Aerospace Science and Technology
Hassan Naseh; Mehran MirShams; Hamid Reza Fazeley
Abstract
Recently, engineering systems are quite large and complicated. Conceptual design process of Space Transportation Systems (STSs) is a multidisciplinary task which must take into account interactions of various disciplines and analysis codes. Current approach for the conceptual design of STSs requires ...
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Recently, engineering systems are quite large and complicated. Conceptual design process of Space Transportation Systems (STSs) is a multidisciplinary task which must take into account interactions of various disciplines and analysis codes. Current approach for the conceptual design of STSs requires the evaluation of a large number of different configurations and concepts. With existing legacy codes, estimating the performance of all design combinations becomes very time consuming and computationally expensive. A possible solution to this problem could be employing of surrogates during design tasks. This paper describes an effort to optimize the design of an entire STS to achieve a low Earth orbit, consisting of multiple stages using an efficient surrogate-based Multidisciplinary Design Optimization (MDO) framework with the goal of minimizing vehicle weight and ultimately vehicle cost. Furthermore, a combination of Response Surface Methodology (RSM) and Kriging surrogates has been used for building surrogate models. The disciplines of aerodynamics, propulsion, trajectory simulation, geometry, and mass properties, have been integrated to produce an engineering system model of the entire vehicle. In addition, the system model has been validated using the existing design data of STS’s trajectory and their subsystems. For the design optimization, in order to ensure that the payload achieves the desired orbit, a hybrid algorithm has been used to minimize the deference between the actual and desired orbital parameters. The objective function of the optimization problem is to minimize the overall system mass, thus minimizing the system cost per launch. The proposed design and optimization methodology provides designers with an efficient and powerful approach in computation during designing space transportation systems and can also be developed for more complex industrial design problems with comparable characteristics.
A. M. Akhlaghi; H. Naseh; Mehran Dr. MirShams; Saeid Irani
Volume 8, Issue 2 , September 2011, , Pages 107-117
Abstract
This paper presents an extension of Bayesian networks (BN) applied to reliability analysis of an open gas generator cycle Liquid propellant engine (OGLE) of launch vehicles. There are several methods for system reliability analysis such as RBD, FTA, FMEA, Markov Chains, and etc. But for complex systems ...
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This paper presents an extension of Bayesian networks (BN) applied to reliability analysis of an open gas generator cycle Liquid propellant engine (OGLE) of launch vehicles. There are several methods for system reliability analysis such as RBD, FTA, FMEA, Markov Chains, and etc. But for complex systems such as LV, they are not all efficiently applicable due to failure dependencies between components, computational complexity and state space explosion problems. So to overcome these problems the BN modeling is preferred for OGLE reliability analysis. In this algorithm first the functional models of OGLE is constructed based on expert knowledge and experiments involving system and subsystems interactions. Then failure modes are derived through performing FMEA. Furthermore by using modeling properties of Bayesian networks, a constructional model for failure propagation is obtained based on the acquired functional model and FMEA. Finally, by allocating quantitative properties to the Bayesian model and inference of it, the reliability of OGLE is obtained. The results are verified to the Monte Carlo simulation results. Comparing the values obtained of two applied methods shows the high accuracy and efficiency of introduced algorithm to reliability analysis of launch vehicle OGLE and other complex systems with dependant failure modes.
