scholarly journals Interval Identification of Thermal Parameters Using Trigonometric Series Surrogate Model and Unbiased Estimation Method

2020 ◽  
Vol 10 (4) ◽  
pp. 1429
Author(s):  
Xiaoguang Wang ◽  
Weiliang He ◽  
Linggong Zhao
Keyword(s):  
Heat Transfer ◽  
Parameter Identification ◽  
Trigonometric Series ◽  
Surrogate Model ◽  
Metal Foam ◽  
Estimation Method ◽  
Estimation Methods ◽  
Unbiased Estimation ◽  
Interval Parameter ◽  
Identification Method

Metal-foam materials have been applied in many engineering fields in virtue of its high specific strength and desirable of thermodynamic properties. However, due to the inherent uncertainty of its attribute parameters, reliable analysis results are often ambiguous to obtain accurately. To overcome this drawback, this paper proposes a novel interval parameter identification method. Firstly, a novel modelling methodology is proposed to simulate the geometry of engineering metal foams. Subsequently, the concept of intervals is introduced to represent the uncertainty relationship between variables and responses in heat transfer systems. To improve computational efficiency, a novel augmented trigonometric series surrogate model is constructed. Moreover, unbiased estimation methods based on different probability distributions are presented to describe system measurement intervals. Then, a multi-level optimization-based identification strategy is proposed to seek the parameter interval efficiently. Eventually, an engineering heat transfer system is given to verify the feasibility of the proposed parameter identification method. This method can rapidly identify the unknown parameters of the system. The identification results demonstrate that this interval parameter identification method can quantify the uncertainty of a metal-foam structure in engineering heat transfer systems efficiently, especially for the actual case without sufficient measurements.

Lower Bound on the Accuracy of Parameter Estimation Methods for Linear Sensorimotor Synchronization Models

2015 ◽  
Vol 3 (1-2) ◽  
pp. 32-51 ◽  
Author(s):  
Nori Jacoby ◽  
Peter E. Keller ◽  
Bruno H. Repp ◽  
Merav Ahissar ◽  
Naftali Tishby
Keyword(s):  
Parameter Estimation ◽  
Lower Bound ◽  
Computational Models ◽  
Linear Models ◽  
Estimation Error ◽  
Explanatory Power ◽  
Estimation Method ◽  
Sensorimotor Synchronization ◽  
Estimation Methods ◽  
Unbiased Estimation

The mechanisms that support sensorimotor synchronization — that is, the temporal coordination of movement with an external rhythm — are often investigated using linear computational models. The main method used for estimating the parameters of this type of model was established in the seminal work of Vorberg and Schulze (2002), and is based on fitting the model to the observed auto-covariance function of asynchronies between movements and pacing events. Vorberg and Schulze also identified the problem of parameter interdependence, namely, that different sets of parameters might yield almost identical fits, and therefore the estimation method cannot determine the parameters uniquely. This problem results in a large estimation error and bias, thereby limiting the explanatory power of existing linear models of sensorimotor synchronization. We present a mathematical analysis of the parameter interdependence problem. By applying the Cramér–Rao lower bound, a general lower bound limiting the accuracy of any parameter estimation procedure, we prove that the mathematical structure of the linear models used in the literature determines that this problem cannot be resolved by any unbiased estimation method without adopting further assumptions. We then show that adding a simple and empirically justified constraint on the parameter space — assuming a relationship between the variances of the noise terms in the model — resolves the problem. In a follow-up paper in this volume, we present a novel estimation technique that uses this constraint in conjunction with matrix algebra to reliably estimate the parameters of almost all linear models used in the literature.

scholarly journals The Estimation of Heritability of Weekly Body Weights in Japanese Quails with MINQUE(Minimum Quadratic Unbiased Estimation) Method

2016 ◽  
Vol 4 (1) ◽  
pp. 58
Author(s):  
Gökhan Tamer Kayaalp ◽  
Mikail Baylan ◽  
Sibel Canoğulları
Keyword(s):  
Variance Components ◽  
Estimation Method ◽  
Estimation Methods ◽  
Unbiased Estimation ◽  
Coturnix Coturnix ◽  
Japanese Quails ◽  
Body Weights

In this study the heritability of body weights of Japanese quails (Coturnix coturnix Japanica) were estimated by using MINQUE (Minimum Quadratic Unbiased Estimation) methods. Firstly the variance components were estimated by using MINQUE method which were later estimated the heritability for weekly body weights. The estimation of heritability of body weights are following: for third week : 0.302±0.018; for fourth week: 0.70±0.15; for fifth week : 0.30±0.067

scholarly journals Research on Parameter Self-Learning Unscented Kalman Filtering Algorithm and Its Application in Battery Charge of State Estimation

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1679
Author(s):  
Fang Liu ◽  
Jie Ma ◽  
Weixing Su ◽  
Hanning Chen ◽  
Maowei He
Keyword(s):  
Parameter Identification ◽  
State Estimation ◽  
Collaborative Optimization ◽  
Estimation Methods ◽  
Model Parameter ◽  
Soc Estimation ◽  
Identification Method ◽  
Model Parameter Identification ◽  
Optimization Mechanism ◽  
Self Learning

