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Understanding reliability is critical in design, maintenance and durability analysis of engineering systems. A reliability simulation methodology is presented in this paper for vehicle fleets using limited data. The method can be used to estimate the reliability of non-repairable as well as repairable systems. It can optimally allocate, based on a target system reliability, individual component reliabilities using a multi-objective optimization algorithm. The algorithm establishes a Pareto front that can be used for optimal tradeoff between reliability and the associated cost. The method uses Monte Carlo simulation to estimate the system failure rate and reliability as a function of time. The probability density functions (PDF) of the time between failures for all components of the system are estimated using either limited data or a user-supplied MTBF (mean time between failures) and its coefficient of variation.

This research paper addresses the ground vehicle reliability prediction process based on a new integrated reliability prediction framework. The integrated stochastic framework combines the computational physics-based predictions with experimental testing information for assessing vehicle reliability. The integrated reliability prediction approach incorporates the following computational steps: i) simulation of stochastic operational environment, ii) vehicle multi-body dynamics analysis, iii) stress prediction in subsystems and components, iv) stochastic progressive damage analysis, and v) component life prediction, including the effects of maintenance and, finally, iv) reliability prediction at component and system level. To solve efficiently and accurately the challenges coming from large-size computational mechanics models and high-dimensional stochastic spaces, a HPC simulation-based approach to the reliability problem was implemented.

The Energy Finite Element Analysis (EFEA) has been developed for evaluating the vibro-acoustic behavior of complex systems. In the past EFEA results have been compared successfully to measured data for Naval, automotive, and aircraft systems. The main objective of this paper is to present information about the process of developing EFEA models for two configurations of a business jet, performing analysis for computing the vibration and the interior noise induced from exterior turbulent boundary layer excitation, and discussing the correlation between test data and simulation results. The structural EFEA model is generated from an existing finite element model used for stress analysis during the aircraft design process. Structural elements used in the finite element model for representing the complete complex aircraft structure become part of the EFEA structural model.

This paper describes modeling and simulation technologies used to simulate the electrical systems of Army vehicles using Matlab/Simulink coupled with graphical user interface software. The models were built using Mathworks' Matlab/Simulink software in conjunction with the SimPowerSystems Toolbox, a toolkit provided by Mathworks that provides models of basic electrical components such as capacitors and inductors, in addition to more advanced components such as diodes and IGBT's. The current results of this ongoing effort are presented and discussed.

A method is presented here that detects aircraft tail surface icing that might normally be unobserved by the flight crew. Such icing can be detected through the action of highly computationally efficient signal processing of existing sensor signals using a so-called failure detection filter (FDF). The FDF creates a unique output signature permitting relatively early detection of tail surface icing. The FDF incorporates a stable state estimator from which the icing signature is created. This estimator is robust to analytical modeling errors or uncertainties, and to process noise (e.g. turbulence). Excellent performance of the method is demonstrated via simulation.