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All sources NASA NTRS DOE OSTI

DOE OSTI2022

Impact of Organic and Volatile Compounds in Produced Water from Unconventional Reservoirs on Direct Contact Membrane Distillation Permeate Quality

The expansion of oil and gas extraction from unconventional reservoirs has led to an increase in the amount of produced water that has to be managed by this industry. Direct contact membrane distillation (DCMD) is a promising technology for treatment of produced water to enable water recovery and reduce the environmental footprint of this industry. The feasibility of DCMD for the treatment of highly saline produced water from the Permian Basin in TX with commercially available polyethylene and polytetrafluoroethylene membranes was evaluated in this study. An increase in water recovery by a DCMD system operated in the batch (concentrating) mode led to an increase in permeate conductivity. Partial removal of organic compounds from the produced water by biodegradation, chemical oxidation, and/or activated carbon adsorption could not resolve deterioration in permeate quality, and none of the organics observed in the permeate contributed to its conductivity. The observed increase in permeate conductivity was attributed to the passage of ammonia vapor from the feed side followed by protonation on the permeate side. Furthermore, this study revealed that permeate conductivity may not always be a reliable indicator of membrane wetting and underscores the importance of understanding the interactions between specific solutes and membrane materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Efficient Super-Resolution of Near-Surface Climate Modeling Using the Fourier Neural Operator

Downscaling methods are critical in efficiently generating high-resolution atmospheric data. However, state-of-the-art statistical or dynamical downscaling techniques either suffer from the high computational cost of running a physical model or require high-resolution data to develop a downscaling tool. Here, we demonstrate a recently proposed zero-shot super-resolution method, the Fourier neural operator (FNO), to efficiently perform downscaling without the need for high-resolution data. Because the FNO learns dynamics in Fourier space, FNO is a resolution-invariant emulator; it can be trained at a coarse resolution and produces emulation at any high resolution. We applied FNO to downscale a 4-km resolution Weather Research and Forecasting (WRF) Model simulation of near-surface heat-related variables over the Great Lakes region. The FNO is driven by the atmospheric forcings, and topographic features used in the WRF model at the same resolution. We incorporated a physics-constrained loss in FNO by using the Clausius-Clapeyron relation to better constrain the relations among the emulated states. Trained on merely 600 WRF snapshots at 4-km resolution, the FNO shows comparable performance with a widely used convolutional network, U-Net, achieving averaged modified Kling-Gupta Efficiency of 0.88 and 0.94 on the test dataset for temperature and pressure, respectively. We then employed the FNO to produce 1-km emulations to reproduce the fine climate features. Further, by taking the WRF simulation as ground truth, we show consistent performances at the two resolutions, suggesting the reliability of FNO in producing high-resolution dynamics. Our study demonstrates the potential of using FNO for zero-shot super-resolution in generating first-order estimation on atmospheric modeling.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Milestone in predicting core plasma turbulence: successful multi-channel validation of the gyrokinetic code GENE

On the basis of several recent breakthroughs in fusion research, many activities have been launched around the world to develop fusion power plants on the fastest possible time scale. In this context, high-fidelity simulations of the plasma behavior on large supercomputers provide one of the main pathways to accelerating progress by guiding crucial design decisions. When it comes to determining the energy confinement time of a magnetic confinement fusion device, which is a key quantity of interest, gyrokinetic turbulence simulations are considered the approach of choice – but the question, whether they are really able to reliably predict the plasma behavior is still open. The present study addresses this important issue by means of careful comparisons between state-of-the-art gyrokinetic turbulence simulations with the GENE code and experimental observations in the ASDEX Upgrade tokamak for an unprecedented number of simultaneous plasma observables.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

First principles modeling of polymer encapsulant degradation in Si photovoltaic modules

