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AIAA Propulsion and Energy Forum / IEEE Electric Aircraft Technologies Symposium (EATS)

作者： Helmerich, Christopher Fuchs, Jared Agnew, Joseph Univ Alabama Huntsville Dept Phys & Astron Huntsville AL 35899 USA Univ Alabama Huntsville Dept Mech & Aerosp Engn Huntsville AL 35899 USA

ISBN: (数字)9781624106118

ISBN: (纸本)9781624106118

Warp drive research has primarily focused on the evaluation of analytic solutions to Einstein's field equations. In this paper, we take a different approach and compute the stress-energy-momentum tensor numerically using Einstein's field equations for a given space-time metric. Once the stress-energy-momentum tensor is computed, a fitness parameter can be calculated based on how efficiently it achieves a certain warp velocity. The initial space-time metric is then updated based on the fitness parameter. This process allows for a much broader range of optimization techniques to be applied since no analytic formula is needed to describe the metric as it is being optimized. In addition, the numerical techniques provide insight into infrequently-discussed parts of warp drive theory, namely, properties of not only the total energy densities needed but also the distribution of momentum, shear stress, and pressure density throughout the warp bubble. These investigations, therefore, provide new insight into the dynamics of warp bubbles.

关键词： Numerical optimization Energy Distribution Shear Stress Greedy algorithm Annealing Streamlines Toroidal Vortex Planets optimization algorithm Reinforcement Learning

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Single and multi-objective optimization of steel moment-resisting frame buildings under vehicle impact using evolutionary algorithms

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Journal of Building Pathology and Rehabilitation 2021年第1期6卷 1-13页

作者： Sadeghi, Abbasali Kazemi, Hamid Samadi, Maysam Department of Civil Engineering Mashhad Branch Islamic Azad University Mashhad Iran

The evaluation of optimization approaches such as single and multi-objective optimization is important for determining the optimal design of structures against abnormal loadings. In this study, the optimal design of steel moment-resisting frames (SMRFs) with 5 and 12-story is investigated and compared by using the mentioned optimization approaches under light vehicle impact loading. Then, the single and multi-objective optimization methodologies are discussed. Next, for single-objective optimization approach, three evolutionary algorithms such as a particle swarm optimization (PSO), a genetic algorithm (GA) and a whale optimization algorithm (WOA) are considered and then for another approach, a multi-objective optimization cuckoo search algorithm (MOCSA) is noticed by linking the nonlinear dynamic analysis responses of OpenSees software to MATLAB software for weight optimization of SMRFs. Finally, the effectiveness and efficiency of these optimization algorithms are evaluated and compared with each other. The results indicated that in comparison to single-objective method, the MOCSA was able to present a solution leading to a more practical and economic selection of minimum total weights and drift. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Light vehicle impact Multi-objective optimization algorithm Single-objective Steel moment-resisting frame

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Estimating an optimal macroeconomic uncertainty index for Australia

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APPLIED ECONOMICS 2022年第38期54卷 4374-4383页

作者： Kaya, Havvanur Feyza Tawadros, George B. Samsun Univ Fac Econ Adm & Social Sci Dept Int Commerce & Business Samsun Turkey Winona State Univ Dept Econ Coll Business Winona MN 55987 USA

In this article, an optimal macroeconomic uncertainty index is constructed for the Australian economy. This index is derived from a small structural macroeconomic model. The structural model is first estimated using GMM to extract the parameter estimates, which are then used to initialize maximum likelihood techniques in order to obtain the optimal coefficient values for the relevant variables. The relevant variables are then weighted by the obtained optimal coefficients and, finally, are aggregated to produce the optimal macroeconomic uncertainty index for Australian economy. The empirical results show that the uncertainty index constructed is a good indicator of the optimal economic conditions in Australia, providing a useful tool to assist the Reserve Bank of Australia in its decision-making process.

