Search NASA⌕ Search

SEARCH · Search NASA

Results for “Application Framework”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Probabilistic Deliverability Assessment of Distributed Energy Resources via Scenario-Based AC Optimal Power Flow

As electric grids decarbonize and distributed energy resources (DERs) become increasingly prevalent, interconnection assessments must evolve to reflect operational variability and control flexibility. This paper highlights key modeling limitations observed in practice and reviews approaches for modeling uncertainty. It then introduces a Probabilistic Deliverability Assessment (PDA) framework designed to complement and extend existing procedures. The framework integrates scenario-based AC optimal power flow (AC OPF), corrective dispatch, and optional multi-temporal constraints. Together, these form a structured methodology for quantifying DER utilization, deliverability, and reliability under uncertainty in load, generation, and topology. Outputs include interpretable metrics with confidence intervals that inform siting decisions and evaluate compliance with reliability thresholds across sampled operating conditions. A case study on Puerto Rico’s publicly available bulk power system model demonstrates the framework’s application using minimal input data, consistent with current interconnection practice. Across staged fossil generation retirements, the PDA identifies high-value DER sites and regions requiring additional reactive power support. Results are presented through mean dispatch signals, reliability metrics, and geospatial visualizations, demonstrating how the framework provides transparent, data-driven siting recommendations. The framework’s modular design supports incremental adoption within existing workflows, encouraging broader use of AC OPF in interconnection and planning contexts.

14 SOLAR ENERGY↗
DOE OSTI2026

From natural language to control signals: a conceptual framework for semantic channel finding in complex experimental infrastructure

Modern experimental platforms such as particle accelerators, fusion devices, telescopes, and industrial process control systems expose tens to hundreds of thousands of control and diagnostic channels, accumulated over decades of hardware evolution. Operators and AI systems alike depend on informal expert knowledge, inconsistent naming conventions, and scattered documentation to locate the signals required for monitoring, troubleshooting, and automated control, creating a persistent bottleneck for reliability, scalability, and emerging language-model-driven interfaces. We formalize semantic channel finding, the task of mapping natural-language intent to concrete control-system signals, as a general problem in complex experimental infrastructure, and introduce a four-paradigm conceptual framework to guide architecture selection based on facility-specific data regimes. The paradigms span (i) direct in-context lookup over small, curated channel dictionaries, (ii) constrained hierarchical navigation through structured trees, (iii) interactive agent exploration using iterative reasoning and tool-based database queries, and (iv) ontology-grounded semantic search that decouples channel meaning from facility-specific naming conventions. We demonstrate the practical feasibility of each paradigm through proof-of-concept implementations at four operational facilities spanning two orders of magnitude in scale: from compact free-electron lasers to large synchrotron light sources, operating under diverse control-system architectures ranging from clean hierarchical naming schemes to legacy environments with decades of heterogeneous conventions. Where evaluated against expert-curated operational queries, these instantiations achieve 90%–97% accuracy, validating the framework’s applicability across real-world deployment scenarios. To accelerate adoption across the broader scientific and industrial control-system community, we release open-source, plug-and-play implementations of all three interactive paradigms-direct lookup, hierarchical navigation, and middle-layer exploration-within the Osprey framework, together with tools for channel database generation, interactive testing, and minimal-configuration deployment. This work establishes semantic channel finding as a foundational capability for human-centric and agentic AI interfaces at large-scale facilities, providing both a systematic framework for architecture design and practical resources to enable adoption without building custom infrastructure from scratch.

channel finding↗
DOE OSTI2020

Extraction of Beam-Spin Asymmetries from the Hard Exclusive π + Channel off Protons in a Wide Range of Kinematics

