Selection of nanoparticles for battery thermal management system using integrated multiple criteria decision‐making approach

Abstract

Summary The selection of suitable nanoparticles is an important task for their applications as heat transfer promoters for the mitigation of thermal issues in energy storage systems. This paper aims to present a systematic framework for the selection of nanoparticles based on multiple attribute decision‐making tools. A hybrid Grey relational analysis‐based technique for order preference by similarity to ideal solution (GRA‐TOPSIS) approach combining the advantages of both GRA and TOPSIS methodologies is proposed and opted for evaluation, comparison, and ranking of different alternatives under the influence of several objective and subjective criteria weights. The criteria importance through inter‐criteria correlation (CRITIC) and entropy weighing methods are employed to determine the objective weights, while the subjective weights are calculated using the analytic hierarchy process (AHP). Also, an attempt is made to present a generalized weight integration formula that combines several objective and subjective weights of attributes. The results demonstrate Al2O3 nanoparticles as the most acceptable alternative. The sensitivity analysis performed proves the robustness and feasibility of the adopted decision framework. Additionally, the rank reversal issue is also addressed to present the qualitative assessment of estimated results. Thus, the evaluation methodology discussed in this paper seems to provide a theoretical reference strategy for nanoparticle selection in heat transfer enhancement applications. Highlights A novel integrated multiple criteria decision making scheme for nanoparticle selection is presented An attempt is made to present a generalized weight integration formula combining several objective and subjective weights of attributes Thermal conductivity and specific surface area are reported as the most significant attributes. Al2O3 is highlighted as the best choice. The sensitivity analysis and rank‐reversal test have also been performed to check the robustness of suggested procedures and Al2O3 has outperformed all other alternatives under consideration.

Parametric optimization of hybrid artificial roughness used in solar air heaters using multiple criteria decision making techniques

“…60,61 In the present work, the Nu has been assigned maximum criteria weight and hence; is considered as a primary attribute with maximum sensitivity affecting the decision-making procedures significantly. The sensitivity tests have been performed by following the procedures discussed by Dwivedi et al 32 The criteria weights corresponding to primary and secondary attributes are termed as the primary weights ( W jp ) and secondary weights ( W js ); respectively. For several gradual alterations made in primary weights (−50% ≤ δW jp ≤ +50%), the modified secondary weights (Wjs′) are recalculated using equations (25) and (26)…”

Evaluation of Air Combat Control Ability Based on Eye Movement Indicators and Combination Weighting GRA-TOPSIS

“…As shown in Table 3, we sorted the literature using relevant methods in recent years. To select suitable nanoparticles to remit the thermal issues in energy storage systems, Dwivedi et al [27] adopted the AHP and EW-CRITIC methods to empower them from both subjective and objective aspects, and the GRA-TOPSIS method was used to evaluate, compare, and rank several different alternatives. Because experts' clustering has been frequently based on their preference similarity, without considering the defect of individual opinion, Chen et al [28] proposed a two-tiered collective opinion generation framework integrating an expertise structure and risk preference to solve this problem.…”

“…This paper follows the principles of scientificity, comprehensiveness, stability, independence, and testability. After consulting a large number of materials [1][2][3][12][13][14][15][16][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40], we conducted on-site research in relevant units, summarized combat training experience, discussed with over twenty experts in the field who are the professors and officers of Air Force, and combined the air combat control process to establish an evaluation indicator system for assessing air combat control ability, as shown in Figure 1.…”