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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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DOE OSTI2026

Digital Assurance for Grid Reliability in the Era of Large Load Growth

The rapid expansion of large electric loads is reshaping the operational and regulatory landscape of the U.S. electric grid. These facilities are reaching new scales of expansion, now exceeding a gigawatt per site, and their highly sensitive, digitally driven behaviors introduce new reliability risks. Recent grid events, including large load losses following routine transmission disturbances, highlight the consequences of limited ride-through capability, inconsistent protection settings, inadequate modeling, and lack of behind-the-meter visibility. Parallels to earlier integration challenges of new grid technologies suggest that the grid’s existing processes, standards, and interconnection frameworks are no longer adequate for emerging large loads. This brief synthesizes lessons from the evolution of inverter-based resource regulation and applies them to large-load integration. It identifies critical gaps in modeling accuracy, interconnection processes, performance standards, and compliance mechanisms. Technical recommendations emphasize advanced monitoring, improved modeling, coordinated communication protocols, modernized substations, and structured behind-the-meter control schemes. Collectively, these measures provide a roadmap to maintain bulk power system reliability while enabling the continued growth of large, electrified digital infrastructure.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Spatial-Temporal Recurrent Graph Neural Networks for Fault Diagnostics in Power Distribution Systems

Fault diagnostics are extremely important to decide proper actions toward fault isolation and system restoration. The growing integration of inverter-based distributed energy resources imposes strong influences on fault detection using traditional overcurrent relays. This paper utilizes emerging graph learning techniques to build new temporal recurrent graph neural network models for fault diagnostics. The temporal recurrent graph neural network structures can extract the spatial-temporal features from data of voltage measurement units installed at the critical buses. From these features, fault event detection, fault type/phase classification, and fault location are performed. Compared with previous works, the proposed temporal recurrent graph neural networks provide a better generalization for fault diagnostics. Moreover, the proposed scheme retrieves the voltage signals instead of current signals so that there is no need to install relays at all lines of the distribution system. Therefore, the proposed scheme is generalizable and not limited by the number of relays installed. The effectiveness of the proposed method is comprehensively evaluated on the Potsdam microgrid and IEEE 123-node system in comparison with other neural network structures.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Design of High Power Density 100 kW Surface Permanent Magnet Machine with No Heavy Rare Earth Material Using Current Source Inverter for Traction Application

Surface permanent magnet (SPM) machines are appealing candidates for traction applications because of high power density and high efficiency. Rare-earth magnets deliver high performance but raise concerns about material supply dependability, particularly if they use heavy rare-earth materials. This paper presents the design of a high-performance SPM machine without any heavy rare-earth material that is optimized specifically for a current-source inverter (CSI) traction drive. This machine is designed for a constant-power speed ratio (CPSR) of 3. A genetic algorithm has been performed to achieve a target of 50 kW/L active power density at speeds up to 20,000 rpm. Operation at a peak line-to-line voltage of 800 V can be achieved using 1200 V power devices with a safety margin. The predicted electromagnetic and rotor structural performance characteristics are presented using both analytical and finite element analysis (FEA) results.

(CPSR)↗
DOE OSTI2024

Numerical Simulation of Infrasound Resonance in Underground Tunnel Structures

Remote observation of infrasound resonant signals emanating from underground tunnel structures could potentially allow the remote quantification of the geometry of the underground structures in which the signals were generated. However, the sensitivity of these observations to tunnel geometry and changes in that geometry are unknown. In this report we outline a numerical simulation study with the following three objectives: 1. Can we model infrasound resonance using numerical simulations? 2. What is the sensitivity of the modeled observations to changes in tunnel geometry or boundary conditions? And 3. Can we accurately model resonant observations of explosions occurring in Redmond Salt Mine in Central Utah. In this report we outline affirmative answers to the first two objectives, but we were unable to accurately model the Redmond Explosions due to numerical instability in the model of the complex structure of the mine. Recommendations for future work emphasize the need to acquire additional datasets and to explore a more data-based approach in which changes in data signatures are detected as a first step towards developing a method which inverts resonant infrasound signals for tunnel geometry.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Seismotectonic evidence for subduction beneath the Eastern Greater Caucasus

