scholarly journals Improved Land Evapotranspiration Simulation of the Community Land Model Using a Surrogate-Based Automatic Parameter Optimization Method

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 943
Author(s):  
Chong Zhang ◽  
Zhenhua Di ◽  
Qingyun Duan ◽  
Zhenghui Xie ◽  
Wei Gong
Keyword(s):  
Sensitivity Analysis ◽  
Parameter Optimization ◽  
Land Surface ◽  
High Efficiency ◽  
Optimization Method ◽  
The Tibetan Plateau ◽  
Optimal Parameters ◽  
Community Land Model ◽  
Optimization Framework ◽  
Sensitivity Analysis Method

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.

Time Integration Incorporated Sensitivity Analysis With Generalized-α Method for Multibody Dynamics Systems

2011 ◽  
Author(s):  
Guang Dong ◽  
Zheng-Dong Ma ◽  
Gregory Hulbert ◽  
Noboru Kikuchi
Keyword(s):  
Sensitivity Analysis ◽  
Topology Optimization ◽  
Dynamic Loading ◽  
Multibody Dynamics ◽  
Equations Of Motion ◽  
Time Integration ◽  
Optimization Method ◽  
Sensitivity Analysis Method ◽  
System Equations ◽  
Consecutive Time

The topology optimization method is extended for the optimization of geometrically nonlinear, time-dependent multibody dynamics systems undergoing nonlinear responses. In particular, this paper focuses on sensitivity analysis methods for topology optimization of general multibody dynamics systems, which include large displacements and rotations and dynamic loading. The generalized-α method is employed to solve the multibody dynamics system equations of motion. The developed time integration incorporated sensitivity analysis method is based on a linear approximation of two consecutive time steps, such that the generalized-α method is only applied once in the time integration of the equations of motion. This approach significantly reduces the computational costs associated with sensitivity analysis. To show the effectiveness of the developed procedures, topology optimization of a ground structure embedded in a planar multibody dynamics system under dynamic loading is presented.

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

Forests ◽  
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.

Optimization and sensitivity analysis of seismic depth migrations from Hibernia Field

Geophysics ◽  
1998 ◽  
Vol 63 (6) ◽  
pp. 2054-2062 ◽  
Author(s):  
Irene Kelly ◽  
Larry R. Lines
Keyword(s):  
Sensitivity Analysis ◽  
Least Squares ◽  
Real Data ◽  
Optimization Method ◽  
Velocity Estimation ◽  
Inversion Method ◽  
Depth Migration ◽  
Velocity Models ◽  
Sensitivity Analysis Method ◽  
Seismic Reflectors

Accurate imaging of seismic reflectors with depth migration requires accurate velocity models. In frontier areas with few well constraints, velocity estimation generally involves the use of methods such as normal moveout analysis, seismic traveltime tomography, or iterative prestack depth migration. These techniques can be effective, but may also be expensive or time‐consuming. In situations where we have information on formation tops from a series of wells which intersect seismic reflectors, we use a least‐squares optimization method to estimate velocity models. This method produces velocity models that optimize depth migrations in terms of well constraints by using least‐squares inversion to match the depth migration images to formation tops. The well log information is used to optimize poststack migration, thereby eliminating some of the time and expense of velocity analysis. In addition to applying an inversion method which optimizes depth migration in terms of formation tops, we can use a sensitivity analysis method of “most‐squares inversion” to explore a range of velocity models which provide mathematically acceptable solutions. This sensitivity analysis quantifies the expected result that our velocity estimates are generally less reliable for thin beds than for thick beds. The proposed optimization method is shown to be successful on synthetic and real data cases from the Hibernia Field of offshore Newfoundland.

scholarly journals Modeling Evapotranspiration over China’s Landmass from 1979 to 2012 Using Multiple Land Surface Models: Evaluations and Analyses

2017 ◽  
Vol 18 (4) ◽  
pp. 1185-1203 ◽  
Author(s):  
Shaobo Sun ◽  
Baozhang Chen ◽  
Quanqin Shao ◽  
Jing Chen ◽  
Jiyuan Liu ◽  
...  
Keyword(s):  
Land Surface ◽  
Regional Scale ◽  
Temporal Variations ◽  
The Tibetan Plateau ◽  
Land Surface Models ◽  
Infiltration Capacity ◽  
Community Land Model ◽  
Variable Infiltration Capacity Model ◽  
Capacity Model ◽  
Surface Models

