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At least 1,765 records · Page 98
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Application of performance portability solutions for GPUs and many-core CPUs to track reconstruction kernels

Next generation High-Energy Physics (HEP) experiments are presented with significant computational challenges, both in terms of data volume and processing power. Using compute accelerators, such as GPUs, is one of the promising ways to provide the necessary computational power to meet the challenge. The current programming models for compute accelerators often involve using architecture-specific programming languages promoted by the hardware vendors and hence limit the set of platforms that the code can run on. Developing software with platform restrictions is especially unfeasible for HEP communities as it takes significant effort to convert typical HEP algorithms into ones that are efficient for compute accelerators. Multiple performance portability solutions have recently emerged and provide an alternative path for using compute accelerators, which allow the code to be executed on hardware from different vendors. We apply several portability solutions, such as Kokkos, SYCL, C++17 std::execution::par, Alpaka, and OpenMP/OpenACC, on two mini-apps extracted from the mkFit project: p2z and p2r. These apps include basic kernels for a Kalman filter track fit, such as propagation and update of track parameters, for detectors at a fixed z or fixed r position, respectively. The two mini-apps explore different memory layout formats. We report on the development experience with different portability solutions, as well as their performance on GPUs and many-core CPUs, measured as the throughput of the kernels from different GPU and CPU vendors such as NVIDIA, AMD and Intel.

Kwok, Ka Hei Martin↗
DOE OSTI2022

Preliminary Reversible Solid Oxide System Specification

This report presents the preliminary documentation of a 10 MWe DC reversible solid oxide cell (rSOC) system designed to use both electrical and thermal energy from a nuclear power plant (NPP). The system is designed to consume 10 MWe DC in electrolysis mode while producing hydrogen from demineralized feedwater. In fuel cell mode, the same stacks produce 2.37 MWe DC of electricity by reacting hydrogen and oxygen, while generating water as a byproduct which is recycled to be used later in the electrolysis mode. The system detailed in this specification is a high-temperature steam electrolysis (HTSE) system when operated in the electrolysis mode. HTSE systems have the benefit of producing hydrogen at a higher efficiency than conventional low-temperature electrolysis (LTE) systems. In this report it is assumed that some of the heat required for HTSE operation comes from an NPP. Heat extraction from an NPP for use in electrolysis mode of the rSOC system allows preheating and vaporization of feedwater before recuperators and trim heaters raise the feed temperature to the approximately 800 °C before entering the solid oxide stacks. The purpose of an rSOC system in a utility company setting is to employ energy arbitrage with a dispatchable demand load which can consume excess electricity generation during times of low grid demand / high generation and can produce electricity for the grid during times of high grid demand / low generation. There is a wide range of energy storage technologies that could be used for utility-scale energy arbitrage (utility-scale battery storage is considered the baseline technology), the object of this work is not to compare and contrast rSOC technology with any of these other technologies, but only to present this preliminary design for consideration and for use in future conceptual or front end engineering design (FEED) work. This document is not meant to be a final specification or definitive description of the rSOC system, but it is meant to showcase preliminary process modeling results, provide boundary conditions and interface requirements such as input feed and utility stream flowrates, temperatures, and pressures as well as thermal and electrical energy requirements, and output conditions in both electrolysis mode and fuel cell mode. These results are intended to inform the future development of a conceptual demonstration-scale study to assess the technical and economic feasibility of a future demonstration of an rSOC integrated project at an NPP.

