首页|Yeungnam University Researchers Yield New Data on Machine Learning [Experimental comparison and optimal machine learning technique for predicting th e thermo-hydraulic performance of Low- GWP refrigerants (R1234yf, R290, and R13I1 /R290) during …]
Yeungnam University Researchers Yield New Data on Machine Learning [Experimental comparison and optimal machine learning technique for predicting th e thermo-hydraulic performance of Low- GWP refrigerants (R1234yf, R290, and R13I1 /R290) during …]
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Researchers detail new data in artific ial intelligence. According to news originating fromGyeongbuk, South Korea, by NewsRx editors, the research stated, “In recent times, employing machinelearnin g techniques (MLTs) to forecast the evaporation/condensation performance of refr igerants, namelythe heat transfer coefficient (HTC) and frictional pressure dro p (FPD), has become increasingly significant.In the current investigation, an e xperimental comparison of the evaporation HTC and FPD of R1234yf,R290, and R13I 1/R290 was explored in an offset strip fin-plate heat exchanger.”
Yeungnam UniversityGyeongbukSouth Ko reaAsiaCyborgsEmerging TechnologiesMachine Learning
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