An outstanding issue in the longevity of photovoltaic (PV) modules is the accelerated degradation caused by the presence of moisture. Moisture leads to interfacial instability, de-adhesion, encapsulant decomposition, and contact corrosion. However, experimental characterization of moisture in PV modules is not trivial and its impacts can take years or decades to establish in the field, presenting a major obstacle to designing high-reliability modules. First principles calculations provide an alternative way to study the ingress of water and its detrimental effect on the structure and decomposition of the polymer encapsulant and interfaces between the encapsulant and the semiconductor, the metal contacts, or the dielectric layer. Here, we use density functional theory (DFT) computations to model single chain, crystalline and cross-linked structures, infrared (IR) signatures, and degradation mechanisms of ethylene vinyl acetate (EVA), the most common polymer encapsulant used in Si PV modules. IR-active modes computed for low energy EVA structures and possible decomposition products match well with reported experiments. The EVA decomposition energy barriers computed using the Nudged Elastic Band (NEB) method show a preference for acetic acid formation as compared to acetaldehyde, are lowered in the presence of a water solvent or hydroxyl ion catalyst, and match well with reported experimental activation energies. This systematic study leads to a clear picture of the hydrolysis-driven decomposition of EVA in terms of energetically favorable mechanisms, possible intermediate structures, and IR signatures of reactants and products.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Moment expansion of polarized dust SED: A new path towards capturing the CMB B -modes with LiteBIRD

Accurate characterization of the polarized dust emission from our Galaxy will be decisive in the quest for the cosmic microwave background (CMB) primordial B-modes. An incomplete modeling of its potentially complex spectral properties could lead to biases in the CMB polarization analyses and to a spurious measurement of the tensor-to-scalar ratio r. It is particularly crucial for future surveys like the LiteBIRD satellite, the goal of which is to constrain the faint primordial signal leftover by inflation with an accuracy on the tensor-to-scalar ratio r of the order of 10 –3 . Variations of the dust properties along and between lines of sight lead to unavoidable distortions of the spectral energy distribution (SED) that cannot be easily anticipated by standard component-separation methods. This issue can be tackled using a moment expansion of the dust SED, an innovative parametrization method imposing minimal assumptions on the sky complexity. In the present paper, we apply this formalism to the B-mode cross-angular power spectra computed from simulated LiteBIRD polarization data at frequencies between 100 and 402 GHz that contain CMB, dust, and instrumental noise. The spatial variation of the dust spectral parameters (spectral index β and temperature T) in our simulations lead to significant biases on r (~21 σ r ) if not properly taken into account. Performing the moment expansion in β, as in previous studies, reduces the bias but does not lead to sufficiently reliable estimates of r. We introduce, for the first time, the expansion of the cross-angular power spectra SED in both β and T, showing that, at the sensitivity of LiteBIRD, the SED complexity due to temperature variations needs to be taken into account in order to prevent analysis biases on r. Thanks to this expansion, and despite the existing correlations between some of the dust moments and the CMB signal responsible for a rise in the error on r, we can measure an unbiased value of the tensor-to-scalar ratio with a dispersion as low as σ r = 8.8 × 10 –4 .

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Platform for Automated Anomaly Detection in the Mercury Process System at the Target System in the Spallation Neutron Source

The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory accelerates proton beams, which are directed toward a mercury target to generate the world’s most intense neutron beams via spallation. The target system consists of several interconnected subsystems and accounts for a major share of the facility’s overall downtime. Early detection of anomalies in the target system response can thus provide the possibility of taking corrective actions to reduce downtime. Accelerator facilities have largely focused on the beam side for data-driven fault prognostics. On the target side, SNS relies on operational shift technicians (OSTs), who respond to alarms and manually flag anomalies onto the System Tracking and Reliability (STAR) platform. This paper presents one of the first studies of using machine learning (ML) to automate anomaly detection in the target system. The study focused on the mercury process system as the first use case and employed reconstruction-based anomaly detection on minutely sampled time series signals. The pipeline was integrated into the STAR platform to autonomously rank and flag anomalies every week. The STAR platform provides a user interface for the OSTs to evaluate the flagged anomalies, thereby incorporating human feedback.