08 HYDROGEN↗
DOE OSTI2025

Dynamics and Formation of Antiferromagnetic Textures in MnBi 2 Te 4 Single Crystal

We report coherent X-ray imaging of antiferromagnetic (AFM) domains and domain walls in MnBi 2 Te 4 , an intrinsic AFM topological insulator. This technique enables direct visualization of domain morphology without reconstruction algorithms, allowing us to resolve antiphase domain walls as distinct dark lines arising from the A-type AFM structure. The wall width is determined to be 550(30) nm, in good agreement with earlier magnetic force microscopy results. The temperature dependence of the AFM order parameter extracted from our images closely follows previous neutron scattering data. Remarkably, however, we find a pronounced hysteresis in the evolution of domains and domain walls: upon cooling, dynamic reorganizations occur within a narrow ∼1 K interval below 𝑇 𝑁 , whereas upon warming, the domain configuration remains largely unchanged until AFM order disappears. These findings reveal a complex energy landscape in MnBi 2 Te 4 , governed by the interplay of exchange, anisotropy, and domain-wall energies, and underscore the critical role of AFM domain-wall dynamics in shaping its physical properties. These sharply defined and hysteretically evolving walls may provide a controllable AFM texture in MnBi 2 Te 4 , hinting at potential use in low-power spintronic devices based on domain-wall dynamics.

36 MATERIALS SCIENCE↗
NASA NTRS1977

Utilization of remote sensing observations in hydrologic models

Most of the remote sensing related work in hydrologic modeling has centered on modifying existing models to take advantage of the capabilities of new sensor techniques. There has been enough success with this approach to insure that remote sensing is a powerful tool in modeling the watershed processes. Unfortunately, many of the models in use were designed without recognizing the growth of remote sensing technology. Thus, their parameters were selected to be map or field crew definable. It is believed that the real benefits will come through the evolution of new models having new parameters that are developed specifically to take advantage of our capabilities in remote sensing. The ability to define hydrologically active areas could have a significant impact. The ability to define soil moisture and the evolution of new techniques to estimate evoportransportation could significantly modify our approach to hydrologic modeling. Still, without a major educational effort to develop an understanding of the techniques used to extract parameter estimates from remote sensing data, the potential offered by this new technology will not be achieved.

Ragan, R. M.↗
NASA NTRS1993

A new clustering algorithm applicable to multispectral and polarimetric SAR images

We describe an application of a scale-space clustering algorithm to the classification of a multispectral and polarimetric SAR image of an agricultural site. After the initial polarimetric and radiometric calibration and noise cancellation, we extracted a 12-dimensional feature vector for each pixel from the scattering matrix. The clustering algorithm was able to partition a set of unlabeled feature vectors from 13 selected sites, each site corresponding to a distinct crop, into 13 clusters without any supervision. The cluster parameters were then used to classify the whole image. The classification map is much less noisy and more accurate than those obtained by hierarchical rules. Starting with every point as a cluster, the algorithm works by melting the system to produce a tree of clusters in the scale space. It can cluster data in any multidimensional space and is insensitive to variability in cluster densities, sizes and ellipsoidal shapes. This algorithm, more powerful than existing ones, may be useful for remote sensing for land use.

