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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Audible Noise Modeling of Hydrogen Release Sonic Hazards in Rail Maintenance Facilities

This study implemented validated literature models to predict audible noise due to pressurized gaseous hydrogen releases through a thermally-activated pressure relief device (TPRD) and attached vent stack. A literature survey discovered limited hydrogen-specific noise prediction models validated by experiments. However, empirical noise prediction models for air flowing through pipes and valves were identified. These empirical models were used to predict noise levels and compared against hydrogen noise data reported in two studies: one experimental study of noise from hydrogen leaking through a pipe and another which modeled hydrogen flowing through a solenoid valve during a fuel cell vehicle refueling. The valve flow model was then applied to predict noise for hydrogen releases through a TPRD. Results show that hydrogen releases through a TPRD can produce harmful noise levels varying from 134 to 150 dB. However, further model validation and additional experimental data are needed to improve prediction confidence and accuracy.

08 HYDROGEN↗
DOE OSTI2025

An Advanced Machine Learning and Artificial Intelligence System for Demonstrating Radiation Regulatory Compliance in DOE Accelerator Facilities

In this Phase II proposal, Applied Research LLC (ARLLC), Thomas Jefferson National Accelerator Facility (Jefferson Lab), and Old Dominion University (ODU) propose the combination of domain knowledge (beam characteristics, fixed structural shielding, earthen burden (the soil and foliage added to the dome of the experimental halls as additional shielding), etc.), machine learning (ML) and/or artificial intelligence (AI) to correlate a variety of multi-modal onsite signals and the radiation fields seen in accessible areas of the accelerator site and the site boundary. The ML/AI will consider the complex influence of environmental parameters affecting the radon contribution of the measurements, focusing on actual data obtained from Jefferson Lab. In Phase I, the coded beam and location data were fed into a deep learning model to predict doses at several designated locations in Jefferson Lab’s facility. Moreover, a dense radiation map was generated using only a sparse collection of the samples in a facility. In Phase II, we will develop a software prototype containing a radiation prediction algorithm, dense radiation map algorithms, and background noise prediction algorithms, with actual data used to evaluate the prototype. This work will provide a framework for evaluation of radiation measurement results around the site based on learned responses. In addition, the proposed approach allows more granular mapping of radiation levels. Better understanding and communication of these levels is related to the overall approach in keeping doses to personnel ALARA.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

A Practical Comparison of Data-Driven Prognostics Methods for Energy Systems

This study explores data-driven prognostics for nuclear power plant (NPP) condensers, focusing on tube fouling. We utilized the Asherah nuclear power plant simulator (ANS) to compare four methods: Random Forest (RF), Support Vector Regressor (SVR), Fully Connected Neural Network (FCNN), and Long Short-Term Memory Neural Network (LSTM). By simulating various fouling scenarios in the ANS, we generated data with different degradation rates under transient operations. The models were trained and tested on these data, with performance evaluated visually and numerically including uncertainty assessment. The LSTM model excelled, exhibiting minimal prediction noise and the most accurate remaining useful life estimates across all degradation levels. Its ability to capture long-term dependencies and produce cleaner outputs makes it a strong candidate, although accurate training data across the entire component lifespan are crucial. The RF model emerged as a robust alternative, providing reliable predictions with high confidence. The FCNN and SVR models, while less effective overall, showed potential under specific conditions. FCNN offers a less complex alternative to LSTM and might benefit from larger datasets. SVR excels in precision when the quality of the training data is high. Furthermore, this study highlights the operational benefits of advanced prognostics in the energy sector and emphasizes the need for further research in NPP condenser health management through real-life experiments.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Priors on red galaxy stochasticity from hybrid effective field theory

