Simulation of Flow Field Past Symmetrical Aerofoil Baffles Using Computational Fluid Dynamics Method
Bibliographic record
Abstract
Compressors are widely used in the petrochemical industry to provide a variety of gas properties that are essential for a variety of chemical processes. Among others, ambient air is predominantly used due to its abundancy in nature and its richness in oxygen. It is customary that clean air is supplied to the compressor by means of a commercial air intake filter placed few meters upstream of the compressor inlet. Various ducting configurations are commercially utilized to link the air filter to the compressor inlet. Embedded baffle-type muzzling units are placed inside the ducting for attenuating acoustic noise generated by the incoming high speed compressible flow. It is imperative that these silencer baffles are aerodynamically structured to maintain constant flow conditions dowsntream of the trailing edge for sustaining an efficient compressor operation. In the Gulf region where ambient temperature condition during hot summer day surpasses 45 o C coupled with saturated moist air, controling and maintaining constant conditions upstream of the compressor inlet pose a great challenge. In this paper, we introduce a numerical study employing Computational Fluid Dynamics (CFD) method for predicting the hydrodynamic conditions within the geometrical entity linking the air filter to the compressor inlet boundary in the presence of a set of symmetrical and cusped trailing-edge aerofoil-shaped silencers. Several baffle locations were investigated to analyze the impact of different locations on the pressure losses and temperature conditions upstream of the compressor inlet. Results show that an optimized silencer location can stabilize the flow conditions upstream of the compressor inlet. Among the considered locations, a recommendation was given for the best location that led to a reduced pressure drop across the bluff body and stabilized inlet conditions to the compressor.
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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 | 0.001 | 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".