Improved Analysis Techniques for Prediction of Avionics Electromagnetic Interference and Vulnerability - IntegratedEMPro System

For modern, complex aircraft systems and avionics subsystems, coupling full-wave solutions to the electronic circuitry models is critical in order to accurately predict RF (radio frequency) system-level performance in the presence of electromagnetic signals originating from other avionics on the aircraft, high-power transmitters on other aircraft or ships, or even from microwave weapons. However, the unavailability of the circuitry models at the full vehicle level makes a comprehensive analysis very challenging. Most of the aircraft components such as Line Replaceable Modules (LRMs) are not always available with their full circuit models. However, many LRMs undergo MIL-STD-461 and MIL-STD-464 compliance testing, which provides radiated emissions/susceptibility and conducted emissions/susceptibility data. Therefore, it is important to integrate these data sets and models into a multi-physics based analysis tool to carry out practical system-level EMI analysis of electronic systems inside an aircraft. This proposed effort is to make significant innovative improvements to physics-based electromagnetic interference/vulnerability (EMI/V) tools by incorporating system performance parameters and characterizations of subsystem components within aircraft transmitters and receivers. A detailed description of the information requirements is developed for field-circuit interaction computational engines and multi-physics simulators. A scattering matrix approach is used for combining time and frequency domain analyses of circuits and LRMs, including their physical model library with aircraft radiating components to achieve a seamless integrated-level solution. Consideration is also given to how the overall prediction accuracy degrades as a function of the level of detail associated with the model library. Proof-of-concept validations will to be performed on a variety of canonical problems and limited number of real-world problems.