首页|Reports Summarize Machine Learning Study Results from Annamalai University (Mach ine Learning Predictions for Enhancing Solar Parabolic Trough Collector Efficiency With Corrugated Tube Receivers)
Reports Summarize Machine Learning Study Results from Annamalai University (Mach ine Learning Predictions for Enhancing Solar Parabolic Trough Collector Efficiency With Corrugated Tube Receivers)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Investigators discuss new findings in Machine Learning. According to news reportingfrom Tamil Nadu, India, by NewsRx journalists, research stated, “This work introduces an innovativemethodology to enhance the efficiency of solar Parabolic Trough Collectors by integrating corr ugated tubereceivers accompanied by conical strip inserts. Conventional optimiz ation techniques involving adjustmentsin size, material composition, and insert configurations often necessitate supplementary energy input.”The news correspondents obtained a quote from the research from Annamalai Univer sity, “A conceptcentered around augmenting turbulence was introduced, employing corrugated tube receivers to addressthis challenge. The study encompassed empi rical investigations employing three corrugated copper tubereceivers, each poss essing distinct pitches (8 mm, 10 mm, and 12 mm) while maintaining uniform corrugation heights (2 mm). These experiments were conducted within a regime of lamin ar flow conditionscharacterized by Reynolds numbers spanning from 700 to 2000. The primary objective was to identify themost favorable absorber geometry, subs equently coupled with three varying pitches of conical strip inserts(20 mm, 30 mm and 50 mm) to intensify heat transference. The findings unveiled that the cor rugatedtube with an 8 mm pitch and 2 mm corrugation height, combined with a con ical strip insert possessing apitch of 20 mm, exhibited promising results. This specific amalgamation yielded remarkable enhancementsin the augmented Nusselt number (132%), friction factor (38%), and thermal effi ciency (9%) in contrastto the unadorned tube operating under analo gous conditions.”
Tamil NaduIndiaAsiaCyborgsEmerging TechnologiesMachine LearningAnnamalai University
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