Wong, Yiu-Fai↗
NASA NTRS2000

Thermal Field Imaging Using Ultrasound

It is often desirable to be able to determine the temperature field in the interiors of opaque fluids forced into convection by externally imposed temperature gradients. To measure the temperature at a point in an opaque fluid in the usual fashion requires insertion of a probe, and to determine the full field therefore requires either the ability to move this probe or the introduction of multiple probes. Neither of these solutions is particularly satisfactory, although they can lead to quite accurate measurements. As an alternative we have investigated the use of ultrasound as a relatively non-intrusive probe of the temperature field in convecting opaque fluids. The temperature dependence of the sound velocity can be sufficiently great to permit a determination of the temperature from timing the traversal of an ultrasound pulse across a chamber. In this paper we will present our results on convecting flows of transparent and opaque fluids. Our experimental cells consist of relatively narrow rectangular cavities made of thermally insulating materials on the sides, and metal top and bottom plates. The ultrasound transducer is powered by a pulser/receiver, the signal output of which goes to a very high speed signal averager. The average of several hundred to several thousand signals is then sent to a computer for storage and analysis. The experimental procedure is to establish a convective flow by imposing a vertical temperature gradient on the chamber, and then to measure, at several regularly spaced locations, the transit time for an ultrasound pulse to traverse the chamber horizontally (parallel to the convecting rolls) and return to the transducer. The transit time is related to the temperature of the fluid through which the sound pulse travels. Knowing the relationship between transit time and temperature (determined in a separate experiment), we can extract the average temperature across the chamber at that location. By changing the location of the transducer it is then possible to find the average temperature at different locations along the chamber, thereby determining the temperature profile along the system. (In the future we will construct an array of transducers. This will give us the capability to determine the temperature profile much more rapidly than at present, an important consideration if time-dependent phenomena are to be studied.) To validate our procedure we introduced encapsulated liquid crystal particles into glycerol. The liquid crystal particles' color varies depending on the temperature of the fluid. A photograph of the fluid through transparent sidewalls therefore gives a picture of the temperature field of the convecting fluid, independent of our ultrasound imaging. A representative result is shown in the Figure 1, which reveals a very satisfying correspondence between the two techniques. Therefore we have a great deal of confidence that the ultrasound imaging approach is indeed measuring the actual temperature profile of the fluid. The technique has also been applied to convecting liquid metal flows, and representative data will be presented from those experiments as well.

Andereck, D.↗
NASA NTRS2010

Highly Siderophile Elements as Tracers for the Subcontinental Mantle Evolution Beneath the Southwestern USA: The San Carlos and Kilbourne Hole Peridotite Xenoliths Revisited

Peridotite xenoliths from San Carlos, Arizona, and Kilbourne Hole, New Mexico, have been studied since the 1970 s to give insights into melting and metasomatism in the subcontinental mantle beneath the southwestern USA. More recently, the highly siderophile elements (HSE; Os, Ir, Ru, Rh, Pt, Pd, and Re) and the included Re-Os isotope system have been established as powerful tools for the study of mantle processes because of their range in compatibility during mantle melting and their siderophile and chalcophile geochemical behavior. Model aluminachron Re-Os ages for San Carlos and Kilbourne Hole, as well as for the nearby Dish Hill and Vulcan's Throne sites, give consistent depletion ages of around 2.2 Ga. This age can be interpreted as a single large scale mantle melting event linked to crustal formation and continental growth under the southwestern USA. Highly siderophile elements, however, may be added to depleted peridotites via melt-rock interaction, especially the more incompatible and hence mobile Pt, Pd, and Re. This may result in overprinting of the signature of melt extraction, thus abating the usefulness of Re-Os mantle extraction model ages. A comprehensive characterization of the suite of mantle xenoliths from the SW USA in terms of HSE concentrations is thus necessary to re-assess the Re-Os system for dating purposes. San Carlos peridotites are depleted to moderately fertile, as indicated by their bulk Al2O3 contents between 0.66 wt% and 3.13 wt%. Bulk Os-187/Os-188 in San Carlos peridotites range from 0.1206 to 0.1357. In contrast, Kilbourne Hole peridotites tend to be more fertile with Al2O3 between 2.11 and 3.78 wt%, excluding one extremely depleted sample with 0.30 wt% Al2O3, and have Os-187/Os-188 between 0.1156 and 0.1272, typical for mantle peridotites. No large fractionation between the more compatible HSE Os, Ir, and Ru are observed. The more incompatible HSE Re, Pd, and to a minor extent, Pt, however, are depleted in a number of samples by factors of up to 4 for Pt, 6 for Pd, and 20 for Re, compared to primitive mantle estimates. This is in agreement with previous studies from the same locales, which demonstrated the presence of different populations of mantle xenoliths having undergone various degrees of melt extraction. The depletion of the more incompatible elements (Re, Pd, and Pt) also suggests that the HSE budgets of the SW USA peridotites were primarily established by extraction of basaltic melt, and reflect only minor influence from later episodes of metasomatism. Model Re-Os ages obtained from San Carlos and Kilbourne Hole xenoliths may thus reflect ages of crustal formation and mantle depletion in the SW USA region.

