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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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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1963

THE DETECTION OF WATER VAPOR ON MARS

We have found eleven weak lines of water vapor on a high dispersion near-infrared spectrogram of Mars taken at the coudè focus of the Mount Wilson 100-inch reflector on April 12/13,1963. We exposed 43 hours on ammoniated IV-N emulsion with a dispersion of 5.6 A/mm; the plate is well exposed at the poles and somewhat overexposed at the center of the planet’s disk. The Martian H 2 O lines in the X 8200 water-vapor band are displaced 0.42 A longward of their telluric counterparts due to the relative velocities of the earth and Mars: AV = +15 km/sec on that date. The Martian lines are strongest over the poles of the planet-we set the spectrograph slit daost north-south to cover both polar regions. This apparent polar strengthening of the Martian H 2 O lines is at least partially due to the increased air mass through the line of sight to high latitudes.

WATER VAPOR↗
NASA NTRS2007

Primary Objective Grating Astronomical Telescope

It has been 370 years since a seventeenth century French mathematician, Mersenne, presciently sketched out an astronomical telescope based on dual parabolic reflectors. Since that time the concept of the primary objective has been virtually unchanged. Now a new class of astronomical telescope with a primary objective grating (POG) has been studied as an alternative. The POG competes with mirrors, in part, because diffraction gratings provide the very chromatic dispersion that mirrors defeat. The resulting telescope deals effectively with long-standing restrictions on multiple object spectroscopy (MOS). Other potential benefits include unprecedented apertures and collection areas. The new design also favors space deployment as a gossamer membrane. The inventor, Tom Ditto, first discovered that higher-order diffraction images contain hidden depth cues, for which he was granted a seminal range finding patent in 1987. Subsequently, he invented and patented 3D localizers, profilometers and microscopes using POGs. The POG telescope was placed in the public domain to expedite research. The function of a telescopes primary objective is to collect flux and to deliver images. Both functions dictate that size matters, and bigger is better. For that reason, there has been a steady push over the past century to ramp up the size of the primary mirror. However, for every doubling of mirror diameter, the elapsed time between initial effort and first light has also doubled. Meanwhile, costs escalated beyond the mirror alone, because larger instruments required larger enclosures and better pointing mechanisms. One key catalog of observation, spectrographic data, is far more difficult to amass than two-dimensional imagery. While the number of observable objects has increased with mirror size, the capacity to take spectra has not increased proportionately. In the best of circumstances, spectrograms are available for one per cent of the all objects surveyed. Spectroscopy was a historical afterthought introduced in the nineteenth century shortly after the invention of the diffraction grating and over a century after Newtons 1670 telescope. Spectroscopy is generally accomplished using a diffraction grating as the disperser in the secondary. The light being delivered to the spectrograph is first captured by a primary mirror which provides no chromatic magnification by itself. Sizeable spectrographs could not be deployed while diffraction gratings were rare commodities scribed using mechanical ruling engines that produced one grating line at a time. Today diffraction gratings are commonplace. Their recent availability is a product of both the invention of holography and the mass replication of surface microstructures. Holography permits all lines in a grating to be made simultaneously in a single photographic exposure. Holograms can then be reproduced by embossing processes. The improvement in replication is analogous to how Gutenberg changed the availability of books. The masters may be expensive, but the copies are not. Computer science is another technology that emerged in the second half of the twentieth century without which our proposed spectrographic instrument could not function due to the complexity of image processing required in data reduction. The employment of very large diffraction gratings as primary objectives for astronomical telescopes requires a novel

spectroscopy↗
DOE OSTI2025

Cyclic Background Noise Variations on Infrasound Microbarometers From Micrometeorology and Human Activity

Infrasound microbarometers deployed on the Earth's surface capture pressure fluctuations and acoustic signatures, revealing trends in surface wind speed and cycles in ambient sound. Previous studies investigated wind noise trends at quarter day resolution and urban acoustic background variations on hourly to weekly scales. Cyclic variations at sub-hourly resolution over local distances remain unaddressed. We show that topography-driven micrometeorology impacts diurnal background noise levels below 10 Hz. Anthropogenic noise occurs over daily and weekly cycles, with inputs from traffic, ventilation systems, and power lines. These noise patterns vary at stations spaced less than a kilometer apart. We observe these phenomena by using a circular spectrogram to visualize frequency trends over a periodic temporal scale. This study demonstrates that infrasound microbarometer deployments can highlight localized wind patterns and time scales of human activity. The results suggest that future microbarometer deployments may benefit from site noise surveys before selecting final sensor locations.

