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检索条件"任意字段=Conference on Simulation-Driven Modeling and Optimization"

共 1171 条记录，以下是431-440 订阅

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Temperature prediction modeling and control parameter optimization based on data driven 5

Temperature prediction modeling and control parameter optimi...

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5th IEEE International conference on Data Science in Cyberspace, DSC 2020

作者： Liu, Qingguang Wei, Jielong Lei, Sining Huang, Qingbao Zhang, Mengqiao Zhou, Xiongbin Guangxi University School of Electrical Engineering Nanning China School of EE Guangxi University China School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Alnan Aluminium Lnc. Alnan Aluminium Co. Ltd Nanning China

ISBN: (数字)9781728195582

ISBN: (纸本)9781728195582

The aluminum smelting process is characterized with multi-variable, strong coupling and large lag, which leads to low efficiency and high energy consumption. Previous study shows that Extreme learning machine (ELM) can be used to model furnace temperature for its high train speed. However, due to the random initialization of the input weight and the hidden layer threshold in the ELM, the furnace temperature prediction model is unstable in the start of each smelting process. To improve the stability, we introduce the restricted Boltzmann machine to optimize an ELM model. In addition, a furnace temperature segmentation optimization strategy based on whale optimization algorithm is proposed to effectively find the optimal values of the control parameters in each furnace temperature section. The simulation results state that the aluminum melting time can be reduced by about 6.67% after optimization. © 2020 IEEE.

关键词： optimization

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9th International Young Scientists conference in Computational Science, YSC 2020

9th International Young Scientists Conference in Computation...

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9th International Young Scientists conference in Computational Science, YSC 2020

The proceedings contain 46 papers. The topics discussed include: induced perturbation network and tiling for modeling the L55P transthyretin amyloid fiber;discovery of the data-driven models of continuous metocean process in form of nonlinear ordinary differential equations;forecasting the air temperature at a weather station using deep neural networks;parallel implementation of a numerical method for solving a three-dimensional transport equation for a mesoscale meteorological model;optimal wells placement to maximize the field coverage using derivative-free optimization;application of hydrochemical simulation model to determination of optimal well pattern for mineral production with in-situ leaching;multi-factor approach to investment attractiveness assessment of urban spaces;and modeling and evaluating the residential urban environment perception.

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Solar-driven Potassium Formate Liquid Desiccant Dehumidification System with Thermal Energy Storage

Solar-Driven Potassium Formate Liquid Desiccant Dehumidifica...

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International conference on modeling, simulation and optimization, CoMSO 2020

作者： Sai Kaushik, A. Bhogilla, Satya Sekhar Indian Institute of Information Technology Design and Manufacturing Kurnool Kurnool India Indian Institute of Technology Jammu Jammu India

ISBN: (纸本)9789811598289

With a conscious effort of shifting toward cleaner sources of energy to reduce the carbon footprint and emissions around the world, the current technological advancements for the production of energy are primarily focused on harnessing the renewable sources of energy for efficient use. Solar photovoltaic energy is one of the most abundant and promising sources of renewable energy with a lot of scope and potential for development in generation of power for useful work. Since the efficiencies of solar cells are very low, coupling the solar panel system with a thermal extraction cycle for supplying energy to various applications can improve the overall efficiency of the combined system. One such application is the usage of the thermal energy obtained from the photovoltaic (PV) panels as input to the liquid desiccant air-conditioning system primarily for dehumidification of air. Considered as the alternative to traditional vapor absorption/compression air conditioning systems, desiccant air conditioning systems are becoming increasingly popular for cooling and dehumidification of air because of their higher efficiencies and ability to use low-grade sources of energy to produce the required output. A comprehensive analysis of the working characteristics of the liquid desiccant dehumidification system using potassium formate as the liquid desiccant solution has been performed in this study. The liquid desiccant dehumidification system has been mathematically modeled using MATLAB R2018b;primarily to study the amount of dehumidification, the system is able to produce using the energy supplied to it from the photovoltaic module. Different configurations of the system have been modeled to identify the best possible system for maximum performance. The motive of implementing the proposed system is to improve the performance and efficiency of the renewable energy systems and to enhance the utilization of eco-friendly technologies in everyday life. As The future heads toward a gr

关键词： Solar panels

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modeling and optimization of thermally excited carbon black and polymer composite actuator

Modeling and optimization of thermally excited carbon black ...

