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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Scheduler Modeling of Distributed Energy Resources for Providing Ancillary Services

Distribution energy resources (DERs) have been integral components of modern power systems, and their capability to provide grid services has been widely studied. To promote the deployment of these resources in providing grid services in real-world utility operations, this paper proposes a day-ahead scheduler model for a distribution system connected DER plant. A certain amount of generation capacity of this DER plant is reserved for frequency services, and some ancillary services for the distribution system-including peak load reduction, voltage regulation, and power factor control-are integrated into the model. The model is tested on a real-world distribution system. From the simulation results, the energy and reserve schedule of the solar photovoltaic (PV) unit and battery energy storage system (BESS) can be determined, and voltage and power factor are well maintained. Additionally, in order to demonstrate the specific characteristics of the co-located and hybrid operation modes for the PV and BESS, these two modes are analyzed both theoretically and through real-time simulation. Simulation results show that most of PV's variability is transferred to the net power in the co-located mode, whereas it is transferred to the BESS in the hybrid mode. This proposed scheduler model and the comparison of co-located and hybrid modes can provide practical guidance for the applications of DER plant in the real-world utility.

14 SOLAR ENERGY↗
DOE OSTI2022

ADMS and DER Coordinated Capability Demonstrations to Assist Utilities to Increase Resilience and Reliability

The proliferation of distributed energy resources (DERs) presents opportunities and challenges to distribution grid operators. The National Renewable Energy Laboratory (NREL) and Survalent are collaborating to demonstrate advanced distribution management system (ADMS) and DER coordinated capability to assist utilities to increase resilience and reliability. ADMS and Distributed Energy Resource Management System (DERMS) Coordination for Peak Load Management (PLM). Fault Location, Isolation, and Service Restoration (FLISR) in the Presence of DERs. Grid-Edge Intelligent Distribution Automation System for Self-Healing Distribution Grids. These laboratory demonstrations use NREL's ADMS Test Bed.

ADMS↗
DOE OSTI2024

Surf-Deformer: Mitigating Dynamic Defects on Surface Code via Adaptive Deformation

In this paper, we introduce Surf-Deformer, a code deformation framework that seamlessly integrates adaptive defect mitigation functionality into the current surface code workflow. It crafts several basic deformation instructions based on fundamental gauge transformations, which can be combined to explore a larger design space than previous methods. This enables more optimized deformation processes tailored to specific defect situations, restoring the QEC capability of deformed codes more efficiently with minimal qubit resources. Additionally, we design an adaptive code layout that accommodates our defect mitigation strategy while ensuring efficient execution of logical operations. Our evaluation shows that Surf-Deformer outperforms previous methods by significantly reducing the end-to-end failure rate of various quantum programs by 35× to 70×, while requiring only about 50% of the qubit resources compared to the previous method to achieve the same level of failure rate. Ablation studies show that Surf-Deformer surpasses previous defect removal methods in preserving QEC capability and facilitates surface code communication by achieving nearly optimal throughput.

Yin, Keyi↗
DOE OSTI2026

Process design for recovering rare-earth elements from mine tailings with low rare-earth concentrations via sequential leaching and solvent extraction

Rare earth elements (REEs) are essential for advanced technologies and yet face significant supply chain risks due to their concentrated global production and limited domestic availability. Addressing this challenge requires efficient processes capable of upgrading low-grade secondary resources such as mine tailings. In this study, we developed a novel separation flowsheet that integrates sequential leaching and 2-stage solvent extraction (SX) processes to recover high-purity heavy REEs (HREEs) and light REEs (LREEs) from a simulated mine-tailing concentrate containing 2.4 wt% total REEs (TREEs; 0.6 wt% LREEs and 1.8 wt% HREEs). Sequential leaching with controlled pH adjustment selectively precipitated REEs while retaining the large amount of impurities in the solution, producing an REE-enriched leachate by following leaching processes with roughly twice the REE concentration and half the impurity concentration compared to that of single-step leaching. The optimized SX flowsheet employed Cyanex 572 to extract HREEs and Fe over LREEs, followed by Fe removal using tributyl phosphate (TBP), while the raffinate stream was processed by SX with di(2-ethylhexyl)phosphoric acid (D2EHPA) to recover LREEs under optimized conditions balancing both extraction efficiency and purity. Although increased extractant availability in the organic phase improved LREE recovery, it also increased co-extraction of Ca, underscoring trade-offs in process optimization. Both HREE- and LREE-rich solutions were subsequently precipitated into solid products via oxalate precipitation, resulting in high-purity REE solids containing ∼92.0 wt% HREEs (∼95.7 wt% TREEs) and ∼92.8 wt% LREEs (∼94.0 wt% TREEs). In conclusion, this proof-of-concept study using simulated mine tailings demonstrates a promising approach for upgrading low-grade REE resources, while highlighting the need for future validation with real materials.