SUMMARY We generated high-resolution 3-D seismic velocity models as well as a relocated earthquake catalogue across the eastern Greater Caucasus and Kura basins. This work was done using data from the recently upgraded Republic Seismological Survey Center's (RSSC) seismic network. We generated our tomographic images of crustal velocity structure in Azerbaijan using double-difference inversions (i.e. tomoDD and hypoDD). Earthquake catalogues from the RSSC between 2011 and 2016 were used; these catalogues include absolute arrival times of 103 288 P- and 120 952 S-wave traveltime picks for 7574 events recorded at 35 stations in Azerbaijan. Beginning with a layered, 1-D velocity model that was estimated using VELEST, we inverted simultaneously for relative location, Vp and Vs on a 3-D grid with dimensions 670 × 445 × 45 km, with a uniform grid spacing of 55 × 55 × 5 km for all of Azerbaijan. We observe that the relocated hypocentres cluster into two depth ranges, at the surface and at depth, that appear to correspond to major fault zones and the top of a subducting plate. Additionally, we note intermediate depth seismicity (∼50–60 km) beneath the Kura Basin, and a northward deepening of earthquake depths. Seismic velocities vary significantly throughout the study region; we observe very slow velocities throughout the Kura Basin between 5 and 15 km, and elevated velocities at 20–35 km. The wholesale velocity structure and seismic structure of Kura Basin strongly mirrors that of the Caspian Sea, which suggests that the geodynamics of the Caspian continue westwards into Azerbaijan. The key results of this study suggest that the northward subduction observed in the Caspian Sea continues beneath the Eastern Greater Caucasus, as well as provides evidence for active faulting along the southern margin of the mountain range.

Gunnels, Michael↗
DOE OSTI2021

INTEGRATE - Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements

The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project is developing a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. This AI-based design technology can capture complex non-linear aerodynamic effects while being 100 times faster than design approaches based on computational fluid dynamics. This project enables innovation in wind turbine design by accelerating time to market through higher-accuracy early design iterations to reduce the levelized cost of energy. INVERTIBLE NEURAL NETWORKS Researchers are leveraging a specialized invertible neural network (INN) architecture along with the novel dimension-reduction methods and airfoil/blade shape representations developed by collaborators at the National Institute of Standards and Technology (NIST) learns complex relationships between airfoil or blade shapes and their associated aerodynamic and structural properties. This INN architecture will accelerate designs by providing a cost-effective alternative to current industrial aerodynamic design processes, including: - Blade element momentum (BEM) theory models: limited effectiveness for design of offshore rotors with large, flexible blades where nonlinear aerodynamic effects dominate - Direct design using computational fluid dynamics (CFD): cost-prohibitive - Inverse-design models based on deep neural networks (DNNs): attractive alternative to CFD for 2D design problems, but quickly overwhelmed by the increased number of design variables in 3D problems AUTOMATED COMPUTATIONAL FLUID DYNAMICS FOR TRAINING DATA GENERATION - MERCURY FRAMEWORK The INN is trained on data obtained using the University of Marylands (UMD) Mercury Framework, which has with robust automated mesh generation capabilities and advanced turbulence and transition models validated for wind energy applications. Mercury is a multi-mesh paradigm, heterogeneous CPU-GPU framework. The framework incorporates three flow solvers at UMD, 1) OverTURNS, a structured solver on CPUs, 2) HAMSTR, a line based unstructured solver on CPUs, and 3) GARFIELD, a structured solver on GPUs. The framework is based on Python, that is often used to wrap C or Fortran codes for interoperability with other solvers. Communication between multiple solvers is accomplished with a Topology Independent Overset Grid Assembler (TIOGA). NOVEL AIRFOIL SHAPE REPRESENTATIONS USING GRASSMAN SPACES We developed a novel representation of shapes which decouples affine-style deformations from a rich set of data-driven deformations over a submanifold of the Grassmannian. The Grassmannian representation as an analytic generative model, informed by a database of physically relevant airfoils, offers (i) a rich set of novel 2D airfoil deformations not previously captured in the data , (ii) improved low-dimensional parameter domain for inferential statistics informing design/manufacturing, and (iii) consistent 3D blade representation and perturbation over a sequence of nominal shapes. TECHNOLOGY TRANSFER DEMONSTRATION - COUPLING WITH NREL WISDEM Researchers have integrated the inverse-design tool for 2D airfoils (INN-Airfoil) into WISDEM (Wind Plant Integrated Systems Design and Engineering Model), a multidisciplinary design and optimization framework for assessing the cost of energy, as part of tech-transfer demonstration. The integration of INN-Airfoil into WISDEM allows for the design of airfoils along with the blades that meet the dynamic design constraints on cost of energy, annual energy production, and the capital costs. Through preliminary studies, researchers have shown that the coupled INN-Airfoil + WISDEM approach reduces the cost of energy by around 1% compared to the conventional design approach. This page will serve as a place to easily access all the publications from this work and the repositories for the software developed and released through this pr...