关键词： Optimal uncertatiny index central bank GMM optimization algorithm

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Multi-criteria Multi-constrained Aerodynamic optimization of Civil Tiltrotor Empennage Surfaces

Multi-criteria Multi-constrained Aerodynamic Optimization of...

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AIAA Aviation Forum and Exposition

作者： Battiston, A. Ponza, R. Benini, E. Bernini, G. Pecoraro, M. HIT09 Srl I-35127 Padua Italy Univ Padua Dept Ind Engn I-35131 Padua Italy Leonardo SpA Helicopter Div I-21017 Cascina Costa Di Samarat Italy

ISBN: (数字)9781624106101

ISBN: (纸本)9781624106101

The present work describes the overall optimization strategy that has been adopted for the enhancement of the aerodynamic performance of a civil tiltrotor empennage surfaces. The optimization process has been designed around GeDEA-II, a Multi-Objective Evolutionary algorithm developed at University of Padua. The optimization algorithm has been used in two different cases: a two-dimensional optimization of the empennage airfoil and a threedimensional optimization of the empennage winglets, patented by Leonardo Helicopters under the name of finlets. Results demonstrate the effectiveness of the optimization strategies for both the cases. A parametric study of the empennage planform has also been conducted with the aid of an artificial neural network, in order to assess the variations in aerodynamic performance for different geometries.

关键词： Tiltrotor Airframes Aerodynamic Performance Symmetric Airfoil Artificial Neural Network optimization algorithm Multi Objective Evolutionary algorithms Leonardo Helicopters Aerodynamic Coefficients Aspect Ratio

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Line start synchronous reluctance motor with improved power factor for agriculture electric pump applications

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JOURNAL OF ENGINEERING-JOE 2022年第3期2022卷 295-310页

作者： Uvaraj, G. Aswin Lenin, N. C. Padmanaban, Sanjeevikumar Khan, Baseem Vellore Inst Technol Sch Elect Engn Chennai Tamil Nadu India Vellore Inst Technol Testing & Res Ctr Deputy Director Elect Vehicles Incubat Chennai Tamil Nadu India Aarhus Univ Dept Business Dev & Technol CTiF Global Capsule Birk Centerpk 15 Herning Denmark Hawassa Univ Dept Elect & Comp Engn Hawassa 05 Ethiopia

Line start synchronous reluctance motor (LSRM) is more efficient than the existing induction motor (IM). However, the power factor is a major concern in LSRM. The power factor of the LSRM depends on the saliency ratio. To increase the saliency ratio, either d-axis inductance (L-d) has to be improved, or q-axis inductance (L-q) reduced. Here, q-axis inductance is decreased by making the minimal or negligible flux variation in the q-axis. Initially, the rotor geometry has been optimized employing an optimization algorithm. Then, finite element analysis is adopted to predict the capability of the designed rotors. Stress analysis has been carried out for both axial and radial rotor structures to verify the mechanical robustness. The fabrication steps such as folding, stacking and screwing, wire cut, die-casting and milling process of both the rotor structures are explained in detail. Further, both the machines are tested with the centrifugal pump. From the test results, it is observed that the proposed rotor structure provides a higher power factor.

关键词： screwing wire cut optimization algorithm folding wire cut negligible flux variation finite element analysis rotors reluctance motors pumps mechanical robustness higher power factor d-axis inductance induction motors improved power factor minimal flux variation stacking wire cut designed rotors LSRM q-axis inductance centrifugal pump power factor axial rotor structures rotor structure agriculture induction motor radial rotor structures line start synchronous reluctance motor stress analysis saliency ratio agriculture electric pump applications rotor geometry

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A Flexible Control Strategy Model for Energy Storage System 4

A Flexible Control Strategy Model for Energy Storage System

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4th Asia Energy and Electrical Engineering Symposium (AEEES)

作者： Zhang, Shaohua Zhang, Rong Wei, Xin Wang, Yunshan Cai, Zhi Kang, Rong China EPPEI Smart Energy Co Ltd Beijing Peoples R China Xian FPA Energy Technol Co Ltd Xian Peoples R China China Elect Power Res Inst Beijing Peoples R China

