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<p>In the chemical industry, an effective gas detection system is fundamental to safety and risk management to prevent accidental leaks that can lead to major fires and explosions. However, the computational demands of CFD simulations increase decision-making time in a gas detection system. The present work addresses the development of a detector optimisation strategy with a reduced number of CFD simulations,&#xa0;by employing a mathematical model based on the kinetic theory of gases that uses a limited number of CFD simulations to estimate the flammable cloud volume. The methodology leveraged the results of the dimensional equations to develop Monte Carlo simulations and generate an exceedance curve to determine the flammable cloud volume with an associated risk criterion. The optimisation method was developed based on the set covering problem, including 2-of-N voting logic as a constraint on the objective function. The results demonstrated that the mathematical model determines the flammable cloud volume with an accuracy comparable to that of the CFD simulations. This provides an advantage for gas detector optimisation, since it significantly reduces the number of CFD simulations and, consequently, the computational time. Furthermore, including voting logic improves the reliability of a gas detection system.</p>

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A novel gas detector optimisation method combining 2-of-N voting systems, kinetic theory of gases, and exceedance curves to minimise CFD requirements

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月2日

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  • Milagritos de María Díaz Díaz,
  • Sávio S. V. Vianna

摘要

In the chemical industry, an effective gas detection system is fundamental to safety and risk management to prevent accidental leaks that can lead to major fires and explosions. However, the computational demands of CFD simulations increase decision-making time in a gas detection system. The present work addresses the development of a detector optimisation strategy with a reduced number of CFD simulations, by employing a mathematical model based on the kinetic theory of gases that uses a limited number of CFD simulations to estimate the flammable cloud volume. The methodology leveraged the results of the dimensional equations to develop Monte Carlo simulations and generate an exceedance curve to determine the flammable cloud volume with an associated risk criterion. The optimisation method was developed based on the set covering problem, including 2-of-N voting logic as a constraint on the objective function. The results demonstrated that the mathematical model determines the flammable cloud volume with an accuracy comparable to that of the CFD simulations. This provides an advantage for gas detector optimisation, since it significantly reduces the number of CFD simulations and, consequently, the computational time. Furthermore, including voting logic improves the reliability of a gas detection system.