Analytical considerations and dimensionless analysis for a description of particle interactions in high pressure processes
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Bibliographic record
Abstract
Abstract High pressures of up to several hundreds of MPa are utilized in a wide range of applications in chemical, bio-, and food engineering, aiming at selective control of (bio-)chemical reactions. Non-uniformity of process conditions may threaten the safety and quality of the resulting products because processing conditions such as pressure, temperature, and treatment history are crucial for the course of (bio-)chemical reactions. Therefore, thermofluid-dynamical phenomena during the high pressure process have to be examined, and numerical tools to predict process uniformity and to optimize the processes have to be developed. Recently applied mathematical models and numerical simulations of laboratory and industrial scale high pressure processes investigating the mentioned crucial phenomena are based on continuum balancing models of thermofluid dynamics. Nevertheless, biological systems are complex fluids containing the relevant (bio-)chemical compounds (enzymes and microorganisms). These compounds are particles that interact with the surrounding medium and between each other. This contribution deals with thermofluid-dynamical interactions of the relevant particulate (bio-)chemical compounds (enzymes and microorganisms) with the surrounding fluid. By consideration of characteristic time and length scales and particle forces, the motion of the (bio-)chemical compounds is characterized. Keywords: mathematical modellinganalytical considerationsdimensionless analysisparticle interactions Acknowledgement This work has been carried out with financial support from the Commission of the European Communities, Framework 6, Priority 5 ‘Food Quality and Safety’, Integrated Project NovelQ FP6-CT-2006-015710, the AiF/FEI (Forschungskreis der Ernährungsindustrie e.V.), and the Bundesministerium für Wirtschaft und Technologie (AiF-FV 16114 N). Furthermore, the authors gratefully acknowledge the funding of the German Research Council (DFG), which, within the framework of its ‘Excellence Initiative’, supports the Cluster of Excellence ‘Engineering of Advanced Materials’ at the University of Erlangen-Nuremberg.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it