Effect of Uncertainties in the Simulation and Design of Thermal Systems
Bibliographic record
Abstract
In most studies on the simulation, design and optimization of thermal processes and systems, wide ranges of the governing parameters are considered with definite values given to the different parameters.This results in a deterministic simulation, which could then form the basis for deterministic design and optimization of the system.However, uncertainties arise in realistic circumstances due to variations in the operating conditions, errors in material properties, changes in the ambient conditions and variations in the gases and raw materials used in the process.Such uncertainties can have a significant effect on the operation and design of the system.Minor variations in the design variables or operating conditions may lead to system failure and thus impact the reliability of the system or process.Therefore, it is important to include the effect of uncertainties to obtain a reliable and realistic system.In this paper, uncertainties in two thermal systems are considered.The optimal points with and without uncertainties are obtained.For multi-objective optimization, Pareto frontiers, which represent a set of nondominated designs where one objective cannot be improved without detriment to another objective, are determined and compared with those for the deterministic cases.The results demonstrate the importance of uncertainties in system simulation and design.This approach thus provides more reliable and realistic solutions than if the uncertainties are neglected.This basic approach may be extended to the design and operation of other thermal problems and processes.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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 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".