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International conference on Advances in Materials and Manufacturing Applications (IConAMMA)

作者： Hiremath, S. Kulkarni, S. M. Natl Inst Technol Karnataka Dept Mech Engn Mangalore 575025 India

As of late, actuators in view carbon black, carbon nanotube and graphene were shown in the extraordinary potential application in the field of drug delivery system, relay switches, robotics, energy harvesting and so on. Now a day electro-thermal and photothermal driven actuator based on polymer composite show larger actuation compare to the traditional thermal actuator. Though, the impact of structural dimensions and material parameters on the actuator execution ought to be investigated further. Since it is a critical point in the design and fabrications of the high-performance actuator. In the present work, finite element (FE) analysis is adopted to simulate the thermally driven bilayer actuator and investigated the performance based on carbon black and polymer composite. Thus, the computational method is carried out to design and optimize the geometry and material parameters such as thickness, the coefficient of thermal expansion and so on. FE simulation results demonstrate that each layer thickness of the actuator has an important role in curvature deformation. A maximum curvature is obtained of 8.9 m(-1) by simulation, which is a decent expected value in light of the geometry and material. Furthermore, a larger change in temperature and CTE between two layers additionally predicts the more prominent bending curvature. Consequently, this investigation is relied upon to give a theoretical baseline to plan and fabrication of CB based thermal actuator for a greater actuation performance. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Carbon black bilayer composite FE analysis

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Utilizing Bayesian optimization and machine learning to find the best inflow control design for horizontal wells

Utilizing Bayesian optimization and machine learning to find...

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Offshore Technology conference 2020, OTC 2020

作者： Shahkarami, Alireza Friedrichs, Meagan Iyer, Naresh Izadi, Ghazal Klenner, Robert Meyer, Elena Moreno, Oscar Becerra Murrell, Glen Baker Hughes United States Ge Global Research United States

ISBN: (纸本)9781613997079

Inflow Control Devices (ICDs) help reduce the adverse consequences of uneven inflow issues in a lateral completion system. The most common uneven inflow consequences are early water breakthrough and gas coning in water-driven and saturated reservoirs. These issues lead to the dominance of undesired fluid production and consequently, reduced well productivity. Typically, uneven inflow issues are caused by different drivers, including heterogenous permeability, an uneven water saturation profile, and/or complex well completion in a lateral section of a given well. ICDs are placed in permanent positions along the lateral section of a well in order to control zonal production and improve well productivity. The goal of utilizing ICDs is to delay water or gas production and equalize the inflow production from the reservoir to wellbore. However, the uncertainty of reservoir characteristics and operational constraints add complexity to the ICD design and complicate optimization strategies. An optimum ICD design entails identifying the number and size of compartments, packer locations, ICD type, and number of ICDs in each compartment, and the ICD settings such as orifice diameter or flow restriction rating. Extensive reservoir modeling work can be performed to accurately quantify the impact of each ICD design on well production. The intent of this paper is to demonstrate that Bayesian optimization and machine learning techniques can help identify an optimized ICD design in a minimum number of reservoir simulation evaluations. These techniques are implemented into the reservoir simulation workflow to enhance the speed of the analysis and resulting value proposition for the operating customer. Using Gaussian Process Regression as a surrogate, Bayesian optimization makes use of a small number of initial reservoir simulation runs to quantify the uncertainty of the surrogate model in the parameter space. It makes use of an appropriate acquisition function (as determined by the de

关键词： Horizontal wells

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Exploratory analysis to address deep uncertainty - Using calibratable system models for exploratory simulation of complex missions

Exploratory analysis to address deep uncertainty - Using cal...

