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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Magnetic pair distribution function data using polarized neutrons and ad hoc corrections

Here, we report the first example of magnetic pair distribution function (mPDF) data obtained through the use of neutron polarization analysis. Using the antiferromagnetic semiconductor MnTe as a test case, we present high-quality mPDF data collected on the HYSPEC instrument at the Spallation Neutron Source using longitudinal polarization analysis to isolate the magnetic scattering cross section. Clean mPDF patterns are obtained for MnTe in both the magnetically ordered state and the correlated paramagnet state, where only short-range magnetic order is present. We also demonstrate significant improvement in the quality of high-resolution mPDF data through the application of ad hoc corrections that require only minimal human input, minimizing potential sources of error in the data processing procedure. We briefly discuss the current limitations and future outlook of mPDF analysis using polarized neutrons. Overall, this work provides a useful benchmark for mPDF analysis using polarized neutrons and provides an encouraging picture of the potential for routine collection of high-quality mPDF data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

An Interactive Spectral Analysis Tool for Chemical Identification and Quantification of Gas-Phase Species in Complex Spectra

A spectral analysis tool has been developed to interactively identify and quantify individual gas-phase species from complex infrared absorbance spectra obtained from laboratory or field data. The SpecQuant program has an intuitive graphical interface that accommodates both reference and experimental data with varying resolution and instrumental lineshape, as well as algorithms to readily align the wavenumber axis of a sample spectrum with the raster of a reference spectrum. Using a classical least squares model in conjunction with reference spectra such as those from the Pacific Northwest National Laboratory (PNNL) gas-phase infrared database or simulated spectra derived from the HITRAN line-by-line database, the mixing ratio of each identified species is determined along with its associated estimation error. After correcting the wavelength and intensity of the field data, SpecQuant displays the calculated mixing ratio versus the experimental data for each analyte along with the residual spectrum with any or all analyte fits subtracted for visual inspection of the fit and residuals. The software performance for multianalyte quantification was demonstrated using moderate resolution (0.5 cm –1 ) infrared spectra that were collected during the time-resolved infrared photolysis of methyl iodide.

Thompson, Christopher J.↗
DOE OSTI2022

New Pulsed Neutron Die-Away Experiments in Light Water

Thermal neutron scattering laws are important data for many nuclear science and engineering applications, especially criticality safety. Recently, pulsed-neutron die-away experiments have been proposed and used as an experiment to validate thermal neutron scattering laws. These experiments involve irradiating a target moderating material with pulses of neutrons from a neutron generator. The physics of thermal scattering greatly affect how the neutron population in the target exponentially decays via absorption and leakage. Herein, we present experimental results from a new pulsed-neutron die-away experiment of light water that was performed at Lawrence Livermore National Laboratory. The experiments were done with cylindrical targets of varying dimensions to modulate the experiment’s sensitivity to thermal scattering and to absorption. We compare the measured integral parameter of the experiment to simulations with MCNP6.2 ® and to past experiments in literature. The integral parameters that were more sensitive to TSLs showed a larger bias. This validation study is known to have errors because the model of the experiment lacks key features about the detectors and neutron generator. These features will be included in the final benchmark evaluation of the experiment which will be submitted to the International Criticality Safety Benchmark Evaluation Project.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Refining localtype primordial non-Gaussianity: Sharpened bϕ constraints through bias expansion

Local-type primordial non-Gaussianity (PNG), predicted by many nonminimal models of inflation, creates a scale-dependent contribution to the power spectrum of large-scale structure tracers. Its amplitude is characterized by the product bϕfNLloc, where bϕ is an astrophysical parameter dependent on the properties of the tracer. However, bϕ exhibits significant secondary dependence on halo concentration and other astrophysical properties, which may bias and weaken the constraints on fNLloc. In this work, we demonstrate that incorporating knowledge of the relation between Lagrangian bias parameters and bϕ can significantly enhance PNG constraints. We employ the hybrid effective field theory approach at the field level and a linear regression model to seek a connection between the bias parameters and bϕ for halo and galaxy samples, constructed using the abacussummit simulation suite and mimicking the luminous red galaxies and quasistellar objects of the Dark Energy Spectroscopic Instrument survey. For the fixed-mass halo samples, our full bias model reduces the uncertainty by more than 70%, with most of that improvement coming from b∇, which we find to be an excellent proxy for concentration. For the galaxy samples, our model reduces the uncertainty on bϕ by 80% for all tracers. By adopting Lagrangian-bias informed priors on the parameter bϕ, future analyses can thus constrain fNLloc with less bias and smaller errors.

