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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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103 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2025

Evaluating the Viability of Compact and Portable X-Ray Systems for an Exploration Medical System in a Ground Demonstration

MOTIVATION FOR INCLUDING X-RAY CAPABILITIES For upcoming exploration missions, the need for enhanced medical care becomes critical due to extended mission durations, significant communication delays, and minimal evacuation opportunities. Previous evidence by our team has revealed that among the 119 medical conditions targeted for management during spaceflight within NASA Exploration Medical Capability’s IMPACT Condition List, at least 36 could benefit from radiography (XR). Utilizing XR for diagnosis and management is hypothesized to significantly improve management of crew health by enabling the immediate evaluation and confirmation of potential injuries or illnesses. Beyond clinical applications, XR also holds potential for non-destructive testing (NDT). This includes applications such as assessing the structural integrity of the spacecraft, analyzing surface and meteorite samples, and inspecting onboard electronics. THREE CANDIDATE X-RAY SYSTEMS CHOSEN FOR GROUND DEMONSTRATION The Exploration Medical Capability Element (ExMC) and the Exploration Medical Integrated Product Team (XMIPT) of the Mars Campaign Office initiated early background work for ground demonstrations. In FY21, ExMC published a Concept of Operations to guide requirements development. By FY23, XMIPT and yet2, a technology scouting and open innovation consulting firm, had completed a market survey and trade study to identify potential miniature XR systems. Selection criteria included commercial-off-the-shelf availability, low mass and volume, and regulatory compliance. The top three candidate devices—Remedi REMEX-KA6, MinXray Impact, and FujiFilm Xair—were acquired to characterize the requirements and capabilities of each device. To facilitate testing, phantoms, and radiographic personal protective equipment (PPE) were purchased, and a dedicated space was designated for XRS usage at Glenn Research Center. During this presentation, the mass, volume, and power requirements for each of the three piloted devices are revealed, as well as information regarding the detector, mA, and kV of the devices. GOAL AND OBJECTIVES OF A MINI XRS GROUND DEMONSTRATION The primary goal of ExMC/XMIPT technology demonstrations is to bridge the gap in available, flight-ready medical device technology by flight-testing diagnostic and treatment technologies essential for managing medical conditions during exploration missions. These technologies must adhere to vehicle constraints such as mass, volume, power, and data requirements, integrate seamlessly with medical decision-support tools, and support increasingly Earth-independent operations. There are three main objectives for the future ground demonstration of these three devices. First, we aim to determine the full capabilities of these three miniature XR systems within the context of the spaceflight environment. While medical applications are the primary focus for the miniature XR, a comprehensive exploration of non-medical uses has been initiated by an XMIPT-sponsored NASA SPARK campaign to identify collaborators. Second, we plan to establish criteria and to use insights gained from evaluating each miniature XR against those criteria to select the most suitable system among the three candidates. Third, we intend to evaluate their suitability for flight certification, which includes assessing its durability for launch, reentry, and exposure to high background radiation, as well as its compatibility with existing data architecture systems. Numerous subject matter experts from NASA and partner institutions will support these objectives.

C A Haddix↗
DOE OSTI2025

Improved guided-wave acoustic defect detection and localization in pipes under varying temperature conditions using deep learning

