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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491-500 订阅

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Deep Reinforcement Learning Empowered Destination driven Computation Offloading in IoT

Deep Reinforcement Learning Empowered Destination Driven Com...

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International conference on Communication Technology (ICCT)

作者： Weizhong Wang Yunkai Wei Qiang Liu Supeng Leng School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9781728181417

ISBN: (纸本)9781728181424

Computation offloading is important for the resource limited devices in Internet of Things (IoT) to conduct complex tasks. Destination driven computation offloading is an emerging offloading pattern that the results of the offloaded tasks should be transmitted to a remote destination instead of the tasks source. Neverthelessly, current studies mostly focus on the traditional source driven pattern, and cannot be directly used in the destination driven computation offloading. In this paper we propose a Deep Reinforcement Learning (DRL) empowered destination driven computation offload mechanism to optimize the network survivability and task delay in this offloading pattern. Each computing server will conduct Double Deep Q Network separately to find the next relay node for the task it is processing. Step by step, the relay nodes will finally form an optimized strategy from the task source to the task destination. Extensive simulations evaluate the performance of our proposed scheme. The simulation results show that the proposed mechanism has significant performance in decreasing the average task latency and extending the network survivability simultaneously in energy constrained scenarios. Besides, it can adaptively switch its strategy according to the scene feature in energy harvesting scenarios.

关键词： Task analysis Delays Energy consumption Computational modeling Servers Reactive power optimization

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A data-driven modeling framework for real-time production planning and control in descrete production systems 49

A data-driven modeling framework for real-time production pl...

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49th International conference on Computers and Industrial Engineering, CIE 2019

作者： Liu, Hongwei Chen, Wenchong Qi, Ershi College of Management and Economics Tianjin University Tianjin China

Advanced technologies (e.g., distributed sensors, RFID, and auto-identification) can gather processing information (e.g., production system status and uncertain events) accurately and real-time. By combining the transparent, detailed and real-time production information with manufacturing system properties, this paper proposes an intelligent and automated modelling framework for real-time production planning and control, in which four key modeling problems are addressed: (1) Constructing dynamic equations with max-plus algebra to describe the dynamic behaviors of manufacturing systems, (2) formulating a real-time production planning framework to generate real-time input sequences of jobs, (3) establishing a model-based event-driven performance evaluation framework to identify real-time production loss, and (4) model data-driven predictive and robust-predictive production control framework to generate optimal release time of jobs. A 0-1 integer programming is established for key problem (2). A data-driven model predictive control framework and a Monte Carlo simulation-based optimization framework are respectively proposed for key problem (3) and (4). The proposed modelling framework utilizes the available real-time sensor data to facilitate predictive production decisions. The effort is a step forward to smart manufacturing in achieving improved system responsiveness and efficiency. © 2019, Computers and Industrial Engineering. All rights reserved.

关键词： Production control

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Large scale subsurface and surface integrated asset modeling - An effective outcome driven approach

Large scale subsurface and surface integrated asset modeling...

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Abu Dhabi International Petroleum Exhibition and conference 2018, ADIPEC 2018

作者： Hafez, Hafez Mansoori, Yousof Al Bahamaish, Jamal Saputelli, Luigi Escorcia, Alvaro Sousa, Sergio Rodriguez, Jose Mijares, Gerardo ADNOC United Arab Emirates Frontender United States Halliburton United States

ISBN: (纸本)9781613996324

Identifying opportunities in the installed capacity and proactively mitigating the limiting factors are paramount objectives for pursuing profitable production assurance. Although integrated asset modeling has been the de facto technology for supporting production planning and optimization work processes, its application is not fully adopted as it presents challenges when attempted to be used in a large-scale of multiple oil and gas assets. This paper describes ADNOC's innovative approach to develop a large scale subsurface to surface integrated asset modeling (LSSSIAM) solution by focusing on the desired business outcome. The paper introduces a new concept of right complexity modeling (RCM) to drive the type and level of complexity of the model/simulation based on the desired business outcome and other factors that influence the quality of the decision-making process. The methodology has been applied on a large-scale of multiple assets for effective production assurance that integrates the subsurface to the surface physical phenomena as required by the desired business outcome -the technical assurance of production plans within the context of a country. For the presented example, the proposed methodology resulted in the design of a solution where the subsurface phenomena are represented with a data-driven model to specifically address the requirements of the decision-making process which the solution supports. This resulted in the development of a first-of-its-kind countrywide production model that rigorously considers the properties and physics from the wells to the point of supply while also considering the subsurface phenomena as related to the production potential of the reservoirs and wells. The solution leverages the rigor of first-principle reservoir models to obtain a data-driven proxy model suitable for integration with a first-principle model covering more than 7,000 wells, multiple network and asset facilities, and a supply point transfer countrywide net

关键词： Digital storage

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Energy system optimization with short term storages respecting nonlinear temperature dependencies 32

Energy system optimization with short term storages respecti...