Mine tailings↗
DOE OSTI2021

Influence of the Anionic Zinc-Adeninate Metal–Organic Framework Structure on the Luminescent Detection of Rare Earth Ions in Aqueous Streams

Rare earth elements (REEs) are critical to numerous technologies; however, a combination of increasing demand, environmental concerns, and monopolistic marketplace conditions has spurred interest in boosting the domestic REE production from sources such as coal utilization byproducts. The economic viability of this approach requires rapid, inexpensive, and sensitive analytical techniques capable of characterizing the REE content during resource exploration and downstream REE processing (e.g., analyzing REE separation, concentration, and purification production steps). Luminescence-based sensors are attractive because many REEs may be sensitized to produce element-specific emission. Hence, a single material may simultaneously detect and distinguish multiple REEs. Metal–organic frameworks (MOFs) can sensitize multiple REEs, but their viability has been hindered by sensitivity and selectivity challenges. Understanding how the MOF structure impacts the REE sensing efficacy is critical to the rational design of new sensors. Here, we evaluate the sensing performance of seven different anionic zinc-adeninate MOFs with different organic linkers and/or structures for the visible-emitting REEs Tb, Dy, Sm, and Eu. The choice of a linker determines which REEs are sensitized and significantly influences their sensitivity and selectivity against competing species (here, Fe(II) and HCl). For a given linker, structural changes to the MOF can further fine-tune the performance. The MOFs produce some of the lowest detection limits (sub-10 ppb for Tb) reported for the aqueous sensitization-based REE detection. Importantly, the most selective MOFs demonstrated the ability to sensitize the REE signal at sub-ppm levels in a REE-spiked acid mine drainage matrix, highlighting their potential for use in real-world sensing applications.

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

Advances in Multiscale Modeling of Lignocellulosic Biomass

Applications and associated processing technologies of lignocellulosic biomass are becoming more important as we endeavor to meet societal demand for fuels, chemicals, and materials from renewable resources. Meanwhile, the rapidly expanding availability and capabilities of high-performance computing present an unprecedented opportunity to accelerate development of technologies surrounding lignocellulose utilization. In order to realize this potential, suitable modeling frameworks must be constructed that effectively capture the multiscale complexity and tremendous variety exhibited by lignocellulosic materials. In our assessment of previous endeavors towards this goal, several important shortcomings have been identified: (1) the lack of multiscale integration strategies that capture emergent properties and behaviors spanning different length scales, and (2) the inability of many modeling approaches to effectively capture the variability and diversity of lignocellulose that arise from both natural and process-induced sources. In this perspective, we survey previous modeling approaches for lignocellulose and simulations processes involving its chemical and mechanical transformation, and suggest opportunities for future development to enhance the utility of computational tools to address barriers to widespread adoption of a renewable bioeconomy.

