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The military aircraft gas turbine engines are often required to undergo a complex set of.operating conditions where the load varies considerably with respect to time. The temperature range for performing such requirements also increases as the thrust increases.The modern design of gas turbine demands very high thrust-to-weight ratio. In order to achieve this, the design is limited in the low cycle regime. The low cycle regime necessarily has the plasticity effect because of fatigue and inelastic time dependent permanent deformation because of creep. Fatigue and creep interaction studies are very important for the safe life design of critical components such as turbine discs and blades.

Gene regulatory studies are of significant importance in many scenarios such as mental illness. 21% of U.S adults experience mental illnesses including 1 in 4 active-duty military personnel. Mental health can be identified in the body by different biomarkers. These biomarkers potentially controlled by riboswitches, which are located in mRNA and switch "ON" or "OFF" depending on the concentration of a biomarker. In this research, a known riboswitch reengineered and its response in the presence of a biomarker investigated. We changed computationally PreQ1, a known riboswitch that has the smallest aptamer, and then experimentally tested against biomarkers, dehydroepiandrosterone-sulfate (DHEA-S), Serotonin, Cortisol, Dopamine, Epinephrine, and Norepinephrine. A total of 7 variant riboswitches were tested in this research, 4 created computationally discussed here and 3 experimentally not covered in this paper. The results from these variants showed that variants 1 and 2 had different responses to DHEA-S then the expected PreQ1 response. A dose response showed downward trend as DHEA-S concentration increased. In conclusion of this research, riboswitches can be re-engineered to have a different response to biomarkers at the same time keeping the same structure.

Turbine engine disk life prediction and understanding the associated risk remains a significant challenge for today's designer. Despite advances made in materials testing and characterization, as well as, the application of damage tolerance and linear elastic fracture mechanics modeling, there remains a void in properly assessing loading, geometry, and material design property variability. Add to this the application of advanced hybrid and composite material systems and the need to accurately deal with material variability is even greater. There still remain incidents of failure of critical components which were not properly accounted for by the existing analytical methods, testing, and inspections employed today. Application of probabilistic methods offers an effective and useful approach to modeling this variability while also providing a means by which to assess random variable sensitivity and risk assessment. Current research, as well as, applicable industry and government regulatory guidelines and publications were examined and will be presented. An assessment of the most effective tools, modeling methods, and predictive risk of failure assessments together with recommendations for future work will be discussed. The potential for probabilistic methods to provide a cost-effective way to manage fleet engine and component usage is presented, as well as, its ability to enhance the safe implementation of Retirement for Cause concepts to fleet management.

Failure of critical engine components such as compressor, fan, and turbine disks during flight can cause the loss of the engine, aircraft, or even life. To reduce the risk of this failure during flight, different methodologies and tools have been developed to determine the safe operating life of these critical disk components. The two most widely used lifing methods, safe-life and damage tolerance, are inherently conservative, retiring all components when a predetermined operating limit is reached. Both methods retire components with theoretical useful life remaining. Additional lifing methods can be used to reduce this conservatism and extend the life of these components. Retirement for cause, developed within the United States Air Force is a lifing method that can extend the life of components by retiring a component only when there is cause to do so. Military and industry standards on lifing methodologies were reviewed. Both deterministic and probabilistic approaches to disk lifing methods are discussed as well as current tools. This paper provides a comparison of the methodologies and tools currently being used today by both the government and industry.