This work addresses issues that appear from wind turbines equipped with frequency converters and presents a new fixed-speed wind turbine (FSWT) concept based on a split power transmission, along with a Mechatronic Con...
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This work addresses issues that appear from wind turbines equipped with frequency converters and presents a new fixed-speed wind turbine (FSWT) concept based on a split power transmission, along with a Mechatronic Control Model. The wind turbine rotor drives the first transmission shaft, while a servomotor with variable speed powers the second input. The differential gear transmission output is linked to the electric grid through an asynchronous generator. To optimize the power extracted from the wind energy while minimizing excessive dynamic loads on the wind turbine, a mechatronic control model of a 750 kW-FSWT is applied using a proportional and integral controller (PI). The paper suggests employing neural networks and evolutionary algorithms for determining appropriate PI gains. For collective servomotor control of a 750 kW-FSWT, a radial basis function (RBF) neural networks based on PI controller is proposed. To acquire an optimum dataset for RBF training, the particle swarm optimization (PSO) evolutionary algorithm is harnessed. The robustness and effectiveness of the proposed model and controller are verified and confirmed through simulation results. Hands-on experience is conducted using the 20-sim software package.
Modern-day Supply Chains (SC) are intricate networks spanning the globe, that are vulnerable to disruptions ranging from natural calamities to changing international policies. Their design, optimization and management...
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ISBN:
(纸本)9798400703638
Modern-day Supply Chains (SC) are intricate networks spanning the globe, that are vulnerable to disruptions ranging from natural calamities to changing international policies. Their design, optimization and management requires a consideration of aspects such as resilience and sustainability alongside profitability. simulation plays a key role in SC design. Despite the existence of commercial tools, there is a dearth of well-documented, open-source libraries for supply chain simulation. This paper presents an outline and work-in-progress towards the development of an open-source Python library for modeling and discrete-event simulation of supply chain networks. The design of the library is guided by a detailed literature survey of supply chain models and applications, through which we have identified a set of configurable model components, their parameters and optimization measures that have wide applicability. A Graph Neural Network (GNN) based meta-model generation and optimization flow is also planned to be integrated with the library for aiding design exploration. We present a brief summary of the review and takeaways along with an outline of the SC modeling library and the planned design exploration flow.
Operating the GaN transistor at high frequencies stimulates additional parasitic effects, which have a strong impact on the RF characteristics of the device. Directly extracting these distributed elements may not be p...
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The channel layouts on bipolar interconnects play an important role in improving the electrochemical performance of solid oxide fuel cells (SOFCs). Most previous layout design studies are performed based on artificial...
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The channel layouts on bipolar interconnects play an important role in improving the electrochemical performance of solid oxide fuel cells (SOFCs). Most previous layout design studies are performed based on artificial predetermination without optimization. In this study, a topology optimization method for the flow channel layout of SOFCs based on the real microstructure of SOFC components is presented. Based on the reconstructed real microstructure, relevant numerical simulations are performed to analyze the multiphysics coupling mechanisms in SOFCs and provide an effective tool for SOFC performance prediction. To further improve SOFC performance, a numerical framework for flow field optimization based on the actual microstructure of a specific SOFC is proposed. The density-based topology optimization is used for the layout of the flow channels by coupling it with the multiphysical fields generated from electrochemical simulation coupled with flow field modeling. The optimization is modified based on the modification of the source term derived from the electrochemical simulation to achieve a significant improvement in the electrochemical performance of the SOFCs.
simulation-based metamodels or surrogate models are simplified models that capture the relationship between inputs and outputs of the simulation model. The analytical expression of metamodels is defined using a sample...
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simulation-based metamodels or surrogate models are simplified models that capture the relationship between inputs and outputs of the simulation model. The analytical expression of metamodels is defined using a sample of input/output points obtained from the simulation model. This analytical expression is then embedded in an optimization process that usually provides solutions much faster than other simulation-based optimization techniques such as metaheuristics or mathematical modeling. The goal of this paper is to describe the simulation-based metamodeling approach and to provide a thorough review of the literature on its applications to emergency healthcare systems. For this purpose, we examine the recent literature (journals and conference proceedings) published in the last 15 years (2008-2022). Finally, we identify findings and avenues of research in simulation-based metamodeling that deserve special attention from the scientific community and allow the potential of this approach to be used for better decision-making in emergency healthcare.
This study investigated mathematical modeling and optimization of the xylene isomerization reaction in a commercial adiabatic reactor. The proposed model, consisting of a set of algebraic and ordinary differential equ...
