Thermal Performance Comparison of Working Fluids for Geothermal Snow Melting with Gravitational Heat Pipe
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Snow and ice accumulation on transportation infrastructure presents significant safety and maintenance challenges in cold regions, while conventional removal methods are both energy-intensive and environmentally detrimental. This study proposes a passive Heat Pipe–Coupled Geothermal Snow Melting System (HP-GSMS) that harnesses shallow geothermal energy to maintain snow-free surfaces without external energy input. Using Fluent-based CFD simulations, the system’s thermal performance was evaluated under various working fluids (ammonia, carbon dioxide, water) and pipe materials (stainless steel, aluminum). A one-dimensional thermal resistance model validated the CFD results under ammonia–stainless steel conditions, predicting a heat flux of 358.6 W/m2 compared to 361.0 W/m2 from the simulation, with a deviation of only 0.66%, confirming model accuracy. Ammonia demonstrated superior phase-change efficiency, with the aluminum–ammonia configuration yielding the highest heat flux (up to 677 W/m2), surpassing typical snow-melting thresholds. Aluminum pipes enhanced radial heat conduction without compromising phase stability, while water exhibited poor phase-change performance and CO2 showed moderate but stable behavior. Additionally, a dynamic three-node RC thermal network was employed to assess transient performance under realistic diurnal temperature variations, revealing surface heat fluxes ranging from 230 to 460 W/m2, with a daily average of approximately 340 W/m2. These findings demonstrate the HP-GSMS’s practical viability in cold climates and underscore the importance of selecting low-boiling-point fluids and high-conductivity materials for scalable, energy-efficient, and low-carbon snow-melting applications in urban infrastructure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it