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Vibration control of a rotor supported by journal bearings and an asymmetric high-static low-dynamic stiffness suspension

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NONLINEAR DYNAMICS 2016年第1期85卷 525-545页

作者： Abbasi, Amirhassan Khadem, S. E. Bab, Saeed Friswell, M. I. Tarbiat Modares Univ Dept Mech Engn POB 14115-177 Tehran Iran Swansea Univ Coll Engn Bay CampusFabian Way Swansea SA1 8EN W Glam Wales

In this paper, the optimum design of an asymmetric model of a high-static low-dynamic stiffness (HSLDS) suspension for the vibration control of a rotating machine is studied. The rotating system consists of a flexible rotor supported by short journal bearings and HSLDS suspensions and is excited by an unbalance force. The equations of motion are solved numerically using the Runge-Kutta method. The bifurcation diagrams, phase portraits, Poincare maps and power spectra show the existence of various response regimes, such as periodic, period-two and quasi-periodic, in the dynamic response of the system. The design procedure is formulated as a multi-objective optimization problem which minimizes objectives including the rotor and bearing vibration, the bearing force transmission and the compliance of the HSLDS suspension under static loading, to obtain the appropriate force-displacement relation for the asymmetricHSLDS suspension. The multi-objective genetic algorithm is applied to identify the best set of the design parameters. The advantages of using an optimum HSLDS suspension to fulfill the design requirements in an operating speed range are demonstrated. It is also shown that the optimum suspension with low-dynamic stiffness can suppress the non-periodic behavior of the rotating machine in the considered operating speed range.

关键词： High-static low-dynamic stiffness Rotor Short journal bearing Bifurcation Phase portraits Poincare maps Power spectra Periodic Period-two Quasi-periodic multi-objective genetic algorithm

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Designing Satellite Constellations to Observe Earth's Time-Varying Gravity from Clock Frequency Comparisons

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JOURNAL OF SPACECRAFT AND ROCKETS 2023年第3期60卷 848-858页

作者： Lee, Kwonhee A. Gunter, Brian C. Georgia Inst Technol Atlanta GA 30332 USA Daniel Guggenheim Sch Aerosp Engn Atlanta GA USA

The nondominated sorting genetic algorithm III (NSGA-III) is used to design nonsymmetric satellite constellations to observe large-scale high-frequency gravity variations that would serve as complementary systems to other dedicated gravity field mapping missions such as the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On missions. The functionality of the NSGA-III method is validated by reproducing optimal surface-coverage satellite constellations from the literature. Then, NSGA-III is used to generate nondominated constellations tuned to detect short-term long-wavelength gravity field variations. The constellations are used to generate subweekly gravity field solutions from clock frequency comparison measurements. The effectiveness of NSGA-III for optimizing nonsymmetric constellations was demonstrated because the constellations obtained from NSGA-III were consistently superior in performance than traditional symmetric constellations. Furthermore, the results showed that the time-varying signals of interest (that is, gravity coefficients through the spherical harmonic degree and order of seven at daily intervals) can be unambiguously resolved by assuming an optical clock with a 4.8x10-18 fractional frequency uncertainty representative of current technology. The ability to monitor these short-term long-wavelength signals on a global scale would observe a spectrum of the gravity field inherently undetectable by GRACE-like missions, and it would provide new insight to the understanding of global mass redistribution processes.

关键词： multi objective genetic algorithm Gravity Recovery and Climate Experiment Geophysics Satellite Geodesy Satellite Constellations Spherical Harmonics multi-objective genetic algorithm Satellite Remote Sensing

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A resource pre-allocation method for cognitive analytics-based social media service in edge computing

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WIRELESS NETWORKS 2024年第6期30卷 6135-6150页

作者： Wang, Ruizhi Zhang, Difei Shanxi Agr Univ Jinzhong 030801 Peoples R China Qufu Normal Univ Sch Math Sci Qufu 273165 Peoples R China

With the rapid development of 5G mobile communication technology, the growing demand for media services in society is becoming a characteristic of smart cities. Technical study has exhaustively investigated the low-latency resource supply of edge computing systems for web hosting services. However, external variables (such as transmission and network delays) impact the transmission between edge servers and service requests, resulting in service request delays. In addition, resource service requests that are constantly updated and in dynamic distribution may overload some servers while others are idle, resulting in poor load balancing. Consequently, this research presents a Resource Pre-Allocation method (RPA) for cognitive analytics-based social media platforms, which aims to improve the load balancing of edge servers while serving requests with strict latency requirements, so as to obtain the optimal resource allocation strategy. First, the resource requirement prediction algorithm is developed based on temporal-spatial demand history. Then, we propose a multi-objective algorithm combined with the optimal solution selection techniques to obtain the ideal resource allocation decisions. Finally, the performance of RPA is tested and evaluated. The experimental results show that RPA can allocate resources better than other methods.

