Bibliographic record
Abstract
Practical tools for the design of capillary tubes for refrigeration systems employing the traditional CFC-12 and HCFC-22 as refrigerants are widely available. Due to environmental concern of the depletion of ozone layer and global warming, CFCs and HCFCs have to be phased out eventually according to the Montreal Protocol. As a result, a number of alternative refrigerants including HFC-134a have emerged as promising chlorine free alternatives to CFC-12 in domestic refrigerators and freezers. The proper size of a capillary tube with a new refrigerant is a critical factor for the optimum performance of a refrigeration system. This study presents a computer model KAPILARI, which has been developed to predict the length of capillary tube used as an expansion device in small refrigerating and air-conditioning systems. The pressure drop through a capillary tube in the single-phase and two-phase regions is modelled in an attempt to predict the size of capillary tube. The model is based on the governing equations of conservation of mass, energy and momentum. Stoecker's basic model was modified with the consideration of various effects due to sub cooling, choked flow and friction factor used in single-phase region. The developed model has been validated by employing available studies and found to agree well with the experimental data and other mathematical model predictions of HFC-134a. The capability of the program has been tested and simulations are carried out to investigate the effect of varying the operating and design parameters of the system, namely cooling capacity, evaporating temperature, degree of superheat, condensing temperature, degree of subcoo1ing and inner diameter of capillary tube, on the length of the capillary tube. The developed model is an effective tool of capillary tube's 377$ design and optimization for systems using HFC-134a
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".