A model parameter correction method based on measured trajectory

Author(s):  
An Jun ◽  
Mu Gang ◽  
Huang Qiaolin ◽  
Li Ping
1990 ◽  
Vol 19 (3) ◽  
pp. 133-140 ◽  
Author(s):  
P. Aloupogiannis ◽  
G. Robaye ◽  
G. Weber ◽  
J. M. Delbrouck-Habaru ◽  
I. Roelandts

2016 ◽  
Vol 55 (15) ◽  
pp. 4073 ◽  
Author(s):  
Shuai Mao ◽  
Pengcheng Hu ◽  
XueMei Ding ◽  
JiuBin Tan

2012 ◽  
Vol 16 (11) ◽  
pp. 4157-4176 ◽  
Author(s):  
S. Stisen ◽  
A. L. Højberg ◽  
L. Troldborg ◽  
J. C. Refsgaard ◽  
B. S. B. Christensen ◽  
...  

Abstract. Precipitation gauge catch correction is often given very little attention in hydrological modelling compared to model parameter calibration. This is critical because significant precipitation biases often make the calibration exercise pointless, especially when supposedly physically-based models are in play. This study addresses the general importance of appropriate precipitation catch correction through a detailed modelling exercise. An existing precipitation gauge catch correction method addressing solid and liquid precipitation is applied, both as national mean monthly correction factors based on a historic 30 yr record and as gridded daily correction factors based on local daily observations of wind speed and temperature. The two methods, named the historic mean monthly (HMM) and the time–space variable (TSV) correction, resulted in different winter precipitation rates for the period 1990–2010. The resulting precipitation datasets were evaluated through the comprehensive Danish National Water Resources model (DK-Model), revealing major differences in both model performance and optimised model parameter sets. Simulated stream discharge is improved significantly when introducing the TSV correction, whereas the simulated hydraulic heads and multi-annual water balances performed similarly due to recalibration adjusting model parameters to compensate for input biases. The resulting optimised model parameters are much more physically plausible for the model based on the TSV correction of precipitation. A proxy-basin test where calibrated DK-Model parameters were transferred to another region without site specific calibration showed better performance for parameter values based on the TSV correction. Similarly, the performances of the TSV correction method were superior when considering two single years with a much dryer and a much wetter winter, respectively, as compared to the winters in the calibration period (differential split-sample tests). We conclude that TSV precipitation correction should be carried out for studies requiring a sound dynamic description of hydrological processes, and it is of particular importance when using hydrological models to make predictions for future climates when the snow/rain composition will differ from the past climate. This conclusion is expected to be applicable for mid to high latitudes, especially in coastal climates where winter precipitation types (solid/liquid) fluctuate significantly, causing climatological mean correction factors to be inadequate.


2020 ◽  
Author(s):  
Min Zhou ◽  
Kang Zhou ◽  
Guolei Zheng ◽  
Tengfei Li

2021 ◽  
Vol 18 (5) ◽  
pp. 1050-1060
Author(s):  
Min Zhou ◽  
Kang Zhou ◽  
Guolei Zheng ◽  
Tengfei Li

Author(s):  
Joachim Kurzke

Ambient conditions have a significant impact on the temperatures and pressures in the flow path and on the fuel flow of any gas turbine. Making observed data comparable requires a correction of the raw data to sea level Standard Day conditions. The most widely applied gas turbine parameter correction method is based on keeping some dimensionless Mach number similarity parameters invariant. These similarity parameters are composed of the quantity to be corrected multiplied by temperature to the power ‘a’ and pressure to the power ‘b’ with exponent ‘a’ being theoretically either 0, +0.5 or −0.5 and ‘b’ either 0 or 1.0. To improve the accuracy of this approach it is common practice to empirically adapt the temperature and pressure exponents ‘a’ and ‘b’ in such a way that the correction process leads to a better correlation of the data. Finding empirical exponents requires either many consistently measured data that cover a wide range of ambient temperatures and pressures or a computer model of the engine. A high fidelity model is especially well suited for creating optimally matched exponents and for exploring the phenomena that make these exponents deviate from their theoretical value. This paper discusses the questions that arise when creating empirical exponents with a thermodynamic model of the gas turbine. The gas turbine parameter correction method based on Mach number similarity parameters can get complex if effects like humidity, bleed air or power off-take, free power turbines, switching between various fuel types (Diesel and natural gas), water respectively steam injection, variable geometry or afterburners have to be considered. In such a case it might be simpler — and certainly more accurate — to use the thermodynamic model for the gas turbine parameter correction. Computing power required for running a model is nowadays of no relevance and the better consistency of the data available for engine performance monitoring can yield a significantly improved performance diagnostic capability.


Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4257 ◽  
Author(s):  
Zeyu Zhang ◽  
Zhiyong Jiao ◽  
Hongbing Xia ◽  
Yuhan Yao

Accurate calculation of the vibration mode and natural frequency of a motor stator is the basis for reducing motor noise and vibration. However, the stator core and winding material parameters are difficult to determine, posing issues which result in modal calculation bias. To address the problem of calibrating the stator material parameters, we developed a parameter correction method based on modal frequency. First, the stator system was simplified to build a stator system finite element model. Secondly, the relationship between modal frequency and material parameters was analyzed by finite element software, the relationship between modal frequency and material parameters was derived, and the anisotropic material parameter correction method was summarized. Finally, a modal experiment was carried out by the hammering method, and the simulation and experimental errors were within 3%, which verified the accuracy of the finite element model. The proposed correction method of anisotropic material can quickly determine the stator material parameters, and the stator core and winding anisotropic material can ensure the accuracy of the modal analysis.


2016 ◽  
Vol 16 (3) ◽  
pp. 1004-1011
Author(s):  
Yonggui Li ◽  
Shuang Wang ◽  
Hua Ji ◽  
Jian Shi ◽  
Surong Huang

Sign in / Sign up

Export Citation Format

Share Document