We have measured beam-spin asymmetries to extract the $\sin\phi$ moment $A_{LU}^{\sin\phi}$ from the hard exclusive $\vec{e} p \to e^\prime n \pi^+$ reaction above the resonance region, for the first time with nearly full coverage from forward to backward angles in the center-of-mass. The $A_{LU}^{\sin\phi}$ moment has been measured up to 6.6 GeV$^{2}$ in $-t$, covering the kinematic regimes of Generalized Parton Distributions (GPD) and baryon-to-meson Transition Distribution Amplitudes (TDA) at the same time. The experimental results in very forward kinematics demonstrate the sensitivity to chiral-odd and chiral-even GPDs. In very backward kinematics where the TDA framework is applicable, we found $A_{LU}^{\sin\phi}$ to be negative, while a sign change was observed near 90$^\circ$ in the center-of-mass. The unique results presented in this paper will provide critical constraints to establish reaction mechanisms that can help to further develop the GPD and TDA frameworks.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

A Meta-Level Framework for Evaluating Resilience in Net-Zero Carbon Power Systems with Extreme Weather Events in the United States

Important changes are underway in the U.S. power industry in the way that electricity is sourced, transported, and utilized. Disruption from extreme weather events and cybersecurity events is bringing new scrutiny to power-system resilience. Recognizing the complex social and technical aspects that are involved, this article provides a meta-level framework for coherently evaluating and making decisions about power-system resilience. It does so by examining net-zero carbon strategies with quantitative, qualitative, and integrative dimensions across discrete location-specific systems and timescales. The generalizable framework is designed with a flexibility and logic that allows for refinement to accompany stakeholder review processes and highly localized decision-making. To highlight the framework’s applicability across multiple timescales, processes, and types of knowledge, power system outages are reviewed for extreme weather events, including 2021 and 2011 winter storms that impacted Texas, the 2017 Hurricane Maria that affected Puerto Rico, and a heatwave/wildfire event in California in August 2020. By design, the meta-level framework enables utility decision-makers, regulators, insurers, and communities to analyze and track levels of resilience safeguards for a given system. Future directions to advance an integrated science of resilience in net-zero power systems and the use of this framework are also discussed.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Catalysis by Metal Organic Frameworks: Perspective and Suggestions for Future Research

Metal organic frameworks (MOFs) have drawn wide attention as potential catalysts, offering high densities of catalytic sites in high-area porous solids, some with stabilities at high temperatures. The field is at an early stage, characterized by numerous discoveries and novel demonstrations of catalytic properties associated with the crystalline structures of MOFs, but applications of MOFs as catalysts are still lacking. In this perspective we summarize advantages and limitations of MOFs as catalysts and fundamental issues to be addressed about their potential applications. MOF framework compositions and pore structures can strongly influence catalytic performance, allowing, for example, shape-selective and bifunctional catalysis, but research is needed to quantify reaction/transport processes in MOFs, identify catalytic sites, and determine intrinsic catalytic reaction rates. Progress is hindered by the lack of understanding of the heterogeneity of MOFs, with catalytic sites sometimes being in structures such as defects not determined by X-ray diffraction crystallography. Determination of the dynamics of MOFs and their catalytic sites, as well as the intrinsic kinetics of catalytic reactions, will help to advance guidelines for synthesizing optimum catalysts. Further, MOFs present challenges related to stability and regeneration as catalysts, some associated with the unique nature of MOFs, such as the node–linker bonds, which can break during catalysis. There are opportunities to understand these matters in depth and to find conditions of catalytic operation that minimize the processes leading to deactivation.

catalysis (heterogeneous), materials and chemistry↗
DOE OSTI2021

Renewable Thermal Hybridization Framework for Industrial Process Heat Applications