关键词： 5G edge computing Resource requirement prediction Resource pre-allocation multi-objective genetic algorithm Optimal solution selection

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multi-objective Waste Load Allocation Model for Optimizing Waste Load Abatement and Inequality Among Waste Dischargers

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WATER AIR AND SOIL POLLUTION 2014年第3期225卷 1-17页

作者： Cho, Jae Heon Lee, Jong Ho Kwandong Univ Dept Hlth & Environm Kangnung 210701 Gangwon Do South Korea Cheongju Univ Dept Urban Planning Cheongju 360764 Chungbuk South Korea

In allocating the waste load of a river basin, the first priority is to achieve a given water quality goal for that river by utilizing several water quality management methods. Minimizing the waste load abatement cost within the river basin through appropriate, efficient water quality management is an important aspect of this process. In the past, it was common to concentrate on economic factors when constructing a waste load allocation (WLA) model. However, environmental resources (e. g., sub-basin area, population, wastewater flow, etc.) vary in each region of a river, and the fairness in the distribution of the treatment efforts among waste dischargersmust be considered. The WLA model in this study was constructed as a multi-objective optimization problem and was established to achieve the economic goal of minimizing waste load abatement and to consider the inequality among waste dischargers. Two types of inequality were introduced into the WLA model. The first type is the inequality in the waste load discharge regarding the environmental resources in each region was computed with the environmental resource-based Gini coefficient. The second type of inequality is the fairness in the distribution of the treatment efforts among waste dischargers. The suitability of this WLA model was verified with its application in a heavily polluted total maximum daily load subject river in South Korea. Furthermore, Pareto-optimal solutions drawn from the multi-objective genetic algorithm were analyzed to infer the least cost solution, the least inequality solution, and the compromise solutions and to verify critical pollution sources.

关键词： WLA Inequality ER-Gini multi-objective genetic algorithm Pareto-optimal solutions Critical pollution sources

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A multi-objective algorithm for task scheduling and resource allocation in cloud-based disassembly

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JOURNAL OF MANUFACTURING SYSTEMS 2016年 41卷 239-255页

作者： Jiang, Hui Yi, Jianjun Chen, Shaoli Zhu, Xiaomin East China Univ Sci & Technol Dept Mech Engn 130 Meilong Rd Shanghai 200237 Peoples R China

Some manufacturers outsource their disassembly tasks to professional factories, each factory of them has specialized in its disassembly ability. Different disassembly facilities are usually combined to execute disassembly tasks. This study proposes the cloud-based disassembly that abstracts ability of the disassembly factory as the disassembly resource, the disassembly resource is then able to be allocated to execute disassembly tasks. Based on this concept, the cloud-based disassembly system is proposed, which provides the disassembly service according to the user requirement. The disassembly service is the execution plan for disassembly tasks, which is the result of scheduling disassembly tasks and allocating disassembly resources. To formally describe the disassembly service, this paper builds a mathematical model that considers the uncertainty nature of the disassembly process and precedence relationships of disassembly tasks. Two objectives including minimizing the expected total makespan and minimizing the expected total cost of the disassembly service are also discussed. The mathematical model is NP complete, a multi-objective genetic algorithm based on non-dominated sorting genetic algorithm II is designed to address the problem. Computation results show that the proposed algorithm performs well, the algorithm generates a set of Pareto optimal solutions. The user can choose a preferred disassembly service among Pareto optimal solutions. (C) 2016 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.

