Performance analysis of two-stage thermoelectric generator model based on Latin hypercube sampling

Zhang, Feng; Liu, Cheng; Wu, Mingying; Xu, Xiayu; Wang, Pengcheng; Liu, Zhongbing

doi:10.1016/j.enconman.2020.113159

Cited by 82 publications

(18 citation statements)

References 28 publications

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“…Common experimental design methods include orthogonal array, central combination design, Latin hypercube design, and optimal Latin hypercube design. Among them, the Latin hypercube design has the ability to fit higher-order nonlinear relationships, and has more effective space-filling ability and higher efficiency than the full factorial design 10 ; while the optimal Latin hypercube design improves the inhomogeneity of the random Latin hypercube design, and can effectively avoid the possibility of losing some design space regions. 11 Therefore, in this paper, the optimal Latin hypercube method is selected for the experimental design, and the five design variables are selected, and the experimental results are shown in Table 3.…”

Section: Sample Construction and Experimental Resultsmentioning

confidence: 99%

Line heating process parameter forecasting method based on optimization concept

Zhou

Wang

et al. 2022

Advances in Mechanical Engineering

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In ship construction, line heating is a necessary process for forming ship plates with complex curvature. The determination of line heating process parameters has a significant impact on the accuracy of the process and the efficiency of shipbuilding. However, previous studies on this subject only stay at the level of predicting deformation, which has not meet the actual processing production requirements. In this paper, a new idea of forecasting line heating parameters proposed. Firstly, an Radial Basis Function (RBF) proxy model between processing parameters and deformation was constructed based on experimental data, and then the reliability of the proxy model is verified. The optimization objective for proxy model was set as the minimum difference between the final deformation and the target deformation. Meanwhile, an improved Multi-Objective Particle Swarm Optimization (MOPSO) method was employed to search the optimal-set of processing parameters with non-dominated solutions. The performance of the optimized parameter set was ranked by using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and the processing parameters combination ranking first was selected as the parameter forecasting result. The results showed that the predicted processing parameters have some reference for the practical engineering.

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Section: Sample Construction and Experimental Resultsmentioning

confidence: 99%

Line heating process parameter forecasting method based on optimization concept

Zhou

Wang

et al. 2022

Advances in Mechanical Engineering

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“…It is easy to utilize in SA with simple concepts and processes, but inefficient computation will appear if some sample points are sparsely distributed while others cluster closely. The LHS method is another methodology for producing a near-random sample of parameter values, between the RS and the stratified sampling technique, from multi-dimensional distribution [35]. LHS can generate more stable results than RS, but its drawbacks include space-filling and uncorrelated samples [36].…”

Section: Sensitivity Analysismentioning

confidence: 99%

An Integrated Sensitivity Analysis Method for Energy and Comfort Performance of an Office Building along the Chinese Coastline

Chen

Tsay

2021

Buildings

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This study aimed to evaluate the comprehensive percentage influence of input parameters on building energy and comfort performance by a new approach of sensitivity analysis (SA) and explore the most reliable and neutral sampling and sensitivity assessment method. The research combined 7 sampling methods with 13 SA methods to comprehensively integrate the percentage influence of 25 input parameters on building energy and comfort performance in 24 coastal cities of China. The results have found that the percentage influence of many important input parameters is affected by geographical position. Considering both energy and comfort performance of the building, the key parameters are heating setpoint, infiltration rate, cooling setpoint, roof U value, roof solar absorptance, window solar heat gain coefficient, equipment, and occupant density, all of which could comprehensively impact 70% of energy demand and comfort performance along the Chinese coastline. This is of great significance for policymakers to formulate relative building regulations. After comparing the F-test and the exceed percentage test, we recommended the Pearson with Quasi-random sampling method as the most reliable SA assessment method in building simulation, followed by the standardized regression coefficient in random sampling and Latin hypercube sampling methods, which can achieve data closest to the average value.

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“…Unlike conventional MCS, LHS is a co-ordinated sampling strategy that relies on stratification of pre-specified input probability distribution. In the LHS technique, cumulative distribution curve is equally divided into n s sections and a sample is randomly drawn from each stratified section [27]. Hence, n s number of samples are generated using LHS.…”

Section: Generating Adequate Samples For Each Random Variablementioning

confidence: 99%

Probabilistic screening and behavior of solar cells under Gaussian parametric uncertainty using polynomial chaos representation model

Samadhiya

Namrata

2021

Complex Intell. Syst.

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The paper presents a hierarchical polynomial chaos expansion-based probabilistic approach to analyze the single diode solar cell model under Gaussian parametric uncertainty. It is important to analyze single diode solar cell model response under random events or factors due to uncertainty propagation. The optimal values of five electrical parameters associated with the single diode model are estimated using six deterministic optimization techniques through the root-mean-square minimization approach. Values corresponding to the best objective function response are further utilized to describe the probabilistic design space of each random electrical parameter under uncertainty. Adequate samples of each parameter corresponding Gaussian uncertain distribution are generated using Latin hypercube sampling. Furthermore, a multistage probabilistic approach is adopted to evaluate the model response using low-cost polynomial chaos series expansion and perform global sensitivity analysis under specified Gaussian distribution. Coefficients of polynomial basis functions are calculated using least square and least angle regression techniques. Unlike the highly non-linear and complex single diode representation of solar cells, the polynomial chaos expansion model provides a low computational burden and reduced complexity. To ensure reproducibility, probabilistic output response computed using proposed polynomial chaos expansion model is compared with the true model response. Finally, a multidimensional sensitivity analysis is performed through Sobol decomposition of polynomial chaos series representation to quantify the contribution of each parameter to the variance of the probabilistic response. The validation and assessment result shows that the output probabilistic response of the solar cell under Gaussian parametric uncertainty correlates to a Rayleigh probability distribution function. Output response is characterized by a mean value of 0.0060 and 0.0760 for RTC France and Solarex MSX83 solar cells, respectively. The standard deviation of $$ \pm $$ ± 0.0034 and $$ \pm $$ ± 0.0052 was observed in the probabilistic response for RTC France and Solarex MSX83 solar cells, respectively.

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Performance analysis of two-stage thermoelectric generator model based on Latin hypercube sampling

Cited by 82 publications

References 28 publications

Line heating process parameter forecasting method based on optimization concept

Line heating process parameter forecasting method based on optimization concept

An Integrated Sensitivity Analysis Method for Energy and Comfort Performance of an Office Building along the Chinese Coastline

Probabilistic screening and behavior of solar cells under Gaussian parametric uncertainty using polynomial chaos representation model

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