ABSTRACT We investigate the stochastic properties of typical red galaxy samples in a controlled numerical environment. We use halo occupation distribution (HOD) modelling to create mock realizations of three separate bright red galaxy samples consistent with data sets used for clustering and lensing analyses in modern galaxy surveys. Second-order Hybrid Effective Field Theory (HEFT) is used as a field-level forward model to describe the full statistical distribution of these tracer samples, and their stochastic power spectra are directly measured and compared to the Poisson shot-noise prediction. While all of the galaxy samples we consider are hosted within haloes with sub-Poisson stochasticity, we observe that the galaxy samples themselves possess stochasticities that range from sub-Poisson to super-Poisson, in agreement with predictions from the halo model. As an application of our methodology, we place priors on the expected degree of non-Poisson stochasticity in cosmological analyses using such samples. We expect these priors will be useful in reducing the complexity of the full parameter space for future analyses using second-order Lagrangian bias models. More generally, the techniques outlined here present the first application of HEFT methods to characterize models of the galaxy–halo connection at the field level, revealing new connections between once-disparate modelling frameworks.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Predicting Seafloor Seismic Noise from Sea Surface Wave Heights and Vice Versa

Seismic hazard monitoring and global seismic tomography efforts are improved by recording seismic signals close to sources and at a variety of azimuths surrounding the source. While seismic networks have been deployed at many locations on land, seafloor stations are still sparse. Inclusion of ocean-based data would greatly improve the global coverage of seismic networks. However, the use of seafloor seismic data to complement land-based detection and characterization of events is complicated by the fact that ambient noise on the seafloor is in general much higher than noise levels on land. This noise is driven primarily by sea surface waves and tides. In this project, I define an empirical relationship between wave height and seafloor seismic displacement, and tie this relationship to the microseism noise theory of Longuet-Higgins and Ursell (1948). I find that while there appears to be a linear relationship between wave height and seismic displacement, wave height values can only be used with low confidence to predict seismic displacement and vice versa, and furthermore, factors other than wave height are significant in determining the level of seismic noise at a particular site.

58 GEOSCIENCES↗
DOE OSTI2021

DeepMC: a deep learning method for efficient Monte Carlo beamlet dose calculation by predictive denoising in magnetic resonance-guided radiotherapy

Abstract Emerging magnetic resonance (MR) guided radiotherapy affords significantly improved anatomy visualization and, subsequently, more effective personalized treatment. The new therapy paradigm imposes significant demands on radiation dose calculation quality and speed, creating an unmet need for the acceleration of Monte Carlo (MC) dose calculation. Existing deep learning approaches to denoise the final plan MC dose fail to achieve the accuracy and speed requirements of large-scale beamlet dose calculation in the presence of a strong magnetic field for online adaptive radiotherapy planning. Our deep learning dose calculation method, DeepMC, addresses these needs by predicting low-noise dose from extremely noisy (but fast) MC-simulated dose and anatomical inputs, thus enabling significant acceleration. DeepMC simultaneously reduces MC sampling noise and predicts corrupted dose buildup at tissue-air material interfaces resulting from MR-field induced electron return effects. Here we demonstrate our model’s ability to accelerate dose calculation for daily treatment planning by a factor of 38 over traditional low-noise MC simulation with clinically meaningful accuracy in deliverable dose and treatment delivery parameters. As a post-processing approach, DeepMC provides compounded acceleration of large-scale dose calculation when used alongside established MC acceleration techniques in variance reduction and graphics processing unit-based MC simulation.

Engineering↗
DOE OSTI2020

Investigation of magnetic noise from conductive shields in the 10–300 kHz frequency range

Here we present experimental and theoretical investigations of magnetic noise originating from radio-frequency (RF) conductive shields of flat geometry in the inductance-dominated impedance regime below 300 kHz. The measurement is based on a Q-factor determination of a coil that provides a sufficient sensitivity, placed at the position where the shield magnetic noise is measured. The theory is based on calculations of magnetic field and inductance of one or a few flat rings that emulate a conductive shield. The theoretical model is found to be in close agreement with experimental data. It can be used to predict the magnetic noise of a conductive shield with different thicknesses, conductivities, and temperatures at different distances for a wide range of frequencies. Although the model can be generalized for a more arbitrary shield geometry, in its presented form, it can be applied to the magnetic noise predictions when the shield surface curvature is not large. One important conclusion is that the RF conductive shield can generate the magnetic noise much lower than femtotesla, and, thus, it can be used in many precision experiments targeting minute high-frequency magnetic signals, such as detection of magnetic resonance imaging and nuclear quadrupole resonance signals and search for axion dark matter.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

A model for excess Johnson noise in superconducting transition-edge sensors

Transition-edge sensors (TESs) are two-dimensional superconducting films utilized as highly sensitive detectors of energy or power. These detectors are voltage biased in the superconducting-normal transition where the film resistance is both finite and a strong function of temperature. Unfortunately, the amount of electrical noise observed in TESs exceeds the predictions of existing noise theories. We describe a possible mechanism for the unexplained excess noise, which we term “mixed-down noise.” The source is Johnson noise, which is mixed down to low frequencies by Josephson oscillations in devices with a nonlinear current–voltage relationship. We derive an expression for the power spectral density of this noise and show that its predictions agree with measured data.

