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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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Depth Thermography: Noninvasive 3D Temperature Profiling Using Infrared Thermal Emission
Here, we introduce a technique based on infrared thermal emission, termed depth thermography, that can remotely measure the temperature distribution beneath the surface of certain objects. Depth thermography utilizes the thermal-emission spectrum in the semitransparent spectral region of the target object to extract its temperature as a function of depth, in contrast with conventional thermography, which uses the spectrally integrated thermally emitted power to measure the surface temperature. Coupled with two-dimensional imaging, for example using an infrared hyperspectral camera or scanning a single-pixel spectrometer, this technique can yield volumetric temperature distributions. Furthermore, we carried out a proof-of-concept experiment on an asymmetrically heated fused-silica window, extracting the temperature distribution throughout the sample. Depth thermography may enable noncontact volumetric temperature measurements of microscopic objects such as multilayer electronic devices or macroscopic volumes of liquids and gases, as well as simultaneous all-optical measurements of optical and thermal properties of materials.
Label-Free, Noninvasive Bone Cell Classification by Hyperspectral Confocal Raman Microscopy
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Noninvasive cardiac modulation via triplet-sensitized photoswitching in the phototherapeutic window
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Digital twins for noninvasively measuring predictive markers of right heart failure
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4D transverse phase space tomography of an operational hydrogen ion beam via noninvasive 2D measurements using laser wires
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Noninvasive beam-size measurements for linear induction accelerators
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A Thermoacoustic Imaging System for Noninvasive and Nondestructive Root Phenotyping
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Noninvasive Thermal Mass Flow Meter for Safeguards
This project addressed the long-standing nuclear safeguards problem of detecting in a timely manner the unauthorized production of significant quantities of highly enriched uranium in gas centrifuge enrichment plants (GCEPs).
Noninvasive Detection, Tracking, and Characterization of Aerogel Implants Using Diagnostic Ultrasound
Medical implants are routinely tracked and monitored using different techniques, such as MRI, X-ray, and ultrasound. Due to the need for ionizing radiation, the two former methods pose a significant risk to tissue. Ultrasound imaging, however, is non-invasive and presents no known risk to human tissue. Aerogels are an emerging material with great potential in biomedical implants. While qualitative observation of ultrasound images by experts can already provide a lot of information about the implants and the surrounding structures, this paper describes the development and study of two simple B-Mode image analysis techniques based on attenuation measurements and echogenicity comparisons, which can further enhance the study of the biological tissues and implants, especially of different types of biocompatible aerogels.
On the Generalizability of Time-of-Flight Convolutional Neural Networks for Noninvasive Acoustic Measurements
Bulk wave acoustic time-of-flight (ToF) measurements in pipes and closed containers can be hindered by guided waves with similar arrival times propagating in the container wall, especially when a low excitation frequency is used to mitigate sound attenuation from the material. Convolutional neural networks (CNNs) have emerged as a new paradigm for obtaining accurate ToF in non-destructive evaluation (NDE) and have been demonstrated for such complicated conditions. However, the generalizability of ToF-CNNs has not been investigated. In this work, we analyze the generalizability of the ToF-CNN for broader applications, given limited training data. We first investigate the CNN performance with respect to training dataset size and different training data and test data parameters (container dimensions and material properties). Furthermore, we perform a series of tests to understand the distribution of data parameters that need to be incorporated in training for enhanced model generalizability. This is investigated by training the model on a set of small- and large-container datasets regardless of the test data. We observe that the quantity of data partitioned for training must be of a good representation of the entire sets and sufficient to span through the input space. The result of the network also shows that the learning model with the training data on small containers delivers a sufficiently stable result on different feature interactions compared to the learning model with the training data on large containers. To check the robustness of the model, we tested the trained model to predict the ToF of different sound speed mediums, which shows excellent accuracy. Furthermore, to mimic real experimental scenarios, data are augmented by adding noise. We envision that the proposed approach will extend the applications of CNNs for ToF prediction in a broader range.
Noninvasive Detection of Mercury on Surfaces
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Noninvasive Spot Size Diagnostic for Linear Induction Accelerators Project #: 22-162 Year 1 of 1
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Noninvasive Spot Size Diagnostic for Linear Induction Accelerators
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Acoustics-based noninvasive wafer defect detection
Techniques are provided for detecting wafer defects. Example techniques include exciting a wafer using an acoustic signal to cause the wafer to exhibit vibrations, measuring one or more of linear frequency response metrics or nonlinear frequency responses metrics associated with the vibrations, and identifying any defects in the wafer based at least in part on one or more of the linear frequency response metrics or nonlinear frequency responses metrics. In embodiments, the wafer includes bismuth telluride (Bi 2 Te 3 ).
Generalizing synthetic data-trained acoustic predictive models to real-world measurements
Acoustic Resonance Spectroscopy (ARS) is highly sensitive to structural properties such as material, geometry, and environmental conditions; as a consequence, it can noninvasively measure internal properties that are unobservable by most other methods. Because of its sensing capabilities and low implementation cost and complexity, ARS has potential as a paradigm shift in noninvasive sensing, characterization, and monitoring applications. However, extracting specific properties from ARS measurements, comprising the vibration spectrum of a test object, is challenging due to the sensitivity of the spectra to other structural changes not being measured, e.g. manufacturing tolerances, component coupling, environmental variation, etc. Neural Networks are promising tools for identifying trends in ARS measurements, but their training typically requires large datasets, which are often impractical to obtain for real-world systems. Synthetic data can be simulated efficiently, but discrepancies between synthetic and real-world data frequently lead to poor generalization when testing on the real-world data. We propose a novel ARS model training framework that enables networks trained exclusively on synthetic ARS data to generalize effectively to real-world measurements. Our approach leverages the Correlation Alignment (CORAL) technique to enforce the extraction of features common to both synthetic and real-world domains. As a case study, we demonstrate noninvasive ARS-based pressure measurements in sealed systems. Finite element method (FEM) simulations were used to generate synthetic training data across diverse vessel configurations and pressure conditions, and model performance was then tested on real-world measurements. We demonstrate that robust machine learning models for ARS can be developed without large real-world datasets, significantly broadening the applicability of ARS for noninvasive sensing. Moreover, the approach is extensible to other sensing modalities where synthetic data are abundant but real-world data are limited.
Non-destructive, whole-plant phenotyping reveals dynamic changes in water use efficiency, photosynthesis, and rhizosphere acidification of sorghum accessions under osmotic stress
Noninvasive phenotyping can quantify dynamic plant growth processes at higher temporal resolution than destructive phenotyping and can reveal phenomena that would be missed by end-point analysis alone. Additionally, whole-plant phenotyping can identify growth conditions that are optimal for both above- and below-ground tissues. However, noninvasive, whole-plant phenotyping approaches available today are generally expensive, complex, and non-modular. We developed a low-cost and versatile approach to noninvasively measure whole-plant physiology over time by growing plants in isolated hydroponic chambers. We demonstrate the versatility of our approach by measuring whole-plant biomass accumulation, water use, and water use efficiency every two days on unstressed and osmotically stressed sorghum accessions. We identified relationships between root zone acidification and photosynthesis on whole-plant water use efficiency over time. Our system can be implemented using cheap, basic components, requires no specific technical expertise, and should be suitable for any non-aquatic vascular plant species.