Abstract Land surface models (LSMs) are useful tools to estimate land evapotranspiration at a grid scale and for long-term applications. Here, the Community Land Model, version 4.0 (CLM4.0); Dynamic Land Model (DLM); and Variable Infiltration Capacity model (VIC) were driven with observation-based forcing datasets, and a multiple-LSM ensemble-averaged evapotranspiration (ET) product (LSMs-ET) was developed and its spatial–temporal variations were analyzed for the China landmass over the period 1979–2012. Evaluations against measurements from nine flux towers at site scale and surface water budget–based ET at regional scale showed that the LSMs-ET had good performance in most areas of China’s landmass. The intercomparisons between the ET estimates and the independent ET products from remote sensing and upscaling methods suggested that there were fairly consistent patterns between each dataset. The LSMs-ET produced a mean annual ET of 351.24 ± 10.7 mm yr−1 over 1979–2012, and its spatial–temporal variation analyses showed that (i) there was an overall significant ET increasing trend, with a value of 0.72 mm yr−1 (p < 0.01), and (ii) 36.01% of Chinese land had significant increasing trends, ranging from 1 to 9 mm yr−1, while only 6.41% of the area showed significant decreasing trends, ranging from −6.28 to −0.08 mm yr−1. Analyses of ET variations in each climate region clearly showed that the Tibetan Plateau areas were the main contributors to the overall increasing ET trends of China.

scholarly journals A Novel Multiobjective Optimization Method Based on Sensitivity Analysis

2016 ◽  
Vol 2016 ◽  
pp. 1-12
Author(s):  
Tiane Li ◽  
Xiaoying Sun ◽  
Zhengzheng Lu ◽  
Yue Wu
Keyword(s):  
Sensitivity Analysis ◽  
Multiobjective Optimization ◽  
Optimization Problems ◽  
Mathematical Problem ◽  
Rank Correlation ◽  
Optimization Method ◽  
Computational Time ◽  
Truss Topology Optimization ◽  
Pareto Optimal Set ◽  
Sensitivity Analysis Method

For multiobjective optimization problems, different optimization variables have different influences on objectives, which implies that attention should be paid to the variables according to their sensitivity. However, previous optimization studies have not considered the variables sensitivity or conducted sensitivity analysis independent of optimization. In this paper, an integrated algorithm is proposed, which combines the optimization method SPEA (Strength Pareto Evolutionary Algorithm) with the sensitivity analysis method SRCC (Spearman Rank Correlation Coefficient). In the proposed algorithm, the optimization variables are worked as samples of sensitivity analysis, and the consequent sensitivity result is used to guide the optimization process by changing the evolutionary parameters. Three cases including a mathematical problem, an airship envelope optimization, and a truss topology optimization are used to demonstrate the computational efficiency of the integrated algorithm. The results showed that this algorithm is able to simultaneously achieve parameter sensitivity and a well-distributed Pareto optimal set, without increasing the computational time greatly in comparison with the SPEA method.

scholarly journals Structural Optimization of Variable Section Slender Manipulator Based on Sensitivity Analysis Method

2011 ◽  
Vol 2-3 ◽  
pp. 291-295
Author(s):  
Zhong Luo ◽  
Le Liang ◽  
Yan Yan Chen ◽  
Fei Wang
Keyword(s):  
Sensitivity Analysis ◽  
Structural Optimization ◽  
Optimization Method ◽  
Design Sensitivity ◽  
Design Sensitivity Analysis ◽  
Analysis Method ◽  
Design Variables ◽  
Variable Section ◽  
Sensitivity Analysis Method ◽  
In The Beginning

A parameter optimization method based on sensitivity analysis is presented for the structural optimization of variable section slender manipulator. Structure mechanism of a polishing robot is introduced firstly, and its stiffness model is established. Then, a design sensitivity analysis method and a sequential liner programming (SLP) strategy are proposed. In the beginning of the optimization, the design sensitivity analysis method can be used to select the sensitive design variables which can make the optimized results more efficient and accurate. And then, it can be used to improve the convergence during the process of the optimization. The design sensitivities are calculated using the finite difference method. The search for the final optimal structure is performed using the SLP method. Simulation results show that the structure optimization method is effective to enhance the stiffness of the manipulator, no matter when the manipulator suffers constant force or variable force. This work lays a theoretical foundation for the structural optimization for such manipulators.

scholarly journals Topology Optimization for Minimizing the Resonant Response of Plates with Constrained Layer Damping Treatment

