INFLUENCE ON PISTON ENGINE PERFORMANCE BY THE BIOCOMPONENTS AND DIFFERENT TYPES OF NANO MATERIALS
Bibliographic record
Abstract
The obviousness of the finiteness of the planet's energy resources makes us constantly concern ourselves with the search for new energy sources and their rational use. The main energy converter is the internal combustion engine and contrary to forecasts, continues to occupy a leading position. Therefore, the issues of improving its working processes, reducing the consumption of mineral fuel, the possibility of using all kinds of alternative fuels and improving the quality of motor fuel continue to be considered throughout the energy world. On the agenda is the dissemination of advances in nanotechnology to the propulsion industry. Improvement of engine performance when using fuel dispersed with nanomaterials of various types is beyond doubt and is used both for pure petroleum and biodiesel and for their mixtures. In the article, against the background of the analysis of studies on the use of alternative biofuels and the introduction of the practice of introducing nanoparticles into petroleum fuel and biofuels as a potential energy carrier to improve the characteristics of toxicity and engine performance, the results of studies of a number of biofuels have been presented. Presented are the results of a study of the performance of a 1Ch 8.5 / 11 diesel engine carried out in the laboratory of IPMash NAS of Ukraine when operating on diesel fuel dispersed with carbon spheroidal nanoadditives of various concentrations, and some comparative results of studies of the indicators of diesel engines with direct injection 2Ch 10.5 / 12 and 4ChN 7.9 / 7.5 ALH, operating on standard and mixed fuels with biocomponents synthesized from rapeseed, sunflower, mustard and corn oils. The thermophysical properties of the fuel (heat of combustion, thermal conductivity, heat capacity, density, kinematic viscosity, convective heat transfer, ignition temperature, cetane number, etc.) undergo significant changes when nanoparticles are introduced into it. The optimal amount of metal nanoparticles, metal oxides, carbon tubes, graphene in mineral, biodiesel or mixed fuel promotes more complete combustion, significantly improves engine performance, and reduces harmful emissions.
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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) | 0.000 | 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".