Renewable thermal energy systems (RTES) could be hybridized with different renewable options (e.g., flat plate collectors (FPCs) with parabolic collectors), or combined with existing heat supplies (e.g., fossil fuels), to give options for targeted solar IPH applications, industrial decarbonization and the reduction of fuel consumption. Buildings and industrial thermal energy applications require different temperature ranges, quantities, and rates of thermal energy, and as such require flexible, cost competitive solutions, able to provide heat over various temperature ranges. Hybrid solutions and thermal energy storage (TES) will be important for the dispatch of heat at optimal times needed by the demand side of the buildings and industrial applications. A variety of tools and platforms, such as the System Advisor Model (SAM), can provide hourly thermal yield simulations from single renewable energy (RE) technology options, including FPCs or CSP for solar IPH. We have investigated a variety of approaches to hybrid system modeling for RTES at different temperatures or combinations of technologies and developed an initial framework. The hybridization framework starts by creating a heat stream and raising the temperature of that heat stream by various combinations of RE technologies and other sources such as fossil fuels, new fuels or electric heating in multiple stages, with options for TES and/or waste heat recovery (WHR).

43 PARTICLE ACCELERATORS↗
DOE OSTI2017

Non-local currents and the structure of eigenstates in planar discrete systems with local symmetries

Local symmetries are spatial symmetries present in a subdomain of a complex system. By using and extending a framework of so-called non-local currents that has been established recently, we show that one can gain knowledge about the structure of eigenstates in locally symmetric setups through a Kirchhoff-type law for the non-local currents. The framework is applicable to all discrete planar Schrödinger setups, including those with non-uniform connectivity. Conditions for spatially constant non-local currents are derived and we explore two types of locally symmetric subsystems in detail, closed-loops and one-dimensional open ended chains. We find these systems to support locally similar or even locally symmetric eigenstates. - Highlights: • We extend the framework of non-local currents to discrete planar systems. • Structural information about the eigenstates is gained. • Conditions for the constancy of non-local currents are derived. • We use the framework to design two types of example systems featuring locally symmetric eigenstates.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

A mathematical framework for thermodynamic computing with applications to chemical reaction networks

The widespread adoption of energy-intensive computing applications has led to a growing need for energy-efficient computing approaches. Thermodynamic computing offers a promising approach for low-energy computation by leveraging the intrinsic computational capabilities of physical, chemical, or biological systems. However, the mathematical foundations of thermodynamic computing require further development to fully realize the potential energy efficiencies, as well as to assess factors like noise and operational speed. In this paper, we establish a mathematical framework for utilizing thermodynamic processes to perform fundamental operations, including addition, subtraction, multiplication, and division. We highlight the use of chemical reactions as potential computational units and explore synthetic chemical and biochemical systems as practical implementations. Additionally, we demonstrate how these principles can be applied to solving complex mathematical problems, such as ordinary differential equations (ODEs) and suggest the necessary components to implement the thermodynamic computing framework using chemical reactions based in a microfluidic device. This work enhances our understanding of thermodynamic processes for natural computing as a basis for scalable, energy-efficient computation in paradigm disruptive next-generation systems.

Cannon, William R. [Pacific Northwest National Lab↗
DOE OSTI2025

A Scale‐Adaptive Urban Hydrologic Framework: Incorporating Network‐Level Storm Drainage Pipes Representation

Abstract Below‐ground urban stormwater networks (BUSNs) significantly influence urban flood dynamics, yet their representation at the watershed or larger scales remains challenging. We introduce a scalable urban hydrologic framework that centers on a novel network‐level BUSN representation, balancing the needs for physical basis, parameter parsimony, and computational efficiency. Our framework conceptualizes an urban watershed into four interacting zones: hillslopes (natural), storm‐sewersheds (urban), a sub‐network channel (tributaries), and a main channel. We develop an innovative Graph Theory‐based algorithm to derive network‐level BUSN parameters from publicly available datasets, enabling efficient, scalable parameterization. We demonstrate this framework's applicability at nine representative watersheds in the Houston metropolitan region, USA, with urban imperviousness ranging from 0% to 64% and drainage areas ranging from 24 to 302 . Our model achieves satisfying computational efficiency, completing hourly time step simulations for 18 years in less than 5 sec per watershed on a standard PC. Validation against observed daily streamflow confirms that the model can capture small‐to‐large flood peaks and seasonal and annual water balance over these watersheds. Comparisons with the National Water Model show better performance in predicting flood peaks and overall water balance, underscoring the promises of our new framework for urban hydrologic modeling at large scales. Furthermore, analysis reveals nonlinear relationships between BUSNs' designed capacities and flood reduction effects. Our approach bridges the gap between detailed hydraulic and large‐scale hydrologic models, providing a valuable tool for urban flood prediction and management across broader spatial and temporal scales.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1996

