Extracting Design Knowledge From Optimization Data: Application to Multi-Split Thermal Management System Configuration
Bibliographic record
Abstract
Abstract As engineering systems become more complex, the traditional sources of design knowledge face limits in the rate and complexity of design knowledge generation. Additional sources of knowledge are needed to guide future design efforts, especially for unprecedented engineering systems that have no design heritage. One promising avenue lies in the analysis of design optimization data, which has the potential to offer valuable insights into unprecedented system design, and to break free from incremental improvements over heritage designs. This paper presents a step toward extracting knowledge from design optimization data. The resulting design knowledge can be used as a basis for successfully synthesizing engineering system configurations that are more complex than those the knowledge was derived from. These methods are studied here using the combined system topology and optimal control design of multi-split fluid-based thermal management systems. This knowledge generation approach offers several advantages over traditional strategies, including applicability in cases where there is no design heritage and the ability to provide normative guidance as opposed to descriptive (i.e., how should systems be designed vs. how have they been designed). Four significant case studies with varying levels of complexity are presented that demonstrate the effectiveness of using knowledge extracted from design optimization data in enhancing the design of complex thermal management systems. Our results show that the knowledge extracted in this way provides a good basis for more general design of complex thermal management systems. It is shown that the objective function value of the estimated optimal configuration closely approximates the true optimal configuration with less than 1 percent error, achieved using basic features based on the system heat loads without involving the corresponding optimal open loop control (OLOC) features. This eliminates the need to solve the OLOC problem, leading to reduced computation costs.
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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".