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This study investigated mathematical modeling and optimization of the xylene isomerization reaction in a commercial adiabatic reactor. The proposed model, consisting of a set of algebraic and ordinary differential equations, is based on a heterogeneous one-dimensional steady-state formulation. To verify the proposed model, the simulation results have been compared to available data from an industrial reactor. A good agreement has been found between the simulation and plant data. The genetic algorithm (GA) method is applied to optimize the reactor operating conditions considering the para-xylene (p-xylene) mole fraction in reactor outlet as the main objective function. According to the simulation results, there is an optimum initial temperature for maximizing the objective function. In the optimization process, the p-xylene mole fraction was enhanced by 3.0% at an optimized feed temperature of 678.04K.
With the increasing adoption of self-service bag drop facilities, modern airports necessitate check-in counter optimization models that strategically allocate passengers between staffed and self-service facilities whi...
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With the increasing adoption of self-service bag drop facilities, modern airports necessitate check-in counter optimization models that strategically allocate passengers between staffed and self-service facilities while balancing operational costs and service quality. This study develops a two-tiered decision framework integrating dynamic integer programming and discrete-event simulation (DES) to minimize manual counter-staffing costs while guaranteeing service level agreements. We first formulate an integer programming model that allocates passengers to manual or self-service channels based on passengers' profiles. To address queue overflow during peak hours, a dynamic capacity scaling factor is introduced to the integer programming model. The DES module then iteratively validates queueing dynamics and feeds back queueing time to optimization layer, triggering increase in the capacity scaling factor and reallocations when predicted wait times exceed 15-min thresholds. applied to Baiyun Airport Terminal 2, the model results show a 9.2% increase in operational cost (from 8824.5 to 9633.75) but a 30.9% reduction in average manual check-in wait time (from 8.1 to 5.6 min), reducing peak-period congestion. This study provides decision support for optimizing check-in counter operations at airports.
Cloud computing has become essential for delivering flexible and scalable resources. In this environment, finding the optimal hardware-software configuration is crucial and often involves solving constrained black-box...
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ISBN:
(纸本)9798331531317;9798331531300
Cloud computing has become essential for delivering flexible and scalable resources. In this environment, finding the optimal hardware-software configuration is crucial and often involves solving constrained black-box problems on discrete multi-dimensional domains. These optimizations must be performed within a limited number of evaluations to contain costs. Bayesian optimization (BO) addresses this problem by providing near-optimal solutions with few iterations. However, the original BO algorithm was designed for continuous and unbounded domains. On the other hand, Machine Learning (ML) models are powerful tools for predicting the values of a black-box function. In this work, we present d-MALIBOO, a framework that integrates BO and ML techniques to improve the efficiency of finding optimal solutions in discrete and bounded domains. While BO suggests the next point to be evaluated by effectively balancing exploration and exploitation, ML models are valuable for determining feasibility regions and focusing the search for the optimum. Experimental results show that our algorithm outperforms alternative methods from the literature in all tested environments, especially in complex optimization scenarios, resulting in an improvement in regret by approximately 2-8 times.
Thermal effect represents one of the main challenges for GaN power transistors. In addition to ambient temperature, the power dissipation-induced self-heating represents the main source of heating. This electrothermal...
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This paper focuses on the investigation of op amp circuit optimization based on Monte Carlo simulation and PVT simulation, focusing on the performance of the op amp in different operating environments and its optimiza...
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ISBN:
(纸本)9798350377040;9798350377033
This paper focuses on the investigation of op amp circuit optimization based on Monte Carlo simulation and PVT simulation, focusing on the performance of the op amp in different operating environments and its optimization method of key performance indexes. First, the performance of the op amp under different conditions is evaluated through pre-simulation and post-simulation performance analysis. Then, the performance under various combinations of process variables is statistically analyzed using Monte Carlo simulation, and the performance indexes such as gain bandwidth product (GBW), common mode rejection ratio (CMRR), and power supply rejection ratio (PSRR) are examined during the study, and the corresponding optimization strategies are given. Then, the selection of the op-amp structure is analyzed, and the design optimization of the current reference and bias circuits is further carried out. Through mathematical modeling and small-signal analysis, this paper proposes a method to optimize the design of the current source, thus improving the bandwidth and stability of the op amp. In addition, the study utilizes small-signal model-based gain and bandwidth calculations, voltage-swing ratio analysis, and power supply rejection ratio calculations. Finally, through design and simulationoptimization, the op-amp design proposed in this paper meets the design specifications in most cases and shows high stability and performance advantages under different conditions.
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