Malach, Amrit Kaur [Sandia National Lab. (SNL-CA),↗
DOE OSTI2026

Learning earthquake ground motions via conditional generative modeling

Predicting high-fidelity ground motions for future earthquakes is crucial for seismic hazard assessment and infrastructure resilience. Conventional empirical simulations suffer from sparse sensor distribution and geographically localized earthquake locations, while physics-based methods are computationally intensive and require accurate representations of Earth structures and earthquake sources. We propose an artificial intelligence (AI) spectrogram generator, Conditional Generative Modeling for Ground Motion (CGM-GM). CGM-GM leverages earthquake magnitudes and geographic coordinates of earthquakes and sensors as inputs, when postprocessed with phase information, capturing spatially continuous Fourier amplitude spectra (FAS) as well as properties such as P and S arrivals, and waveform durations, without explicit physics constraints. This is achieved through a probabilistic autoencoder that extracts latent distributions in the time-frequency domain and variational sequential models for prior and posterior distributions. We evaluate the performance of CGM-GM using small-magnitude earthquake records from the San Francisco Bay Area, a region with high seismic risks. Here, we report that CGM-GM demonstrates potential for complementing physics-based simulations and non-ergodic empirical ground motion models, as well as shows promise in seismology and beyond.

geophysics↗
DOE OSTI2025

Pedestal formation via different trajectories in the stability space in response to the timing scan of neutral beam heating in DIII-D

The frequency of type-I ELMs decreases as the initiation of the neutral beam injection (NBI) heating is delayed with respect to the time when plasma current (I p ) reaches flat-top in the ITER Baseline Scenario discharges in DIII-D. Henceforth, the time gap between the NBI initiation and I p flat-top will be referred to as “heating delay.” As the heating delay is modified, pedestal formation follows different trajectories in the edge current density–pedestal pressure gradient (j edge -∇p e ped ) space from the L-H transition toward the first ELM event. During the stationary phase after the first ELM, the ELM frequency (f ELM ) decreases by a factor of ~2 as the heating delay is increased. A longer pedestal recovery time in the inter-ELM period is observed for the low f ELM discharges as compared to the high f ELM discharges. Both low and high f ELM discharges show nearly identical profiles of electron density and temperature and have a similar MHD stability just before an ELM crash. However, a marked difference is observed in the magnetic spectrogram of the high and low f ELM discharges in response to the variation in the heating delay. The main difference is in the 200–400 kHz range of the magnetic spectra. A quasi-coherent mode (QCM) at 220 kHz and weaker broadband fluctuations are observed in the high f ELM discharges, while only strong broadband fluctuations are prevalent in the low f ELM discharges. ELM-synchronized analysis shows that the time evolution of these modes is different for the high and low f ELM discharges. The localization of both these modes is confirmed at the maximum gradient region of the pedestal. We hypothesize that these modes cause important pedestal transport and that the difference in the pedestal recovery of the high and low f ELM discharges is a result of the difference in transport driven by these modes, as they change with changes in the heating delay. It is demonstrated experimentally for the first time that discharges with similar pedestal parameters can carry the history of the heating delay into the stationary phase and that changes in turbulent-driven transport are a likely cause of changes in f ELM observed with variations of heating delay.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

In situ characterization of two unknown ultrashort laser pulses using four-wave mixing in gas

Accurate characterization of two ultrashort laser pulses is of great interest in many ultrafast pump-probe experiments. We demonstrate a method based on four-wave mixing (FWM) in a gas which could be easily implemented into many existing pump-probe setups with minimal modifications for accurate, in situ characterization of both unknown pulses. This technique is tested on pairs of unknown pulses at wavelengths of 400/800 nm, and 266/400 nm. We measured the spectrogram of the pulse generated through FWM of the two unknown pulses by scanning the delay between two unknown pulses. The retrieval algorithm converges to accurately predict the intensity and the phase profiles of both unknown pulses with a trace error of < 1% and the accuracy is verified using an independent pulse characterization device.

Nambu, Noa (ORCID:0000000197303400)↗
DOE OSTI2025

Denoising Seismograms in the Time Domain Using a Deep Learning Model

Deep learning has emerged as a transformative tool for enhancing the extraction of reliable information from seismograms, addressing the increasing demand for precise and efficient seismic data analysis. We introduce an innovative encoder–decoder deep learning model, named WaveDenoiser, designed for noise reduction in the time domain, thereby eliminating the need for spectrogram computations that have been used for existing deep learning tools and significantly improving processing speed. Utilizing the benchmark dataset that is Stanford Earthquake Dataset, we developed three models of varying sizes: base, medium, and large. Notably, the large (referred to as WaveDenoiser) model demonstrated superior performance, achieving a median signal‐to‐noise ratio improvement of 8.8 dB on in‐distribution unseen data (in the same geographic region) and 7.7 dB on out‐distribution unseen data (in a new geographic region), outpacing both the base and medium models. Further evaluation of the WaveDenoiser model revealed a reduction in median arrival‐time errors by 0.02 s for P waves and 0.01 s for S waves when processing waveforms prior to phase picking using PhaseNet on in‐distribution unseen data. When tested on out‐distribution unseen data, the model also effectively reduced the P‐wave median arrival‐time error by 0.02 and 0.01 s in median arrival‐time error for S waves. Importantly, the application of WaveDenoiser resulted in a significant reduction of phase picking outliers by 1.1% to 3.6% for both P and S waves. In addition, we achieved over five times acceleration in processing speed compared with the seisBench implementation of DeepDenoiser. Our findings underscore the potential of WaveDenoiser as a powerful tool for improving seismic data analysis and processing efficiency.

