Assessment of particle image velocimetry applied to high-speed organic vapor flows
Bibliographic record
Abstract
Abstract Compressible flows of fluids whose thermophysical properties are related by complex equations are quantitatively and can be qualitatively different from high-speed flows of ideal gases. Nonideal compressible fluid dynamics (NICFD) is concerned with these fluid flows, which are relevant in many processes and power and propulsion systems. Typically, NICFD effects occur if the fluid is an organic compound and its vapor state is close to the vapor–liquid critical point, at high-reduced temperature and pressure (even supercritical). Current design and analysis of devices operating in the nonideal compressible regime demand for validated simulation software, characterized in terms of uncertainty. Moreover, experiments are needed to further validate related theory. Experimental data are limited as generating and measuring these flows is challenging given their high pressure or temperature or both. In addition, flows of organic compounds can be flammable, can thermally decompose, and sealing may demand for special materials. Recently, more research has been devoted to the measurement of these flows using both intrusive and less intrusive techniques relying on optical access and lasers. The transparency and refractive properties of these dense vapors pose additional problems. The ORCHID (organic Rankine cycle hybrid integrated device) at the Aerospace Propulsion and Power Laboratory of Delft University of Technology is a closed-loop facility, used to generate a continuous nonideal supersonic flow of siloxane MM with the vapor at 4 $${{\textrm{bar}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mtext>bar</mml:mtext> </mml:math> and 220 °C at the inlet of the test section. Within this work, we have employed particle image velocimetry for the first time to obtain the velocity field in a de Laval nozzle in such flows. Measured velocity fields (expanded uncertainty within 1.1% of the maximum velocity) have been compared with those resulting from a CFD simulation. The comparison between experimental and simulated data is satisfactory, with deviation ranging from 0.1 to 10 % from the throat to the outlet, respectively. This discrepancy is attributed to hardware limitations, which will be overcome in the future experiments. The feasibility of PIV with uncontrolled but fixed seeding density to measure high-speed vapors of organic vapors has been demonstrated, and future experimental campaigns will target flows for which nonideal effects are more pronounced, other paradigmatic configurations, and improvements to the measurement techniques.
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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) | 0.001 | 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 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".