Bayesian inference of structural error in inverse models of thermal response tests

Choi, Wonjun; Menberg, Kathrin; Kikumoto, Hideki; Heo, Yeonsook; Choudhary, Ruchi; Ooka, Ryozo

doi:10.1016/j.apenergy.2018.06.147

Cited by 22 publications

(5 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The role of the hyper-parameters in the GP models and the calibration process is further explained in Supplementary Appendix 6. Further information on Bayesian inference can be found in works such as Gelman et al (2013), Chong and Menberg (2018), Choi et al (2018), and Menberg et al (2019).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian parameter inference for shallow subsurface modeling using field data and impacts on geothermal planning

Kreitmair

Makasis²,

Menberg³

et al. 2022

DCE

Self Cite

View full text Add to dashboard Cite

Understanding the subsurface is crucial in building a sustainable future, particularly for urban centers. Importantly, the thermal effects that anthropogenic infrastructure, such as buildings, tunnels, and ground heat exchangers, can have on this shared resource need to be well understood to avoid issues, such as overheating the ground, and to identify opportunities, such as extracting and utilizing excess heat. However, obtaining data for the subsurface can be costly, typically requiring the drilling of boreholes. Bayesian statistical methodologies can be used towards overcoming this, by inferring information about the ground by combining field data and numerical modeling, while quantifying associated uncertainties. This work utilizes data obtained in the city of Cardiff, UK, to evaluate the applicability of a Bayesian calibration (using GP surrogates) approach to measured data and associated challenges (previously not tested) and to obtain insights on the subsurface of the area. The importance of the data set size is analyzed, showing that more data are required in realistic (field data), compared to controlled conditions (numerically-generated data), highlighting the importance of identifying data points that contain the most information. Heterogeneity of the ground (i.e., input parameters), which can be particularly prominent in large-scale subsurface domains, is also investigated, showing that the calibration methodology can still yield reasonably accurate results under heterogeneous conditions. Finally, the impact of considering uncertainty in subsurface properties is demonstrated in an existing shallow geothermal system in the area, showing a higher than utilized ground capacity, and the potential for a larger scale system given sufficient demand.

show abstract

Section: Methodsmentioning

confidence: 99%

“…A large value for a given (and, correspondingly, a small value for ) indicates that the related term absorbs only a small portion of the variance in the model output, i.e. explains a small amount of the variation in (Choi et al, 2018). The correlation hyper-parameters are an indication for the smoothness of the covariance functions.…”

Section: Tablementioning

confidence: 99%

Bayesian parameter inference for shallow subsurface modeling using field data and impacts on geothermal planning

Kreitmair

Makasis²,

Menberg³

et al. 2022

DCE

Self Cite

View full text Add to dashboard Cite

show abstract

“… Validation of a parameter estimation algorithm Shared TPT datasets can be used for the validation purpose when a numerical or stochastic parameter estimation algorithm is developed. The estimation results of the effective ground thermal conductivity and the borehole thermal resistance using the Bayesian inference technique [14] , [15] were presented in the original research article [1] . …”

Section: Usage Of Shared Datasetsmentioning

confidence: 99%

Two thermal performance test (TPT) datasets of a single U-tube borehole heat exchanger with inlet setpoint temperatures of 30 °C and 40 °C

2018

Self Cite

View full text Add to dashboard Cite

The presented thermal performance test (TPT) datasets were related to the research article “New perspectives in thermal performance test: Cost-effective apparatus and extended data analysis” (Choi et al., 2018), where a new TPT apparatus was developed by adding a solid-state-relay and a proportional–integral–derivative (PID) controller to a thermal response test apparatus. Using the developed apparatus connected to a 50-m-long vertical ground heat exchanger, two TPTs were conducted for 144 h with inlet setpoint temperatures of 30 °C and 40 °C. The raw data were measured at 5 s intervals and consisted of the inlet, and outlet fluid temperatures, and the flow rate. The attached MATLAB script allows users to easily filter the data at user-specified time intervals. Moreover, the execution of code provides two additional quantities: heat injection rate and unit heat exchange rate. The datasets are shared for the following purposes: (1) performance comparison of various ground heat exchangers using the unit heat exchange rate (2) comparison of the control performance of a newly developed TPT apparatus, (3) validation of an analytical or numerical thermal response model, and (4) validation of a parameter estimation algorithm.

show abstract

“…Hidden Markov models [17,18], reinforcement learning and control [19]. VB is also applicable to energy management problems [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Evaluating uncertainties in electrochemical impedance spectra of solid oxide fuel cells

Žnidarič,

Nusev,

Morel

et al. 2021

Preprint

View full text Add to dashboard Cite

Electrochemical impedance spectroscopy (EIS) is a widely used tool for characterization of fuel cells and electrochemical conversion systems in general. When applied to the on-line monitoring in context of infield applications, the disturbances, drifts and sensor noise may cause severe distortions in the evaluated spectra, especially in the low-frequency part. Failure to account for the random effects can implicate difficulties in interpreting the spectra and misleading diagnostic reasoning. In the literature, this fact has been largely ignored. In this paper, we propose a computationally efficient approach to the quantification of the spectral uncertainty by quantifying the uncertainty of the equivalent circuit model (ECM) by means of the variational Bayes approach (VB) approach. To assess the quality of the VB posterior estimates, we compare the results of VB approach with those obtained with the Markov chain Monte Carlo (MCMC) algorithm. Namely, MCMC algorithm is expected to return accurate posterior distributions, while VB approach provides the approximative distributions. By using simulated and real data we show that VB approach generates approximations, which although slightly over-optimistic, are still pretty close to the more realistic MCMC estimates. A great advantage of the VB method for online monitoring is low computational load, which is several orders of magnitude lighter than that of MCMC. The performance of VB algorithm is demonstrated on a case of ECM parameters estimation in a 6 cell solid-oxide fuel cell (SOFC) stack.The complete numerical implementation for recreating the results can be found at https://repo.ijs.si/ lznidaric/variational-bayes-supplementary-material.

show abstract

Bayesian inference of structural error in inverse models of thermal response tests

Cited by 22 publications

References 43 publications

Bayesian parameter inference for shallow subsurface modeling using field data and impacts on geothermal planning

Bayesian parameter inference for shallow subsurface modeling using field data and impacts on geothermal planning

Two thermal performance test (TPT) datasets of a single U-tube borehole heat exchanger with inlet setpoint temperatures of 30 °C and 40 °C

Evaluating uncertainties in electrochemical impedance spectra of solid oxide fuel cells

Contact Info

Product

Resources

About

Bayesian inference of structural error in inverse models of thermal response tests

Cited by 22 publications

References 43 publications

Bayesian parameter inference for shallow subsurface modeling using field data and impacts on geothermal planning

Bayesian parameter inference for shallow subsurface modeling using field data and impacts on geothermal planning

Two thermal performance test (TPT) datasets of a single U-tube borehole heat exchanger with inlet setpoint temperatures of 30 °C and 40 °C

Evaluating uncertainties in electrochemical impedance spectra of solid oxide fuel cells

Contact Info

Product

Resources

About

Two thermal performance test (TPT) datasets of a single U-tube borehole heat exchanger with inlet setpoint temperatures of 30 °C and 40 °C