P-waves↗
DOE OSTI2024

PDV Inspection and Analysis Demonstration: 2024 PDV Workshop

This document walks a user through a demonstration of working with PDV digitizer data using python. This demonstration and included suggested exercises will be used at the 2024 PDV workshop hands-on session as an example and skill-development training session. The tutorial allows the user to generate synthetic but realistic PDV waveform data and visualize/inspect the results using spectrograms and waveform viewing tools.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Towards AI Based Data Classification for Decision Making During Testing

During the development of high-consequence items, test systems should be capable of differentiating between test failures resulting from narrowly missing requirements versus those indicating potentially catastrophic faults. In many instances, classifying the data corresponds to simply identifying whether measured waveforms have approximately the anticipated shape. Cast in this light, the problem reduces to converting raw data into a form optimal for use with neural network classifiers. This manuscript investigates different means of representing raw data for image classification. Raw data plots and Short Time Fourier Transform (STFT) spectrograms are classified by both custom built, small-scale, Convolution Neural Networks (CNN) and open-source, multi-million parameter, pre-trained deep CNNs. In the case of time varying frequency content, the STFTs provide images with greater detail and can be accurately classified with simpler networks. This requires less memory and runs faster than classifying the raw data using the more sophisticated options—making STFTs optimal for applications with memory constraints. STFTs are not a panacea. In some cases the time-domain signal contains useful information that should not be discarded. Rather than using raw data or STFTs, the images can be constructed from both by using red and green channels of an RGB image to visualize the real and imaginary components of the transform, with the raw data occupying the blue channel.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Toroidal Alfven Wave Coupling (Nonlinear Wave-Wave Interactions) on DIII-D

Connect DIII-D physics with space plasma phenomena. In this case of using the toroidal Alfvén eigenmodes and frequency-chirping Reversed-Shear Alfvén eignmodes in DIII-D, we will document how the nonlinear interactions among dipolar Kinetic Alfvén Wave eigenmodes in space plasmas may determine saturation levels of these fluctuations. We seek evidence of nonlinear energy transfer and wave-wave coupling during 3-wave interactions mediated by a much lower-frequency mode. In FY2019, we found evidence of nonlinear “wave-wave” interactions in 175 relevant shots of archival DIII-D data. Toroidal mode number was identified and spectrograms were produced from each shot’s Mirnov coil data. Bispectral analysis was performed using a preliminary version of a new user friendly code derived from a 1995 M.S. thesis at WVU. These results formed a part of a May 2019 M.S. thesis at WVU.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Improving Chirped Fiber Bragg Grating Resolution for Position-Sensitive Sensors in Shock- and Detonation-Driven Experiments

Chirped fiber Bragg gratings (CFBGs) are robust diagnostic sensors that are widely used to track detonation-driven and shock wave propagation. CFBGs are inscribed with a linearly chirped periodic index of refraction changes that alter the Bragg wavelength along the length of the probe. The light return of each individual Bragg element is captured by a detector at a unique time to map the full reflected spectrum. The CFBG spectrum is measured with a dispersive Fourier transform of the reflected light that temporally stretches the spectrum to increase spatial resolution and make a one-to-one map of the wavelength on a time axis. Here, we propose an improvement of CFBG temporal resolution by incorporating two co-linear laser pulses with orthogonal polarization states and a 5 ns time offset. The two separate signals were split and tracked by two separate detectors. An oscilloscope captured good separation in the signals, and two separate spectrograms were generated and interleaved in the post-processing of the data. This novel technique doubled the CFBG temporal resolution and led to a doubled location resolution. As a proof-of-concept of this technique, the resolution improvement was compared between standard CFBG measurements and the two polarization states method on a position-sensitive CFBG sensor. CFBG resolution doubling will advance sensor capabilities and will have a direct impact on improving capture and analysis in dynamic, high-explosive experiments.

42 ENGINEERING↗
DOE OSTI2026

Advancing Industry 4.0: Multimodal Sensor Fusion for AI-Based Fault Detection in 3D Printing

Additive manufacturing, particularly fused deposition modeling, is transforming modern production by enabling rapid prototyping and complex part fabrication. However, its layer-by-layer process remains vulnerable to faults such as nozzle clogging, filament runout, and layer misalignment, which compromise print quality and reliability. Traditional inspection methods are costly, time-intensive, and often limited to post-process analysis, making them unsuitable for real-time intervention. In this current study, the authors developed a novel, low-cost, and portable faultdetection system that leverages multimodal sensor fusion and artificial intelligence for real-time monitoring in FDM-based 3D printing. The system integrates acoustic, vibration, and thermal sensing into a non-intrusive architecture, capturing complementary data streams that reflect both mechanical and process-related anomalies. Acoustic and thermal sensors operate in a fully contactless manner, while the vibration sensor requires minimal attachment such that it will not interfere with printer hardware, thereby preserving portability and ease of deployment. The multimodal signals are processed into spectrograms and time-frequency features, which are classified using convolutional neural networks for intelligent fault detection. The proposed system advances Industry 4.0 objectives by offering an affordable, scalable, and practical monitoring solution that improves faultdetection accuracy, reduces waste, and supports sustainable, adaptive manufacturing.

42 ENGINEERING↗