aerodynamics↗
DOE OSTI2026

Analytical desmearing of Bonse–Hart ultra-small-angle neutron scattering data via truncated Abel inversion

A non-iterative analytical framework based on the truncated Abel inversion is developed for desmearing Bonse–Hart ultra-small-angle neutron scattering (USANS) data. The method directly inverts the slit-averaged intensity without empirical extrapolation or iterative regularization, establishing a closed-form relationship between the measured and intrinsic scattering profiles. Numerical benchmarks on representative models, including a rigid-line form factor, a Lorentzian function and a fractal structural model, demonstrate quantitative recovery of the ground-truth intensity across the full Q range. Application to a deuterated polystyrene/poly(2-vinylpyridine) blend further confirms that the approach yields smooth continuous profiles consistent with companion small-angle neutron scattering data. The truncated Abel inversion thus provides a stable, model-independent and physically transparent route for accurate desmearing of Bonse–Hart USANS measurements.

Huang, Guan-Rong [National Tsing Hua University, T↗
DOE OSTI2023

The Cause of Insufficient Damping in Phase-Locked-Loop and Its Influence

Phase-locked loop (PLL) is a critical component that synchronizes grid-following (GFL) inverter-based resources (IBR) to the grid. Insufficient damping in PLL has been identified as a cause associated with real-world weak grid stability issues. This paper reveals that the second-order low-pass filter deployed in a sophisticated PLL structure can have negative influence on damping performance and lead to system instability when the grid is weak and power generation is low. Improving the PLL damping is illustrated as one of the mitigation approaches. The dynamic study is carried out in electromagnetic transient (EMT) simulation environment. In addition to EMT demonstration, a simplified analytical model of PLL is developed to represent a PLL with the sophisticated structure. The resulting overall analytical model of the entire system is then used for eigenvalue- based analysis to illustrate the influence of PLL on dynamic modes.

Wang, Zhengyu↗
DOE OSTI2024

Three-dimensional cooperative inversion of airborne magnetic and gravity gradient data using deep-learning techniques

Using multiple geophysical methods has become a prevailing approach in numerous geophysical applications to investigate subsurface structures and parameters. These multimethod-based exploration strategies have the potential to greatly diminish uncertainties and ambiguities encountered during geophysical data analysis and interpretation. One of the applications is the cooperative inversion of airborne magnetic and gravity gradient data for the interpretation of data obtained in mineral, oil and gas, and geothermal explorations. In this paper, a unified cooperative inversion framework is designed by combining the standard separate inversions with a deep neural network (DNN), which serves as the link between different types of data. A well-trained DNN takes the separately inverted susceptibility and density models as the inputs and provides improved models that will be used as the initial models of deterministic inversions. A two-round iteration strategy is adopted to guarantee the reasonability of the recovered models and overall efficiency of the inversion. In addition, this deep-learning (DL)-based framework demonstrates excellent generalization abilities when tested on models that are entirely distinct from the training data sets. The framework can easily incorporate multiphysics without necessitating any structural changes to the network. Synthetic experiments validate that our DL-based method outperforms conventional separate inversions and cross-gradient-based joint inversion in view of the accuracy of the recovered models and inversion efficiency. Successful application to field data further verifies the effectiveness of our DL-based method.