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32nd International conference on Efficiency, Cost, optimization, simulation and Environmental Impact of Energy Systems, ECOS 2019

作者： Powilleit, Marion Wasserfall, Joram Kirschbaum, Stefan Tretau, Anne Steinmann, Wolf-Dieter Society for the Advancement of Applied Computer Science Berlin Germany Brick and Tile Research Institute Essen regd. Essen Germany German Aerospace Center Stuttgart Germany

ISBN: (纸本)9788361506515

The field of operational optimization of energy systems is strongly dominated by linear optimization techniques (MILP). Due to their numerical performance advantages (good convergence, short computing time), even large and complex systems can be managed. Nevertheless, while modeling only in linear energetic terms many simplifications have to be accepted. A problem lies in the integration of thermal storage devices, whose model descriptions decisively deal with nonlinear correlations (regarding temperatures and material properties). Neglecting these nonlinear relations can lead to inaccurate results and the ability to depict temperature-sensitive processes is deprived. The work presents an approach for the modeling of sensible or latent heat storage devices integrated into an energy system. The sensible storage is represented by a solid matter storage with a fan-driven air flow as heat transfer medium. A classic PCM material combination with water as heat transfer medium is chosen as the latent heat storage. Both models have in common that the heat transfer depends, among other things, on the prevailing temperature conditions. Therefore, the input, output, or storage temperature must be considered as free optimizable variables. This leads to nonlinear equations. To cover these, the MILP model is enhanced by specifically placed bilinear terms and solved by an efficient spatial branch and bound algorithm. Nonlinearities are taken into account when necessary, but the unprecedented convergence and computing properties of MILP are largely retained. This model can be used to answer important questions: Is it economically feasible to increase the inlet temperature in order to achieve better heat transfer? Below which temperature is it not feasible to load/unload the storage because the ventilation costs exceed the thermal advantage? How to operate the storage in order to guarantee a minimum outlet temperature? The established MILP models cannot take these points into accoun

关键词： Heat transfer

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WISDOM: Watering Intelligently at Scale with Distributed optimization and modeling 19

WISDOM: Watering Intelligently at Scale with Distributed Opt...

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17th ACM conference on Embedded Networked Sensor Systems (SenSys)

作者： Winkler, Daniel A. Cerpa, Alberto E. Univ Calif Merced CA 95343 USA

ISBN: (纸本)9781450369503

As lawn irrigation is estimated to consume 7 billion gallons of scarce fresh water each day in North America alone, lawn irrigation systems are a high priority for improvements in efficiency. To this end, recent work has introduced several key advancements in irrigation control. Distributed actuation systems allow the irrigation system to apply water completely independently across the field allowing flexibility of control, and the use of fluid flow modeling and optimization allows more efficient schedules to be computed automatically, significantly improving the irrigation quality of service as well. However, the proposed systems are designed with centralized architectures that introduce single points of failure, computational bottlenecks in data processing, and significant network energy for data forwarding used by the centralized data-driven modeling strategies. In response to these challenges, we propose and demonstrate WISDOM, whose novel and flexible hardware and processing pipelines enable the use of a distributed system for the management of irrigation systems of any scale, with energy independence by way of energy harvesting. Across 4 weeks of live system deployment, we find that the WISDOM system can save up to 32.9% of water in comparison to industry-best, while maintaining a perfect quality of service to the plant. Furthermore, with substantial analysis in simulation we find that in addition to practical system improvements, the use of the proposed distributed system within typical operating conditions will provide all of the efficiency and quality-of-service benefits of the globally-modeled, centrally controlled systems, while allowing the robust control of irrigation systems of any size.

