Sensitivity analysis in machining parameter optimization

Land surface evapotranspiration (ET) is important in land-atmosphere interactions of water and energy cycles. However, regional ET simulation has a great uncertainty. In this study, a highly-efficient parameter optimization framework was applied to improve ET simulations of the Community Land Model version 4.0 (CLM4) in China. The CLM4 is a model at land scale, and therefore, the monthly ET observation was used to evaluate the simulation results. The optimization framework consisted of a parameter sensitivity analysis (also called parameter screening) by the multivariate adaptive regression spline (MARS) method and sensitivity parameter optimization by the adaptive surrogate modeling-based optimization (ASMO) method. The results show that seven sensitive parameters were screened from 38 adjustable parameters in CLM4 using the MARS sensitivity analysis method. Then, using only 133 model runs, the optimal values of the seven parameters were found by the ASMO method, demonstrating the high efficiency of the method. For the optimal parameters, the ET simulations of CLM4 were improved by 7.27%. The most significant improvement occurred in the Tibetan Plateau region. Additional ET simulations from the validation years were also improved by 5.34%, demonstrating the robustness of the optimal parameters. Overall, the ASMO method was found to be efficient for conducting parameter optimization for CLM4, and the optimal parameters effectively improved ET simulation of CLM4 in China.

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Experimental study and machining parameter optimization in milling thin-walled plates based on NSGA-II

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-016-9265-1 ◽

2016 ◽

Vol 89 (5-8) ◽

pp. 2399-2409 ◽

Cited By ~ 16

Author(s):

Sheng Qu ◽

Jibin Zhao ◽

Tianran Wang

Keyword(s):

Experimental Study ◽

Parameter Optimization ◽

Nsga Ii ◽

Thin Walled ◽

Machining Parameter

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Experiment and Analysis on Side Milling Deformation of Thin-Walled Workpieces Using Laddered Symmetrical Tool Path

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.710.233 ◽

2013 ◽

Vol 710 ◽

pp. 233-237

Author(s):

Yong Qiang He

Keyword(s):

Parameter Optimization ◽

Tool Path ◽

Cutting Parameters ◽

Deformation Characteristics ◽

Side Milling ◽

Machining Center ◽

Thin Walled ◽

Aluminum 7075 ◽

Machining Parameter ◽

Deformation Error

The aluminum 7075 workpieces are machined on a vertical machining center KX650 using laddered symmetrical tool path. The deformation characteristics are studied under different cutting conditions. Different cutting parameters are changed one by one in side milling tests to find out their impact on deformation error. The analyzed result provides a solid basis for machining parameter optimization in side milling thin-walled workpieces.

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Parameter Optimization of the 3PG Model Based on Sensitivity Analysis and a Bayesian Method

Forests ◽

10.3390/f11121369 ◽

2020 ◽

Vol 11 (12) ◽

pp. 1369

Author(s):

Chenjian Liu ◽

Xiaoman Zheng ◽

Yin Ren

Keyword(s):

Sensitivity Analysis ◽

Parameter Optimization ◽

Bayesian Method ◽

Growth Models ◽

Probability Distributions ◽

Model Parameters ◽

Simulation Accuracy ◽

Sensitivity Analysis Method ◽

Improved Model ◽

Posterior Estimation

Sensitivity analysis and parameter optimization of stand models can improve their efficiency and accuracy, and increase their applicability. In this study, the sensitivity analysis, screening, and optimization of 63 model parameters of the Physiological Principles in Predicting Growth (3PG) model were performed by combining a sensitivity analysis method and the Markov chain Monte Carlo (MCMC) method of Bayesian posterior estimation theory. Additionally, a nine-year observational dataset of Chinese fir trees felled in the Shunchang Forest Farm, Nanping, was used to analyze, screen, and optimize the 63 model parameters of the 3PG model. The results showed the following: (1) The parameters that are most sensitive to stand stocking and diameter at breast height (DBH) are nWs(power in stem mass vs. diameter relationship), aWs(constant in stem mass vs. diameter relationship), alphaCx(maximum canopy quantum efficiency), k(extinction coefficient for PAR absorption by canopy), pRx(maximum fraction of NPP to roots), pRn(minimum fraction of NPP to roots), and CoeffCond(defines stomatal response to VPD); (2) MCMC can be used to optimize the parameters of the 3PG model, in which the posterior probability distributions of nWs, aWs, alphaCx, pRx, pRn, and CoeffCond conform to approximately normal or skewed distributions, and the peak value is prominent; and (3) compared with the accuracy before sensitivity analysis and a Bayesian method, the biomass simulation accuracy of the stand model was increased by 13.92%, and all indicators show that the accuracy of the improved model is superior. This method can be used to calibrate the parameters and analyze the uncertainty of multi-parameter complex stand growth models, which are important for the improvement of parameter estimation and simulation accuracy.

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