Wessels, Abigail (ORCID:0000000173918339)↗
DOE OSTI2025

Computational Performance Bounds Prediction in Quantum Computing With Unstable Noise

Quantum computing has significantly advanced in recent years, boasting devices with hundreds of quantum bits (qubits), hinting at its potential quantum advantage over classical computing. Yet, noise in quantum devices poses significant barriers to realizing this supremacy. Understanding noise’s impact is crucial for reproducibility and application reuse; moreover, the next-generation quantum-centric supercomputing essentially requires efficient and accurate noise characterization to support system management (e.g., job scheduling), where ensuring correct functional performance (i.e., fidelity) of jobs on available quantum devices can even be higher-priority than traditional objectives. However, noise fluctuates over time, even on the same quantum device, which makes predicting the computational bounds for on-the-fly noise is vital. Noisy quantum simulation can offer insights but faces efficiency and scalability issues. Here, in this work, we propose a data-driven workflow, namely QuBound, to predict computational performance bounds. It decomposes historical performance traces to isolate noise sources and devises a novel encoder to embed circuit and noise information processed by a Long Short-Term Memory (LSTM) network. For evaluation, we compare QuBound with a state-of-the-art learning-based predictor, which only generates a single performance value instead of a bound. Experimental results show that the result of the existing approach falls outside of performance bounds, while all predictions from our QuBound with the assistance of performance decomposition better fit the bounds. Moreover, QuBound can efficiently produce practical bounds for various circuits with over 106 speedup over simulation; in addition, the range from QuBound is over 10× narrower than the state-of-the-art analytical approach.

Li, Jinyang [George Mason Univ., Fairfax, VA (Unit↗
DOE OSTI2023

BICEP Array: 150 GHz Detector Module Development

The Background Imaging of Cosmic Extragalactic Polarization (BICEP)/Keck (BK) collaboration is currently leading the quest for the highest-sensitivity measurements of the polarized cosmic microwave background (CMB) anisotropies on a degree scale with a series of cryogenic telescopes, of which BICEP Array (BA) is the latest Stage-3 upgrade with a total of ~ 32,000 detectors. The instrument comprises 4 receivers spanning 30–270 GHz, with the low-frequency 30/40 GHz deployed to the South Pole Station in late 2019. The full complement of receivers is forecast to set the most stringent constraints on the tensor-to-scalar ratio r. Building on these advances, the overarching small-aperture telescope concept is already being used as the reference for further Stage-4 experiment design. This paper describes the development of the BICEP Array 150 GHz detector module and its fabrication requirements, with highlights on the high-density time division multiplexing (TDM) design of the cryogenic circuit boards. The low-impedance wiring required between the detectors and the frst stage of superconducting quantum interference device amplifers is crucial to maintaining a stable bias current on the detectors. Here, a novel multilayer FR4 Printed Circuit Board with superconducting traces, capable of reading out up to 648 detectors, is detailed along with its validation tests. An ultra-high-density TDM detector module concept we developed for a CMB-S4-like experiment that allows up to 1920 detectors to be read out is also presented. TDM has been chosen as the detector readout technology for the Cosmic Microwave Background Stage-4 (CMB-S4) experiment based on its proven low-noise performance, predictable costs, and overall maturity of the architecture. The heritage for TDM is rooted in mm- and sub-mm-wave experiments dating back 20 years and has since evolved to support a multiplexing factor of 64x in Stage-3 experiments.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Applying Bayesian inference and deterministic anisotropy to retrieve the molecular structure ∣Ψ(R)∣2 distribution from gas-phase diffraction experiments

