Virginia Tech receives 5 Department of Defense instrumentation awards

Virginia Tech has received five Department of Defense awards that support the purchase of research equipment.

Through the Defense University Research Instrumentation Program (DURIP), annual awards have been granted to 172 university researchers at 91 institutions across 40 states, totaling approximately $49 million. With these awards, undergraduate and graduate students will gain valuable experience in state-of-the-art facilities, train on the latest technologies, and ultimately work to advance military research.

The awards are the result of a merit competition for DURIP funding conducted by the Army Research Office, Office of Naval Research, and Air Force Office of Scientific Research. The annual awards process is highly competitive, with 724 proposals requesting $295 million in funding for 2020.

Virginia Tech recipients are:

Associate Professor Olivier Coutier-Delgosha of the Kevin T. Crofton Department of Aerospace and Ocean Engineering received nearly $192,000 from the Office of Naval Research for the purchase of a high-frequency imaging system to analyze cavitation bubble collapse.

The new imaging system will comprise four optical devices, including a high-speed spectrometer to measure the bubble composition; a fast responding pressure sensitive paint to measure the pressure evolution within the bubble; a high frequency fiber-optic hydrophone to detect the pressure wave generated by the bubble collapses; and an ultra-high speed camera for high resolution imaging.

The new system will enhance the Virginia Tech Cavitation Lab and supplement the existing instrumentation that creates cavitation bubbles of various diameters. The Cavitation Lab also houses high-speed flow facilities of different scales that investigate instabilities in hydrodynamic cavitation and subsequent effects like wall erosion, noise, and vibrations.

The instrumentation will directly impact several research projects already funded by the Office of Naval Research, including Coutier-Delgosha’s research with measuring temperature in a cavitation bubble and cavitation bubble inception, deformation and collapse.

This award will also benefit Virginia Tech’s collaborative efforts planned with University of Washington, George Washington University, and University of Michigan to study cavitation-induced erosion and develop a modelling of bubble clouds.

Ayman Karim, associate professor in chemical engineering, will build a “one of its kind” nano-calorimeter instrument that is capable of very sensitive measurements of adsorption and reactions of different molecules of interest to the Army on catalytic surfaces. The award of nearly $123,000 and the purchase of the nano-calorimeter will benefit ongoing catalysis work in chemical engineering and chemistry at Virginia Tech.

The proposed system is designed to measure adsorption and reaction enthalpies of small molecules and simulants on catalysts being developed in the Karim lab. The novelty of the proposed system is in the integration of a very sensitive calorimeter, gas dosing system, and mass spectrometer to allow measurements not previously possible.

The results from this work are expected to advance the basic science of heterogeneous catalysis by revealing unprecedented details on the interaction of molecules with single atom and subnanometer clusters to help design the next generation oxidation catalysts.

Alumni Distinguished Professor and Christopher C. Kraft Endowed Professor of Mechanical Engineering Wing Ng, received nearly $650,000 from the Office of Naval research to procure an aircraft engine test bed/test cell and an additional $750,000 from Rolls-Royce, who also committed $100,000 per year to support laboratory continuity.

The new test bed will be installed in the Advanced Power and Propulsion Lab in the spring of 2020. The equipment was part of a proposal called “A turboshaft engine test stand for particle ingestion research.”

Initially, the new equipment will test the effect of sand ingestion on the performance of aircraft engines. According to Ng, the equipment will also be an important factor in further developing the lab, increasing the capability and capacity of engine research, and positioning Virginia Tech as a prime innovator in propulsion environmental impact research.

In recent years, the Hume Center for National Security and Technology at Virginia Tech has established itself as a leading innovator in advancing radio frequency machine learning research and the application of state-of-the-art deep machine learning concepts to wireless communication and electronic warfare applications in support of our national defense partners. In the past four years, the center has been awarded more than $10 million in funding from commercial and government sponsors in such key innovation areas as signal detection and estimation, signal format identification, specific emitter identification, dynamic spectrum access, enhancements and robustness, and distributed radio frequency machine learning,

Research Assistant Professor Bradley Davis will use the DURIP funding to purchase a test and measurement system to conduct high-sensitivity, radio-frequency and microwave measurements using industry standard equipment and techniques. This capability will help support research in applied electromagnetics and multidisciplinary projects in material development and characterization for application to radar, communications, electronic warfare, signature control, and signal intelligence.

Expanding horizons for research and development in artificial (metamaterial) and nano/macro composite materials will bring together multidisciplinary teams across Virginia Tech’s academic departments in engineering and the sciences.

Research Assistant Professor Chris Headley will use his DURIP award for a GPU-based deep learning training system and data storage solution in order to increase the ability to engage the rapidly growing student base with interest in this critical research area. This equipment will decrease the data transfer and training times of our deep learning solutions by multiple orders of magnitude and allow investigation of much deeper neural network architectures to tackle much more challenging and sophisticated RFML problems.

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