Project Grant DESC0025538

This National Science Foundation (NSF) Engineering Research Initiation (ERI) award, funded under CFDA 47.041, supports research at Michigan Technological University to explore a novel, non-contact ultrasonic method using Rayleigh waves to optimize delay timing in three-dimensional (3D) concrete printing. The $199,581 project aims to develop a real-time scanning system that monitors concrete surface properties without physical contact, enabling better understanding of how concrete properties...

This Project Grant award from the National Science Foundation (CFDA 47.049 - Mathematical and Physical Sciences) provides $220,000 in funding from September 1, 2025 to August 31, 2028 to the University of North Carolina at Chapel Hill (UNC-CH) to support collaborative research on improving the use of deep learning for computational wave imaging. The project aims to develop innovative techniques to address data scarcity and improve the generalizability of deep learning models in solving complex...

This $592,223 project grant from the National Science Foundation's Engineering program (CFDA 47.041) will fund the development of an ultrasonic diffuse wave field method to enable volumetric characterization of microstructures in engineering metals. Led by The Pennsylvania State University, researchers will design experimental transduction mechanisms and signal processing techniques to extract quantitative data on properties like diffusivity, attenuation, and scattering from ultrasonic...

FBS Inc., doing business as Guidedwave, was awarded a $398,766 Project Grant from the Department of Energy Office of Science on June 27, 2022 to support research titled "Ultrasonic Imaging of Mechanical Degradation in Superconducting Magnets." The award will fund work through August 20, 2023 under the Office of Science Financial Assistance Program (CFDA #81.049). The project aims to advance ultrasonic imaging techniques to detect mechanical degradation in superconducting magnets. The...

This National Science Foundation (NSF) Project Grant under the Engineering program (CFDA 47.041) awarded $200,000 to Lawrence Technological University (LTU) to develop integrated non-contact ultrasound generation and detection systems. The project aims to enable high-resolution, non-contact tissue functional imaging and biomechanical characterization, addressing limitations of current ultrasound imaging methods that require direct contact. The research plan focuses on integrating photoacoustic...

This $354,485 Project Grant awarded by the National Science Foundation's (NSF) Mathematical and Physical Sciences program will support collaborative research aimed at developing fast, accurate computational models (digital twins) for simulating how high-frequency sound waves interact with complex materials. The research will focus on creating reduced-order models that can efficiently simulate wave propagation, enabling more accurate and responsive guidance for medical procedures that use...

This $359,990 project grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) supports research at Rutgers, The State University to establish a physics-informed machine learning (PIML) framework that can accurately predict the fatigue life and scattering behavior of metal components produced through laser powder bed fusion (LPBF) additive manufacturing (AM) processes. The key objectives are to: 1) characterize the quality and fatigue properties of...

Echo Mind Ai Corp. received a $269,978 National Science Foundation Technology, Innovation, and Partnerships Project Grant to develop artificial intelligence-enabled ultrasound imaging technology for diagnosing musculoskeletal injuries. The goal is to advance AI models that can analyze ultrasound image sequences to determine proper probe alignment and identify tendon tears, while providing guidance to aid novice practitioners in consistent image capture. Once complete, the AI-guided ultrasound...

This $249,999 Project Grant awarded by the National Science Foundation (NSF) under the Engineering program (CFDA 47.041) aims to establish a physics-informed machine learning (PIML) framework to enable accurate and transparent predictions of fatigue life and its variation for metal additive manufacturing (AM) components. The project will fabricate baseline fatigue samples of SS316L and Ti-6Al-4V alloys produced via laser powder bed fusion (LPBF) AM and post-processing, characterize their...

The National Science Foundation awarded a $200,000 Project Grant to the University of Chicago under the Engineering (47.041) federal grant program. The grant will support the creation of two open-source, high-performance computing-enabled deep learning frameworks to significantly improve the reconstruction speed and quality of full waveform inversion-based ultrasound computed tomography. One framework will incorporate adjoint tomography theory into a generative adversarial network to...