Search NASA⌕ Search

DOE OSTI · 1312959

Optimal Ground Source Heat Pump System Design

Abstract

Despite the facts that GSHPs first gained popularity as early as the 1940’s and they can achieve 30 to 60 percent in energy savings and carbon emission reductions relative to conventional HVAC systems, the use of geothermal energy in the U.S. has been less than 1 percent of the total energy consumption. The key barriers preventing this technically-mature technology from reaching its full commercial potential have been its high installation cost and limited consumer knowledge and trust in GSHP systems to deliver the technology in a cost-effective manner in the market place. Led by ENVIRON, with support from University Hartford and University of Vermont, the team developed and tested a software-based a decision making tool (‘OptGSHP’) for the least-cost design of ground-source heat pump (‘GSHP’) systems. OptGSHP combines state of the art optimization algorithms with GSHP-specific HVAC and groundwater flow and heat transport simulation. The particular strength of OptGSHP is in integrating heat transport due to groundwater flow into the design, which most of the GSHP designs do not get credit for and therefore are overdesigned.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ozbek, Metin, Yavuzturk, Cy, Pinder, George. 2015-04-01. Optimal Ground Source Heat Pump System Design. https://doi.org/10.2172/1312959

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