Anomaly detection↗

DOE OSTI2022

Improved scaling law for the prediction of deuterium retention in beryllium co-deposits

Hydrogen isotope co-deposition with Be eroded from the first wall is expected to be the main fusion fuel retention mechanism in ITER. Since good fuel accounting is crucial for economic and safety reasons, reliable predictions of hydrogen isotope retention are needed. Furthermore, this study builds upon the well-established empirical De Temmerman scaling law that predicts D/Be ratios in co-deposited layers based on deposition temperature, deposition rate, and deuterium particle energy. Expanding the data used in the original development of the scaling law with an additional dataset obtained with more recent measurements using a different technique to the original De Temmerman approach, allows us to obtain new values for free parameters and improve the prediction capabilities of the new scaling law. In an effort to improve the model even further, scaling with D 2 background pressure was included and a new two-term model derived, describing D retention in low- and high-energy traps separately.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Toward machine learning interatomic potentials for modeling uranium mononitride

Uranium mononitride (UN) is a promising accident-tolerant fuel because of its high fissile density and high thermal conductivity. In this study, we developed the first machine learning interatomic potentials for reliable atomic-scale modeling of UN at finite temperatures. We constructed a training set using density functional theory (DFT) calculations that was enriched through an active learning procedure, and two neural network potentials were generated. Both potentials successfully reproduce key thermophysical properties of interest, such as temperature-dependent lattice parameter, specific heat capacity, and bulk modulus. We also evaluated the energy of stoichiometric defect reactions and defect migration barriers and found close agreement with DFT predictions, demonstrating that our potentials can be used for modeling defects in UN. Additional tests provide evidence that our potentials are reliable for simulating diffusion, noble gas impurities, and radiation damage.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Implementation of Advanced Grid Support Functionalities by Smart Operation of Residential Loads with low Cost Converter Interface

This paper investigates a grid-supportive load concept for small-scale residential appliances, focusing on a residential refrigerator. Power consumption is adjusted based on grid conditions to achieve IEEE-1547 grid support functions. Two key aspects are presented: a low-cost refrigerator converter with Lyapunov energy function-based local controllers for speed control, and the impact on a standard microgrid system, demonstrating advanced grid support from the load side. This method enhances grid resilience and reliability and can be extended to other residential loads. The study contributes to efficient and robust grid-supportive load management systems, showing promising performance. This approach has the potential to improve overall grid stability and can be adapted for various types of residential appliances. The modeling and simulations in MATLAB/Simulink and PLECS confirm the feasibility and effectiveness of the proposed solution. Future work will explore real-world implementation and scalability of this concept for broader applications.

grid supportive loads (GSL)↗

DOE OSTI2025

SVM-Based Synchronized Fault Detection for 100% Renewable Microgrids

Traditional protection schemes face significant challenges when applied to microgrids with high penetrations of renewables with inverter-based resources (IBRs). The proliferation of advanced sensing and communication technologies has generated copious data, offering an opportunity to overcome these limitations using data-driven machine learning approaches. This work proposes a novel approach based on a support vector machine (SVM) for detecting faults within a 100% renewable microgrid. The approach encompasses a systematic offline training stage for the development of a linear SVM-based fault detection algorithm. This process covers offline data collection from the microgrid under study, the extraction of features such as positive- and negative-sequence components and the total harmonic distortion of the voltage and current measurements of the relays, and the design of the linear SVM-based classifier. During the online implementation, however, different classifiers can exhibit asynchronicity in detecting the fault inception at different subcycle-to-cycle period-level delays. To circumvent this asynchronicity issue, a separate algorithm is developed for each relay to estimate the fault inception time as close to the real fault time. The performance of the proposed SVM-based synchronized fault detection method is evaluated using online time-domain simulation studies on a microgrid test system. The results corroborate the reliability of the fault detection scheme when tested under various fault cases (fault types, locations, and impedances) and non-fault cases during both grid-tied and islanded operation modes.

