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A hybrid-heuristic machine learning methodology, based on hybrid genetic algorithm (GA) and artificial neural network (ANN) data classification methods is proposed as an expert system for assessing viability of surrogates of a two-stage-to-orbit (TSTO) vehicle. The methodology is integral to the inverse design method for spaceplane systems. Since spaceplanes do not exist therefore archival mass-model data is also non-existent and inverse design method is used to generate optimal vehicle configuration data. The GA-ANN offers an expert system whereby when a new vehicle configuration is evolved its mass-model is first optimized using GA and then the optimal solution is processed through the ANN classifier to assess the viability of solution. If classification result fails the process is repeated until a qualified result is obtained. Results are validated using mass-model parameters of HTSM (hypersonic transport system Munich) vehicles.

A linear, multi-variable decision method called DF-APSO (difference-fractional actual, perfect, scaled, optimized) figure of merit is proposed for quantitative down-selection of comparative aerothermodynamic configurations of highly-integrated hypersonic compression system. It uses non-dimensional fractions of difference parameters to yield scaled figures-of-merit. A total of five unique configurations, for a mixed internal–external compression system, are studied by using their respective aerothermodynamic parameters of systemʼs performance. The results obtained by this method quantitatively agree with the order-of-merit solution obtained from weighted matrix method. Sensitivity analysis also demonstrates robustness and stability of the method.

A two-step improved particle swarm optimization (TIPSO) algorithm was recently proposed and used for the optimization of flexible satellite-control parameters. It was found to be much more stable and much less complex than other evolutionary algorithms. In this article the efficacy of the TIPSO algorithm is investigated for multidisciplinary optimization of aerothermodynamic parameters of performance, cowl deflection angle and shock–boundary layer separation on a cone-derived, wedge-integrated hypersonic (waverider) compression system. This algorithm uses an aggregate objective function. Optimization results from the TIPSO algorithm are compared with those obtained from a hybrid genetic algorithm, particle swarm optimization using an inertial weight approach and a multi-objective genetic algorithm. Since optimality of the forebody configuration is the basic requirement, the optimization variables selected are the isolator exit Mach number, static pressure ratio across the forebody–inlet configuration, cyc...

A framework methodology for multidisciplinary multiobjective optimization and analysis is proposed. It is based on analytical aerothermodynamics and mass-modeling parameters of highly-integrated forebody-inlet configuration and representative hypersonic flight vehicle respectively. A complex configuration for a highly-integrated waverider forebody attached to planar compression ramps and planar sidewalled-inlet system is studied. Optimization and analytical solutions are obtained using SHWAMIDOF-FI design tool. Results show substantial improvement in geometric, performance and flow parameters as compared to baseline configuration.

In this paper we propose a conceptual design and optimization strategy for a multistage ground-based interceptor (GBI) comprising of three stage solid propulsion system for an exo-atmospheric boost phase intercept using Particle Swarm Optimization (PSO). The mission of the interceptor is to deliver the Kinetic Kill Vehicle (KKV) to an optimal position in space to allow it to complete the intercept. The design objective is to minimize the Gross Lift Off Mass (GLOM) (Mg) of the interceptor under the mission constraints of miss distance (m), intercept time (s), lateral divert (m/s), velocity at intercept (km/s), g-loads and stage configuration requirements. Solid Rocket Motor (SRM) envelope constraints include length to diameter ratios, nozzle expansion ratios, propellant burn rates and grain geometry constraints like web fraction and volumetric loading efficiency. Interceptor conceptual design problem was posed to optimizer and it successfully solved under the given conditions and satisfied the interceptor trajectory/performance objectives.

This paper presents Robust Design Method (RDM) for the conceptual design of a multistage boost phase interceptor to obtain an optimum system configuration insensitive to uncertainties in the form of design variable variations. The mission of Ground Based Interceptor is to deliver Kinetic Kill Vehicle to an optimal position in space to allow it to complete the intercept. RDM is implemented using First Order Orthogonal Design Matrix (FOODM) to calculate the worst-case variation which is then utilized to evaluate the mean and variance of a system output. Latin Hypercube Sampling (LHS) is used for its good space filling properties to extract maximum useful information about ‘mean’ of quality function. The performance of Robust Design is compared with the Optimal Design. Genetic Algorithm (GA) is used as optimizer to search the design space for optimal and robust solution. Real/exact simulation analyses are used instead of surrogate models. To compare the robustness of the solutions obtained, a sensitivity analysis is performed using Monte Carlo simulation runs. The results indicate that the proposed method can find a robust configuration without compromising the performance objectives and design constraints.

In this paper we propose meta-model based design and optimization strategy for multistage ground based interceptor comprising of three stage solid propulsion system for an exo-atmospheric boost phase intercept. An efficient Least Square Support Vector Regression technique is used to approximate the current problem. The mission of Ground Based Interceptor is to deliver Kinetic Kill Vehicle to an optimal position in space to allow it to complete the intercept. The modules for propulsion characteristics, mass properties and flight dynamics have been integrated to produce a high fidelity model of the entire vehicle. For the present effort, the design objective is to minimize the Gross Lift off Mass of the ground based interceptor under the mission constraints of miss distance, intercept time, lateral divert, velocity at intercept, g-loads and stage configuration requirements as Solid Rocket Motor envelope constraints which comprise of length to diameter ratios, nozzle expansion ratios, propellant burn rates and grain geometry constraints like web fraction, volumetric loading efficiency etc. Though, the optimization results and performance are to be considered as preliminary (proof-of-concept) only, but they can be compared to existing systems and used for conceptual design of ground based interceptors. The proposed design and optimization methodology provides the designer with a time efficient and powerful approach for the design of interceptor systems.

This article proposes a multidisciplinary design and optimization (MDO) strategy for the conceptual design of a multistage ground-based interceptor (GBI) using hybrid optimization algorithm, which associates genetic algorithm (GA) as a global optimizer with sequential quadratic programming (SQP) as a local optimizer. The interceptor is comprised of a three-stage solid propulsion system for an exoatmospheric boost phase intercept (BPI). The interceptor's duty is to deliver a kinetic kill vehicle (KKV) to the optimal position in space to accomplish the mission of intercept. The modules for propulsion, aerodynamics, mass properties and flight dynamics are integrated to produce a high fidelity model of the entire vehicle. The propulsion module comprises of solid rocket motor (SRM) grain design, nozzle geometry design and performance prediction analysis. Internal ballistics and performance prediction parameters are calculated by using lumped parameter method. The design objective is to minimize the gross lift off mass (GLOM) of the interceptor under the mission constraints and performance objectives. The proposed design and optimization methodology provide designers with an efficient and powerful approach in computation during designing interceptor systems.