Abstract Currently, our general approach to retrieving molecular structures from ultrafast gas-phase diffraction heavily relies on complex ab initio electronic or vibrational excited state simulations to make conclusive interpretations. Without such simulations, inverting this measurement for the structural probability distribution is typically intractable. This creates a so-called inverse problem. Here we address this inverse problem by developing a broadly applicable method that approximates the molecular frame structure ∣Ψ( R , t )∣ 2 distribution independent of these complex simulations. We retrieve the vibronic ground state ∣Ψ( R )∣ 2 for both simulated stretched NO 2 and measured N 2 O. From measured N 2 O, we observe 40 mÅ coordinate-space resolution from 3.75 Å −1 reciprocal space range and poor signal-to-noise, a 50X improvement over traditional Fourier transform methods. In simulated NO 2 diffraction experiments, typical to high signal-to-noise levels predict 100–1000X resolution improvements, down to 0.1 mÅ. By directly measuring the width of ∣Ψ( R )∣ 2 , we open ultrafast gas-phase diffraction capabilities to measurements beyond current analysis approaches. This method has the potential to effectively turn gas-phase ultrafast diffraction into a discovery-oriented technique to probe systems that are prohibitively difficult to simulate.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2026

Dynamically suppressing cavity dephasing induced by frequency fluctuations of a coupled nonlinear mode

High-coherence superconducting cavities offer a promising platform for quantum information, with long coherence times and negligible intrinsic dephasing. However, cavity control generally relies on nonlinear Josephson elements whose frequency fluctuations are inherited by the cavity as dephasing, potentially limiting control fidelities and eroding the noise bias used in error-correction protocols. Here, we introduce Stark-Assisted Flux-noise Evasion (SAFE), a hardware-efficient protocol that protects the cavity from inherited dephasing using only a weak off-resonant microwave drive applied to the nonlinear element. As a concrete setup, we analyze a 3D superconducting cavity dispersively coupled to a flux-tunable transmon (FTT) subject to $1/f$ flux noise. Analytical predictions are confirmed by Monte Carlo simulations with realistic parameters, which show that SAFE can extend the cavity dephasing time by more than an order of magnitude while keeping residual drive-induced decoherence subdominant.

Lu, Yunwei [Northwestern U.] (ORCID:00000003401756↗
DOE OSTI2024

Challenges of measuring spin Seebeck noise

Just as electronic shot noise in driven conductors results from the granularity of charge and the statistical variation in the arrival times of charge carriers, there are predictions for fundamental noise in magnon currents due to angular momentum being carried by discrete excitations. The inverse spin Hall effect as a transduction mechanism to convert spin current into charge current raises the prospect of experimental investigations of such magnon shot noise. Spin Seebeck effect measurements have demonstrated the electrical detection of thermally driven magnon currents and have been suggested as an avenue for accessing spin current fluctuations. Using spin Seebeck structures made from yttrium iron garnet on gadolinium gallium garnet, we demonstrate the technical challenges inherent in such noise measurements. While there is a small increase in voltage noise in the inverse spin Hall detector at low temperatures associated with adding a magnetic field, the dependence on field orientation implies that this is not due to magnon shot noise. We describe theoretical predictions for the expected magnitude of magnon shot noise, highlighting ambiguities that exist. Further, we show that magnon shot noise detection through the standard inverse spin Hall approach is likely impossible due to geometric factors. Implications for future attempts to measure magnon shot noise are discussed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Multi-modal effects on indirect noise induced by turbulent entropy fields

Planar entropy waves are commonly assumed for predicting indirect combustion noise. However, the non-planar and turbulent nature of flows found in most practical combustors challenges this assumption. In the present paper, we examine the indirect noise generated by non-planar and turbulent entropy fields through subsonic nozzles. Firstly, we introduce a new transfer function framework that accounts for the contribution of non-planar Fourier modes of the entropy field to the indirect noise spectra. When applied to a turbulent flow field, this method demonstrates a significant improvement in spectral predictions compared with a conventional approach that only considers the planar mode. Secondly, simulations show that non-planar Fourier modes become significant above a threshold frequency ƒ thresh , found in the mid- to high-frequency range. This contribution of non-planar modes is explained by two-dimensional shear effects that distort the entropy waves. A scaling relation that uses residence times along streamlines is developed for ƒ thresh , showing good agreement with simulation results. Finally, we show that the indirect noise from non-planar entropy modes found in aviation combustors can be significant at frequencies below 1 kHz, which might be relevant in situations of thermo-acoustic instabilities coupled to indirect noise.

