Fast calculation of the <i>g</i>-functions of geothermal borehole fields using similarities in the evaluation of the finite line source solution

Cimmino, Massimo

doi:10.1080/19401493.2017.1423390

Cited by 32 publications

(35 citation statements)

References 22 publications

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“…No modifications are required to the methodology used to compute these g-functions. Cimmino has produced significant quantification regarding the speed of these methods [39] and has shown that for borefields with up to 64 boreholes-which is realistically expected to capture most of the WBES use cases-g-functions can be computed in times from a few seconds to a few minutes. Note that the code by Cimmino is written in Python; the computation time could likely be reduced to some extent after being implemented in a compiled programming language.…”

Section: Methodology Summary and Discussionmentioning

confidence: 99%

“…Others have developed methods for computing response factors with series or parallel arrangements [16,37,38]. Cimmino [39] later developed a "fast" method for calculating g-functions that takes advantage of symmetry with the borefield to simplify and reduce computations. Cimmino later extended this model to compute g-functions for GHE with series-or parallel-connected boreholes [40,41].…”

Section: Response Factor Modelsmentioning

confidence: 99%

“…The borehole wall boundary temperature is updated each timestep using the response factor calculation seen in Equation (4). g-function response factors are computed using the pygfunction library [42], which uses FLS methods for long-timestep g-functions [39]. Short-timestep g-functions are computed using the 1D, radial finite volume model developed by Xu & Spitler [58].…”

Section: Simple Borehole Trc Modelmentioning

confidence: 99%

“…These have been discussed in the literature review and are not repeated here. The methods developed by Cimmino [39] will be used through the pygfunction Python library [42] for computing the long-timestep g-functions. Short-timestep borehole wall temperature g-functions are computed using the 1D radial finite-volume model developed by Xu & Spitler [58].…”

Section: Borehole Wall Temperature Response Factor Generationmentioning

confidence: 99%

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An Enhanced Vertical Ground Heat Exchanger Model for Whole-Building Energy Simulation

Mitchell

Spitler

2020

Energies

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This paper presents an enhanced vertical ground heat exchanger (GHE) model for whole-building energy simulation (WBES). WBES programs generally have computational constraints that affect the development and implementation of component simulation sub-models. WBES programs require models that execute quickly and efficiently due to how the programs are utilized by design engineers. WBES programs also require models to be formulated so their performance can be determined from boundary conditions set by upstream components and environmental conditions. The GHE model developed during this work utilizes an existing response factor model and extends its capabilities to accurately and robustly simulate at timesteps that are shorter than the GHE transit time. This was accomplished by developing a simplified dynamic borehole model and then exercising that model to generate exiting fluid temperature response factors. This approach blends numerical and analytical modeling methods. The existing response factor models are then extended to incorporate the exiting fluid temperature response factor to provide a better estimate of the GHE exiting fluid temperature at short simulation timesteps.

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Section: Methodology Summary and Discussionmentioning

confidence: 99%

Section: Response Factor Modelsmentioning

confidence: 99%

Section: Simple Borehole Trc Modelmentioning

confidence: 99%

Section: Borehole Wall Temperature Response Factor Generationmentioning

confidence: 99%

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An Enhanced Vertical Ground Heat Exchanger Model for Whole-Building Energy Simulation

Mitchell

Spitler

2020

Energies

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“…The ground model computes the effective borehole wall temperature in the borefield, T b , from the temporal superposition of the g-function, g(t) [9,28,29] as presented by Equation 7:…”

Section: Borefield Model 215mentioning

confidence: 99%

Fluid temperature predictions of geothermal borefields using load estimations via state observers

Figueroa

Cimmino

Drgoňa

et al. 2020

Journal of Building Performance Simulation

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Fluid temperature predictions of geothermal borefields usually involve temporal superposition of its characteristic g-function, using load aggregation schemes to reduce computational times. Assuming that the ground has linear properties, it can be modeled as a linear state space system where the states are the aggregated loads. However, the application and accuracy of these models is compromised when the borefield is already operating and its load history is not registered or there are gaps in the data. This paper assesses the performance of state observers to estimate the borefield load history to obtain accurate fluid predictions. Results show that both Time-Varying Kalman Filter (TVKF) and Moving Horizon Estimator (MHE) provide predictions with average and maximum errors below 0.1 • C and 1 • C respectively. MHE outperforms TVKF in terms of n-step ahead output predictions and load history profile estimates at the expense of about 5 times more computational time.

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Advanced Topics in GSHP Analysis

Lamarche

2023

Fundamentals of Geothermal Heat Pump Systems

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Fast calculation of the g-functions of geothermal borehole fields using similarities in the evaluation of the finite line source solution

Cited by 32 publications

References 22 publications

An Enhanced Vertical Ground Heat Exchanger Model for Whole-Building Energy Simulation

An Enhanced Vertical Ground Heat Exchanger Model for Whole-Building Energy Simulation

Fluid temperature predictions of geothermal borefields using load estimations via state observers

Advanced Topics in GSHP Analysis

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