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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81-90 订阅

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A Novel Approach for Gas Delivery optimization of a Complex Pipeline Network System Using Hybrid Physics-Machine Learning modeling

A Novel Approach for Gas Delivery Optimization of a Complex ...

引用

2024 SPE Annual Technical conference and Exhibition, ATCE 2024

作者： Fadhl, Ahmed Atmaca, Serdar Zhang, Cathy Mooney, Kyle SLB HoustonTX United States Geminus CambridgeMA United States

ISBN: (纸本)9781959025375

Traditionally, physics-driven simulation models have primarily been employed for design and long-term forecasting purposes. However, maintaining the accuracy of the model's boundary conditions and achieving convergence for operational decisions have posed significant challenges. Consequently, operators do not commonly use these physics-based models for optimizing daily operations, relying instead on personal judgment and experience. In this work, we introduce a novel and agile approach for optimizing gas delivery using hybrid physics-machine learning (ML) modeling. It is applied in a complex onshore pipeline system connected to multiple midstream sales points. Given the fluctuating gas prices and daily operational constraints with the need to meet the daily production quotas, there is a significant demand for a robust, agile optimization solution. This solution must manage day-to-day decision-making processes, considering the ever-changing field and market conditions. The system in this project comprises 80 pads and four delivery sites, each with its unique set of flow and pressure constraints. These include minimum and maximum flow rates at delivery points and a cap on the maximum allowable pressure within the network. The flow into delivery points is regulated through per-site choke adjustments, adding another layer of complexity to the optimization process. The core objective of the proposed solution is to maximize total revenue. This is achieved by optimizing gas production across the four delivery sites while adhering to all flow and pressure constraints. This balance is crucial for operational efficiency and profitability. The solution is built on a novel hybrid of physics-ML models, employing surrogate modeling techniques. This approach combines the accuracy of physics-based models with the efficiency and adaptability of ML. The model is trained on thousands of scenarios generated from high-fidelity physics-based flow simulations. These simulations incorporat

关键词： Application programs

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Study and simulation of the Transient Thermal Behavior of a Rim-driven Motor with Submerged Air Gap for the Purpose of Geometry optimization 4th

Study and Simulation of the Transient Thermal Behavior of a ...

引用

4th International conference on Electronic Engineering and Renewable Energy Systems, ICEERE 2024

作者： Amri, Lahcen Senhaji, Abdelhamid Zouggar, Smail Charpentier, Jean-Frederic Kebdani, Mohamed Elhafyani, Mohamed Larbi Allaoui, Tarik El Electrical Engineering and Maintenance Laboratory ESTO Mohamed I University Oujda Morocco Arts Et Métiers Institute of Technology CNAM LIFSE HESAM University Paris75013 France French Naval Academy Brest29240 France

ISBN: (纸本)9789819606436

Mastering the thermal behavior of an electric machine is a primordial step in its pre-sizing. Indeed, the optimization of the geometry of this one requires limiting the temperature in the active parts of the machine. This is why this article provides thermal modeling of the active part of the permanent magnet synchronous machine (PMSM). This machine is used for rim driving the turbine of an under-development pump. The machine is supposed to be cooled by water that flows through the submerged airgap, while natural air circulates on the external side. The geometry optimization algorithm considered the temperature in slots (which are the hottest area) as a constraint. A slot temperature of less than 155 °C was chosen, and the resulting geometry from this study perfectly fulfilled the specifications. The transient thermal behavior was studied and simulated for an optimized machine to highlight the sensitivity of the temperature of active components to different heat sources within the motor. It is shown that a. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Synchronous machinery

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Parameter Modification of Hydraulic System Model

Parameter Modification of Hydraulic System Model

引用

2024 International conference on Guidance, Navigation and Control

作者： Jiang Nan Li Shu Zhao Yiran Dong Shaopeng Shenyang Aircraft Design & Res Inst Shenyang 110000 Liaoning Peoples R China Beihang Univ Beijing 100191 Peoples R China