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Zhanpeng Fang ◽  
Ling Zheng
Keyword(s):  
Sensitivity Analysis ◽  
Topology Optimization ◽  
Damping Ratio ◽  
Optimization Method ◽  
Constrained Layer Damping ◽  
Resonant Response ◽  
Optimal Layout ◽  
Analysis Method ◽  
Topology Optimization Problem ◽  
Sensitivity Analysis Method

A topology optimization method is proposed to minimize the resonant response of plates with constrained layer damping (CLD) treatment under specified broadband harmonic excitations. The topology optimization problem is formulated and the square of displacement resonant response in frequency domain at the specified point is considered as the objective function. Two sensitivity analysis methods are investigated and discussed. The derivative of modal damp ratio is not considered in the conventional sensitivity analysis method. An improved sensitivity analysis method considering the derivative of modal damp ratio is developed to improve the computational accuracy of the sensitivity. The evolutionary structural optimization (ESO) method is used to search the optimal layout of CLD material on plates. Numerical examples and experimental results show that the optimal layout of CLD treatment on the plate from the proposed topology optimization using the conventional sensitivity analysis or the improved sensitivity analysis can reduce the displacement resonant response. However, the optimization method using the improved sensitivity analysis can produce a higher modal damping ratio than that using the conventional sensitivity analysis and develop a smaller displacement resonant response.

scholarly journals Efficiency Optimization Design of L-LLC Resonant Bidirectional DC-DC Converter

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3123
Author(s):  
Jing Lu ◽  
Xiangqian Tong ◽  
Jianwu Zeng ◽  
Ming Shen ◽  
Jun Yin
Keyword(s):  
Parameter Optimization ◽  
Optimization Design ◽  
High Efficiency ◽  
Design Method ◽  
Optimization Method ◽  
Switching Loss ◽  
Intrinsic Mechanism ◽  
Parasitic Parameters ◽  
New Type ◽  
Parameter Optimization Design

The new type of L-LLC resonant bidirectional DC-DC converter (L-LLC-BDC) has merits of high efficiency, high-power density and wide gain and power ranges, and it is suitable for energy interface between energy storage systems and DC micro grid. However, the resonances are sensitive to the parasitic parameters, which will deteriorate the efficiency. This paper investigates the intrinsic mechanism of parasitic parameters on the L-LLC-BDC operating principle and working characteristics based on the analysis of working modes and resonance tank. By taking the oscillation of parasitic parameters produced in the stage for the freewheeling stage into consideration, a parameter optimization method is proposed to reduce the resonant current oscillation while maintaining the characteristic of the natural soft switching. The experiment results not only validated the proposed parameter optimization design method, but also testified to the improvement of the efficiency through the minimization of the conduction and switching loss.

scholarly journals Evaluating and improving simulations of diurnal variation in land surface temperature with the Community Land Model for the Tibetan Plateau

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11040
Author(s):  
Xiaogang Ma ◽  
Jiming Jin ◽  
Lingjing Zhu ◽  
Jian Liu
Keyword(s):  
Thermal Conductivity ◽  
Tibetan Plateau ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Soil Layer ◽  
Surface Model ◽  
The Tibetan Plateau ◽  
Soil Thermal Conductivity ◽  
Community Land Model

This study evaluated and improved the ability of the Community Land Model version 5.0 (CLM5.0) in simulating the diurnal land surface temperature (LST) cycle for the whole Tibetan Plateau (TP) by comparing it with Moderate Resolution Imaging Spectroradiometer satellite observations. During daytime, the model underestimated the LST on sparsely vegetated areas in summer, whereas cold biases occurred over the whole TP in winter. The lower simulated daytime LST resulted from weaker heat transfer resistances and greater soil thermal conductivity in the model, which generated a stronger heat flux transferred to the deep soil. During nighttime, CLM5.0 overestimated LST for the whole TP in both two seasons. These warm biases were mainly due to the greater soil thermal inertia, which is also related to greater soil thermal conductivity and wetter surface soil layer in the model. We employed the sensible heat roughness length scheme from Zeng, Wang & Wang (2012), the recommended soil thermal conductivity scheme from Dai et al. (2019), and the modified soil evaporation resistance parameterization, which was appropriate for the TP soil texture, to improve simulated daytime and nighttime LST, evapotranspiration, and surface (0–10 cm) soil moisture. In addition, the model produced lower daytime LST in winter because of overestimation of the snow cover fraction and an inaccurate atmospheric forcing dataset in the northwestern TP. In summary, this study reveals the reasons for biases when simulating LST variation, improves the simulations of turbulent fluxes and LST, and further shows that satellite-based observations can help enhance the land surface model parameterization and unobservable land surface processes on the TP.

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