关键词： Quality of service

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A Combined Netflow-driven and Agent-Based Social modeling Approach for Building Evacuation 1

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22nd International conference on Principles and Practice of Multi-Agent Systems (PRIMA)

作者： Dugdale, Julie Moghaddam, Mahyar T. Muccini, Henry Narayanankutty, Hrishikesh Univ Aquila I-67100 Laquila Italy Univ Grenoble Alps F-38401 Grenoble France Amrita Vishwa Vidyapeetham Univ Clappana 690525 Kerala India

ISBN: (数字)9783030337926

ISBN: (纸本)9783030337926;9783030337919

In an emergency, finding safe egress pathways in a short period of time is crucial. In this paper we use a network flow (netflow) algorithm that acts as the core of a real-time recommender system to be used by building occupants and decision-making bodies. However, a purely optimization approach can lack realism since building occupants may not evacuate immediately, stopping to look for their friends or trying to assess if the alert is for real or just a drill, etc. Furthermore, they may not always follow the recommended optimal paths. Thus, in order to assess the egress in a physical space and to test our evacuation algorithms, we use a simulation-optimization (S/O) approach. The model allows us to test more realistic evacuation scenarios and compare them with an optimal approach. The S/O uses both a netflow algorithm and an agent-based approach to model and simulate individual human behaviours. People are modeled as agents with specific characteristics, such as social attachment to others, variation in speed of movement, etc. Furthermore, a Belief-Desire-Intention (BDI) agent architecture is used to model the individual differences in people and to more accurately describe the heterogeneity of the building occupants in terms of their current beliefs about the situation and goals. The real geospatial data obtained from three experiments is set as the model input. The results confirm the usefulness of using such S/O approach to improve design-time and real-time evacuation systems.

关键词： Agent-based modeling Network optimization Emergency evacuation

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Data-driven Evolutionary Optimisation for the design parameters of a Chemical Process: A Case Study

Data-Driven Evolutionary Optimisation for the design paramet...

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International conference on Information Fusion

作者： L Stander M Woolway T. L van Zyl School of Computer Science and Applied Mathematics University of the Witwatersrand Johannesburg South Africa

ISBN: (数字)9780578647098

ISBN: (纸本)9781728168302

A significant challenge faced within the field of chemical plant design and optimisation is the uncertainty in the reaction parameter, indeterminate component failures and their impacts on the design and operation. Additionally, real-world data accumulated from these plants would contain stochastic elements that are difficult to model. To this end, stochastic and deterministic methods have been proposed to simulate the uncertainty and enable an understanding of the plant and how it may be optimised. Within the existing literature investigated, the optimisation is done under the assumption that the simulation (target function) is non-stochastic. We have found that the use of an Evolutionary Algorithm in the form of a Genetic Algorithm can find an optimal solution even when we allow the simulation to behave stochastically as it would in practical applications. Further, we note that the use of a surrogate Machine Learning model as a substitute for the stochastic simulation model leads to substantively improved solutions in significantly less time (1.82 times speedup). We argue that the use of Genetic Algorithms in the optimisation of chemical plant design, taking into account the stochastic nature of the plant and including indeterminate failures, is a worthwhile solution and that surrogate assisted evolutionary algorithms will improve this solution further.

关键词： Sociology Statistics optimization Computational modeling Chemicals Genetic algorithms Feeds

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Integration of an open source blade geometry generator using a physics based parameterization with the engineering sketch pad

Integration of an open source blade geometry generator using...

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ASME Turbo Expo 2020: Turbomachinery Technical conference and Exposition, GT 2020

作者： Sharma, Mayank Dannenhoffer Iii, John F. Holder, Justin Turner, Mark G. Gas Turbine Simulation Laboratory Department of Aerospace Engineering University of Cincinnati CincinnatiOH45221 United States Department of Mechanical and Aerospace Engineering Syracuse University SyracuseNY13244 United States