Hadzhiyska, Boryana↗
DOE OSTI2022

Quantifying modeling uncertainty in simplified beam models for building response prediction

The use of simple models for response prediction of building structures is preferred in earthquake engineering for risk evaluations at regional scales, as they make computational studies more feasible. The primary impediment in their gainful use presently is the lack of viable methods for quantifying (and reducing upon) the modeling errors/uncertainties they bear. This study presents a Bayesian calibration method wherein the modeling error is embedded into the parameters of the model. Here, the method is specifically described for coupled shear-flexural beam models here, but it can be applied to any parametric surrogate model. The major benefit the method offers is the ability to consider the modeling uncertainty in the forward prediction of any degree-of-freedom or composite response regardless of the data used in calibration. The method is extensively verified using two synthetic examples. In the first example, the beam model is calibrated to represent a similar beam model but with enforced modeling errors. In the second example, the beam model is used to represent the detailed finite element model of a 52-story building. Both examples show the capability of the proposed solution to provide realistic uncertainty estimation around the mean prediction.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Waveform processing using neural network algorithms on the front-end electronics

In a multi-channel radiation detector readout system, waveform sampling, digitization, and raw data transmission to the data acquisition system constitute a conventional processing chain. The deposited energy on the sensor is estimated by extracting peak amplitudes, area under pulse envelopes from the raw data, and starting times of signals or time of arrivals. However, such quantities can be estimated using machine learning algorithms on the front-end Application-Specific Integrated Circuits (ASICs), often termed as “edge computing”. Edge computation offers enormous benefits, especially when the analytical forms are not fully known or the registered waveform suffers from noise and imperfections of practical implementations. In this work, we aim to predict peak amplitude from a single waveform snippet whose rising and falling edges containing only 3 to 4 samples. We thoroughly studied two well-accepted neural network algorithms, Multi-Layer Perceptron (MLP) and Convolutional Neural Network (CNN) by varying their model sizes. Further, to better fit front-end electronics, neural network model reduction techniques, such as network pruning methods and variable-bit quantization approaches, were also studied. By combining pruning and quantization, our best performing model has the size of 1.5 KB, reduced from 16.6 KB of its full model counterpart. It can reach mean absolute error of 0.034 comparing to that of a naive baseline of 0.135. Such parameter-efficient and predictive neural network models established feasibility and practicality of their deployment on front-end ASICs.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Reconstruction of the Density Power Spectrum from Quasar Spectra using Machine Learning

We describe a novel end-to-end approach using Machine Learning to reconstruct the power spectrum of cosmological density perturbations at high redshift from observed quasar spectra. State-of-the-art cosmological simulations of structure formation are used to generate a large synthetic dataset of line-of-sight absorption spectra paired with 1-dimensional fluid quantities along the same line-of-sight, such as the total density of matter and the density of neutral atomic hydrogen. With this dataset, we build a series of data-driven models to predict the power spectrum of total matter density. We are able to produce models which yield reconstruction to accuracy of about 1% for wavelengths $k \leq 2 h Mpc^{-1}$, while the error increases at larger $k$. We show the size of data sample required to reach a particular error rate, giving a sense of how much data is necessary to reach a desired accuracy. This work provides a foundation for developing methods to analyse very large upcoming datasets with the next-generation observational facilities.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

DESI DR2 Baryon Acoustic Oscillations from the Lyman Alpha Forest Multipoles

We present an alternative measurement of the Baryon Acoustic Oscillation (BAO) using the Legendre multipole representation of the Ly$α$ forest correlation functions from the second data release (DR2) of the Dark Energy Spectroscopic Instrument survey. Compressing the auto- and cross-correlation functions into Legendre multipoles yields a positive-definite covariance matrix without any smoothing -- unlike the baseline DR2 analysis -- thanks to a significantly reduced data vector size. We introduce the statistical corrections required to debias the finite-sample covariance matrix estimate and demonstrate that monopole and quadrupole terms for both auto- and cross-correlations can be used even when the correlation functions are distorted by continuum errors and contaminated by metals. This formalism has slightly diminished the constraining power of the BAO scale, while considerably weakening constraints on nuisance parameters. We measure the isotropic BAO scale with $0.93\%$ precision at $z_\mathrm{eff}=2.35$, the Hubble parameter $H(z_\mathrm{eff})=(239.5\pm3.4)~(147.09~\mathrm{Mpc}/r_d) ~\mathrm{km~s}^{-1}~\text{Mpc}^{-1}$, and the transverse comoving distance $D_M(z_\mathrm{eff})=(5.80 \pm 0.10)~(r_d/147.09~\mathrm{Mpc})$~Gpc for a given value of the sound horizon ($r_d$). Our BAO results are entirely consistent with the baseline DR2 analysis.

