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

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Multi-generation modeling and building energy use optimization based on a natural gas driven internal combustion engine 47

Multi-generation modeling and building energy use optimizati...

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47th National Solar conference of the American Solar Energy Society, SOLAR 2018

作者： Okafor, Gabriel Taherian, Hessam University of Alabama at Birmingham BirminghamAL United States

ISBN: (纸本)9783981465990

Science and technological advancement, environmental pollution, government policies and rising energy costs have long since begun redefining the way power is generated, distributed and utilized. Considering the much higher standards relative to common practices, distributed energy power generation has proven to be a viable alternative to centralized power generation due to the many advantages such as energy generation and control independence, lower greenhouse emissions and significant reduction in transmission losses. This research considers a natural gas-powered internal combustion engine (ICE) with heat recovery and trigeneration capabilities or combined cooling heating and power (CCHP) as well as renewable energy incorporation including solar panels and possibly solar thermal. Research objective is to create an accurate working numerical model that in future can be compared with an experimental model for verification and optimization. TRNSYS software is used to create a numerical model by assembling the components as it would be in real life and running a simulation. In modeling the building, Sketchup and OpenStudio will be used to create thermal zones as in a singlefamily two story detached house with conventional floor plans. The model will then be imported into the TRNSYS environment for further analysis. optimization of the system would be primary, and with a targeted energy utilization factor of 95%, this research will seek to provide substantial and convincing data for future installations. © 2018 The Authors.

关键词： Natural gas

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DATA-driven COMPUTING WITH CONVOLUTIONAL NEURAL NETWORKS FOR TWO-PHASE FLOWS: APPLICATION TO WAVE-STRUCTURE INTERACTION 37

DATA-DRIVEN COMPUTING WITH CONVOLUTIONAL NEURAL NETWORKS FOR...

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37th ASME International conference on Ocean, Offshore and Arctic Engineering

作者： Mao, X. Joshi, V. Miyanawala, T. P. Jaiman, Rajeev K. Natl Univ Singapore Dept Mech Engn Singapore 117575 Singapore

ISBN: (纸本)9780791851210

Fluctuating wave force on a bluff body is of great significance in many offshore and marine engineering applications. We present a Convolutional Neural Network (CNN) based data-driven computing to predict the unsteady wave forces on bluff bodies due to the free-surface wave motion. For the full-order modeling and high-fidelity data generation, the air-water interface for such wave-body problems must be captured accurately for a broad range of physical and geometric parameters. Originated from the thermodynamically consistent theories, the physically motivated Allen-Cahn phase-field method has many advantages over other interface capturing techniques such as level-set and volume-of-fluid methods. The Allen-Cahn equation is solved in the mass-conservative form by imposing a Lagrange multiplier technique. While a tremendous amount of wave-body interaction data is generated in offshore engineering via both CFD simulations and experiments, the results are generally underutilized. Design space exploration and flow control of such practical scenarios are still time-consuming and expensive. An alternative to semi-analytical modeling, CNN is a class of deep neural network for solving inverse problems which is efficient in parametric data-driven computation and can use the domain knowledge. It establishes a model with arbitrarily generated model parameters, makes predictions using the model and existing input parametric settings, and adjusts the model parameters according to the error between the predictions and existing results. The computational cost of this prediction process, compared with high-fidelity CFD simulation, is significantly reduced, which makes CNN an accessible tool in design and optimization problems. In this study, CNN-based data-driven computing is utilized to predict the wave forces on bluff bodies with different geometries and distances to the free surface. The discrete convolution process with a non-linear rectification is employed to approximate the ma

关键词： Data-driven computing CNN-based deep learning Allen-Cahn phase-field Parametric deep kernel learning Wave-structure interaction

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12th International Scientific conference on Parallel Computational Technologies, PCT 2018

12th International Scientific Conference on Parallel Computa...