Geochemistry & Geophysics↗
DOE OSTI2023

Hydrologic Impacts of a Strike-Slip Fault Zone: Insights from Joint 3D Body-Wave Tomography of Rock Valley

The Rock Valley fault zone (RVFZ), an intraplate strike-slip fault zone in the southern Nevada National Security Site (NNSS), hosted a series of very shallow (<3 km) earthquakes in 1993. The RVFZ may also have hydrological significance within the NNSS, potentially playing a role in regional groundwater flow, but there is a lack of local hydrological data. In the Spring of 2021, we collected active-source accelerated weight drop seismic data over part of the RVFZ to better characterize the shallow subsurface. Here, we manually picked ~17,000 P-wave travel times and over 14,000 S-wave travel times, which were inverted for P-wave velocity (V P ), S-wave velocity (V S ), and V P /V S ratio in a 3D joint tomographic inversion scheme. Seismic velocities are imaged as deep as ~700 m in areas and generally align with geologic and structural expectations. V P and V S are relatively reduced near mapped and inferred faults, with the most prominent lower V P and V S zone around the densest collection of faults. We image V P /V S ratios ranging from ~1.5 to ~2.4, the extremes of which occur at a depth of ~100 m and are juxtaposed across a fault. One possible interpretation of the imaged seismic velocities is enhanced fault damage near the densest collection of faults with relatively higher porosity and/or crack density at ~100 m depth, with patches of semiperched groundwater present in the sedimentary rock in higher V P /V S areas and drier rock in lower V P /V S areas. A relatively higher V P /V S area beneath the densest faults persists at depth, which suggests percolation of groundwater via the fault damage zone to the regionally connected lower carbonate aquifer. Potentially, the presence and movement of groundwater may have played a role in the 1993 earthquake aftershocks.

58 GEOSCIENCES↗
DOE OSTI2022

Invertible Neural Networks for Airfoil Design

We report the airfoil design problem, in which an engineer seeks a shape with desired performance characteristics, is fundamental to aerodynamics. Design workflows traditionally rely on iterative optimization methods using low-fidelity integral boundary-layer methods as higher-fidelity adjoint-based computational fluid dynamics methods are computationally expensive. Surrogate-based approaches can accelerate the design process but still rely on some iterative inverse design procedure. In this work, we leverage emerging invertible neural network (INN) tools to enable the rapid inverse design of airfoil shapes for wind turbines. INNs are specialized deep-learning models with well-defined inverse mappings. When trained appropriately, INN surrogate models are capable of forward prediction of aerodynamic and structural quantities for a given airfoil shape as well as inverse recovery of airfoil shapes with specified aerodynamic and structural characteristics. The INN approach offers a roughly 100 times speed-up compared to adjoint-based methods for inverse design. We demonstrate the INN tool for inverse design on three test cases of 100 airfoils each that satisfy the performance characteristics close to those of airfoils used in wind-turbine blades. All generated shapes satisfy the desired aerodynamic characteristics, demonstrating the success of the INN approach for inverse design of airfoils.

17 WIND ENERGY↗
DOE OSTI2020

Single Stage PLL-less Decoupled Active and Reactive Power Control for Weak Grid Interactive Inverters

This paper presents a single stage phase locked loop-less (PLL-less) active and reactive power (PQ) control for single-phase weak grid interactive inverters. The absence of the PLL requirement in the proposed PQ control enhances the stability margin comparing to conventional current control approaches in both the stationary or the synchronous reference frame. Additionally, the proposed control scheme enables direct PQ control with a single loop control structure. This paper demonstrates that the proposed PLL-less PQ control scheme is asymptotically stable if the controller gains are positive. The necessary conditions for supplying the rated active power into the weak grid are derived. The provided analysis shows that certain amount of reactive power injection is necessary to avoid point of common coupling (PCC) voltage collapse when the rated active power is injected into the weak grid. Several case studies are provided to demonstrate the performance of the proposed PLL-less PQ control scheme under weak grid condition.

