Optimizing vertical ground heat exchanger modelling through GPU-accelerated computation strategies

Reza Moghanni and Ali Hakkaki-Fard

Renewable Energy, 2024, vol. 221, issue C

Abstract: Enhancing Vertical Ground Heat Exchanger (VGHE) modelling for efficient Vertical Ground-Coupled Heat Pump (VGCHP) systems, renowned for their eco-friendly operation, has garnered significant attention. Optimally designing and sustaining the long-term efficiency of VGCHP systems necessitates rigorous, protracted VGHE modelling, a process fraught with computational intensity. To surmount this challenge, this contribution introduces an accelerated VGHE modelling paradigm grounded in Graphics Processing Unit (GPU) processing. This entails employing the Finite Line Source (FLS) model based on Fast Fourier Transform (FFT). To ameliorate computational demands, the processing capabilities of both Central Processing Units (CPUs) and GPUs are harnessed, leveraging CPU- and GPU-based FFT and inverse FFT (IFFT). Hybrid and non-hybrid processing modes are explored, differentiating between CPU- and GPU-based FFT in non-hybrid scenarios, while hybrid approaches combine both for distinct simulation periods. By scrutinizing two processing systems with varying specifications, diverse CPU- and GPU-based FFT combinations are assessed. Findings unequivocally underscore the superiority of CPU-based FFT for initial years, succeeded by GPU-based FFT for ensuing years, in terms of computational efficiency. The efficacy of the hybrid computation approach is demonstrated through a reduction of total computational time in 20-year VGCHP operation simulations by up to 33.5 % compared to conventional CPU-based FFT methods.

Keywords: Graphics processing unit (GPU); GPU-based FFT; Hybrid processing; Vertical ground-coupled heat pump; Vertical ground heat exchanger modelling; Finite line source model (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148123017056
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:221:y:2024:i:c:s0960148123017056

DOI: 10.1016/j.renene.2023.119790

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().