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12th International Scientific conference on Parallel Computational Technologies, PCT 2018

ISBN: (纸本)9783319996721

The proceedings contain 24 papers. The special focus in this conference is on . The topics include: Fine-grained parallel algorithms in TIM-3D code;modified componentwise gradient method for solving structural magnetic inverse problem;parallel multipoint approximation method for large-scale optimization problems;high-performance computation of initial boundary value problems;a study of euclidean distance matrix computation on intel many-core processors;parallel method of pseudoprojection for linear inequalities;GPU acceleration of bubble-particle dynamics simulation;VM2D: Open source code for 2D incompressible flow simulation by using vortex methods;modeling of nonstationary two-phase flows in channels using parallel technologies;a toolkit for the development of data-driven functional parallel programmes;supercomputer simulation of cathodoluminescence transients in the vicinity of threading dislocations;supercomputer simulation of promising nanocomposite anode materials for lithium-ion batteries: New results;parallel solution of sediment and suspension transportation problems on the basis of explicit schemes;three-dimensional mathematical model of wave propagation towards the shore;supercomputer modeling of hydrochemical condition of shallow waters in summer taking into account the influence of the environment;machine learning techniques for detecting supercomputer applications with abnormal behavior;Role-dependent resource utilization analysis for large HPC centers;high-performance reconfigurable computer systems with immersion cooling;hybrid supercomputer desmos with torus angara interconnect: Efficiency analysis and optimization;performance of elbrus processors for computational materials science codes and fast fourier transform;performance and energy analysis of nighttime satellite image archive processing module;fully homomorphic encryption for parallel implementation of approximate methods for solving differential equations.

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A robust control and design of generator-side converter for a multi-megawatt hydraulic energy conversion system 4

A robust control and design of generator-side converter for ...

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4th International conference on optimization and Applications, ICOA 2018

作者： Benzazah, Chirine Lazrak, Loubna El Daoudi, Soukaina Lafkih, Mustapha Ait Electrical Engineering Department University of Sultan Moulay Slimane Faculty of Sciences and Technology B.P: 523 Beni-Mellal23000 Morocco

ISBN: (纸本)9781538642252

This paper focus on an optimal control of a multi pole permanent magnet synchronous generator MP-PMSG direct driven by multi-megawatt hydraulic turbine system connected to the grid. The control strategy is applied to extract the full power output from the hydraulic turbine through a DC/DC boost converter connected to the generator by an uncontrolled three phase rectifier. This control method consists of two cascaded loops, a fast inner loop of DC inductor current, gives the instantaneous duty ratio d to the boost converter switch. The second outer speed loop controls the generator speed to balance well the power flow into the grid. Concerning the control strategy of the DC/AC converter grid side, the three levels NPC inverter controls the DC link voltage and injected active/reactive powers by the (d, q)-axis currents respectively. The reactive power generated by the entire installation is adjusted at zero so that unity power factor is maintained. simulation results show that the suggested control strategy method and system modeling operate satisfactorily. © 2018 IEEE.

关键词： Permanent magnets

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Numerical modeling of a flexural displacement-converter mechanism to excite a flat acoustic source driven by piezoelectric stack actuators

Numerical modeling of a flexural displacement-converter mech...

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International conference on Noise and Vibration Engineering (ISMA) / International conference on Uncertainty in Structural Dynamics (USD)

作者： Tajdari, F. Berkhoff, A. P. de Boer, A. Univ Twente Fac Engn Technol Drienerlolaan 5 NL-7500 AE Enschede Netherlands TNO Tech Sci Acoust & Sonar Oude Waalsdorperweg 63 NL-2597 AK The Hague Netherlands

ISBN: (纸本)9789073802995

This paper studies an acoustic source with a relatively small thickness and high bending stiffness. The acoustic source operates in the low frequency, quasi-static regime. The focus of the current study is on the actuation part in order to design an appropriate excitation mechanism. A flexural mechanism is modeled in combination with piezoelectric actuators to convert an in-plane displacement of the actuators to a perpendicular out-of-plane direction. First, an optimization simulation is used to determine the size of the required piezoelectric actuator. Then an equivalent electrical circuit of the lumped acoustic source is developed. This equivalent circuit can directly be connected to the electrical model of a switching amplifier. Finally, a coupled numerical finite element analysis is carried out by using COMSOL Multiphysics software package to model the combination of both flexural mechanism and piezoelectric device. The suggested flexural mechanism is sufficiently narrow to overcome the space limitation challenge in the design.

关键词： Piezoelectric actuators

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Data-driven Switched Affine modeling for Model Predictive Control

Data-driven Switched Affine Modeling for Model Predictive Co...