Khan, Ahmad↗
DOE OSTI2020

Na-functionalized Ir Te 2 monolayer: Suppressed charge ordering and electric field tuned topological phase transition

Two-dimensional materials with atomic thickness usually possess superior tunability by surface adsorption than their bulk counterparts, showing great promise for novel nanotechnologies. In layered transition-metal dichalcogenide IrTe 2 there exhibits complex structural distortions induced by charge ordering, resulting in difficulties for the applications of its corresponding monolayer material. Here, using first-principles calculations, we demonstrate that depositing Na on the surface of the IrTe 2 monolayer can suppress the structural distortion to form a stable NaIrTe 2 sheet. It naturally breaks the inversion symmetry to enable a Rashba-type spin splitting for potential spintronic applications. In addition, the introduced empty Na s band and the valence band of the IrTe 2 monolayer can be inverted by the application of a perpendicular electric field, achieving a quantum phase transition from normal to topological insulator. Such an electric field-controlled topological phase transition is promising for the realization of topological field-effect transistors. Furthermore, these findings not only provide a feasible approach to stabilizing the IrTe 2 monolayer, but also broaden its applications in spintronics and low-dissipation topoelectronics.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Neural Networks-Based Inverter Control: Modeling and Adaptive Optimization for Smart Distribution Networks

The optimal voltage control of inverter-based resources, especially under the high penetration of solar photovoltaics, is critical to the stability of the distribution power system. However, the computational complexity as well as the coordinated operation performance of the voltage control optimization in the distribution power system limits the real-time applications. To mitigate this issue, a model-free based adaptive optimal control scheme for the smart inverter is proposed to maximize the active power generation, minimize the power loss, and maintain the bus voltages in smart distribution networks. An inverter-based optimization model for coordinated operation is first established, considering the uncertainties of renewable power generation. Subsequently, by collecting the data and control strategies, the neural networks (NNs) based algorithm is proposed to efficiently predict the best possible control strategy. The main objective of this scheme is to accurately predict candidate optimal solutions with near-negligible feasibility and optimization gaps, with the advantage of avoiding complicated iteration-based numerical algorithms. Thereafter, the co-simulation among OpenDSS, MATLAB, and Python is set up to fully take advantage of the three individual software. Experiments are conducted based on different control parameter characteristics and structures of NNs. Finally, the results reveal that an average mean squared error of 0.013 and 1 ms response time are achieved, which is lower than some state-of-the-art methods.

42 ENGINEERING↗
DOE OSTI2023

Pragmatic Uncertainty Quantification and Propagation in Inverse Estimation of Structural Dynamics Parameters given Material Property Uncertainties and Limited Sensor Data

In this report we demonstrate some relatively simple and inexpensive methods to effectively account for various sources of epistemic lack-of-knowledge type uncertainty in inverse problems. The demonstration problem involves inverse estimation of six parameters of a bolted joint that attaches a kettlebell shaped object to a thick plate. The parameters are efficiently inverted in a modal-based model calibration using gradient-based optimization. Two material properties of the kettlebell are treated as uncertain to within given epistemic uncertainty bounds. We apply and test interval and sparse-sample probabilistic approaches to account for uncertainty in the estimated parameters (and various scalar functionals of the parameters as generic quantities of interest, QOIs) due to uncertainties in the material properties. We also investigate the error effects of limited numbers of vibration sensors (accelerometers) on the kettlebell and plate, and therefore abbreviated excitation/response information in the parameter inversions. We propose and demonstrate a Leave-K-Sensors-Out “cross-prediction” UQ approach to estimate related uncertainties on the parameters and QOI functionals. We indicate how uncertainties from material properties and limited sensors are treated in a combined manner. The economical combined UQ approach involves just three to five samples (i.e. three to five inverse simulations), with no added complication or error/uncertainty from use of surrogate models for affordability. Finally, we describe a related economical UQ approach for handling potential parameter solution non-uniqueness and numerical optimization related precision uncertainties in the estimated parameter values. Indicated further research is identified.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Geometric Interpretation of the Cluster Location Problem Part II: Application to the Pahala, Hawaii, Earthquake Sequence

