Investigation of Immersion Cooling in Battery Packs with PIV and Simultaneous Heat Flux Measurement in an Optical Oil Flow Channel
Bibliographic record
Abstract
We combine flow investigations with particle image velocimetry (PIV) and simultaneous heat flux measurement across the surface of real battery modules using the atomic layer thermopile (ALTP) technique in a new optical oil flow channel. It represents the dimensions of a realistic immersion cooled battery pack. The objective is to compare the cooling performance of different oils, where some received viscoelastic properties through additives. In previous studies, we performed characterization that is more fundamental in benchmark geometries [1]. The channel cross section is of rectangular shape with a height of 100 mm and width of 47.5 mm. Along the main flow direction, there are four battery cells of 147 mm length lined up with a spacing of 27.5 mm. They cover the height of the channel. On both sides, a gap of 10.4 mm remains. The channel walls are made from acrylic glass, allowing for optical access. We perform time-resolved two-dimensional two-component PIV with a high-speed camera. We vary the flow conditions in terms of Reynolds numbers. Through a closed flow loop, we secure and survey the upstream temperature of the oil. We charge and discharge the batteries at different rates to reproduce realistic thermal loads. An electric load monitors the output voltage and current. Additionally, we position ALTP sensors on the side faces of the cell, where the heat transfer to the oil takes place. The sensors facilitate direct measurement of the heat flux, obviating the necessity to calculate it from temperatures and to base this calculation on assumptions. In a recent study, we demonstrate the experimental principle in a benchmark case [2]. Each sensor is of rectangular shape with an active area of 100 mm x 8 mm. Additionally we employ dummy cells, which we can heat in a controlled and reproducible manner. There the heating rates can significantly exceed the rates that results from charging and discharging the real cells. We compare our experimental results to computational fluid dynamics (CFD). Karathanassis et al. recently reported preliminary results from related simulations [3]. The new flow channel allows us to conclude the preparative, fundamental work we did on heat transfer in viscoelastic oils. By including and discharging the batteries, we take a big step closer to the real-world application.
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.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".