vanAcken, D.↗
DOE OSTI2025

Machine Learning Analysis of Temperature-Strain Relationships for Structural Health Monitoring of Pipes: Self-powered wireless sensor system for health monitoring of liquid-sodium cooled fast reactors

This report presents machine learning (ML) analysis of temperature-strain relationships for structural health monitoring of nuclear reactor stainless steel (SS) pipes with the strain gauge sensor directly printed on the pipe with a 3D conformal aerosol jet printer. We investigate correlations for two sensor pairs installed on the same SS304 pipe: commercial K-type thermocouple with a printed gold strain gauge (TC3-SG3), and commercial K-type thermocouple with commercial Kyowa strain gauge (TC0-SG0). The temperature ranges for the sensor pairs TC0-SG0 and TC3-SG3 are 20.00°C to 266.37°C and 39.95°C to 219.28°C respectively. ML algorithms in this study include Linear Regression (baseline method), Ridge Regression, Lasso Regression, and Gradient Boosting. Performance evaluation metrics include Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), R 2 Score, and Explained Variance. Using advanced feature engineering techniques, we extracted 27 temperature-based features and 30 strategic inclusion features. The best performance was obtained with the Gradient Boosting method, which achieves prediction accuracy of R 2 = 0.9999 and RMSE = 7.69 μStrain for TC0-SG0, and R 2 = 0.9998 and RMSE = 18.03 μStrain for TC3-SG3. While the temperature-strain correlations are weaker for the gauge directly printed on the pipe than for the commercial strain gauge, deployment-ready performance exceeding industry standards is achieved for both sensor pairs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Robust Event Classification Using Imperfect Real-world PMU Data

Here, this paper studies robust event classification using imperfect real-world phasor measurement unit (PMU) data. By analyzing the real-world PMU data, we find it is challenging to directly use this dataset for event classifiers due to the low data quality observed in PMU measurements and event logs. To address these challenges, we develop a novel machine learning framework for training robust event classifiers, which consists of three main steps: data preprocessing, fine-grained event data extraction, and feature engineering. Specifically, the data preprocessing step addresses the data quality issues of PMU measurements (e.g., bad data and missing data); in the fine-grained event data extraction step, a model-free event detection method is developed to accurately localize the events from the inaccurate event timestamps in the event logs; and the feature engineering step constructs the event features based on the patterns of different event types, in order to improve the performance and the interpretability of the event classifiers. Based on the proposed framework, we develop a workflow for event classification using the real-world PMU data streaming into the system in real time. Using the proposed framework, robust event classifiers can be efficiently trained based on many off-the-shelf lightweight machine learning models. Numerical experiments using the real-world dataset from the Western Interconnection of the U.S power transmission grid show that the event classifiers trained under the proposed framework can achieve high classification accuracy while being robust against low-quality data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Data-Driven Analysis of Multipactor Dynamics via Dynamic Mode Decomposition

Multipactor effect is a performance-limiting kinetic plasma effect that can occur in high-power microwave and radio frequency (RF) devices. Multipactor effect is of special concern in vacuum or near-vacuum conditions such as those in particle accelerators and spaceborne devices. In this work, we present a data-driven reduced-order model (ROM) based on dynamic mode decomposition (DMD) for modeling of multipactor effects. We study multipactor effects and the resulting nonlinear harmonic generation by processing high-fidelity data generated from electromagnetic particle-in-cell (EMPIC) simulations using the DMD algorithm. We also investigate time-delay embedding extensions of DMD with improved generalizability and accuracy for modeling the electron plasma current density behavior. Here, the results show that DMD provides valuable insights into multipactor phenomena by extracting relevant modal spatiotemporal patterns and frequencies. In addition, DMD offers the potential to time extrapolate EMPIC simulations at a minimal cost, thereby reducing overall simulation time.