09 BIOMASS FUELS↗
DOE OSTI2022

Lattice Resonances of Nanohole Arrays for Quantum Enhanced Sensing

We report periodic arrays of nanoholes perforated in metallic thin films interact strongly with light and produce large electromagnetic near-field enhancements in their vicinity. As a result, the optical response of these systems is very sensitive to changes in their dielectric environment, thus making them an exceptional platform for the development of compact optical sensors. Given that these systems already operate at the shot-noise limit when used as optical sensors, their sensing capabilities can be enhanced beyond this limit by probing them with quantum light, such as squeezed or entangled states. Motivated by this goal, here, we present a comparative theoretical analysis of the quantum enhanced sensing capabilities of metallic nanohole arrays with one and two holes per unit cell. Through a detailed investigation of their optical response, we find that the two-hole array supports resonances that are narrower and stronger than its one-hole counterpart, and therefore have a higher fundamental sensitivity limit as defined by the quantum Cramer-Rao bound. We validate the optical response of the analyzed arrays with experimental measurements of the reflectance of representative samples. The results of this work advance our understanding of the optical response of these systems and pave the way for developing sensing platforms capable of taking full advantage of the resources offered by quantum states of light.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2026

Real-time heralded resource-state generator for non-Gaussian teleportation

We experimentally demonstrate a heralded two-mode resource state (with fidelity F = 0.973 ± 0.005) capable of real-time use in quantum networking protocols, enabled by a newly developed photon-subtraction orchestrator system and a synchronized homodyne detection server.

Chapman, Joseph [ORNL] (ORCID:0000000233460914)↗
DOE OSTI2020

A Novel use of Direct Simulation Monte-Carlo to Model Dynamics of COVID-19 Pandemic Spread

In this report, we evaluate a novel method for modeling the spread of COVID-19 pandemic. In this new approach we leverage methods and algorithms developed for fully-kinetic plasma physics simulations using Particle-In-Cell (PIC) Direct Simulation Monte-Carlo (DSMC) models. This approach then leverages Sandia-unique simulation capabilities, and High-Performance Computer (HPC) resources and expertise in particle-particle interactions using stochastic processes. Our hypothesis is that this approach would provide a more efficient platform with assumptions based on physical data that would then enable the user to assess the impact of mitigation strategies and forecast different phases of infection. This work addresses key scientific questions related to the assumptions this new approach must make to model the interactions of people using algorithms typically used for modeling particle interactions in physics codes (kinetic plasma, gas dynamics). The model developed uses rational/physical inputs while also providing critical insight; the results could serve as inputs to, or alternatives for, existing models. The model work presented was developed over a four-week time frame, thus far showing promising results and many ways in which this model/approach could be improved. This work is aimed at providing a proof-of-concept for this new pandemic modeling approach, which could have an immediate impact on the COVID-19 pandemic modeling, while laying a basis to model future pandemic scenarios in a manner that is timely and efficient. Additionally, this new approach provides new visualization tools to help epidemiologists comprehend and articulate the spread of this and other pandemics as well as a more general tool to determine key parameters needed in order to better predict pandemic modeling in the future. In the report we describe our model for pandemic modeling, apply this model to COVID-19 data for New York City (NYC), assess model sensitivities to different inputs and parameters and , finally, propagate the model forward under different conditions to assess the effects of mitigation and associated timing. Finally, our approach will help understand the role of asymptomatic cases, and could be extended to elucidate the role of recovered individuals in the second round of the infection, which is currently being ignored.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Center for Integrated Simulation of Fusion Relevant RF Actuators

This project was part of the “Center for Integrated Simulation of Fusion Relevant RF Actuators” SciDAC-4 project, led by Dr. Paul Bonoli (MIT). Rather than use an acronym (CISFRRFA), the project will be referred to in this document as the “RF-SciDAC4”. The larger SciDAC-4 project goals were to: 1. Develop an integrated simulation of the antenna + sheath + scrape-off-layer + core plasma system which fully utilizes leadership class computing resources to move towards a quantitative predictive capability for the response to RF power. 2. Work closely with the SciDAC-4 Whole Device Modeling (WDM) community to make both our new code development efforts, as well as the established hierarchy of RF tools, available within their environment, and to utilize WDM technologies to implement the couplings below. 3. Validate this predictive capability on appropriately diagnosed experiments including dedicated RF test stands, linear devices, and existing tokamaks. 4. Use these tools to inform design of robust, impurity-mitigating RF heating and current drive sources for future fusion devices.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Land Model Testbed: Accelerating Development, Benchmarking and Analysis of Land Surface Models