Early defect detection in pipelines is critical across industries, particularly in the oil and gas sector, where failures result in significant maintenance costs and operational disruptions. Acoustic guided-wave techniques are widely used for nondestructive evaluation of pipeline defects due to their long-distance propagation capability. However, environmental variations, sensitivity limitations, and complex signal interpretation challenges limit the effectiveness of traditional signal processing approaches with guided-wave signals. Recent advances in deep learning methods have demonstrated remarkable success in solving complex real-world problems in many fields. In particular, deep-learning-based signal processing holds substantial promise to overcome limitations and challenges of conventional signal processing. This study presents a deep learning framework for pipeline inspection using acoustic guided-wave signals under temperature varying environments. The proposed framework employs a dual-path one-dimensional convolutional autoencoder that combines defect detection, localization, and temperature prediction functions. The proposed system utilizes multi-mode and broadband acoustic waves with an optimized number of sensors that provide high accuracy while retaining practical simplicity. Experimental validation is performed on a carbon steel pipe. The results indicate exceptional defect detection accuracy and precise defect localization with a mean absolute error of 66 mm. The proposed technique also predicts the effective average temperature of the pipe with a mean absolute error of 0.2°C. Comparative analysis shows superior performance of the proposed method over a traditional method previously developed by the authors' team. These results highlight the potential of integrating deep learning methods into guided-wave pipeline inspection systems to improve reliability under varying environmental conditions.

42 ENGINEERING↗
DOE OSTI2026

Non-Contact Eddy Current Method for Assessing Proton Conductivity in Nafion Membranes

Accurate measurement of proton conductivity in Proton Exchange Membranes (PEMs) is vital for fuel cell performance and durability. This study explores eddy current testing as a non-destructive, non-contact and high-rate quality control (QC) method for measuring Nafion membrane proton conductivity, with potential for high-volume manufacturing applications.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2000

Virtual Reality Robotic Operation Simulations Using MEMICA Haptic System

There is an increasing realization that some tasks can be performed significantly better by humans than robots but, due to associated hazards, distance, etc., only a robot can be employed. Telemedicine is one area where remotely controlled robots can have a major impact by providing urgent care at remote sites. In recent years, remotely controlled robotics has been greatly advanced. The robotic astronaut, "Robonaut," at NASA Johnson Space Center is one such example. Unfortunately, due to the unavailability of force and tactile feedback capability the operator must determine the required action using only visual feedback from the remote site, which limits the tasks that Robonaut can perform. There is a great need for dexterous, fast, accurate teleoperated robots with the operator?s ability to "feel" the environment at the robot's field. Recently, we conceived a haptic mechanism called MEMICA (Remote MEchanical MIrroring using Controlled stiffness and Actuators) that can enable the design of high dexterity, rapid response, and large workspace system. Our team is developing novel MEMICA gloves and virtual reality models to allow the simulation of telesurgery and other applications. The MEMICA gloves are designed to have a high dexterity, rapid response, and large workspace and intuitively mirror the conditions at a virtual site where a robot is simulating the presence of the human operator. The key components of MEMICA are miniature electrically controlled stiffness (ECS) elements and Electrically Controlled Force and Stiffness (ECFS) actuators that are based on the sue of Electro-Rheological Fluids (ERF). In this paper the design of the MEMICA system and initial experimental results are presented.

NDE NDT composite materials defects inspection↗
NASA NTRS2001

Residue detection for real-time removal of paint from metallic surfaces

Paint stripping from large steel ships and other metallic surfaces is a major issue in the maintenance and refurbishing of structures, and environmental concerns are greatly limiting the possible options. As a result, waterjet with water recycling has become the leading form of paint stripping and robotic manipulators with scanning bridges were constructed by various manufacturers to address this need. The application of such scanning bridges is slow and their access is constrained by the complex shape of the ship hull and various features on the surface. To overcome these limitations, a robotic system that is called Ultrastrip (UltraStrip Systems, Inc., Stuart, FL) is developed. This system uses magnetic wheels to attach the stripper to the structure and travel on it while performing paint stripping. To assure efficient paint stripping feedback data is required to control the travel speed by monitoring the paint thickness before and during the stripping process. Efforts at JPL are currently underway to develop the required feedback capability to assure effective paint stripping. Various possible sensors were considered and issues that can affect the sensitivity, reliability and applicability of the sensors are being investigated with emphasis on measuring the initial conditions of the paint. Issues that affect the sensory data in dynamic conditions are addressed while providing real-time real feedback for the control of the paint stripper speed of travel.

NDT↗