ISBN: (纸本)9780791884089

This paper reviews an in-house turbomachinery blade generation system which integrates easily in an MDAO driven design process while providing the designer with a flexible and parsimonious parameterization scheme along with insights gained from interaction with 3D geometry. T-Blade3 is an open source 3D parametric blade geometry generator which uses a novel parameterization scheme based on the specification of the second derivative of the mean-line and a continuous modified NACA four digit thickness distribution as B-spline control points. The second derivative parameterization ensures curvature and slope of curvature continuity on the airfoil surface which leads to a smooth surface pressure distribution. Use of spanwise B-spline control points ensures the creation of smooth 3D geometries and keeps the overall parameterization space small. The Engineering Sketch Pad (ESP) is an open-source interactive web-enabled solid modeling system based onWebViewer and OpenCSM. The open-source nature of OpenCSM allows the use of user-defined primitives to create solid blade models using T-Blade3 geometries. OpenCSM also allows the computation of the sensitivity of surface points to design parameters using a combination of analytical derivatives and finite differences. Thus, the TBlade3/ESP combinations allows a designer to interact with parametrically generated geometries and make improvements to the design using sensitivity information and a gradient based optimization loop. Furthermore, ESP enables the addition of flends (similar to fillets) to solid blade models. The resulting blades with applied centrifugal and pressure loads are then analyzed with a commercial FEM structural solver to get factor of safety relative to yield strength, natural frequencies and mode shapes. © 2020 ASME.

关键词： Parameterization

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Robust Dispatch of Integrated Electric-Heat Systems Considering Weather-Parameter-driven Residential Thermal Demands

Robust Dispatch of Integrated Electric-Heat Systems Consider...

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IEEE conference on Energy Internet and Energy System Integration (EI2)

作者： Guanghui Hua Chen Li Yong Zhang Dan Li Chuang Liu Cheng Wang State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institut Nanjing China National Power Dispatching and Control Center Bejing China State Grid Hebei Electric Power Co. Ltd. Shijiazhuang China State Grid DC Engineering Construction Company Bejing China School of Electrical and Electronic Engineering North China Electric Power University Bejing China

ISBN: (数字)9781728196060

ISBN: (纸本)9781728196077

Conventional day-ahead scheduling strategies of integrated electricity and heating system (IEHS) are oversimplified for the thermal demand modeling and cannot meet the thermal comfort of the users. Furthermore, this unbalanced power may lead to reserve capacity deficiency in the power system, due to the thermal-electric coupling of the combined heat and power (CHP) units in IEHS. This paper derives a tractable and accurate residential thermal demand (RTD) model, which comprehensively considers the impact of weather conditions. Then, a robust scheduling strategy is employed to tackle the uncertainties of the renewable generation outputs and RTDs, suggesting a double-stage optimization model. The first stage is to identify the unit commitment with minimum operational cost. In the second stage, the feasibility of the first stage unit commitment would be checked to minimize the summation of slack variables. Besides, the robust optimization model is converted into a mixed-integer linear program via the big-M method and solved by column and constraint generation (C & CG) algorithm. The simulation results show the effectiveness of the proposed RTD model, as well as the robust scheduling strategy for IEHS.

关键词： Uncertainty simulation Cogeneration Stochastic processes System integration optimization Meteorology

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Reliable modeling of Antenna Input Characteristics by Means of Domain Confinement and Variable-Fidelity EM simulations

Reliable Modeling of Antenna Input Characteristics by Means ...

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International conference on Microwaves, Radar and Wireless Communications

作者： Anna Pietrenko-Dabrowska Slawomir Koziel Faculty of Electronics Telecommunications and Inf. Gdansk University of Technology Gdansk Poland Engineering Optimization & Modeling Center Reykjavik University Reykjavik Iceland

ISBN: (数字)9788394942175

ISBN: (纸本)9781728157870

Electromagnetic (EM) simulation is the single most important tool in the design of modern antenna structures. Its versatility and reliability comes at a price of considerable evaluation cost, which may be impractical or even prohibitive when multiple simulations are to be executed, e.g., pertaining to parameter tuning or uncertainty quantification. Fast replacement models (or surrogates) may mitigate this problem but modeling of antenna characteristics is a challenging endeavor due to their nonlinearity and typically high-dimensionality of the parameter space. Conventional data-driven modeling methods fail under such circumstances. In this paper, a novel technique is presented which combines the nested kriging framework and variable-fidelity computational models. The former is employed to confine the surrogate model domain to a region containing designs that are of high-quality from the point of view of the considered performance figures. This dramatically reduces the number of required training data samples. Incorporation of low- and high-fidelity simulations is realized using co-kriging. The proposed approach is illustrated using a wideband monopole antenna modeled for wide ranges of substrate permittivity (2.0 ≤ ε r ≤ 5.0) and height (0.5 mm ≤ h ≤ 1.5 mm). Benchmarking indicates that constrained variable-fidelity model outperforms both the conventional surrogates but also the constrained surrogate constructed using high-fidelity EM data only.

关键词： Microwave antennas Computational modeling Radar antennas Numerical models Broadband antennas Reliability Wideband

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