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Development of design guidelines using probabilistic framework for the development of smart thickening fluid based ultra resistant adaptive kinematic soft human armor (SURAKSHA)

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  • Kumar, Suman
  • Saxena, Sanchit
  • Sharma, Hrishikesh
  • Gangolu, Jaswanth
  • Prabhu, T. Ajeeth

Abstract

In the present era, the form of war has been changing constantly, and it has taken various shapes such as guerilla warfare, insurgency, and similar. With the rapid development in technology, arms and ammunition have modernized. Due to the rapid development in the lethality of bullets, there is an immense demand for the development of enhanced protective suit which can safeguard against these threats and are simultaneously flexible and light. This paper presents the design and development of a probabilistic models of Shear Thickening Fluid Based Ultra Resistant Adaptive Kinematic Soft Human Armor (SURAKSHA) and proposed the design guidelines based on the outcome to counter extreme threats arising from the from standard arms and ammunition. The STF encapsulated bubble wrap configuration consists of STF filled in bubbles and these bubbles are either uniformly or randomly placed to prepare a layer of bubbles. The numerical strategies along with the probabilistic model for modelling of STF encapsulated bubble wrap configuration is proposed and presented. This new configuration is found to be more efficient under the impact of projectile of BR I, II A, II, and III A as per NIJ standards compared to STF treated fabric in terms of ballistic performance.

Suggested Citation

  • Kumar, Suman & Saxena, Sanchit & Sharma, Hrishikesh & Gangolu, Jaswanth & Prabhu, T. Ajeeth, 2023. "Development of design guidelines using probabilistic framework for the development of smart thickening fluid based ultra resistant adaptive kinematic soft human armor (SURAKSHA)," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    • Handle: RePEc:eee:reensy:v:236:y:2023:i:c:s0951832023001928
    DOI: 10.1016/j.ress.2023.109277
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    References listed on IDEAS

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    1. Dinis, D. & Teixeira, A.P. & Guedes Soares, C., 2020. "Probabilistic approach for characterising the static risk of ships using Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
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    3. Kishore, Katchalla Bala & Gangolu, Jaswanth & Ramancha, Mukesh K. & Bhuyan, Kasturi & Sharma, Hrishikesh, 2022. "Performance-based probabilistic deflection capacity models and fragility estimation for reinforced concrete column and beam subjected to blast loading," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    4. Cho, Jaehyun & Lee, Sang Hun & Kim, Jaewhan & Park, Seong Kyu, 2022. "Framework to model severe accident management guidelines into Level 2 probabilistic safety assessment of a nuclear power plant," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    5. Gangolu, Jaswanth & Kumar, Ajay & Bhuyan, Kasturi & Sharma, Hrishikesh, 2022. "Probabilistic demand models and performance-based fragility estimates for concrete protective structures subjected to missile impact," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    6. Bhuyan, Kasturi & Sharma, Hrishikesh, 2022. "Reliability analysis & performance-based code calibration for slabs/walls of protective structures subject to air blast loading," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
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