A Land Model Testbed (LMT), designed to provide a computational framework for systematically assessing model fidelity and supporting rapid development of complex multiscale models, offers a general-purpose workflow for conducting large ensemble simulations of multiple land surface models, post-processing large volumes of model output, and evaluating model results. It leverages existing tools for launching model simulations and the International Land Model Benchmarking (ILAMB) package for assessing model fidelity through comparison with best-available observational datasets. Increased complexity and proliferation of uncertain parameters in process representations in land surface models has driven the need for frequent and intensive testing and evaluating of models to quantify uncertainties and optimize parameters such that results are consistent with observations. The LMT described here meets these needs by providing tools to run thousands of ensemble simulations simultaneously and post-process their output files, by automating execution of an enhanced version of ILAMB with site-specific benchmarks and multivariate functional relationships, and by offering ensemble diagnostics and a customizable dashboard for displaying model performance metrics and associated graphics. We envision the LMT capabilities will serve as a foundational computational resource for a proposed user facility focused on terrestrial multiscale model--data integration.

Sreepathi, Sarat↗
DOE OSTI2025

SymbolNet: neural symbolic regression with adaptive dynamic pruning for compression

Abstract Compact symbolic expressions have been shown to be more efficient than neural network (NN) models in terms of resource consumption and inference speed when implemented on custom hardware such as field-programmable gate arrays (FPGAs), while maintaining comparable accuracy (Tsoi et al 2024 EPJ Web Conf. 295 09036). These capabilities are highly valuable in environments with stringent computational resource constraints, such as high-energy physics experiments at the CERN Large Hadron Collider. However, finding compact expressions for high-dimensional datasets remains challenging due to the inherent limitations of genetic programming (GP), the search algorithm of most symbolic regression (SR) methods. Contrary to GP, the NN approach to SR offers scalability to high-dimensional inputs and leverages gradient methods for faster equation searching. Common ways of constraining expression complexity often involve multistage pruning with fine-tuning, which can result in significant performance loss. In this work, we propose S y m b o l N e t , a NN approach to SR specifically designed as a model compression technique, aimed at enabling low-latency inference for high-dimensional inputs on custom hardware such as FPGAs. This framework allows dynamic pruning of model weights, input features, and mathematical operators in a single training process, where both training loss and expression complexity are optimized simultaneously. We introduce a sparsity regularization term for each pruning type, which can adaptively adjust its strength, leading to convergence at a target sparsity ratio. Unlike most existing SR methods that struggle with datasets containing more than O ( 10 ) inputs, we demonstrate the effectiveness of our model on the LHC jet tagging task (16 inputs), MNIST (784 inputs), and SVHN (3072 inputs).

Tsoi, Ho Fung (ORCID:0000000225502184)↗
DOE OSTI2026

Bipolar Membranes Electrodialysis for Water Purification and Resource Recovery

Reverse osmosis concentrate (ROC) from inland brackish water desalination and wastewater recycling presents moderate salinity, posing significant challenges for treatment and disposal. Existing treatment technologies predominantly rely on energy-intensive thermal methods, large-area evaporation strategies, or deep-well disposal, which often result in the loss of water value. Electrified membrane brine treatment offers a promising alternative by utilizing renewable energy sources, enabling complete water recycling. This work proposes the development of next-generation bipolar membrane electrodialysis (BMED) systems capable of desalinating brine while simultaneously recovering valuable resources. However, several critical challenges remain in the areas of membrane engineering for BMED. These include: 1) decreased current efficiency during desalination due to the low conductivity of feed chambers, 2) fouling and scaling from multivalent ions and contaminants in the feed, and 3) the retention and carryover of ROC contaminants, including PFAS compounds. Our research focuses on the development of novel anion exchange membranes (AEMs) and cation exchange membranes (CEMs) based on synthesized multiblock copolymers. We investigate the effects of block length, ion exchange capacity (IEC), and polymer backbone structure on cation and anion transport (e.g., Na+, Cl-, OH-, H+) for both our synthesized membranes and commercial membranes. Fundamental properties of these membranes, such as water uptake, IEC, and ionic conductivity, are also evaluated to optimize performance for BMED applications.