Karaçaylı, Naim Göksel [Chicago U., KICP; Ohio Sta↗
DOE OSTI2022

The Octopus processor for the CMS L1 muon trigger for High Luminosity LHC

The upgraded L1 muon trigger system of the CMS experiment in the High Luminosity Large Hadron Collider is based on custom processors featuring large Field Programmable Gate Arrays (FPGAs) connected by large numbers of optical links. These provide the I/O bandwidth and power necessary to process the complex algorithms used during the collection of physics data. The design and performance requirements of these processors creates significant challenges in signal integrity, power delivery, and thermal management. In this paper we describe the Octopus processor, featuring a large Xilinx Virtex Ultrascale+ FPGA and up to 128 links interfaced to optics through high quality twin-ax copper cables. Results on signal integrity at 25 Gb/s and the first demonstration of 50+ Gb/s links with pluggable optics in CMS are also shown, demonstrating bit error rates below 10 –15 at a 95% confidence level. The thermal performance is measured inside an Advanced-TCA crate with acceptable thermal margins up to 200 W of chip power. Future improvements are mentioned, potentially allowing operation at up to 300 W.

Instruments & Instrumentation↗
DOE OSTI2023

Multi-parameter distributed fiber optic sensing using double-Brillouin peak fiber in Brillouin optical time domain analysis

In this paper, we demonstrate a multi-parameter fiber sensing system based on stimulated Brillouin scattering in a double-Brillouin peak specialty fiber with enhanced Brillouin gain response. The amplitude level of the second Brillouin gain peak, which originated from the higher-order acoustic modes, has been improved with an approximately similar amplitude level to the first Brillouin gain peak from the fundamental acoustic mode. Compared to other multi-Brillouin peak fibers presented in the literature, the proposed fiber significantly reduces the measured Brillouin frequency shift error, thus improving strain and temperature accuracies. By utilizing the sensitivity values of the strain and temperature associated with each Brillouin gain spectrum (BGS) peak, a successful discriminative measurement of strain and temperature is performed with an accuracy of ±13 μɛ, and ±0.5 °C, respectively. The proposed double-Brillouin peak fiber appears to be a possible alternative to other multi-BGS peak fibers, for instance, large effective area fiber and dispersion compensating fibers, which are inherently accompanied by large measurement errors due to the weak Brillouin gain values originating from the higher-order acoustic modes. The demonstrated results show different strain and temperature coefficients of 47 kHz/µɛ, 1.15 MHz/°C for peak 1 and 51 kHz/µɛ, 1.37 MHz/°C for peak 2. Moreover, the enhanced BGS peak gains having nearly the same amplitude levels enable the discriminative measurement of strain and temperature. Such fibers in Brillouin interrogation eliminate the need for complex monitoring setups and reduce measurement errors. We recommend that for long-distance natural gas pipeline monitoring, where discriminative strain and temperature measurement is crucial, the proposed double-Brillouin peak fiber can be highly beneficial.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Ibis Networks/WattIQ (IN2 Final Report)

Ibis Networks is a full-stack cleantech company that provides plug-level energy monitoring and control to solve energy and asset management problems for the enterprise. During DATES – DATES, an NREL research team worked with the Ibis team to validate their product, the InteliSocket. The Ibis InteliSocket is a pass-through plug-load energy monitor and controller that is designed to reduce energy wasted by common 120 V plug-in devices in commercial office buildings, such as computer peripherals, conference room AV equipment, and break-room appliances. The system can shut off supply power to these end uses via remote control, manual switches, pre-set schedules, or automated control algorithms. The scope of this IN2 project was the development and refinement of “smart” learning behavior algorithms (LBAs), which could help installation processes and dramatically expand the sockets’ capabilities and energy-saving potential by suggesting suitable control schedules that are based on monitored use patterns. While Ibis has the analytical and software expertise for algorithm development, the lack of test data, both in a controlled laboratory setting and in real-life deployment scenarios, represented a key barrier toward commercialization of the product. Assistance through the IN2 program provided an opportunity to conduct the needed “trial and error” algorithm development. The project included baseline field-data collection, laboratory testing, and field validation components, all of which were conducted at the NREL campus between April 2017 and July 2019.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Physics-assisted generative adversarial network for X-ray tomography