ISBN: (纸本)9789819622504;9789819622481;9789819622474

Hydraulic systems are vital in industry, yet accurately modeling them presents significant computational challenges. Traditional methods for adjusting parameters are often inefficient. In this study, we employ a data-driven optimization strategy that utilizes a particle swarm optimization algorithm with surrogate models to effectively identify model parameters, ensuring feasible solutions at reduced computational costs. Experimental validation conducted on a servo valve-controlled hydraulic cylinder simulation model confirms the efficiency and accuracy of our approach in adjusting parameters. This research addresses challenges in hydraulic system model parameter identification by focusing on enhancing computational efficiency and effectiveness. We propose a data-driven optimization approach, which begins by defining an optimization problem based on credibility indicators. Using a Particle Swarm optimization (PSO) algorithm paired with surrogate models, our method employs dynamic partitioning and parallel optimization to swiftly identify potential solutions within constrained computational costs. Experimental validation on a servo-valve-controlled hydraulic cylinder system confirms the effectiveness of our approach in parameter identification and optimization. This method significantly improves optimization efficiency and holds promise for similar applications in hydraulic systems, contributing to practical solutions in system modeling.

关键词： Hydraulic System Model Parameter Identification Particle Swarm optimization Bayesian

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Data-driven Geometric Programming for System-Level Performance optimization 14

Data-Driven Geometric Programming for System-Level Performan...

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14th International conference on Information and Communication Technology Convergence, ICTC 2023

作者： Gonmei, Soihem Lee, Junhwan Kwon, Taesoo Seoul National University of Science and Technology Department of Computer Science and Engineering Seoul Korea Republic of Electronics and Telecommunications Research Institute Daejeon Korea Republic of

ISBN: (纸本)9798350313277

More than often, the output of a comprehensive network-wide performance modeling is a non-linear and non-convex function of the input data. To estimate the non-linear relationship of such a procedure, this paper employs a data-driven methodology. By formulating the non-linear objective as a geometric program, we leverage Levenberg-Marquardt algorithm to fit a convex log-sum-exponential function to data obtained through system-level simulation. Focusing on the downlink energy efficiency in mmWave cellular networks, we formulate a convex optimization problem and numerically obtain the optimal BS density and transmit power. © 2023 IEEE.

关键词： Energy Efficiency Geometric Programming Levenberg-Marquardt algorithm (LMA) mmWave Regression

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DATA-driven INVESTIGATION OF LATENT HEAT THERMAL ENERGY STORAGE PERFORMANCE UNDER DYNAMIC OPERATING CONDITIONS 37

DATA-DRIVEN INVESTIGATION OF LATENT HEAT THERMAL ENERGY STOR...

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37th International conference on Efficiency, Cost, optimization, simulation and Environmental Impact of Energy Systems, ECOS 2024

作者： Safaee, Raman Ward, Phil Ding, Yulong Sciacovelli, Adriano University of Birmingham School of Chemical Engineering Birmingham Centre for Energy Storage Birmingham United Kingdom Eskimo Products Ltd. Studio 53 Sydney Row Spike Island BristolBS1 6UX United Kingdom University of Birmingham School of Chemical Engineering Birmingham Centre for Energy Storage Birmingham Energy Institute Birmingham United Kingdom