100% microgrid↗

DOE OSTI2020

Optimized Highly Efficient SSCB Using Organic Substrate Packaging for Electric Vehicle Applications

Solid State Circuit Breakers (SSCBs) are an attractive protection solution for their arcless current interruption and fast actuation speeds over mechanical breakers. This paper proposes a Bidirectional SSCB (BSSCB) with a thermally defined and digitally controlled current time profile for fault protection in EV and other low-voltage DC systems. The paper proposes an organic packaging approach utilizing flex circuitry to develop a reliable, cost-effective power module for BSSCBs. The paper studies transient heat transfer in the power modules using finite element analysis (FEA). An RC themal ladder network is extracted to define a fusing curve. To demonstrate and verify the design, a 1kV/50 A SiC MOSFET BSSCB prototype is fabricated and tested, having a power density of 60 W/cm3 and 4x reduction in form factor over presently researched breakers. Also, given are results for 750 V/150 A operation showing interruption in 2.4 μs.

organic power packaging↗

DOE OSTI2025

femto-PIXAR: a self-supervised neural network method for reconstructing femtosecond X-ray free electron laser pulses

X-ray Free Electron Lasers (X-FELs) operate in a wide range of lasing configurations for a broad variety of scientific applications at ultrafast time-scales such as structural biology, materials science, and atomic and molecular physics. Shot-by-shot characterization of the X-FEL pulses is crucial for analysis of many experiments as well as tuning the X-FEL performance. However, for the weak pulses found in advanced configurations, e.g. those needed for coherent, two-pulse studies of quantum materials, there is no current method for reliably resolving pulse profiles. Here we show that a physics-based U-net model can reconstruct the individual pulse power profiles for sub-picosecond pulse separation without the need for simulations. Using experimental data from weak X-FEL pulse pairs, we demonstrate we can learn the pulse characteristics on a shot-by-shot basis when conventional methods fail.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Reference Station Risk Assessment for Refueling Heavy-Duty Vehicles Using HyRAM+

The deployment of heavy-duty (HD) hydrogen fuel cell vehicles that are entering the market now is driving the need for expanded HD hydrogen refueling station infrastructure to meet demand. This expansion must prioritize safety and reliability, necessitating careful consideration of the associated risks. In this study, we use a light-duty (LD) hydrogen refueling station as a comparative tool to quantify the risks for a HD station, which is essentially a scaled-up version of a LD station.

08 HYDROGEN↗

DOE OSTI2024

X-ray chemical imaging for assessing redox microsites within soils and sediments

Redox reactions underlie several biogeochemical processes and are typically spatiotemporally heterogeneous in soils and sediments. However, redox heterogeneity has yet to be incorporated into mainstream conceptualizations and modeling of soil biogeochemistry. Anoxic microsites, a defining feature of soil redox heterogeneity, are non-majority oxygen depleted zones in otherwise oxic environments. Neglecting to account for anoxic microsites can generate major uncertainties in quantitative assessments of greenhouse gas emissions, C sequestration, as well as nutrient and contaminant cycling at the ecosystem to global scales. However, only a few studies have observed/characterized anoxic microsites in undisturbed soils, primarily, because soil is opaque and microsites require µm-cm scale resolution over cm-m scales. Consequently, our current understanding of microsite characteristics does not support model parameterization. To resolve this knowledge gap, we demonstrate through this proof-of-concept study that X-ray fluorescence (XRF) 2D mapping can reliably detect, quantify, and provide basic redox characterization of anoxic microsites using solid phase “forensic” evidence. First, we tested and developed a systematic data processing approach to eliminate false positive redox microsites, i.e., artefacts, detected from synchrotron-based multiple-energy XRF 2D mapping of Fe (as a proxy of redox-sensitive elements) in Fe-“rich” sediment cores with artificially injected microsites. Then, spatial distribution of Fe II and Fe III species from full, natural soil core slices (over cm-m lengths/widths) were mapped at 1–100 µm resolution. These investigations revealed direct evidence of anoxic microsites in predominantly oxic soils such as from an oak savanna and toeslope soil of a mountainous watershed, where anaerobicity would typically not be expected. We also revealed preferential spatial distribution of redox microsites inside aggregates from oak savanna soils. We anticipate that this approach will advance our understanding of soil biogeochemistry and help resolve “anomalous” occurrences of reduced products in nominally oxic soils.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Characterizing Seasonal Variation of the Atmospheric Mixing Layer Height Using Machine Learning Approaches

