首页|Research in the Area of Artificial Intelligence Reported from University of Tabr iz (Combining artificial intelligence and computational fluid dynamics for optim al design of laterally perforated finned heat sinks)
Research in the Area of Artificial Intelligence Reported from University of Tabr iz (Combining artificial intelligence and computational fluid dynamics for optim al design of laterally perforated finned heat sinks)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on artificial intell igence are discussed in a new report. According to news reporting originating fr om Tabriz, Iran, by NewsRx correspondents, research stated, "The efficient desig n of heat sinks is a severe challenge in thermo-fluid engineering. A creative an d innovative way is applying lateral perforations to parallel finned heat sinks. " Our news journalists obtained a quote from the research from University of Tabri z: "The significance of achieving an optimal design for perforated finned heat s inks (PFHSs) has inspired the present authors to introduce a novel hybrid design ing approach that combines computational fluid dynamics (CFD), machine learning (ML), multi-objective optimization (MOO), and multi-criteria decision-making (MC DM). The design variables considered include the size (0.25<ph<0.5) and shape (square, circular, and hexagonal) of the perforations, as well as the airflow Reynolds number (2000<Re<5000). The design objectives have been redefined as dime nsionless parameters to assess heat dissipation, pressure drop, and heat sink we ight. These modified objectives encompass thermal performance (TP), thermo-hydra ulic performance (THP), and thermo-volumetric performance (TVP). The modeling pr ocess showed that both stepwise mixed selection (SMS) and GMDH-NN techniques exh ibited comparable performance in most modeling scenarios. Nevertheless, the SMS approach demonstrated more reliability in modeling diverse objectives. Furthermo re, the optimization results demonstrated that the optimal size of the perforati ons is strongly dependent on their shapes. In PFHSs with square perforations, ap proximately 54% of the Pareto points had a ph-value greater than 0 .45."
University of TabrizTabrizIranAsiaArtificial IntelligenceEmerging TechnologiesMachine Learning
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