ISBN: (纸本)9781665479141

As the share of wind power participating in the electricity market grows, it is becoming increasingly important to control energy storage systems in order to increase the profitability of wind farms. This paper presents a flexible control model of energy storage system that may be utilized to assist wind farms in controlling energy storage system efficiently in real time, hence improving wind farm controllability and economic benefits. Based on the rolling forecast of ultra-short-term power and real-time electricity prices, the model in this paper can adjust the control strategy of the energy storage system in real time according to actual application scenarios, so as to help wind farms avoid deviation recovery losses and obtain excess profits as much as possible. To begin, a constrained optimization model of an energy storage system is established with the maximization of income generated by energy storage system as the objective function. Then, the CPLEX solver is used to solve the constrained optimization problem. Finally, data from a wind farm in Shanxi Province is used to verify the model proposed in this paper, and the result indicates the reliability and effectiveness of the model.

关键词： energy storage system flexible control strategy optimization algorithm wind power

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Forecasting very short-term wind power generation using deep learning, optimization and data decomposition techniques 24

Forecasting very short-term wind power generation using deep...

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24th International Conference on Electrical Machines and Systems (ICEMS)

作者： Hossain, Md Alamgir Gray, Evan MacA Islam, Md Rabiul Chakrabortty, Ripon K. Pota, Hemanshu R. Griffith Univ Queensland Micro & Nanotechnol Ctr Nathan Qld 4111 Australia Univ New South Wales Canberra Sch Engn & IT Campbell ACT 2612 Australia

ISBN: (纸本)9788986510218

Although accurate wind power prediction can improve the reliability, security and economic operation of a power system, the prediction task is complex due to the intermittent nature of wind speed its strong dependence on weather conditions. This paper proposes a novel framework, consisting of hybrid deep learning models, an optimization algorithm and a data decomposition technique, to improve the forecasting accuracy of ultra-short-term wind power generation. The data of the wind power generation collected from a real wind farm are pre-processed and decomposed using a variational mode decomposition (VMD). A deep-learning model (long short-term memory (LSTM) with dropout regularization) is designed to accurately predict the decomposed spectra. The hyper-parameters of the model are optimized by applying the grey-wolf optimization (GWO) algorithm to choose the best hyper-parameters. The combination of deep learning and optimization algorithm plays a key role in achieving better prediction accuracy. The effectiveness of the proposed framework is measured by applying it to two data sets, and the framework is compared with other forecasting models, such as a hybrid deep-learning and empirical wavelet transform (EWT) with LSTM. Comparison with experimental results demonstrates that the novel hybrid framework has the best prediction accuracy in forecasting ultra short-term wind power generation.

关键词： Very-short term prediction wind power generation VMD data decomposition deep learning optimization algorithm Boco Rock wind farm

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A Multi-objective approach for Robust Structural Topology optimization

A Multi-objective approach for Robust Structural Topology Op...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： Nathan Palar, Pramudita Satria Zuhal, Lavi Rizki Fac Mech & Aerosp Engn Inst Teknol Bandung Bandung 40132 West Java Indonesia

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

This paper presents a robust multi-objective approach for structural topology optimization. The performance and the stability are two conflicting objectives, hence, it is sometimes preferable to treat them as multiple objectives to gain important insight regarding various optimal topologies. The optimization problem is formulated as a simultaneous minimization of expectation and standard deviation of compliance in the presence of uncertainties in loading magnitude, boundary conditions, or material properties. It is imperative to obtain a good representation of the Pareto front with as few finite element simulations as possible. This paper proposes the combination of topology optimization with Polynomial Chaos Expansion (PCE) and Pareto Optimal Tracing (POT) to seek diverse Pareto optimal solutions efficiently. A benchmark test case with two and three random loads is used to test efficacy of the proposed method and the results are compared to the deterministic optimization. We studied the impact of the PCE to reduce the number of realizations and POT to reduce the number of iterations on the estimation of the Pareto front with various combinations of weights assigned to the expectation and the standard deviation. Results show that robust topology optimization with POT and PCE yielded better and diverse topologies than the conventional approach, which restarts the search from the uniform distributed density as the baseline design.