X-ray tomography is capable of imaging the interior of objects in three dimensions non-invasively, with applications in biomedical imaging, materials science, electronic inspection, and other fields. The reconstruction process can be an ill-conditioned inverse problem, requiring regularization to obtain satisfactory results. Recently, deep learning has been adopted for tomographic reconstruction. Unlike iterative algorithms which require a distribution that is known a priori , deep reconstruction networks can learn a prior distribution through sampling the training distributions. In this work, we develop a Physics-assisted Generative Adversarial Network (PGAN), a two-step algorithm for tomographic reconstruction. In contrast to previous efforts, our PGAN utilizes maximum-likelihood estimates derived from the measurements to regularize the reconstruction with both known physics and the learned prior. Compared with methods with less physics assisting in training, PGAN can reduce the photon requirement with limited projection angles to achieve a given error rate. The advantages of using a physics-assisted learned prior in X-ray tomography may further enable low-photon nanoscale imaging.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

From Obsolete to Optimal (ATR Demineralized Water System)

My project was migrating a Human-Machine Interface (HMI) and Programmable Logic Controller (PLC) program from legacy software to a modern platform, enhancing operational efficiency and eliminating unsupported, obsolete equipment. Initially, I used a migration tool to transfer the programs to the new software, then manually updated variables to align with the new PLC. I optimized the PLC code by leveraging new scaling features in the I/O cards, removing over 45 obsolete rungs, and enhancing clarity with functional tag aliases. Next, I modernized the HMI's visuals with an updated color scheme and 3D buttons. I developed multiple interface versions iteratively refining them based on operator feedback. I also designed and created a dedicated "Rounds" screen incorporating insights from a previous intern’s project to streamline daily rounds. To improve clarity, consistency, and efficiency I redesigned the entire interface. Adding visual indicators to enhance operational awareness: red is used for values outside normal specifications, green for normal operating modes, yellow for manual mode, and grey for offline units. Additionally, I added a dynamic visual representation of tank levels, creating PLC logic to show the level in inches as well as inches and feet. I then incorporated a navigation sidebar to facilitate efficient screen transitions. Risk of human error was mitigated by defining narrow input ranges for tank level shutoff and password protecting setpoint changes. I also fixed issues with datatypes that caused inaccuracies in the original code. Overall, this comprehensive upgrade significantly enhanced the HMI's functionality, user experience, and operational reliability.

47 - OTHER INSTRUMENTATION↗
DOE OSTI2021

Validation of SAS4A/SASSYS-1 for predicting steady-state single-phase natural circulation

The validation of system analysis codes for nuclear reactor systems is required for the development and application of these computational tools. Designed as a comprehensive system analysis code for advanced nuclear reactors, SAS4A/SASSYS-1 requires validation of its physics model for capturing single-phase natural circulation behavior. To support the validation of SAS4A/SASSYS-1, high-precision experiments are performed capturing steady-state single-phase natural circulation on a scaled facility with comprehensive instrumentation. In this work, dedicated tests are performed quantifying the critical modeling parameters, and a single-phase natural circulation benchmark dataset is obtained with well-documented uncertainty and comprehensive facility description. The validation is then performed against the dataset examining the capability of SAS4A/SASSYS-1 in simulating steady-state single-phase natural circulation. The experimental facility is modeled in the candidate code. Solution verification is performed using Richardson-extrapolation-based estimators which quantify and restrict numerical errors from discretization. Input uncertainty provided by the benchmark dataset is forward propagated through the candidate code, quantifying the output uncertainty in a Monte Carlo approach. The composition of the output uncertainty is also quantified through a variance-based sensitivity analysis. With the uncertainty quantified for each individual condition, a detailed comparison between the simulation results and experimental data is performed covering the whole dataset. The results show consistent agreement for all primary parameters. The current validation activity provides a valuable benchmark dataset for the validation of system analysis codes in capturing single-phase natural circulation and demonstrates satisfactory prediction capability of SAS4A/SASSYS1 for steady-state single-phase natural circulation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Constraints on neutrino physics from DESI DR2 BAO and DR1 full shape