An Optimal Order Nonnested Mixed Multigrid Method for Generalized Stokes Problems

A multigrid algorithm is developed and analyzed for generalized Stokes problems discretized by various nonnested mixed finite elements within a unified framework. It is abstractly proved by an element-independent analysis that the multigrid algorithm converges with an optimal order if there exists a 'good' prolongation operator. A technique to construct a 'good' prolongation operator for nonnested multilevel finite element spaces is proposed. Its basic idea is to introduce a sequence of auxiliary nested multilevel finite element spaces and define a prolongation operator as a composite operator of two single grid level operators. This makes not only the construction of a prolongation operator much easier (the final explicit forms of such prolongation operators are fairly simple), but the verification of the approximate properties for prolongation operators is also simplified. Finally, as an application, the framework and technique is applied to seven typical nonnested mixed finite elements.

Deng, Qingping↗
DOE OSTI2014

NRC Licensing Status Summary Report for NGNP

The Next Generation Nuclear Plant (NGNP) Project, initiated at Idaho National Laboratory (INL) by the U.S. Department of Energy (DOE) pursuant to provisions of the Energy Policy Act of 2005, is based on research and development activities supported by the Department of Energy Generation IV Nuclear Energy Systems Initiative. The principal objective of the NGNP Project is to support commercialization of high temperature gas-cooled reactor (HTGR) technology. The HTGR is a helium-cooled and graphite moderated reactor that can operate at temperatures much higher than those of conventional light water reactor (LWR) technologies. The NGNP will be licensed for construction and operation by the Nuclear Regulatory Commission (NRC). However, not all elements of current regulations (and their related implementation guidance) can be applied to HTGR technology at this time. Certain policies established during past LWR licensing actions must be realigned to properly accommodate advanced HTGR technology. A strategy for licensing HTGR technology was developed and executed through the cooperative effort of DOE and the NRC through the NGNP Project. The purpose of this report is to provide a snapshot of the current status of the still evolving pre-license application regulatory framework relative to commercial HTGR technology deployment in the U.S. The following discussion focuses on (1) describing what has been accomplished by the NGNP Project up to the time of this report, and (2) providing observations and recommendations concerning actions that remain to be accomplished to enable the safe and timely licensing of a commercial HTGR facility in the U.S.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2019

A scalable real-time framework for Thomson scattering analysis: Application to NSTX-U

A detailed description of a prototype setup for real-time (RT) Thomson scattering (TS) analysis is presented and implemented in the multi-point Thomson scattering (MPTS) diagnostic system at the National Spherical Torus Experiment Upgrade (NSTX-U). The data acquisition hardware was upgraded with RT capable electronics (RT-analog digital converters and a RT server) that allow for fast digitization of the laser pulse signal of eight radial MPTS channels. In addition, a new TS spectrum analysis software for a rapid calculation of electron temperature ( T e ) and electron density ( n e ) was developed. Testing of the RT hardware and data analysis software was successfully completed and benchmarked against the standard, post-shot evaluation. Timing tests were performed showing that the end-to-end processing time was reproducibly below 17 ms for the duration of at least 5 s, meeting a 60 Hz deadline by the laser pulse repetition rate over the length of a NSTX-U discharge. The presented RT framework is designed to be scalable in system size, i.e., incorporation of additional radial channels by solely adding additional RT capable hardware. In conclusion, it is scalable in its operation duration and was continuously running for up to 30 min, making it an attractive solution for machines with long discharges such as advanced, non-inductive tokamaks or stellarators.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