08 HYDROGEN↗
DOE OSTI2022

Blackstart Capability and Survivability of Wind Turbines with Fully Rated Converters

We report the blackstart capability of wind turbines is critical for the recovery of wind-dominant power systems from blackouts. If wind power plants are not able to restore a power system, the incorporation of wind resources into electric grids could be limited by blackstart capability. This paper tackles this problem by setting forth a grid-forming controller for Type 4 wind turbines which employ fully rated power converters. To this end, we engineer two-axis anti-windup proportional-integral regulators that serve to ride through restoration disturbances. We also design an active protection system to prevent wind turbines from stalling during recovery from blackouts. We demonstrate in a high-fidelity simulation environment that Type 4 wind turbines with the developed controllers and protection subsystem can: (i) re-energize a notional wind-dominant grid; (ii) ride through asymmetrical faults; and (iii) survive low wind speed events.

17 WIND ENERGY↗
DOE OSTI2020

Integrated Resource Supply-Demand-Routing Model for the COVID-19 Crisis

As part of the Department of Energy response to the novel coronavirus disease (COVID-19) pandemic of 2020, a modeling effort was sponsored by the DOE Office of Science. Through this effort, an integrated planning framework was developed whose capabilities were demonstrated with the combination of a treatment resource demand model and an optimization model for routing supplies. This report documents this framework and models, and an application involving ventilator demands and supplies in the continental United States. The goal of this application is to test the feasibility of implementing nationwide ventilator sharing in response to the COVID-19 crisis. Multiple scenarios were run using different combinations of forecasted and observed patient streams, and it is demonstrated that using a "worst-case forecast for planning may be preferable to best mitigate supply-demand risks in an uncertain future. There is also a brief discussion of model uncertainty and its implications for the results.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

FORCE-DISPATCHES Integration - Initial Demonstration

Integrated energy systems (IES) combine, in mutually beneficial ways, power from variable renewable energy sources and nuclear power plants (NPP) to improve economic viability under uncertain market and weather conditions. The open-source Framework for Optimization of Resources and Economics (FORCE) tool suite, developed at Idaho National Laboratory (INL), has enabled comprehensive modeling and simulation of IES. The capabilities within FORCE include grid portfolio optimization through the Holistic Energy Resource Optimization Network (HERON) and the transient process model analysis library HYBRID, among others. Continuous efforts and investments from the IES programs have been made to expand and improve the versatility of the FORCE toolset in fiscal year 2022. Code-coupling and cross-tool communication have been important methods for improving this versatility. This report focuses on an additional workflow in the HERON tool for capacity and dispatch stochastic optimization through integration with the external tool Design Integration and Synthesis Platform to Advance Tightly Coupled Hybrid Energy Systems (DISPATCHES). DISPATCHES was primarily developed by the National Energy Technology Laboratory, in collaboration with other national laboratories, which included INL, universities, and industry partners. It is coupled to a library of algebraic models for specific plant components, and to a framework for stochastic optimization different from that provided in the current Risk Analysis Virtual Environment (RAVEN)-running-RAVEN algorithm in HERON. HERON currently conducts stochastic optimization via an outer-inner loop: it optimizes over variable capacity on the outer loop, and at each step within the capacity parameter space, conducts an inner optimization over scenarios (of market signals, demand, and/or weather patterns) and hourly dispatch throughout a user-specified number of years. On the other hand, DISPATCHES conducts stochastic optimization via an “all-at-once” strategy in which capacity variables are optimized at the same level as dispatch variables, as all scenarios are considered at once. The latter method works especially well for projects of limited size and project length, as the necessary computational power and memory increases with the number of variables and scenarios. The new capability to use the DISPATCHES workflow in HERON enhances standalone simulations by leveraging FORCE tools—namely, the economic metrics from the Tool for Economic Analysis (TEAL) and reduced-order model (ROM) sampling from RAVEN. The initial demonstration of the DISPATCHES workflow simulates an existing nuclear-case flowsheet within the DISPATCHES repository—this models a NPP with a secondary revenue stream for hydrogen production. Electrical output from the plant is converted to hydrogen via a proton-exchange membrane (PEM) electrolyzer, hydrogen tanks are used for storage, and an additional turbine is added for hydrogen combustion. Continued work regarding this FORCE-DISPATCHES integration will include automatic generation of DISPATCHES models from HERON inputs, offering analysts the option of using either the RAVEN-runsRAVEN or DISPATCHES workflow to solve technoeconomic optimization problems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Bayesian Analysis of TRISO Fuel: Quantifying Model Inadequacy, Incorporating Lower-Length-Scale Effects, and Developing Parallel Active Learning Capabilities

