Insight to Innovation: An Overview of Research Journey of Dr. Satish Kandlikar
Bibliographic record
Abstract
Abstract Professor Satish G. Kandlikar has been an outstanding researcher in the field of heat transfer having published some of the most widely cited publications over the last 30 years. Through the years he has co-authored 212 journal paper in various areas of heat transfer. The present paper provides a compressive look at Professor Kandlikar’s research work over the years. The research work has been broadly categorized into 1) flow boiling correlations, 2) fluid flow and heat transfer in microchannels, 3) roughness effect at microscale, 4) pool boiling heat transfer and CHF modeling, 5) surface enhancements for pool boiling, 6) numerical modeling of bubble growth in boiling, 7) modeling liquid-vapor and liquid-liquid interfaces, 8) water transport in PEM fuel cells and 9) infrared imaging to detect breast cancer. The research conducted in each of these areas has produced some landmark findings, some of the most widely used theoretical models and an abundance of high quality experimental data. The focus of this paper is to collate major finding and highlights some of the common themes that guided the research in Professor Kandlikar’s group. This will help the readers gain a comprehensive understanding of each of the areas of study in Professor Kandlikar’s group and place the findings of the paper in a larger context.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".