Multi-deconvolution in non-stationary conditions applied to experimental thermal response test analysis to obtain short-term transfer functions
Bibliographic record
Abstract
Thermal response test interpretation methods usually rely on the assumptions of constant operating conditions in time. However, through desired or undesired processes, these conditions often vary in time. Since interpretation is usually done with stationary methods, no current algorithm allows to account for non-stationarity in thermal response test, as encountered with varying flow rate. The goal of this article is to apply a multi-deconvolution algorithm to retrieve a set of short-term transfer functions during a thermal response test with changing operating conditions. The deconvolution algorithm uses an optimization-based technique as the inverse model, while considering non-stationarity in the forward model through a recent non-stationary convolution algorithm. By optimizing a set of nodes on each estimated short-term transfer function, precise reconstruction of the experimental temperatures is possible. Results show that temperature reconstruction is as precise as an error of 0.06 °C on numerical cases and 0.07 °C on field cases. The usable transfer function duration and an analysis of the objective function’s optimum are also demonstrated. With the proposed algorithm, only the dataset of a thermal response test is needed to obtain short-term transfer functions when operating conditions are changing.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".