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2020 Spring simulation Multiconference, SpringSim 2020

作者： Tolk, Andreas Dinh, Khuong Comer, Kevin Scott, Steve MITRE Corporation 903 Enterprise Parkway #200 HamptonVA23666 United States MITRE Corporation 7515 Colshire Drive McLeanVA22102 United States

ISBN: (纸本)9781713812883

Traditional optimization methods and sometimes even the underlaying concept of experimentation design for optimization problems are not always successful in complex solution spaces. This is not only driven by the complexity itself, but also by the deep uncertainty in estimating the model parameters. Applying exploratory analysis to better understand the solution space has been recognized as a promising approach for several years, but with the new generation of computers the community now has the means to practically apply it as well. We define the terms, derive a conceptual model, provide an application case from the defense domain, and show the possibility to generalize the results, namely that parameter sweeps are possible and lead to deeper insights of the solution space, allowing for stable and robust solutions that often are more important in practice than optimized point solutions. © 2020 Society for modeling & simulation International (SCS)

关键词： Uncertainty analysis

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Data-driven simulation and optimization for Covid-19 Exit Strategies 20

Data-driven Simulation and Optimization for Covid-19 Exit St...

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26th ACM SIGKDD International conference on Knowledge Discovery and Data Mining (KDD)

作者： Ghamizi, Salah Rwemalika, Renaud Cordy, Maxime Veiber, Lisa Bissyande, Tegawende F. Papadakis, Mike Klein, Jacques Le Traon, Yves Univ Luxembourg SnT Luxembourg Luxembourg

ISBN: (纸本)9781450379984

The rapid spread of the Coronavirus SARS-2 is a major challenge that led almost all governments worldwide to take drastic measures to respond to the tragedy. Chief among those measures is the massive lockdown of entire countries and cities, which beyond its global economic impact has created some deep social and psychological tensions within populations. While the adopted mitigation measures (including the lockdown) have generally proven useful, policymakers are now facing a critical question: how and when to lift the mitigation measures? A carefully-planned exit strategy is indeed necessary to recover from the pandemic without risking a new outbreak. Classically, exit strategies rely on mathematical modeling to predict the effect of public health interventions. Such models are unfortunately known to be sensitive to some key parameters, which are usually set based on rules-of-thumb. In this paper, we propose to augment epidemiological forecasting with actual data-driven models that will learn to fine-tune predictions for different contexts (e.g., per country). We have therefore built a pandemic simulation and forecasting toolkit that combines a deep learning estimation of the epidemiological parameters of the disease in order to predict the cases and deaths, and a genetic algorithm component searching for optimal trade-offs/policies between constraints and objectives set by decision-makers. Replaying pandemic evolution in various countries, we experimentally show that our approach yields predictions with much lower error rates than pure epidemiological models in 75% of the cases and achieves a 95% R-2 score when the learning is transferred and tested on unseen countries. When used for forecasting, this approach provides actionable insights into the impact of individual measures and strategies.

关键词： Covidl9 Pandemic Prediction Exit strategies SEIR Deep Learning Search-based optimization

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Credibility Computation Offloading Based Task-driven Routing Strategy for Emergency UAVs Network

Credibility Computation Offloading Based Task-Driven Routing...

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2021 IEEE Global Communications conference (GLOBECOM)

作者： Buyun Ma Zhiyuan Ren Wenchi Cheng State Key Laboratory of Integrated Services Networks Xidian University Xi'an China

