Recent progress of efficient low-boom design and optimization methods

Reducing the sonic boom to a community-acceptable level is a fundamental challenge in the configuration design of the next-generation supersonic transport aircraft. This paper conducts a survey of recent progress in developing efficient low-boom design and optimization methods, and provides a perspective on the state-of-the-art and future directions. First, the low- and high-fidelity sonic boom prediction methods used in metric of low-boom design are briefly introduced. Second, efficient low-boom inverse design methods are reviewed, such as the classic Jones–Seebass–George–Darden (JSGD) method (and its variants), the high-fidelity near-field-overpressure-based method, and the mixed-fidelity method. Third, direct numerical optimization methods for low-boom designs, including the gradient-, surrogate-, and deep-learning-based optimization methods, are reviewed. Fourth, the applications of low-boom design and optimization methods to representative low-boom configurations are discussed, and the challenging demands for commercially viable supersonic transports are presented. In addition to providing a comprehensive summary of the existing research, the practicality and effectiveness of the developed methods are assessed. Finally, key challenges are identified, and further research directions such as full-carpet-low-boom-driven multidisciplinary design optimization considering mission requirements are recommended.