In the companion “Theory” article, we presented a new framing of the seismic location problem in terms of differential geometry (Harris et al., 2025). From that viewpoint, we developed a “project and correct” approach for estimating the relative locations of earthquakes. Here, in this study, we use project and correct to estimate high-precision relative locations of events from an earthquake sequence beneath the town of Pahala, Hawaii, using high-precision correlation-derived picks. The sequence was active from 2020 through 2022 and produced many highly correlated signals at Hawaii Volcano Observatory (HVO) stations on the island of Hawaii. The data we inverted consisted of 2882 events with observations at 5 HVO stations. For comparison with the travel-time image, we also produced conventional hypocenter solutions using both the Bayesloc program (Myers et al., 2007, 2009) and a purpose-built double-difference code. There were obvious structural elements in the resulting image, the resolution of which we used to test the performance of the project and the correct algorithm. For the projection step, we first produced a 3D local basis using an singular value decomposition (SVD) of the 2882 groups of times. Projection of the travel-time vectors into this basis resulted in an image with structures similar to those produced by our conventional locators, but with distortion as predicted by theory. Removing the distortion requires an inverse operator generated from the metric tensor at the geometric centroid of the events. We compared two approaches to obtaining such an inverse operator. The first uses an estimate of the geographic centroid of the event cloud from the centroid of the travel-time data. The second approach uses the centroid of the conventionally produced locations. The first approach produces a corrected image very similar to the conventional results, but with a rotation. The corrected image produced using the conventionally derived centroid is a near-exact match to the conventional locations.

Dodge, Douglas A. [Lawrence Livermore National Lab↗
DOE OSTI2022

New Industry Partnerships: Solar Energy Technologies Office Support for Early Projects in the Energy Systems Integration Facility: An Agreement Closeout Summary Report

The U.S. Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy (EERE) Solar Energy Technologies Office (SETO)-funded New Industry Partnerships (NIPs) agreement established multiple new cooperative research projects with industry partners. Each project demonstrated the use of the National Renewable Energy Laboratory's Energy Systems Integration Facility (ESIF) as a national asset for research and development, testing, and validation of new technologies to support high penetrations of solar energy on the electric grid. The agreement was launched in 2013 and represented the SETO portion of the DOE EERE-wide Integrated Network Testbed for Energy Grid Research and Technology Experimentation (INTEGRATE) program. The focus of INTEGRATE was on multi-energy system testing and on testing the interactions of energy systems with information technology, communications, and telecommunications (ICT) systems. The NIPs agreement was a bit different from traditional research projects because it was specifically structured to enable partnerships for ESIF testing and as such required at least a 1:1 funds-in cost share from industry partners. In the end, the project engaged six different industry partnerships and resulted in testing a large number of innovative technologies. These included tiny inverters; power-to-gas technologies; and advanced simulation, analysis, and power-hardware-in-the-loop testing techniques. The agreement also provided significant impact for both research and industrial communities. This summary report provides a brief, high-level overview of these projects and their highlights along with lists of citations and other impacts. Readers are referred to the corresponding project reports for more in-depth information.

14 SOLAR ENERGY↗
DOE OSTI2021

Resilience in an Evolving Electrical Grid

Fundamental shifts in the structure and generation profile of electrical grids are occurring amidst increased demand for resilience. These two simultaneous trends create the need for new planning and operational practices for modern grids that account for the compounding uncertainties inherent in both resilience assessment and increasing contribution of variable inverter-based renewable energy sources. This work reviews the research work addressing the changing generation profile, state-of-the-art practices to address resilience, and research works at the intersection of these two topics in regards to electrical grids. The contribution of this work is to highlight the ongoing research in power system resilience and integration of variable inverter-based renewable energy sources in electrical grids, and to identify areas of current and further study at this intersection. Areas of research identified at this intersection include cyber-physical analysis of solar, wind, and distributed energy resources, microgrids, network evolution and observability, substation automation and self-healing, and probabilistic planning and operation methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