ISBN: (纸本)9781728181059

By offloading tasks on the drones in the transmission route over an emergency UAVs network, the rescue efficiency can be significantly enhanced. Most routing algorithms and task offloading strategies relied on the drones' future location information. However, due to the harsh conditions of the disaster scene, drones' future locations are unpredictable, which results in extremely dynamic topology and brings a great challenge for the credibility of the scheduling result. Motivated by the aforementioned problem, a credibility Computation Offloading based Task-driven routing (COT) strategy for emergency UAVs network is proposed, where a two-step Weighted time Expanded Graph (WEG) is constructed to cope with the network dynamics. Then, based on the two-step WEG, the COT is formulated to offload the tasks on the data transmission route to minimize the task processing latency and realize the computing while transmitting. Moreover, a novel credibility model is conceived to enhance the credibility of the scheduling result. Finally, the binary particle swarm optimization (BPSO) algorithm is adopted to solve this problem. The simulation results validate that the proposed COT leads to significant performance improvement in latency compared with cloud computing and local computing.

关键词： Processor scheduling Network topology simulation Computational modeling Routing Dynamic scheduling Topology

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Learning Free-Surface Flow with Physics-Informed Neural Networks

Learning Free-Surface Flow with Physics-Informed Neural Netw...

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International conference on Machine Learning and Applications (ICMLA)

作者： Raphael Leiteritz Marcel Hurler Dirk Pflüger Institute of Parallel and Distributed Systems University of Stuttgart Stuttgart Germany

ISBN: (纸本)9781665443388

The interface between data-driven learning methods and classical simulation poses an interesting field offering a multitude of new applications. In this work, we build on the notion of physics-informed neural networks (PINNs) and employ them in the area of shallow-water equation (SWE) models. These models play an important role in modeling and simulating free-surface flow scenarios such as in flood-wave propagation or tsunami waves. Different formulations of the PINN residual are compared to each other and multiple optimizations are being evaluated to speed up the convergence rate. We test these with different 1-D and 2-D experiments and finally demonstrate that regarding a SWE scenario with varying bathymetry, the method is able to produce competitive results in comparison to the direct numerical simulation with a total relative L 2 error of 8.9e−3.

关键词： Learning systems Neural networks optimization methods Machine learning Differential equations Tsunami Numerical simulation

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PSO based on Generator Scheduling for Low Voltage Direct Current Distribution System onboard Ship with Mechanical Propulsion System

PSO based on Generator Scheduling for Low Voltage Direct Cur...

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International conference on Advanced Mechatronics, Intelligent Manufacture, and Industrial Automation (ICAMIMIA)

作者： Dennis Chaniago Ramadhan Adi Kurniawan Indra Ranu Kusuma Marine Engineering Department Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (纸本)9781665499705

Along with advances in power electronics technology at the end of this decade, the application of a low-voltage direct current (L VDC) electric power distribution system began to be redeveloped. It is considered to have advantages in fuel economy, space efficiency due to the absence of transformer components, and lower emissions. To get the benefits of a direct current distribution system in fuel economy is necessary to optimize generator scheduling. One of the optimization methods used is Particle Swarm optimization (PSO), which is inspired by the movement of living things such as birds and fish that adapt to the environment. This study aims to conduct a generator scheduling optimization using the PSO method on a mechanical propulsion-driven vessel. The ship with mechanical propulsion was chosen because it is considered the ship most often operates in Indonesian. The optimization process will be carried out by modeling the characteristics of the generator used on the study object ship with two methods of second interpolation and third interpolation. The results of the calculation of fuel consumption for the LV AC and L VDC systems with conventional load sharing show savings on the LVDC system of 18.8953 kg/h, 81.7884 kg/h, 19.5244 kg/h, 46.5792 kg/h in sailing, maneuvering, cargo handling and at port modes. The simulation results for the LV AC and L VDC systems with load sharing using the PSO method show fuel consumption savings on the L VDC system of 18.8953 kg/h, 81.7884 kg/h, 19.5244 kg/h, 59.98 57 kg/h in sailing, maneuvering, cargo handling and at ports modes. The simulation results for the L VDC system with conventional load sharing and PSO only show savings in the at port mode of 13.41 kg/h. However, the simulation results for the LVAC system with conventional load sharing and PSO are similar.

关键词： Low voltage Interpolation simulation Propulsion Fuel economy Current distribution Generators

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