A scalable real-time framework for Thomson scattering analysis: Application to NSTX-U

A detailed description of a prototype setup for real-time (rt) Thomson scattering (TS) analysis is presented and implemented in the multi-point Thomson scattering (MPTS) diagnostic system at the National Spherical Torus Experiment Upgrade(NSTX-U). The data acquisition hardware was upgraded with rt capable electronics (rt-analog digital converters (ADCs) and a rt server) that allow for fast digitization of the laser pulse signal of eight radial MPTS channels. In addition, a new TS spectrum analysis software for a rapid calculation of electron temperature (Te) and electron density (ne) was developed. Testing of the rt hardware and data analysis soft-ware was successfully completed and benchmarked against the standard, post-shot evaluation. Timing tests were performed showing that the end-to-end processing time was reproducibly below 17 ms for the duration of at least 5 s, meeting a 60 Hz deadline by the laser pulse repetition rate over the length of a NSTX-U discharge. The presented rt framework is designed to be scalable in system size, i.e. incorporation of additional radial channels by solely adding additional rt capable hardware. Furthermore, it is scalable in its operation duration and was continuously run for up to 30 min, making it an attractive solution for machines with long discharge duration such as advanced, non-inductive tokamaks or stellarators.

Plasma, Tokamak, Diagnostic, Thomson scattering, r↗
DOE OSTI2026

Deep Gaussian process-based cost-aware batch Bayesian optimization for complex materials design campaigns

The accelerating pace and expanding scope of materials discovery demand optimization frameworks that efficiently navigate vast design spaces with complex response surfaces while judiciously allocating limited evaluation resources. We present a cost-aware, batch Bayesian optimization scheme powered by deep Gaussian process (DGP) surrogates and a heterotopic querying strategy. Our DGP surrogate, formed by stacking GP layers, models complex hierarchical relationships among high-dimensional compositional features and captures correlations across multiple target properties, propagating uncertainty through successive layers. We integrate evaluation cost into an upper-confidence-bound acquisition extension, which, together with heterotopic querying, proposes small batches of candidates in parallel, balancing exploration of under-characterized regions with exploitation of high-mean, low-variance predictions across correlated properties. Applied to refractory high-entropy alloys for high-temperature applications, our framework converges to optimal formulations in fewer iterations with cost-aware queries than conventional GP-based BO, highlighting the value of deep, uncertainty-aware, cost-sensitive strategies in materials campaigns.

36 MATERIALS SCIENCE↗
DOE OSTI2015

User applications driven by the community contribution framework MPContribs in the Materials Project

This paper discusses how the MPContribs framework in the Materials Project (MP) allows user-contributed data to be shown and analyzed alongside the core MP database. The MP is a searchable database of electronic structure properties of over 65,000 bulk solid materials, which is accessible through a web-based science-gateway. We describe the motivation for enabling user contributions to the materials data and present the framework's features and challenges in the context of two real applications. These use cases illustrate how scientific collaborations can build applications with their own 'user-contributed' data using MPContribs. The Nanoporous Materials Explorer application provides a unique search interface to a novel dataset of hundreds of thousands of materials, each with tables of user-contributed values related to material adsorption and density at varying temperature and pressure. The Unified Theoretical and Experimental X-ray Spectroscopy application discusses a full workflow for the association, dissemination, and combined analyses of experimental data from the Advanced Light Source with MP's theoretical core data, using MPContribs tools for data formatting, management, and exploration. The capabilities being developed for these collaborations are serving as the model for how new materials data can be incorporated into the MP website with minimal staff overhead while giving powerful tools for data search and display to the user community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