The U.S. Department of Energy (DOE)’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program aims to develop predictive capabilities by applying computational methods to the analysis and design of advanced reactor and fuel-cycle systems. This program has been providing engineering-scale support for the continued development of BISON, a high-fidelity, high-resolution fuel performance tool. Fuel behavior in nuclear reactors is governed by a complex network of mechanisms that interact with various other physics aspects in the reactor system. Any model developed to represent fuel behavior will likely be idealized, resulting in uncertainties when comparing their predictions against the observed data. In Fiscal Year (FY)-23, we initiated the Uncertainty Quantification (UQ) work by using Bayesian methods to establish a level of model trustworthiness and further improve it, with a particular emphasis on TRI-Structural isOtropic (TRISO) nuclear fuel. This year, we further expanded on that UQ work by investigating an approach to quantifying model inadequacy and accounting for lower-length scale (LLS) effects in TRISO silver (Ag) release modeling. Furthermore, we are implementing parallel active learning capabilities to reduce the computational cost (i.e., required computational resources and elapsed time) of performing UQ. Specifically, we utilized The Kennedy O’Hagan framework for Bayesian uncertainty quantification (KOH) to account for model inadequacy in TRISO Ag release predictions made by BISON. The KOH framework represents an improvement over the standard Bayesian framework used in FY-23. Explicitly accounting for model inadequacy in the Bayesian framework helps establish the level of experimental noise uncertainty in the Advanced Gas Reactor (AGR) data. We compared the inverse UQ results obtained from both the standard Bayesian and KOH frameworks in light of the AGR-2/3/4 data, and also compared the predictive UQ results obtained from these two frameworks in light of the AGR-1 data. Next, we investigated the impact of considering LLS effects in the Ag release simulations. We developed an expanded database of LLS simulated effective diffusivities for Ag, covering a wide range of microstructures and temperatures. Using this database, we developed a framework for incorporating LLS effects into the engineering-scale Ag release UQ. We developed both parametric and non-parametric approaches for bridging the length scales. We then investigated the inverse UQ results in light of the AGR-2/3/4 data and the predictive UQ results in light of the AGR-1 data, and compared the LLS-informed approach and the Arrhenius equation, which does not include microstructure information. Finally, we discussed implementing parallel active learning capabilities in the Multiphysics Object Oriented Simulation Environment (MOOSE)/BISON to reduce the computational cost (i.e., computational resources and elapsed time) of Bayesian UQ. For verification purposes, we first tested these new capabil ities on a species interaction problem. We then demonstrated them on the TRISO Ag release application, showing that parallel active learning capabilities can enhance the accuracy of UQ while also substantially reducing the computational cost in comparison to the reference methods developed in FY-23.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Ground Source Heat Pump Screening Tool and Related Resources

This archive contains both a link to Oak Ridge National Laboratory's Ground Source Heat Pump (GSHP) Screening Tool and related resources documenting its development, technical features, and applications. The web-based screening tool is a techno-economic analysis resource designed to evaluate the feasibility, costs, and benefits of implementing GSHP systems in buildings across various U.S. climate zones. It is intended for use by building owners, HVAC system designers, and installers to support decision-making in residential and commercial applications, incorporating advanced modeling capabilities such as ground heat exchanger design. The included resources detail the tool's creation and use, covering topics such as innovative ground heat exchanger design methodologies, the development of an open library of g-functions for borehole configurations, and studies on GSHP performance and economic viability in retrofitting single-family homes.

15 GEOTHERMAL ENERGY↗