42 ENGINEERING↗
DOE OSTI2025

Noise in Intense Electron Bunches (Final Technical Report)

The goal of this project is to investigate density fluctuations in electron bunches. Noise and density fluctuations in relativistic electron bunches, accelerated in a linac, are of critical importance to various Coherent Electron Cooling (CEC) concepts as well as to free-electron lasers (FELs). For CEC, the beam noise results in additional diffusion that counteracts cooling; and if this noise is not controlled at sufficiently low level, the noise heating effects can overcome cooling. There have been several proposals in the past to suppress the noise in the beam in the frequency range of interest in order to optimize the cooling effects. In SASE FELs a microwave instability starts from the initial noise in the beam and eventually leads to the beam microbunching yielding coherent radiation, and the initial noise in the FEL bandwidth plays a useful role. In seeded FELs, in contrast, such noise interferes with the seed signal, so that reducing noise at the initial seed wavelength would lower the seed laser power requirement. Our major goals were (1) to measure the electron beam density noise level in a 0.5 – 10 um wavelength range, (2) to predict the beam noise level in order to compare with the measurements, and (3) as a stretch goal, to find mechanisms that affect the beam noise to control its level in a predictable manner.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

A New Crater Near InSight: Implications for Seismic Impact Detectability on Mars

A new 1.5 m diameter impact crater was discovered on Mars only ~40 km from the InSight lander. Context camera images constrained its formation between 21 February and 6 April 2019; follow-up High Resolution Imaging Science Experiment images resolved the crater. During this time period, three seismic events were identified in InSight data. In this paper, we derive expected seismic signal characteristics and use them to evaluate each of the seismic events. However, none of them can definitively be associated with this source. Atmospheric perturbations are generally expected to be generated during impacts; however, in this case, no signal could be identified as related to the known impact. Using scaling relationships based on the terrestrial and lunar analogs and numerical modeling, we predict the amplitude, peak frequency, and duration of the seismic signal that would have emanated from this impact. The predicted amplitude falls near the lowest levels of the measured seismometer noise for the predicted frequency. Hence, it is not surprising this impact event was not positively identified in the seismic data. Finding this crater was a lucky event as its formation this close to InSight has a probability of only ~0.2, and the odds of capturing it in before and after images are extremely low. We revisit impact-seismic discriminators in light of real experience with a seismometer on the Martian surface. Using measured noise of the instrument, we revise our previous prediction of seismic impact detections downward, from ~a few to tens, to just ~2 per Earth year, still with an order of magnitude uncertainty.

58 GEOSCIENCES↗
DOE OSTI2024

DeepUQ: Assessing the Aleatoric Uncertainties from two Deep Learning Methods

Assessing the quality of aleatoric uncertainty estimates from uncertainty quantification (UQ) deep learning methods is important in scientific contexts, where uncertainty is physically meaningful and important to characterize and interpret exactly. We systematically compare aleatoric uncertainty measured by two UQ techniques, Deep Ensembles (DE) and Deep Evidential Regression (DER). Our method focuses on both zero-dimensional (0D) and two-dimensional (2D) data, to explore how the UQ methods function for different data dimensionalities. We investigate uncertainty injected on the input and output variables and include a method to propagate uncertainty in the case of input uncertainty so that we can compare the predicted aleatoric uncertainty to the known values. We experiment with three levels of noise. The aleatoric uncertainty predicted across all models and experiments scales with the injected noise level. However, the predicted uncertainty is miscalibrated to $\rm{std}(\sigma_{\rm al})$ with the true uncertainty for half of the DE experiments and almost all of the DER experiments. The predicted uncertainty is the least accurate for both UQ methods for the 2D input uncertainty experiment and the high-noise level. While these results do not apply to more complex data, they highlight that further research on post-facto calibration for these methods would be beneficial, particularly for high-noise and high-dimensional settings.

Nevin, Rebecca↗