Tolerance‐based reliability and optimization design of switched‐mode power supply

Zeng, Ying; Li, Yanfeng; Li, Xiang‐Yu; Huang, Hong‐Zhong

doi:10.1002/qre.2556

Cited by 8 publications

(5 citation statements)

References 25 publications

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“…In this engineering case, a bridge system is taken as an example for verifying the reliability redundancy allocation optimization performance of different algorithms 41 . Figure 3 shows the reliability block diagram.…”

Section: Engineering Applications Of Dtpso In Reliability Optimizationmentioning

confidence: 99%

“…In this engineering case, a bridge system is taken as an example for verifying the reliability redundancy allocation optimization performance of different algorithms. 41 Figure 3 shows the reliability block diagram. In addition, for reliability redundancy allocation, each unit in this system has only two states including normal and failure, moreover, the states of units are independent.…”

Section: Reliability Redundancy Allocation Optimizationmentioning

confidence: 99%

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Direct position updating‐based trying‐mutation particle swarm optimization algorithm and its application on reliability optimization

Zheng

Zhang

et al. 2023

Quality & Reliability Eng

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In order to improve the global optimization of particle swarm optimization (PSO), enhance the performance of PSO in dealing with these complex, highdimensional, multimodal optimization problems, and furthermore, promote the optimization effect in reliability optimization applications, a direct position updating-based trying-mutation PSO (DTPSO) was proposed. In this algorithm, the direct position updating strategy and trying-mutation strategy were designed, which could effectively maintain the population diversity, balance the exploitation and exploration, and increase the probability of obtaining global optimal solution. After being verified and compared by nine complex test functions, the rationality of algorithm design and the excellent global optimization performances were proved. In the reliability optimization, the reliability redundancy allocation and reliability allocation were optimized by different advanced improved algorithms, the comparison results proved the stability and optimization performance of DTPSO. Due to its stable and efficient global optimization performance, it was concluded the proposed DTPSO could provide effective technical support for practical applications such as reliability optimization.

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Section: Engineering Applications Of Dtpso In Reliability Optimizationmentioning

confidence: 99%

Section: Reliability Redundancy Allocation Optimizationmentioning

confidence: 99%

Direct position updating‐based trying‐mutation particle swarm optimization algorithm and its application on reliability optimization

Zheng

Zhang

et al. 2023

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…Choi [22] and others [23,24], is designed to narrow the gap between such real-world industrial uncertainties and deterministic optimization based on probabilistic theory. It has been observed that uncertainties such as tolerances that can occur in actual industrial processes can be reflected by considering the probability distribution of each design variable, and through this, it is possible to derive an optimal design solution that guarantees reliability [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Reliability-Based Design Optimization of the PEMFC Flow Field with Consideration of Statistical Uncertainty of Design Variables

Heo,

Choi,

Park

et al. 2024

Energies

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Recently, with the fourth industrial revolution, the research cases that search for optimal design points based on neural networks or machine learning have rapidly increased. In addition, research on optimization is continuously reported in the field of fuel cell research using hydrogen as fuel. However, in the case of optimization research, it often requires a large amount of training data, which means that it is more suitable for numerical research such as CFD simulation rather than time-consuming research such as actual experiments. As is well known, the design range of fuel cell flow channels is extremely small, ranging from hundreds of microns to several millimeters, which means the small tolerance could cause fatal performance loss. In this study, the general optimization study was further improved in terms of reliability by considering stochastic tolerances that may occur in actual industry. The optimization problem was defined to maximize stack power, which is employed as objective function, under the constraints such as pressure drop and current density standard deviation; the performance of the optimal point through general optimization was about 3.252 kW/L. In the reliability-based optimization problem, the boundary condition for tolerance was set to 0.1 mm and tolerance was assumed to occur along a normal distribution. The optimal point to secure 99% reliability for the given constraints was 2.918 kW/L, showing significantly lower performance than the general optimal point.

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“…To overcome this challenge, many scholars have applied machine learning 27,28 to improve the efficiency of structural reliability analysis. By integrating optimization design 29 and reliability analysis methodologies, 30,31 reliability metrics can be embedded into the design phase, 32,33 thus ensuring the reliability level of turbine shaft during the fatigue optimization design. 34 To enhance the life expectancy of a turbine shaft under CCF loading, this study proposes a fatigue reliability analysis and optimization design method.…”

Section: Introductionmentioning

confidence: 99%

A reliability analysis and optimization method for a turbine shaft under combined high and low cycle fatigue loading

Bai,

Zeng,

Huang

et al. 2024

Quality & Reliability Eng

Self Cite

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The combined high and low cycle fatigue (CCF) loading condition and random uncertainty exert a considerable impact on the design of turbine shafts. To enhance the fatigue life and reliability, this research proposes a CCF reliability analysis and optimization method for turbine shafts. A CCF fatigue reliability analysis framework is established, which focuses on the quantification of CCF loading characteristics and random uncertainty. The consideration of CCF loading characteristics contain the loading frequency ratio of high cycle fatigue (HCF) to low cycle fatigue (LCF), the stress amplitude ratio of HCF to LCF, as well as their interaction. The consideration of random uncertainty contains material, geometry and load, and a surrogate model‐based method is introduced to improve the quantification efficiency. Through the validation by comparing with experimental data and traditional methods, the proposed method is with higher accuracy and efficiency. By integrating the proposed fatigue reliability analysis method with design optimization, optimal design values for the turbine shaft were identified. This method theoretically extends the shaft's CCF life and provides practical engineering guidance for its reliability analysis and design.

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Tolerance‐based reliability and optimization design of switched‐mode power supply

Cited by 8 publications

References 25 publications

Direct position updating‐based trying‐mutation particle swarm optimization algorithm and its application on reliability optimization

Direct position updating‐based trying‐mutation particle swarm optimization algorithm and its application on reliability optimization

Reliability-Based Design Optimization of the PEMFC Flow Field with Consideration of Statistical Uncertainty of Design Variables

A reliability analysis and optimization method for a turbine shaft under combined high and low cycle fatigue loading

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