ISBN: (纸本)9798331307660

This paper presents a novel data-driven approach for characterizing the performance of Latent Heat Thermal Energy Storage (LHTES) systems employing Phase Change Materials (PCM) across a broad spectrum of operating conditions. LHTES systems are increasingly utilized in various sectors, notably in domestic space heating. Traditional research in this field has largely been confined to examining individual LHTES systems under fixed, predefined conditions. However, the practical deployment of these systems is expected to entail operation under a wide range of conditions, such as fluctuating heat transfer fluid (HTF) input temperatures, flow rates, and states of charge (SoC). The study introduces and tests a comprehensive data-driven framework to characterize, under the wider possible spectrum of conditions, an LHTES prototype device (~1kWh, Ts ~ 42-43 °C) designed for building applications. Unique to the approach is its reliance on exclusively experimental data, which has been gathered through a comprehensive testing campaign. The proposed data-drive framework is employed to assess efficiency, heat transfer, inlet/outlet temperatures, and SoC, relying solely on experimental data. The findings of the study can provide sufficient inputs for data reduction techniques such as Respond Surface Methodology (RSM) and circumvent the need for high-fidelity parameters required in traditional first principle LHTES modeling. The findings indicate that LHTES units maintain highly stable performance variations over different operating conditions. Notably, increasing the input temperature from 48 °C to 54 °C in charging operation reduces the full charging time by up to 100%, while variations in flow rate influence the performance by 5 to 50% depending on the Reynolds numbers and flow regimes. The HTF flow rate influence on the performance drastically decreases at Reynolds numbers around 6500, which corresponds to turbulent flow regime characterized with high internal heat transfer coeff

关键词： Reynolds number

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Research on Dynamic modeling and Cable Tension optimization of Marine Cable-driven Parallel Cleaning Robot 8

Research on Dynamic Modeling and Cable Tension Optimization ...

引用

8th International conference on Advances in Machinery, Materials Science and Engineering Application (MMSE)

作者： Li, Jian Chen, Yizong Wang, Shenghai Han, Guangdong Sun, Yuqing Luo, Weirong Dalian Maritime Univ Coll Marine Engn Dalian 116026 Peoples R China

ISBN: (纸本)9781643683225;9781643683232

Ship cleaning is mainly done manually, which is high operation risk and low work efficiency. For that, it is proposed to apply the cable-driven parallel robot to the field of ship cleaning. The related research of the traditional cable-driven parallel robot is mainly focused on the fixed base, which largely ignores the influence of the base motion on its work performance. Considering the influence of ship motion caused by wave excitation on modeling accuracy, the dynamic model of the system is established by Newton-Euler method. And then, the least variance method of correlation force was used to optimize the cable tension. Finally, the correctness and rationality of the proposed theory are verified by Simulink-Adams co-simulation. The simulation results show that the trajectory comparison effect is good, which verifies the accuracy and validity of the proposed theory.

关键词： CDPR ship cleaning dynamic model tension optimization

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Enhanced Decision Making and Asset optimization for Unconventional Resources with Type Wells (DCA), RTA-based Numerical modeling and Machine Learning

Enhanced Decision Making and Asset Optimization for Unconven...

引用

2024 SPE/AAPG/SEG Unconventional Resources Technology conference, URTC 2024

作者： Haghighat, Alireza Burrough, Toby S&P Global United States

Optimizing asset development for unconventional resources can be challenging due to uncertainties in data and reservoir assessment. There are a wide variety of upstream data sources like geology, engineering, and cost that add to the uncertainty due to errors in measurement, missing values, and outliers. Additionally, each production forecasting method can represent a degree of ambiguity due to assumptions, limitations, and approximations. Reducing the uncertainty in production forecasts requires exploring different techniques with a range of outcomes. This paper will showcase three different techniques for asset evaluation/optimization in the Marcellus play. Type wells, or decline curve analysis, can be a good start to understand a well’s future performance;however, uncertainty is high for decision makers with this approach. Physics based models that incorporate static and dynamic field measurements in Rate Transient Analysis (RTA) and simulation are very practical to characterize reservoir performance and predict production forecasts. These physics-based approaches have been shown to be powerful in addressing uncertainty but come with limitations and approximations. In the presence of big data, machine learning is showing promise as a complimentary approach to asset evaluation and optimization for its inclusion of many data sources and ability to learn complex data relationships on problems like production forecasting. The last part of this study demonstrates the use of different data sources for 300 Marcellus wells to feed a Machine Learning algorithm to build a data driven model with acceptable accuracy to predict production under different development scenarios. Moreover, data-driven models can rank and show key drivers of production, isolate each attribute from one another to allow the study of the contribution of a single attribute on production for each well, and the ability to analyze changing each attribute (geology or engineering) on production. Uncertain

关键词： Machine learning

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Key technology and application of gun agile optimization design based on dynamic simulation 3

Key technology and application of gun agile optimization des...