关键词： Cloud-based disassembly multi-objective genetic algorithm Task scheduling and resource allocation Cloud manufacturing

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Improving the Summer Power Generation of a Hydropower Reservoir Using the Modified multi-Step Ahead Time-Varying Hedging Rule (vol 36, 853, 2022)

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WATER RESOURCES MANAGEMENT 2022年第8期36卷 2889-2889页

作者： Thiha, Soe Shamseldin, Asaad Y. Melville, Bruce W. Univ Auckland Dept Civil & Environm Engn Private Bag 92019 Auckland 1142 New Zealand

This paper aims to improve summer power generation of the Yeywa Hydropower Reservoir in Myanmar using the modified multi-step ahead time-varying hedging (TVH) rule as a case study. The results of the TVH rules were compared with the standard operation policy (SOP) rule, the binary standard operation policy (BSOP) rule, the discrete hedging (DH) rule, the standard hedging (SH) rule, the one-point hedging (OPH) rule, and the two-point hedging (TPH) rule. The multi-objective genetic algorithm (MOGA) was utilized to drive the optimal Pareto fronts for the hedging rules. The results demonstrated that the TVH rules had higher performance than the other rules and showed improvements in power generation not only during the summer period but also over the entire period.

关键词： Yeywa Hydropower Reservoir Myitnge River Time-varying hedging rules multi-objective genetic algorithm Summer power generation

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Simultaneous multi-objective Optimization of Semi-active Intermediate Isolation System and Building Structure

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INTERNATIONAL JOURNAL OF STEEL STRUCTURES 2021年第2期21卷 604-612页

作者： Kim, Hyun-Su Kang, Joo-Won Sunmoon Univ Div Architecture Asan South Korea Yeungnam Univ Sch Architecture Gyongsan South Korea

Recently, to reduce seismic responses, dozens of high-rise buildings in Japan and Korea have adopted the intermediate isolation system (IIS). These applications have shown successful response reduction performance for tall buildings. The important dynamic responses of building structures with the IIS are the peak intermediate isolator drift (IID), and the peak inter-story drift (ISD). The semi-active intermediate isolation system (SAIIS) was developed to more effectively reduce these seismic responses. In previous study, the authors showed that the SAIIS using magnetorheological dampers successfully reduced both the IID and the ISD. However, the optimal design of the SAIIS only was conducted, without considering the building structure. If both the SAIIS and the building structure properties are considered in the optimal design procedure, more effective optimal design for both the SAIIS and the building structure can be achieved. In this research, a simultaneous multi-objective optimization (MOO) method of the SAIIS and the building structure is proposed to achieve this. genetic algorithm was selected for simultaneous MOO of the SAIIS and the building structure. The fuzzy inference controller was selected as a control algorithm. The authors show that in comparison to the sequential optimization procedure that practical applications generally use, the simultaneous MOO method can provide much better control capacity and structural design process.

关键词： Semi-active intermediate isolation system multi-objective genetic algorithm Simultaneous structural optimization Seismic response reduction

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Assessing the effective spatial characteristics of input features through physics-informed machine learning models in inundation forecasting during typhoons

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HYDROLOGICAL SCIENCES JOURNAL 2022年第10期67卷 1527-1545页

作者： Jhong, Bing-Chen Lin, Chung-Yi Jhong, You-Da Chang, Hsiang-Kuan Chu, Jung-Lien Fang, Hsi-Ting Natl Taiwan Univ Sci & Technol Dept Civil & Construct Engn Taipei 106335 Taiwan Lehigh Univ Dept Civil & Environm Engn Bethlehem PA 18015 USA Feng Chia Univ Coll Engn & Sci Taichung Taiwan Natl Taiwan Univ Ctr Weather Climate & Disaster Res Taipei Taiwan Natl Sci & Technol Ctr Disaster Reduct Meteorol Div Taipei Taiwan Taiwan Integrated Disaster Prevent Technol Engn C Dept Hydraul Engn Taipei Taiwan

This study aimed to assess the effective spatial characteristics of input features by using physics-informed, machine learning (ML)-based inundation forecasting models. To achieve this aim, inundation depth data were simulated using a numerical hydrodynamic model to obtain training and testing data for these ML-based models. Effective spatial information was identified using a back-propagation neural network, an adaptive neuro-fuzzy inference system, support vector machine, and a hybrid model combining support vector machine and a multi-objective genetic algorithm. The conventional average rainfall determined using the Thiessen polygon method, raingauge observations, radar-based rainfall data, and typhoon characteristics were used as the inputs of the aforementioned ML models. These models were applied in inundation forecasting for Yilan County, Taiwan, and the hybrid model had the best forecasting performance. The results show that the hybrid model with crucial features and appropriate lag lengths gave the best performance.