43 PARTICLE ACCELERATORS↗
NASA NTRS2011

Be/X-Ray Pulsar Binary Science with LOFT

Accretion disks are ubiquitous in astronomical sources. Accretion powered pulsars are a good test bed for accretion disk physics, because unlike for other objects, the spin of the neutron star is directly observable allowing us to see the effects of angular momentum transfer onto the pulsar. The combination of a sensitive wide-field monitor and the large area detector on LOFT will enable new detailed studies of accretion powered pulsars which I will review. RXTE observations have shown an unusually high number of Be/X-ray pulsar binaries in the SMC. Unlike binaries in the Milky Way, these systems are all at the same distance, allowing detailed population studies using the sensitive LOFT WFM, potentially providing connections to star formation episodes. For Galactic accreting pulsar systems, LOFT will allow measurement of spectral variations within individual pulses, mapping the accretion column in detail for the first time. LOFT will also provide better constraints on magnetic fields in accreting pulsars, allowing measurements of cyclotron features, observations of transitions into the centrifugal inhibition regime, and monitoring of spin-up rate vs flux correlations. Coordinated multi-wavelength observations are crucial to extracting the best science from LOFT from these and numerous other objects.

Wilson-Hodge, Colleen A.↗
NASA NTRSDate not supplied

Algorithmic Classification of Raman Spectra Biosignatures: Improving Life Detection Confidence

“Agnostic” biosignatures – indicators of life (or the absence of life), independent of a particular biochemistry – are increasingly considered a high standard for life detection. The Ladder of Life Detection (2018) called for investigating how combinations of independent and different potential biosignatures affect confidence. To address this gap, statistical classification of elemental abundances, isotopic fractionation, and reflectance spectroscopy (VNIR) has been implemented. Raman spectroscopy, highly desirable due to its wide availability, has the potential to improve this predictive power. This work implemented biosignature classification algorithms on Raman data alone, in preparation for combination with the other data types. Raman spectroscopy data was collected from published databases and papers as part of a manually curated dataset of “indicative” and “non-indicative of life” samples. These currently include 61 non-indicative samples (meteorites, magnetite); 3 indicative living samples (bacteria); 20 indicative non-living samples (chalk, bone); and 12 indicative mixed (with non-indicative material) samples (soil, microbial mats). Laboratory work is ongoing to characterize additional samples, particularly a greater breadth of mixed systems. Spectra were interpolated, filtered with the Savitzsky-Golay filter, and de-noised. For a preliminary examination, agnostic features were manually extracted including mean intensity, number of peaks, and mean peak width. Different peak prominences and filtering polynomials were used to refine features. Classification algorithms were implemented: k-nearest neighbors (KNN), logistic regression (LR), linear support vector machines (SVM), random forest (RF), Gaussian naïve bayes (GNB). Lastly, Monte Carlo simulations on 1,000 50%-train-test-splits were used to validate classification performance and feature significance. The preliminary feature set achieved its highest AUC of 0.52 with LR, with no strongly discriminatory features. Work to improve feature extraction, such as through deep learning with back propagation, is planned. In future work, the Raman data will be combined with the other data types, and potentially new data types such as enantiomeric excess. This project was partially supported through the NASA Ames Project EXcellence (APEX) incubator program.