引用

3rd International conference on Defence Technology, ICDT 2022

作者： Xu, Y.F. Gu, B. Shan, C.L. Liu, P.K. Northwest Institute of Mechanical & Electrical Engineering Shaanxi Xianyang712099 China

The new generation of artillery equipment is showing the development trend of lightweight, long-range, intelligent et al. However, the traditional development mode based on physical prototype test cannot meet these needs of equipment development. In the development, there are some problems, such as lack of systematic guidance in the overall design scheme, low degree of digitalization and parameterization of the model, and long period of serial iterative development, which need to be solved by optimization design. Parametric modeling technology, hard point driven template model technology and structural optimization design technology are briefly introduced, which are the foundation of equipment optimization design. These key technologies, combined with the overall scheme design, chassis system design and ammunition loading timing optimization design of a large-caliber tracked self-propelled gun, showing the basic development route of artillery performance improvement. The optimization design of artillery type equipment are summarized in recent years, which provide reference for other similar work. © Published under licence by IOP Publishing Ltd.

关键词： Structural optimization

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DATA-driven UNCERTAINTY REVENUE modeling FOR COMPUTATION RESOURCE ALLOCATION IN RECOMMENDATION SYSTEMS

DATA-DRIVEN UNCERTAINTY REVENUE MODELING FOR COMPUTATION RES...

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2024 Winter simulation conference, WSC 2024

作者： Liu, Feixue Miao, Yuqing Ye, Yun Wang, Peisong Liu, Xinglu Zhang, Kai Chan, Wai Kin Tsinghua-Berkeley Shenzhen Institute Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Institute of Data and Information Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Ant Group Shanghai China

ISBN: (纸本)9798331534202

In recent years, as the resource consumption of computation-intensive recommendation systems (RS) significantly increased, and the supply of large-scale resources encountered bottleneck, computation resource allocation for improving computing efficiency grabbed the attention of the industry. The simulation of revenue is a focal point in the allocation problem. However, due to the complex engineering architecture of RS, no existing research proposes a simulation model that addresses the relationship between resource allocation strategies and benefits. This paper, based on real data from Alipay’s advertising RS and integrating queuing theory, models the relationship from resource allocation decisions to revenue considering exposure randomness of traffic. We further merge allocation tasks with capacity planning (CP) to establish a two-stage joint optimization model and use the revenue model above as the objective. The proposed model outperforms the baseline with 1.9% in revenue, and represents flexible adaptation of exposure rate, providing insights for the simulation of industrial RS. © 2024 IEEE.

关键词： Queueing theory

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A Data-driven Inductor modeling Technique Using Parametric Circuit simulation and Deep Learning

A Data-Driven Inductor Modeling Technique Using Parametric C...

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2023 IEEE International Magnetic conference - Short Papers, INTERMAG Short Papers 2023

作者： Motomatsu, Takehiro Koga, Takahiro Shigei, Noritaka Yamaguchi, Masahiro Itagaki, Atsushi Ishizuka, Yoichi Kagoshima University Kagoshima890-0065 Japan Nagasaki University Nagasaki852-8521 Japan Tohoku University Sendai980-8579 Japan Ryowa Electronics Co. Ltd Sendai984-0805 Japan

ISBN: (纸本)9798350338362

optimization of magnetic components design, such as power inductors and transformers, is most needed to improve the performance of future power electronics. However, power electronics designers are faced with the problem of not having sufficient magnetic component models available for their designs. In this paper, we propose a method to construct a unique nonlinear magnetic component model using parametric circuit simulation and deep learning. © 2023 IEEE.

关键词： Timing circuits

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