As machine learning becomes more integrated into atmospheric science, XGBoost has gained popularity for its ability to assess the relative contributions of influencing factors in the atmospheric boundary layer height. To examine how these factors vary across seasons, a seasonal analysis is necessary. However, dividing data by season reduces the sample size, which can affect result reliability and complicate factor comparisons. To address these challenges, this study replaces default parameters with grid search optimization and incorporates cross-validation to mitigate dataset limitations. Using XGBoost with four years of data from the atmospheric radiation measurement (ARM) (Southern Great Plains (SGP) C1 site, cross-validation stabilizes correlation coefficient fluctuations from 0.3 to within 0.1. With optimized parameters, the R value can reach 0.81. Analysis of the C1 site reveals that the relative importance of different factors changes across seasons. Lower tropospheric stability (LTS, ~0.53) is the dominant factor at C1 throughout the year. However, during DJF, latent heat flux (LHF, 0.44) surpasses LTS (0.22). In SON, LTS (0.58) becomes more influential than LHF (0.18). Further comparisons among the four long-term SGP sites (C1, E32, E37, and E39) show seasonal variations in relative importance. Notably, during JJA, the differences in the relative importance of the three factors across all sites are lower than in other seasons. This suggests that boundary layer development in the summer is not dominated by a single factor, reflecting a more intricate process likely influenced by seasonal conditions such as enhanced convective activity, higher temperatures, and humidity, which collectively contribute to a balanced distribution of parameter impacts. Furthermore, the relative importance of LTS gradually increases from morning to noon, indicating that LTS becomes more significant as the boundary layer approaches its maximum height. Consequently, the LTS in the early morning in autumn exhibits greater relative importance compared to other seasons. This reflects a faster development of the mixing layer height (MLH) in autumn, suggesting that it is easier to retrieve the MLH from the previous day during this period. The findings enhance understanding of boundary layer evolution and contribute to improved boundary layer parameterization.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

A Comparison of Time-Series Gap-Filling Methods to Impute Solar Radiation Data: Preprint

Complete solar resource data sets play a critical role at every stage of solar energy projects; however, measured or modeled solar resource data come with significant uncertainties and usually suffer from several issues, including, but not limited to, data gaps and data quality issues. To mitigate these issues, an appropriate data imputation method should be implemented to build a complete and reliable temporal (and spatial) database. Motivated by this, in this study, we extensively compare the performance of eight different gap-filling methods by creating random and artificial data gaps in (i) hourly irradiance data for 1 year using a few locations of the National Solar Radiation Database (NSRDB) and (ii) 1-minute ground measurement data sets from the Surface Radiation Budget Network (SURFRAD) and the National Renewable Energy Laboratory (NREL) stations.

clearness index↗

DOE OSTI2023

AI-Driven Smart Community Control for Accelerating PV Adoption and Enhancing Grid Resilience

The U.S. Department of Energy (DOE) has launched a Connected Communities program that supports projects that expand DOE's network of grid-interactive, efficient building communities nationwide. As an early pilot of Connected Communities, the National Renewable Energy Laboratory (NREL) and its partners have developed and demonstrated a community-scale, hierarchical control solution to address the potential grid issues arising from high penetration of solar photovoltaics (PV) as well as to improve grid reliability and resilience in this residential community. A field pilot study has been performed at an affordable housing development called the Basalt Vista Community, which was built for school teachers and other professionals in the local workforce and represents an autonomous energy grid with all-electric, energy-efficient homes. With no natural gas line in the community, this is the first all-electric net-zero community in rural Colorado. In the field pilot study, NREL has validated the performance of the hierarchical control solution, which consists of NREL's foresee home energy management systems (HEMS) and community aggregators, in increasing demand flexibility and self-consuming PV, reducing potential over-voltages, and supporting critical loads during emergency events. In this presentation, we will present the methodology, simulation and field pilot results, and lessons learned from the project.

all-electric community↗

DOE OSTI2024

SVM-Based Synchronized Fault Detection for 100% Renewable Microgrids: Preprint

100% microgrid↗