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International Journal of Quantitative Research and Modeling
Published by Research Collaboration Community
ISSN : 27225046     EISSN : 2721477X     DOI : https://doi.org/10.46336/ijqrm
Core Subject : Science, Social, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Environmental Science Physics
International Journal of Quantitative Research and Modeling (IJQRM) is published 4 times a year and is the flagship journal of the Research Collaboration Community (RCC). It is the aim of IJQRM to present papers which cover the theory, practice, history or methodology of Quatitative Research (QR) and Mathematical Moodeling (MM). However, since Quatitative Research (QR) and Mathematical Moodeling (MM) are primarily an applied science, it is a major objective of the journal to attract and publish accounts of good, practical case studies. Consequently, papers illustrating applications of Quatitative Research (QR) and Mathematical Modeling (MM) to real problems are especially welcome. In real applications of Quatitative Research (QR) and Mathematical Moodeling (MM): forecasting, inventory, investment, location, logistics, maintenance, marketing, packing, purchasing, production, project management, reliability and scheduling. In a wide variety of environments: community Quatitative Research (QR) and Mathematical Moodeling (MM), education, energy, finance, government, health services, manufacturing industries, mining, sports, and transportation. In technical approaches: decision support systems, expert systems, heuristics, networks, mathematical programming, multicriteria decision methods, problems structuring methods, queues, and simulation Computational Intelligence Computing and Information Technologies Continuous and Discrete Optimization Decision Analysis and Decision Support Mathematics Education Engineering Management Environment, Energy and Natural Resources Financial Engineering Heuristics Industrial Engineering Information Management Information Technology Inventory Management Logistics and Supply Chain Management Maintenance Manufacturing Industries Marketing Engineering Markov Chains Mathematics Actuarial Sciences Big Data Analysis Operations Research Military and Homeland Security Networks Operations Management Planning and Scheduling Policy Modeling and Public Sector Production Management Queuing Theory Revenue & Risk Management Services Management Simulation Statistics Stochastic Models Strategic Management Systems Engineering Telecommunications Transportation Risk Management Modeling of Economics And so on
Arjuna Subject : Matematika - Matematika (Lain-Lain)
Articles 391 Documents
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Optimization of an Al₂O₃–Water Nanofluid Loop: Response Surface Modeling, ANOVA and Desirability Analysis for Thermoelectric Cooling Victor Chimdike Obinani; Chinedum Ogonna Mgbemena; Olusegun David Samuel
International Journal of Quantitative Research and Modeling Vol. 7 No. 3 (2026): International Journal of Quantitative Research and Modeling (IJQRM)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v7i3.1382

Abstract

A closed Al₂O₃–water nanofluid loop was built to reject heat from the hot junctions of a dual-thermoelectric household water dispenser, and its operating window was mapped experimentally and then optimized statistically. A full factorial design of 48 runs was executed on an instrumented rig, combining four nanoparticle sizes (10, 20, 30 and 50 nm), four volume concentrations (0.5, 2.0, 4.0 and 5.0 vol%) and three volumetric flow rates (0.5, 1.2 and 2.4 L min⁻¹) at a fixed module current of 4.0 A and an ambient temperature of 27 ± 1 °C, with a deionized-water baseline recorded separately. Six responses were measured or derived for every run: hot-junction temperature, pressure gradient, Darcy friction factor, Nusselt number, performance evaluation criterion (PEC) and system coefficient of performance (COP). Five responses were fitted with quadratic response surfaces and PEC with a linear model; each model was reduced by backward elimination at α = 0.05 under the hierarchy principle, tested by Type-III analysis of variance, and checked against externally studentized residual diagnostics and leave-one-out PRESS statistics. The COP model was highly significant (F = 46.15, p < 0.0001) with R² = 0.916, adjusted R² = 0.896 and predicted R² = 0.874. Volume concentration was the dominant term (F = 85.36, p < 0.0001) and its negative quadratic partner (F = 49.59, p < 0.0001) located an interior maximum at 4.28 vol%, a reversal that is also present at 5.0 vol% in every raw series and in the directly measured hot-junction temperature. The linear PEC model was significant overall (F = 4.30, p = 0.0096) but was governed by flow rate (F = 8.35, p = 0.0060) rather than by concentration, and accounted for only 22.7% of the observed variance, so the factor that controls loop efficiency is not the factor that controls device efficiency. Derringer–Suich desirability optimization over all six responses under equal weighting converged on a particle size of 11.4 nm, a concentration of 4.30 vol% and a flow rate of 2.4 L min⁻¹, with a composite desirability of 0.649, a predicted COP of 0.667 against 0.42 for the deionized-water baseline and a predicted hot-junction temperature of 32.7 °C. At the nearest tested condition, five of the six responses were predicted to within 8% and COP to within 4.7%.

Page 40 of 40 | Total Record : 391

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