A Decision-Support System for Cryogenic Storage Tank Material Selection Based on WASPAS

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Authors: Ramya Sharma
Cited by: 0
References: 31

Abstract

Material selection problem of cryogenic storage tank the material selection for cryogenic storage tanks is a critical and complex problem due to the extreme operating conditions and specific requirements of cryogenic applications. Cryogenic storage tanks are designed to store liquefied gases at very low temperatures, typically below -150°C (-238°F) Common materials used for cryogenic storage tanks include stainless steels, aluminum alloys, and certain low-temperature carbon steels. Additionally, specialized materials such as nickel alloys or composite materials may be utilized for specific applications where enhanced properties are required. The material selection process for cryogenic storage tanks involves a comprehensive evaluation of the aforementioned factors, often utilizing material databases, testing, and simulation tools. It is essential to consult with experts in cryogenic engineering and consider the specific requirements of the intended application to ensure the optimal material choice for cryogenic storage tanks. he Weighted Aggregated Sum Product Assessment (WASPAS) methodology is a multi-criteria decision-making (MCDM) technique used to solve problems where multiple criteria need to be considered for decision-making. The WASPAS methodology allows decision-makers to systematically evaluate alternatives based on multiple criteria and their relative importance. It provides a structured approach for decision-making that takes into account the preferences and priorities of the decision-maker. The weights assigned to the criteria play a crucial role in determining the final ranking of the alternatives. 2024 aluminium in the T6 temper, 2024 aluminium in the O temper, 301 Full Hard Tempered Stainless Steel, Stainless Steel 310, TC4, Ti64, or ASTM Grade 5, nickel-chromium-molybdenum super alloy, Brass. Yield Strength, Elastic Modulus, Toughness Index, Density, Specific Heat, Thermal Expansion. From the result we can see that ss301-FH got 1st rank and AL5052-0 got last rank. by using waspas method we obtained that ss301-FH got 1st rank and AL5052-0 got last rank .

Fields of Study & Subjects

Material Selection and Properties Metallurgical and Alloy Processes Machine Learning in Materials Science

Publication details

Journal / source
Aeronautical and Aerospace Engineering
Volume
3
Issue
3
Pages
30–30
Publication date
10 January 2026