AMReX v2024

The software framework, AMReX, supports the development of block-structured adaptive mesh refinement (AMR) algorithms for solving systems of partial differential equations. AMR reduces the computational cost and memory footprint compared to a uniform mesh while preserving the essential local descriptions of different physical processes in complex multiphysics algorithms. AMR uses a hierarchical representation of the solution at multiple levels of resolution where the solution on each level is defined on the union of data containers at that resolution. These data containers, which represent the solution over a logically rectangular subregion of the domain, can contain field data defined on a mesh, Lagrangian particles or combinations of both. In addition to these basic data types, AMReX supports a multilevel embedded boundary representation of complex geometry; linear solvers for cell-centered and nodal data; asynchronous I/O in a native format readable by ParaView, VisIt and yt; and interfaces to hypre and PETSc solvers. AMReX enables applications to run on distributed memory architectures with multicore CPUs and with GPU accelerators. AMReX uses a lightweight abstraction layer that effectively hides the details of the architecture from the application. The framework currently supports CUDA, HIP and SYCL for GPU acceleration and OpenMP for multi-core CPU architectures.

Almgren, Ann↗
DOE OSTI2025

Physics consistent machine learning framework for inverse modeling with applications to ICF capsule implosions

In high energy density physics (HEDP) and inertial confinement fusion (ICF), predictive modeling is complicated by uncertainty in parameters that characterize various aspects of the modeled system, such as those characterizing material properties, equation of state (EOS), opacities, and initial conditions. Typically, however, these parameters are not directly observable. What is observed instead is a time sequence of radiographic projections using X-rays. In this work, we define a set of sparse hydrodynamic features derived from the outgoing shock profile and outer material edge, which can be obtained from radiographic measurements, to directly infer such parameters. Our machine learning (ML)-based methodology involves a pipeline of two architectures, a radiograph-to-features network (R2FNet) and a features-to-parameters network (F2PNet), that are trained independently and later combined to approximate a posterior distribution for the parameters from radiographs. We show that the machine learning architectures are able to accurately infer initial conditions and EOS parameters, and that the estimated parameters can be used in a hydrodynamics code to obtain density fields, shocks, and material interfaces that satisfy thermodynamic and hydrodynamic consistency. Finally, we demonstrate that features resulting from an unknown EOS model can be successfully mapped onto parameters of a chosen analytical EOS model, implying that network predictions are learning physics, with a degree of invariance to the underlying choice of EOS model. To the best of our knowledge, our framework is the first demonstration of recovering both thermodynamic and hydrodynamic consistent density fields from noisy radiographs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Framework for assessing land‐management effects on at‐risk species: Example of SE USA wood pellet production and gopher tortoise ( Gopherus polyphemus )

Abstract Land‐management choices made for economic and societal gains intrinsically influence landscapes and species that are dependent upon them. We propose a simple analysis framework to examine critical intersections between land‐management choices and the life‐history conditions of selected species of concern, thereby facilitating the identification of mitigation practices that can reduce negative impacts on species at risk. We test the proposed framework through application to gopher tortoise ( Gopherus polyphemus ), a keystone species that is the focus of conservation efforts across the southeastern region of United States of America, where wood pellets are being produced for bioenergy. Production of these wood pellets for export to Europe and Asia has drastically increased in the past decade, raising concerns about potential harm to biodiversity since many species in the forests sourcing pellet production were already at risk prior to the development of this new commodity. Identifying the mechanisms of potential impacts of wood pellet production on species of concern is essential to establishing meaningful management recommendations that can enhance conservation efforts while supporting sustainable bioenergy. By considering the intersections between life‐history conditions of gopher tortoise and forest‐management practices related to woody biomass extraction for pellet production, we identify several mechanisms by which the wood‐pellet industry might affect this species of concern, both positively and negatively. We then identify mitigation practices that can help offset the potential impacts of logging, thinning, and dead wood removal on gopher tortoise. Our analysis framework may be transferable to other species of concern and land‐management practices across diverse landscapes. This article is categorized under: Bioenergy > Economics and Policy Bioenergy > Climate and Environment

54 ENVIRONMENTAL SCIENCES↗