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6th IFAC conference on Analysis and Design of Hybrid Systems (ADHS) / Federated Logic conferences (FLOC)

作者： Smarra, Francesco Jain, Achin Mangharam, Rahul D'Innocenzo, Alessandro Univ Aquila Dept Informat Engn Comp Sci & Math Via Vetoio I-67100 Laquila Italy Univ Penn Dept Elect & Syst Engn 200 South 33rd St Philadelphia PA 19104 USA

Model Predictive Control (MPC) is a well consolidated technique to design optimal control strategies, leveraging the capability of a mathematical model to predict the systems behavior over a predictive horizon. However, building physics-based models for large scale systems, such as buildings and process control, can be cost and time prohibitive. To overcome this problem we propose in this paper a methodology to exploit machine learning techniques (i.e. regression trees and random forests) in order to build a state-space switched affine dynamical model of a large scale system only using historical data. Finite Receding Horizon Control (RHC) setup using control-oriented data-driven models based on regression trees and random forests is presented as well. A comparison with an optimal MPC benchmark and a related methodology is provided on an energy management system to show the performance of the proposed modeling framework. simulation results show that the proposed approach is very close to the optimum and provides better performance with respect to the related methodology in terms of cost function optimization. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Data-driven modeling data-driven model predictive control machine learning switched systems

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Application of data science and machine learning for well completion optimization

Application of data science and machine learning for well co...

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Offshore Technology conference, OTC 2018

作者： Pankaj, Piyush Geetan, Steve MacDonald, Richard Shukla, Priyavrat Sharma, Abhishek Menasria, Samir Xue, Han Judd, Tobias Schlumberger United States EP Energy Corporation United States

ISBN: (纸本)9781510862531

In today's data-driven economy, operators that integrate vast stores of fundamental reservoir and production data with the highperformance predictive analytics solutions can emerge as winners in the contest of maximizing estimated ultimate recovery (EUR). The scope of this study is to demonstrate a new workflow coupling earth sciences with data analytics to operationalize well completion optimization. The workflow aims to build a robust predictive model that allows users to perform sensitivity analysis on completion designs within a few hours. Current workflows for well completion and production optimization in unconventional reservoirs require extensive earth modeling, fracture simulation, and production simulations. With considerable effort and wide scale of sensitivity, studies could enable optimized well completion design parameters such as optimal cluster spacing, optimal proppant loading, optimal well spacing, etc. Yet, today, less than 5% of the wells fractured in North America are designed using advanced simulation due to the required level of data, skillset, and long computing times. Breaking these limitations through parallel fracture and reservoir simulations in the cloud and combining such simulation with data analytics and artificial intelligence algorithms helped in the development of a powerful solution that creates models for fast, yet effective, completion design. The approach was executed on Eagle Ford wells as a case study in 2016. Over 2000 data points were collected with completion sensitivity performed on a multithreaded cluster environment on these wells. Advanced machine learning and data mining algorithms of data analytics such as random forest, gradient boost, linear regression, etc. were applied on the data points to create a proxy model for the fracturing and numerical production simulator. With the gradient boost technique, over 90% accuracy was achieved between the proxy model and the actual results. Hence, the proxy model could predict

关键词： Sensitivity analysis

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Need for speed: Data analytics coupled to reservoir characterization fast tracks well completion optimization

Need for speed: Data analytics coupled to reservoir characte...

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SPE Canada Unconventional Resources conference 2018, URC 2018

作者： Pankaj, Piyush Geetan, Steve MacDonald, Richard Shukla, Priyavrat Sharma, Abhishek Menasria, Samir Judd, Tobias Schlumberger United States EP Energy Corporation United States