关键词： typhoon inundation forecasting input feature spatial characteristics machine learning multi-objective genetic algorithm

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Thermal management of GaN HEMT devices using serpentine minichannel heat sinks

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APPLIED THERMAL ENGINEERING 2018年 140卷 622-636页

作者： Al-Neama, Ahmed F. Kapur, Nikil Summers, Jonathan Thompson, Harvey M. Univ Leeds Sch Mech Engn Inst Thermofluids Leeds LS2 9JT W Yorkshire England Univ Mosul Dept Mech Engn Fac Engn Mosul Iraq

An experimental and numerical investigation of water-cooled serpentine rectangular minichannel heat sinks (MCHS) has been performed to assess their suitability for the thermal management of gallium nitride (GaN) high-electron-mobility transistors (HEMTs) devices. A Finite Element-based conjugate heat transfer model is developed, validated experimentally and used to determine the optimal minichannel width and number of minichannels for a case with a uniform heat flux of 100 W/cm(2). The optimisation process uses a 30 point Optimal Latin Hypercubes Design of Experiments, generated from a permutation genetic algorithm, and accurate metamodels built using a Moving Least Square approach. A Pareto front is then constructed to enable the compromises available between designs with a low pressure drop and those with low thermal resistance to be explored and an appropriate minichannel width and number of minichannels to be chosen. These parameters are then used within conjugate heat transfer models of a serpentine MCHS with silicon, silicon carbide, diamond and graphene heat spreaders placed above a GaN HEMT heating source of area 4.8 x 0.8 mm(2), generating 1823 W/cm(2). A nanocrystalline diamond (NCD) layer with thickness of 2 pm is mounted on the top surface of the GaN HEMT to function as a heat spreader to mitigate the hot spots. The effect of volumetric flow rate and heat spreader thickness on the chip temperature has been investigated numerically and each of these has been shown to be influential. For example, at a volumetric flow rate of 0.10 l/min, the maximum chip temperature can be reduced from 124.7 degrees C to 96.7 degrees C by employing a 25 pm thick graphene heat spreader attached to the serpentine MCHS together with a NCD layer compared with a serpentine MCHS without these heat spreaders.

关键词： Serpentine minichannel heat sink Conjugate heat transfer CFD GaN HEMTs Heat spreader multi-objective genetic algorithm Nanocrystalline diamond layer

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Optimization of Residential Demand Response Program Cost With Consideration for Occupants Thermal Comfort and Privacy

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IEEE ACCESS 2024年 12卷 15194-15207页

作者： Nematirad, Reza Ardehali, M. M. Khorsandi, Amir Mahmoudian, Arash Kansas State Univ Elect & Comp Engn Dept Manhattan KS 66506 USA Amirkabir Univ Technol Tehran Polytech Dept Elect Engn Tehran *** Iran Bellevue Univ Coll Sci & Technol Bellevue NE 68005 USA

Residential consumers can optimize their participation in demand response programs (DRPs) using home energy management systems (HEMS). By automatically adjusting air conditioning (AC) setpoints and shifting certain appliances to off-peak hours, HEMS can lead to significant cost reductions. While HEMS aims to adjust AC temperature setpoints, it is important to consider the occupants thermal comfort. This study aims to develop a multi-objective model for the application of DRPs in a smart residential house. The objectives of the model are to achieve (a) reduction in electrical load demand, (b) adjustment in thermal comfort temperature setpoints, and (c) minimization of consumer costs subject to the related constraints. Determining occupancy status through HEMS provides more economic benefits and thermal comfort for consumers. However, traditional methods such as direct occupancy monitoring are often costly, inaccurate, and can intrusively collect data on residents' activities, locations, and routines, compromising their privacy. To tackle these challenges, this study introduces advanced forecasting algorithms such as random forest, light gradient boosting machine, and multilayer perception artificial neural networks to predict occupancy by utilizing indirect data sources, such as energy consumption patterns. This approach enables the prediction of residential presence without direct monitoring. However, inherent uncertainty associated with predicted parameters can compromise the effectiveness of DRPs, and potentially lead to non-optimal energy savings, jeopardizing consumer comfort, and even system instability. To address these uncertainties, this study integrates robust counterpart optimization techniques, augmented with uncertainty budgets to control uncertainty variation making them less conservative. Simulations show uncertainty increases costs by 36% and reduces AC temperature setpoints. Further, implementing DRPs reduces demand by shifting some appliances to off

关键词： Demand response program thermal comfort occupant privacy occupancy forecasting learning algorithms robust counterpart optimization multi-objective genetic algorithm

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