantile Mapping, and 0 for Machine Learning-based corrections. Concurrently, the CVRMSE values registered reductions from an original 51 to 50.8 for Quantile Mapping, and 38.56 for Machine Learning-based corrections, pointing towards the effectiveness of specific bias correction methods in refining the precision of UBEM energy predictions. Such accurate estimations are paramount for informed energy planning and urban policy-making.

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international conference on Artificial Intelligence and Cloud Computing, ICAICC 2020

International Conference on Artificial Intelligence and Clou...

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2nd international conference on Artificial Intelligence and Cloud Computing, ICAICC 2020

The proceedings contain 13 papers. The topics discussed include: research on hybrid index method of double-layer B+ tree for power big data considering knowledge graph;research on modeling and simulation of smart substation cyber-physical system considering coupling analysis;end-to-end bone age assessment based on attentional region localization;study on the correlation between the fingerprint of rosa rugosa and its formula granules;study on HPLC fingerprint of atractylodes macrocephala and atractylodes macrocephala dispensing granules;an exploration on using mobile smart terminals to promote the reform of college English teaching and learning;research on computer network security emergency response systematization;and study on agricultural green efficiency and its influencing factors in Chinese Yangtze River economic belt: the FDI perspective.

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Complex for Remote Execution of Laboratory Work on Radioelectronics

Complex for Remote Execution of Laboratory Work on Radioelec...

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IEEE Region 8 international conference on Computational Technologies in Electrical and Electronics Engineering, SIBIRCON

作者： Yuriy Davydov Alexander Baranov Ruslan Latypov Fail Mukhametzyanov Maxim Popov Rufat Babaev Institute of Physics Kazan Federal University Kazan Russian Federation

ISBN: (纸本)9781665464819

In the modern world there is a rapid transition to distance education in higher education institutions. In the case of lectures and seminars, solutions already exist, but organizing remote laboratory works is much more difficult. This paper describes one approach to the organization of remote laboratory work on the basics of radioelectronics. At the moment, there are approaches using virtual instruments and computer simulations. The presented approach, in contrast to analogs, is based on the use of real electronic components and equipment and excludes the use of virtual instruments and computer simulation. The shown complex consists of several key blocks as laboratory stands, control unit of complex and special software, the purpose and principle of creation of which are presented in the article. At the same time, one complex supports the execution of one laboratory work, but the identity of the design and operation of the laboratory stands allows you to quickly switch the laboratory work being performed simply by replacing the stand with another. The laboratory complex includes 7 laboratory works on the basics of radio electronics and allows you to perform the tasks described in the methodological manuals of laboratory work.

关键词： Seminars Remote laboratories Instruments computer simulation Computational modeling Education Switches

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Analysing the contributions of lower limb muscles to eccentric cycling using musculoskeletal modeling and simulation

Analysing the contributions of lower limb muscles to eccentr...

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Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Stefan Venter Paul J. Stapley Joel A. Walsh Roy Cheung Manish Sreenivasa School of Mechanical Materials Mechatronic and Biomedical Engineering Faculty of Engineering and Information Sciences University of Wollongong Wollongong Australia School of Medical Indigenous and Health Sciences Faculty of Science Medicine and Health University of Wollongong Wollongong Australia School of Health Sciences Western Sydney University Campbell-Town Australia

Eccentric (ECC) cycling, compared to traditional concentric cycling, has been shown to improve muscle strength and neuromuscular control at a lower metabolic cost. Despite the popularity of this exercise in the sports and rehabilitation contexts, there is a gap in our knowledge of which muscles are behaving eccentrically during ECC cycling. To this end, we used a musculoskeletal model and computer simulations to calculate joint kinematics and muscle lengths during ECC cycling. Movements were recorded using 3D motion capture technology while cycling eccentrically on a custom-built semi-recumbent ergometer. The software Opensim was used to calculate joint kinematics and muscle lengths from recorded movements. We found that among the primary knee extensors, it was predominantly the Vastii muscles that acted eccentrically in the ECC cycling phase, with other lower limb muscles showing mixed eccentric/concentric activation. Additionally, the muscle force-length and force-velocity factors in the ECC phase suggest that changes to the participant’s pose and pedaling speed may elicit larger active muscle forces. Our work provides an interesting application of musculoskeletal modeling to ECC cycling, and an alternative way to help understand in-vivo muscle mechanics during this activity.

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Improving Cloud Energy Efficiency through Machine Learning Models

Improving Cloud Energy Efficiency through Machine Learning M...

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Euromicro conference on Parallel, Distributed and Network-Based Processing

作者： Eugenio Cesario Paolo Lindia Federica Lobello Andrea Vinci Shabnam Zarin Santina Capalbo DICES Department University of Calabria Rende Italy DIMES Department University of Calabria Rende Italy CNRNational Research Council of Italy Institute for HPC and Networking (ICAR) Rende Italy Computer Science Department Monmouth University West Long Branch NJ USA

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

The rapid growth of Cloud Computing and the expansion of large data centers have led to significant increases in energy consumption for hardware and cooling systems. This surge in power usage has raised operational costs, making energy efficiency a pressing issue for data center management. Virtual Machines (VMs) consolidation is a widely studied strategy to mitigate these costs by minimizing the number of active physical servers while maintaining user Service Level Agreements (SLAs). However, the success of consolidation largely depends on accurately forecasting VM resource needs. This paper presents the design and development of an energy-aware VMs allocation system that leverages predictive machine learning models to forecast future computational demands (CPU) of each VM. By anticipating these needs, the system optimally allocates VMs to available servers, effectively balancing energy savings with performance. A preliminary experimental evaluation, conducted on synthetic data, leverages predictive modeling within a server simulation to assess CPU and energy usage, demonstrating the effectiveness of energy-aware VMs allocation in optimizing resource utilization and reducing energy consumption.

关键词： Energy consumption Data centers Cloud computing Machine learning Predictive models Virtual machines Energy efficiency Servers Resource management Optimization

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