ISBN: (纸本)9781613995822

In today's data-driven economy, operators that integrate vast stores of fundamental reservoir and production data with the high-performance predictive analytics solutions can emerge as winners in the contest of maximizing estimated ultimate recovery (EUR). The scope of this study is to demonstrate a new workflow coupling earth sciences with data analytics to operationalize well completion optimization. The workflow aims to build a robust predictive model that allows users to perform sensitivity analysis on completion designs within a few hours. Current workflows for well completion and production optimization in unconventional reservoirs require extensive earth modeling, fracture simulation, and production simulations. With considerable effort and wide scale of sensitivity, studies could enable optimized well completion design parameters such as optimal cluster spacing, optimal proppant loading, optimal well spacing, etc. Yet, today, less than 5% of the wells fractured in North America are designed using advanced simulation due to the required level of data, skillset, and long computing times. Breaking these limitations through parallel fracture and reservoir simulations in the cloud and combining such simulation with data analytics and artificial intelligence algorithms helped in the development of a powerful solution that creates models for fast, yet effective, completion design. As a case study, the approach was executed on Eagle Ford wells. Over 2000 data points were collected with completion sensitivity performed on a multithreaded cluster environment on these wells. Advanced machine learning and data mining algorithms of data analytics such as random forest, gradient boost, linear regression, etc. were applied on the data points to create a proxy model for the fracturing and numerical production simulator. With the gradient boost technique, over 90% accuracy was achieved between the proxy model and the actual results. Hence, the proxy model could predict the w

关键词： Sensitivity analysis

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Design-oriented constrained modeling of antenna structures 12

Design-oriented constrained modeling of antenna structures

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12th European conference on Antennas and Propagation, EuCAP 2018

作者： Koziel, Slawomir Sigurðsson, Ari T. Engineering Optimization and Modeling Center Reykjavik University Reykjavik101 Iceland Faculty of Electronics Telecommunications and Informatics Gdansk University of Technology Gdansk80-233 Poland

ISBN: (纸本)9781785617911

Fast surrogate models are crucially important to reduce the cost of design process of antenna structures. Due to curse of dimensionality, standard (data-driven) modeling methods exhibit serious limitations concerning the number of independent geometry parameters that can be handled but also (and even more importantly) their parameter ranges. In this work, a design-oriented modeling framework is proposed in which the surrogate is constructed in the region of interest defined by a set of reference designs, optimized with respect to user-selected figures of interest (e.g., operating frequencies, substrate permittivity, etc.). The model domain is spanned by the simplexes obtained by triangulation of the reference designs, further extended into their orthogonal complements. Constraining the surrogate model domain this way allows for considerable reduction of the number of training data samples necessary to build the model, as compared to the traditional approach where the sampling is normally performed over a hypercube defined by the lower/upper bounds of the antenna parameters. Our considerations are illustrated using two examples: a UWB monopole, and a dual-band patch antenna. © Institution of Engineering and *** Rights Reserved.

关键词： Triangulation

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optimization of completion and well spacing for development of multi-stacked reservoirs using integration of data analytics, geomechanics and reservoir flow modeling

Optimization of completion and well spacing for development ...

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SPE/AAPG/SEG Unconventional Resources Technology conference 2018, URTC 2018

作者： Min, Kyoung Suk Sen, Vikram Ji, Lujun Sullivan, Richard B. Anadarko Petroleum Corporation United States

In multi-stacked unconventional development, optimization of completion and well spacing is a major challenge. Because of complex interactions between tightly placed wells in different target zones, it is necessary to model the interference between the wells together with depletion and flow across zones. This integrated scenario modeling is a pre-requisite for full field development and we have developed a new workflow combining physics-based geomechanical reservoir simulation and data-driven analytics. Using the hybrid method, we can efficiently identify key drivers for completion and well spacing optimization. Our method involves statistically selecting multiple single wells from our portfolio, using them in Dynamic Stimulated Reservoir (DSRV) modeling (using geomechanically influenced permeabilities during fracturing and production) to build prediction functions, and then validating them against actual multi-well field data. For field development scenarios of multi-stacked reservoirs, we applied Design of Experiment (DoE) method and Multivariate analysis (MVA) to screen and rank scenarios. For completion optimization, multiple production profiles predicted from simulated cases were used to generate response surfaces. These in turn were used to project the outcome from various permutations and combinations of completion parameters and well spacing in a multi-stacked configuration and fed into economic analyses. Through this study, we have identified that the resultant impact of key completion drivers dynamically change over production periods as well as well spacing. Thus, some completion parameters have a high impact at only early times, while other completion parameters dominate for later production. Furthermore, when well spacing and targeting are changed, the key completion derivers may also change. This demonstrates to us that completion design, targeting, well control and well spacing should be optimized based on the field-wide development plan, honoring int

关键词： Multivariant analysis

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