A novel state estimation algorithm based on the parameters of a self-learning unscented Kalman filter (UKF) with a model parameter identification method based on a collaborative optimization mechanism is proposed in this paper. This algorithm can realize the dynamic self-learning and self-adjustment of the parameters in the UKF algorithm and the automatic optimization setting Sigma points without human participation. In addition, the multi-algorithm collaborative optimization mechanism unifies a variety of algorithms, so that the identification method has the advantages of member algorithms while avoiding the disadvantages of them. We apply the combination algorithm proposed in this paper for state of charge (SoC) estimation of power batteries and compare it with other model parameter identification algorithms and SoC estimation methods. The results showed that the proposed algorithm outperformed the other model parameter identification algorithms in terms of estimation accuracy and robustness.

Development of Temperature Estimation Method for a Gas Turbine Transition Piece

2016 ◽  
Author(s):  
Mitsutoshi Okada ◽  
Toshihiko Takahashi ◽  
Susumu Yamada ◽  
Takayuki Ozeki ◽  
Tomoharu Fujii
Keyword(s):  
Heat Transfer ◽  
Temperature Distribution ◽  
Gas Turbine ◽  
Estimation Method ◽  
Life Assessment ◽  
Estimation Methods ◽  
Test Time ◽  
Internal Cooling ◽  
Temperature Estimation ◽  
Transition Piece

Temperature estimation methods for a transition piece of a gas turbine are developed in terms of microstructural changes and computational fluid dynamics (CFD) for life assessment. Temperature is estimated to be low around the center of the component where thermal barrier coating (TBC) is deposited on the Ni-base superalloy and a combination of internal cooling and film cooling is also applied. Test specimens are prepared from the above area for a high-temperature heating test in air. The microstructure in the superalloy and TBC is investigated after the test. The thermally grown oxide (TGO) formed on the bondcoat surface increases with the square root of the test time, and on the basis of this relation, a temperature-estimation equation is obtained. The estimated temperature distribution is compared with a numerical heat transfer simulation by means of CFD. The geometry of the transition piece with internal cooling structure is acquired using an X-ray computerized radiography and a laser digitizer, and it is modeled for the numerical simulation. The heat conduction analysis is applied to the transition piece, and the convection and radiation heat transfer analyses are applied to the gas path and internal cooling flow. These analyses are conjugated to estimate the temperature distribution. The simulation result agrees well with the estimation using TGO thickness.

scholarly journals A new biased estimation method based on Neumann series for solving ill-posed problems

2019 ◽  
Vol 16 (4) ◽  
pp. 172988141987205 ◽  
Author(s):  
QW Yang
Keyword(s):  
Estimation Method ◽  
Neumann Series ◽  
High Order ◽  
Estimation Methods ◽  
Unbiased Estimation ◽  
Biased Estimation ◽  
Ridge Estimation ◽  
Rank Defect ◽  
Ill Posed ◽  
Neumann Series Expansion

The ill-posed least squares problems often arise in many engineering applications such as machine learning, intelligent navigation algorithms, surveying and mapping adjustment model, and linear regression model. A new biased estimation (BE) method based on Neumann series is proposed in this article to solve the ill-posed problems more effectively. Using Neumann series expansion, the unbiased estimate can be expressed as the sum of infinite items. When all the high-order items are omitted, the proposed method degenerates into the ridge estimation or generalized ridge estimation method, whereas a series of new biased estimates can be acquired by including some high-order items. Using the comparative analysis, the optimal biased estimate can be found out with less computation. The developed theory establishes the essential relationship between BE and unbiased estimation and can unify the existing unbiased and biased estimate formulas. Moreover, the proposed algorithm suits for not only ill-conditioned equations but also rank-defect equations. Numerical results show that the proposed BE method has improved accuracy over the existing robust estimation methods to a certain extent.

scholarly journals A New Physical Parameter Identification Method for Two-Axis On-Road Vehicles: Simulation and Experiment

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Minyi Zheng ◽  
Peng Peng ◽  
Bangji Zhang ◽  
Nong Zhang ◽  
Lifu Wang ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Parameter Identification ◽  
Physical Parameter ◽  
Mass Matrix ◽  
Estimation Method ◽  
Least Square Method ◽  
Least Square ◽  
Percentage Error ◽  
Physical Parameters ◽  
Identification Method

A new physical parameter identification method for two-axis on-road vehicle is presented. The modal parameters of vehicle are identified by using the State Variable Method. To make it possible to determine the matricesM,C, andKof the vehicle, a known mass matrixΔMis designed to add into the vehicle in order to increase the number of equations ensuring that the number of equations is more than the one of unknowns. Therefore, the physical parameters of vehicle can be estimated by using the least square method. To validate the presented method, a numerical simulation example and an experiment example are given in this paper. The numerical simulation example shows that the largest of absolute value of percentage error is 1.493%. In the experiment example, a school bus is employed in study for the parameter identification. The simulation result from full-car model with the estimated physical parameters is compared with the test result. The agreement between the simulation and the test proves the effectiveness of the proposed estimation method.

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