Astrobiology↗
DOE OSTI2022

Microwave Heating-Induced Temperature Gradients in Liquid–Liquid Biphasic Systems

Microwaves (MWs) can enable the electrification and intensification of chemical manufacturing. They have been applied to various unit separations, such as drying, distillation, and extraction, entailing gas–liquid and solid–liquid systems. However, a limited quantitative understanding of MW-heated liquid–liquid biphasic systems related to extraction exists. This work measures the temporal and spatial temperature difference between an aqueous and an organic phase in batch and continuous microfluidic modes. We demonstrate permanent temperature differences between phases over 35 °C and spatiotemporal periodic and quasiperiodic oscillations modulated by the flow patterns. The temperature differences are primarily driven by the faster absorption rate of MW irradiation by the aqueous phase versus the slower heat transfer from the aqueous phase to the organic phase. These are amplified by low specific interfacial area and modifications of the electromagnetic field. We employ a multiphysics model to predict the temperature difference in a batch system. The model is in good agreement with the experiments. We demonstrate a strong effect of input power, dielectric properties of organic solvents, the volume of solvents, and the volume ratio between phases on the temperature difference. A simple analytical model describes the temperature difference and provides design principles. Furthermore, the combined approach offers new insights into the design and optimization of the MW-heated biphasic systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Helicity at small x: oscillations generated by bringing back the quarks

We construct a numerical solution of the recently-derived large-N c &N f small-x helicity evolution equations with the aim to establish the small-x asymptotics of the quark helicity distribution beyond the large-N c limit explored previously in the same framework. (Here N c and N f are the numbers of quark colors and flavors.) While the large-N c helicity evolution involves gluons only, the large-N c &N f evolution includes contributions from quarks as well. We find that adding quarks to the evolution makes quark helicity distribution oscillate as a function of x. Our numerical results in the large-N c &N f limit lead to the x-dependence of the flavor-singlet quark helicity distribution which is well-approximated by $$ {\left.\Delta \Sigma \left(x,{Q}^2\right)\right|}_{\mathrm{large}\hbox{-} {N}_c\&{N}_f}\sim {\left(\frac{1}{x}\right)}^{\alpha_h^q}\cos \left[{\omega}_q\ln \left(\frac{1}{x}\right)+{\varphi}_q\right]. $$. The power $α^{q}_{h}$ exhibits a weak N f -dependence, and, for all Nf values considered, remains very close to $α^{q}_{h}$(N f =0)=$\left(4/\sqrt{3}\right)\sqrt{\alpha_s{N}_c/\left(2\pi \right)}$ obtained earlier in the large-N c limit. The novel oscillation frequency ω q and phase shift φ q depend more strongly on the number of flavors N f (with ω q = 0 in the pure-glue large-N c limit). The typical period of oscillations for ΔΣ is rather long, spanning many units of rapidity. We speculate whether the oscillations we find are related to the sign variation with x seen in the strange quark helicity distribution extracted from the data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Model Agnostic Bayesian Framework for Online Anomaly/Event Detection in PMU Data

Phasor measurement units (PMU) are integral to the modernization and automation plan of the electric power industry. A PMU data signature contains system-level events (e.g., faults, generation/load change, etc.) and any measurement/device-related errors. Therefore, the reliable and resilient operation of power systems is equivalent to the quality of the PMU data and the situation awareness provided by its data signature. Despite recent progress, current state-of-the-art methods are not fool-proof and have certain limitations tracing an error/abnormality to sensor sub-components and grid systems. This is because of technical challenges imposed by the scarcity of the labeled information, loss of data quality, and non-stationarity of data. In this paper, we consider the online PMU data stream as an output of a stochastic process and pose the anomaly/event detection as a changepoint detection problem dealing with detecting parameter changes in the underlying stochastic processes. The proposed model-agnostic framework relies on: (a) feature extraction utilizing the minimum volume enclosing ellipsoids (MVEE) method from raw PMU observations and (b) a Bayesian framework of changepoint detection. The validity of the proposed methodology is discussed through numerical experiments on real-world utility-scale PMU data.