The Dark Energy Spectroscopic Instrument (DESI) Collaboration has obtained robust measurements of baryon acoustic oscillations in the redshift range 0.1 < 𝑧 < 4.2, based on the Lyman-𝛼 forest and galaxies from data release 2. We combine these measurements with cosmic microwave background (CMB) data from Planck and the Atacama Cosmology Telescope to place our tightest constraints yet on the sum of neutrino masses. Assuming the cosmological Λ⁢ CDM model and three degenerate neutrino states, we find ∑𝑚 𝜈 < 0.0642 eV (95%) with a marginalized error of 𝜎⁡(∑𝑚 𝜈 ) = 0.020 eV. We also constrain the effective number of neutrino species, finding 𝑁 eff = 3.2⁢3$^{+0.35}_{−0.34}$ (95%), in line with the Standard Model prediction. When accounting for neutrino oscillation constraints, we find a preference for the normal mass ordering and an upper limit on the lightest neutrino mass of 𝑚 𝑙 < 0.023 eV (95%). However, we determine using frequentist and Bayesian methods that our constraints are in tension with the lower limits derived from neutrino oscillations. Correcting for the physical boundary at zero mass, we report a 95% Feldman-Cousins upper limit of ∑𝑚 𝜈 < 0.053 eV, breaching the lower limit from neutrino oscillations. Considering a more general Bayesian analysis with an effective cosmological neutrino mass parameter, ∑𝑚 𝜈,eff , that allows for negative energy densities and removes unsatisfactory prior weight effects, we derive constraints that are in 3⁢𝜎 tension with the same oscillation limit, while the error rises to 𝜎⁡(∑𝑚 𝜈,eff ) = 0.053 eV. In the absence of unknown systematics, this finding could be interpreted as a hint of new physics not necessarily related to neutrinos. The preference of DESI and CMB data for an evolving dark energy model offers one possible solution. In the 𝑤 0 ⁢𝑤 𝑎 ⁢CDM model, we find ∑𝑚 𝜈 < 0.163 eV (95%), relaxing the neutrino tension. These constraints all rely on the effects of neutrinos on the cosmic expansion history. Using full-shape power spectrum measurements of data release 1 galaxies, we place complementary constraints that rely on neutrino free streaming. Our strongest such limit in Λ ⁢CDM, using selected CMB priors, is ∑𝑚 𝜈 < 0.193 eV (95%).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Implementing machine learning methods on QICK hardware for qubit readout & control

Quantum readout and control is a fundamental aspect of quantum computing that requires accurate measurement of qubit states. Errors emerge in all stages, from initialization to readout, and identifying errors in post-processing necessitates resource-intensive statistical analysis. In our work, we use a lightweight fully-connected neural network (NN) to classify states of a transmon system with no prior processing. Our NN accelerator yields higher fidelities (92%) than the classical matched filter method (84%). By exploiting the natural parallelism of NNs and their placement near the source of data on field-programmable gate arrays (FPGAs), we can achieve ultra-low latency on the Quantum Instrumentation Control Kit (QICK). Integrating machine learning methods on QICK opens several pathways for efficient real-time processing of quantum circuits.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Di-CNN: Domain-Knowledge-Informed Convolutional Neural Network for Manufacturing Quality Prediction

In manufacturing, convolutional neural networks (CNNs) are widely used on image sensor data for data-driven process monitoring and quality prediction. However, as purely data-driven models, CNNs do not integrate physical measures or practical considerations into the model structure or training procedure. Consequently, CNNs’ prediction accuracy can be limited, and model outputs may be hard to interpret practically. This study aims to leverage manufacturing domain knowledge to improve the accuracy and interpretability of CNNs in quality prediction. A novel CNN model, named Di-CNN, was developed that learns from both design-stage information (such as working condition and operational mode) and real-time sensor data, and adaptively weighs these data sources during model training. It exploits domain knowledge to guide model training, thus improving prediction accuracy and model interpretability. A case study on resistance spot welding, a popular lightweight metal-joining process for automotive manufacturing, compared the performance of (1) a Di-CNN with adaptive weights (the proposed model), (2) a Di-CNN without adaptive weights, and (3) a conventional CNN. The quality prediction results were measured with the mean squared error (MSE) over sixfold cross-validation. Model (1) achieved a mean MSE of 6.8866 and a median MSE of 6.1916, Model (2) achieved 13.6171 and 13.1343, and Model (3) achieved 27.2935 and 25.6117, demonstrating the superior performance of the proposed model.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

The Effects of Gamma Ray Integrated Dose on a Commercial 65-nm SRAM Device

Here this work shows that the static random access memory (SRAM) error rate for a commercial 65-nm device in a dose rate environment can be highly dependent upon the integrated dose (dose rate × pulse duration). While the typical metric for such testing is dose rate upset (DRU) level in rad(Si)/s, a series of dose rate experiments at Little Mountain Test Facility (LMTF) shows dependence on the integrated dose. The error rate is also found to be dependent on the core voltage, and the preradiation value of the bits. We believe that these effects are explained by a well charge depletion caused by gamma ray photocurrent.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