关键词： Topology optimization Standard Deviation Material Properties Poisson's Ratio Simply Supported Beams Structural optimization optimization algorithm Monte Carlo Simulation Stiffness Matrices Collocation Method

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optimization-based reference calculation for Modular Multilevel Converters in balanced and unbalanced network conditions 23

Optimization-based reference calculation for Modular Multile...

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23rd European Conference on Power Electronics and Applications (EPE ECCE Europe)

作者： Spier, Daniel Westerman Prieto-Araujo, Eduardo Lopez-Mestre, Joaquim Gomis-Bellmunt, Oriol Control Intelligent Energia Coop CINERGIA Carrer Can Baletes 7 Argentona 08310 Spain Univ Politecn Cataluna CITCEA UPC Avinguda Diagonal 647 Barcelona 08028 Spain

ISBN: (纸本)9789075815375

The paper addresses an optimization-based algorithm to calculate the references of the Modular Multi-level Converter (MMC) during normal and constrained scenarios (when the prioritization of quantities is required). The optimization problem prioritizes to satisfy the positive- and negative-sequence active and reactive current set-points demanded by the Transmission System Operator (TSO) through the corresponding grid code. If the TSO's requirements are achieved, the algorithm minimizes the arm impedances losses. Otherwise, it attempts to reduce the error between the current components and the TSO's current set-points. The optimization-based current reference calculation is derived based on the steady-state equations of the MMC, considering the maximum currents that can flow through the MMC's arms, the maximum and minimum arm applied voltages and the maximum sub-module capacitor's voltage. Simulation in the time-domain have been conducted and the results indicate that this method can be potentially employed to calculate the converter's references during both normal and faulted conditions.

关键词： Modular Multilevel Converters (MMC) HVDC optimization algorithm

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An Adaptive and Efficient Network Traffic Measurement Method Based on SDN in IoT 13th

An Adaptive and Efficient Network Traffic Measurement Method...

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13th European-Alliance-for-Innovation (EAI) International Conference on Simulation Tools and Techniques (EAI SIMUtools)

作者： Cai, Wansheng Song, Xi Liu, Chuan Jiang, Dingde Huo, Liuwei State Grid Elect Power Res Inst Nanjing 210003 Peoples R China State Grid Gansu Elect Power CORP Lanzhou 730000 Peoples R China Global Energy Interconnect Res Inst Co Ltd Nanjing 210003 Peoples R China Univ Elect Sci & Technol China Sch Astronaut & Aeronaut Chengdu 611731 Peoples R China

ISBN: (纸本)9783030971243;9783030971236

The Internet of Things (IoT) is a worldwide information network that connects thousands of technological gadgets. We incorporate the SDN network architecture into IoT networks and investigate the characteristics of SDN-based IoT networks in order to make the IoT more flexible and extendable. SDN (Software Defined Networking) is a logical control center with a centralized control plane that makes network management more flexible and efficient. For IoT network management, fine-grained and reliable traffic information is critical. Then, in SDN-based IoT networks, we construct a network traffic model by analyzing the self-similarity of network traffic in IoT network. Then, we collect some traffic statistics in OpenFlow-based switches as the source data and use it to train the proposed network traffic estimation model. Using the measured network traffic in the IoT network, we use the Kalman Filtering to measure and estimate each flow, this scheme just increases a little overhead. Then, we propose to an algorithm to search the more accuracy of traffic. Finally, we run additional simulations to ensure that the suggested measuring system is accurate. Simulation findings suggest that using intelligent optimization approaches, we can improve the granularity and accuracy of traffic data.

关键词： Internet of Things Software Defined Network optimization algorithm Network measurement

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