Hossain, Ramij Raja↗
DOE OSTI2022

The COS Legacy Archive Spectroscopy Survey (CLASSY) Treasury Atlas

Far-ultraviolet (FUV; ~1200–2000 Å) spectra are fundamental to our understanding of star-forming galaxies, providing a unique window on massive stellar populations, chemical evolution, feedback processes, and reionization. The launch of the James Webb Space Telescope will soon usher in a new era, pushing the UV spectroscopic frontier to higher redshifts than ever before; however, its success hinges on a comprehensive understanding of the massive star populations and gas conditions that power the observed UV spectral features. This requires a level of detail that is only possible with a combination of ample wavelength coverage, signal-to-noise, spectral-resolution, and sample diversity that has not yet been achieved by any FUV spectral database. We present the Cosmic Origins Spectrograph Legacy Spectroscopic Survey (CLASSY) treasury and its first high-level science product, the CLASSY atlas. CLASSY builds on the Hubble Space Telescope (HST) archive to construct the first high-quality (S/N 1500 Å ≳ 5/resel), high-resolution (R ~ 15,000) FUV spectral database of 45 nearby (0.002 < z < 0.182) star-forming galaxies. The CLASSY atlas, available to the public via the CLASSY website, is the result of optimally extracting and coadding 170 archival+new spectra from 312 orbits of HST observations. The CLASSY sample covers a broad range of properties including stellar mass (6.2 < log M * (M ⊙ ) < 10.1), star formation rate (–2.0 < log SFR (M ⊙ yr –1 ) < +1.6), direct gas-phase metallicity (7.0 < 12+log(O/H) < 8.8), ionization (0.5 < O 32 < 38.0), reddening (0.02 < E(B – V) < 0.67), and nebular density (10 < n e (cm –3 ) < 1120). CLASSY is biased to UV-bright star-forming galaxies, resulting in a sample that is consistent with the z ~ 0 mass–metallicity relationship, but is offset to higher star formation rates by roughly 2 dex, similar to z ≳ 2 galaxies. This unique set of properties makes the CLASSY atlas the benchmark training set for star-forming galaxies across cosmic time.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Value-based Insights from the Implementation of Hierarchical Control for Energy Savings and Demand Response in Residential Premises

As the adoption of distributed energy resources and electric vehicles at residential customer premises increases exponentially, behind-the-meter assets can be utilized to achieve energy cost reduction and demand response through coordination and control strategies. A hierarchical control architecture from the utility headend to residential premises is implemented to attain these objectives. This paper extracts the values from the development, implementation, and deployment of that control hierarchy. The development of the control philosophy is built upon the existing advanced metering infrastructure, communication protocols, and industry-compatible application programming interfaces. Results are presented visually with analytical insights by utilizing the data from hardware-in-the-loop testing and simulation analysis out of the collected data from the field.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Prospects for measuring the time variation of astrophysical neutrino sources at dark matter detectors

We study the prospects for measuring the time variation of solar and atmospheric neutrino fluxes at future large-scale xenon and argon dark matter detectors. For solar neutrinos, a yearly time variation arises from the eccentricity of Earth’s orbit and, for charged current interactions, from a smaller energy-dependent day-night variation due to flavor regeneration as neutrinos travel through Earth. For a 100-ton xenon detector running for ten years with a xenon-136 fraction of ≲ 0.1 % , in the electron recoil channel a time-variation amplitude of about 0.8% is detectable with a power of 90% and the level of significance of 10%. This is sufficient to detect time variation due to eccentricity, which has amplitude of ∼ 3 % . In the nuclear recoil channel, the detectable amplitude is about 10% under current detector resolution and efficiency conditions, and this generally reduces to about 1% for improved detector resolution and efficiency, the latter of which is sufficient to detect time variation due to eccentricity. Our analysis assumes both known and unknown periods. We provide scalings to determine the sensitivity to an arbitrary time-varying amplitude as a function of detector parameters. Identifying the time variation of the neutrino fluxes will be important for distinguishing neutrinos from dark matter signals and other detector-related backgrounds and extracting properties of neutrinos that can be uniquely studied in dark matter experiments. Published by the American Physical Society 2024

Zhuang, Yi (ORCID:0000000277138724)↗