Award Data

Through this MDA Phase I STTR program, NanoSonic shall work with the high energy laboratory at Penn State and with input from a major US integrator of hypersonic vehicles to develop improved high temperature radome materials for uncooled aerodynamic reentry and hypersonic vehicles. Through prior work, NanoSonic has developed and demonstrated high temperature materials with good thermal insulating ...

NanoSonic will work with Penn State to develop inexpensive automated manufacturing processes for the production of ceramic matrix composite thermal protective system materials with high temperature thermal performance and high thermal insulation propertiesWe will fabricate materials using automated robotic production methods, and perform testing, including high energy irradiation at Penn State’s ...

The innovation proposed here is a novel carbon-carbon composite (CCC) manufacturing method based on polymer infusion and polymerization (PIP) using a novel precursor polymer with exceptionally high char yields. This results in a material that has the promise of excellent quality and mechanical properties, while offering the breakthrough advantages of (1) greatly reduced or eliminated need for back ...

This topic identifies the need creating a Modeling and Simulation (M&S) development and execution environment that significantly decreases the time to execute statistically significant batches of stochastic simulation runs for the purpose of estimating scenario output and outcome distributions while improving statistical knowledge of the outcome distributions. The strategy sought by this topic is ...

To advance simulation techniques, such as high-fidelity computational fluid dynamics (CFD), to accelerate maturation of DACS design through design-time trade studies, there is a need for new, test-validated models that improve both computational performance and the accuracy of the reacting jet in hypersonic crossflow simulations. ATA and CUBRC (a research institution with leading expertise in aero ...

krtkl (“critical”) will conduct a Phase I Feasibility Study to identify the best approach for reducing aviator cognitive load by optimizing information delivery and decision-making based on a thorough analysis of existing platforms, sensors, data sources, and onboard compute resources. This information will be used to identify Artificial Intelligence and Machine Learning based algorithms for p ...

Sensor systems aboard aircrafts address unique problems and are siloed in their objectives. A data silo is a term used to describe a data system that is insulated from other data systems. While keeping information categorized may lead to easier organization, the costs often outweigh the benefits. In aviation systems, data silos often lead to miscommunication, cognitive overload, and waste. These d ...

Simulations are designed to reproduce the behavior of end-to-end systems or their critical components. When designed under a stochastic framework, they enable us to identify ensembles of possible system evolutions and their associated uncertainties and sensitivities of outputs relative to the inputs. When the intent is to reproduce sensor observations they need to account for the sensor and data p ...

Cynnovative proposes Explainable Deep Reinforcement Learning with Symbolically Guided Transitions (X DRLSGT) to improve the transparency and, thus, the explainability of deep reinforcement learning (DRL) algorithms. The inability to understand the reasoning behind an Artificial Intelligence’s (AI) decision is a major limiting factor that prevents AI-enabled physical systems from being deployed a ...

In manned aviation environments – both military and commercial – AI support is being developed and researched for ground-based planning and operational decision support. Using AI in real-time with crews poses additional questions and issues. This research will provide the means to understand the measures and requirements to architect potential AI-Agent solutions before implementation. This fea ...