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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Fast methods for multisite charge transfer. Processes II. Analytic nuclear gradients and nonadiabatic dynamics for cCASSCF(1,n) and cCASSCF(2n-1,n) wavefunctions

In this work we derive and implement analytic nuclear gradients and derivative couplings for a constrained complete active space self-consistent field with a small active space designed to model electron or hole transfer. Using a Lagrangian formalism, we are able to differentiate both the CASSCF energy and the constraint (which is required for smooth surfaces over a wide range of parameter space), and the resulting efficient algorithm can be immediately applied to nonadiabatic dynamics simulations of charge transfer processes. Here, we run initial surface-hopping simulations of a proton coupled electron transfer event for a phenoxyl–phenol system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Analytic gradients for compressed multistate pair-density functional theory

Photochemical reactions often involve states that are closely coupled due to near degeneracies, for example by proximity to conical intersections. Therefore, a multistate method is used to accurately describe these states; for example, ordinary perturbation theory is replaced by quasidegenerate perturbation theory. Multiconfiguration pair-density functional theory (MC-PDFT) provides an efficient way to approximate the full dynamical correlation energy of strongly correlated systems, and we recently proposed compressed multistate pair-density functional theory (CMS-PDFT) to treat closely coupled states. In the present paper, we report the implementation of analytic gradients for CMS-PDFT in both OpenMolcas and PySCF, and here we illustrate the use of these gradients by applying the method to the excited states of formaldehyde and phenol.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics With Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis

Detecting and analyzing complex patterns in multivariate time-series data is crucial for decision-making in urban and environmental system operations. However, challenges arise from the high dimensionality, intricate complexity, and interconnected nature of complex patterns, which hinder the understanding of their underlying physical processes. Existing AI methods often face limitations in interpretability, computational efficiency, and scalability, reducing their applicability in real-world scenarios. This paper proposes a novel visual analytics framework that integrates two generative AI models, Temporal Fusion Transformer (TFT) and Variational Autoencoders (VAEs), to reduce complex patterns into lower-dimensional latent spaces and visualize them in 2D using dimensionality reduction techniques such as PCA, t-SNE, and UMAP with DBSCAN. These visualizations, presented through coordinated and interactive views and tailored glyphs, enable intuitive exploration of complex multivariate temporal patterns, identifying patterns’ similarities and uncover their potential correlations for a better interpretability of the AI outputs. The framework is demonstrated through a case study on power grid signal data, where it identifies multi-label grid event signatures, including faults and anomalies with diverse root causes. Additionally, novel metrics and visualizations are introduced to validate the models and assess the performance, efficiency, and consistency of latent maps generated by VAE, which have been utilized in prior studies for latent space cartography and used as a benchmark in this study, and the emerging TFT architecture under various configurations. These analyses provide actionable insights for model parameter tuning and reliability improvements. Comparative results highlight that TFT achieves shorter run times and superior scalability to diverse time-series data shapes compared to VAE. This work advances fault diagnosis in multivariate time series, fostering explainable AI to support critical system operations.

Explainable AI↗
DOE OSTI2024

Graph Analytics on Jellyfish topology

Because large unstructured datasets is important for many science domains, distributed graph analytics is critical to many scientists. Unfortunately, obtaining scaling and performance for irregular communication is challenging because contemporary network interconnects are primarily designed to maximize bandwidths of fixed-neighborhoods large-message exchanges (e.g., stencils). Although there is no consensus on the “best” network topologies for irregular communication, unstructured graph-based interconnects can be more suitable. We analyze three popular graph workloads – clustering, pattern enumeration, and traversal — on comparable networks (in terms of resources and costs) constructed from Jellyfish Random Regular, Dragonfly and Fat tree topologies, varying the routing algorithms. Using packet-level simulations, we demonstrate up to 60% improvement in communication time with Jellyfish due to diversity of the short paths between arbitrary endpoints, which can reduce overall network stalls and congestion.

Graph Analytics, network topology, interconnect, H↗
DOE OSTI2021

A Semi-Analytical Solution Approach for Solving Constant-Coefficient First-Order Partial Differential Equations

Simulation and control of many dynamic systems involve solving partial differential equations (PDE). This letter proposes a semi-analytical solution (SAS) approach for fast and high-quality solution of first-order PDEs. The region of interest of the studied PDE is divided into a grid, and an SAS is derived for each grid cell in the form of the multivariate polynomials, of which the coefficients are identified using initial value and boundary value conditions. The solutions are solved in a “time-stepping” manner, i.e. within one time step, the coefficients of the SAS are identified and the initial value of the next time step is evaluated. This approach achieves a significantly larger grid cell than the widely used finite difference method, and thus enhances the computational efficiency significantly. Furthermore, the simulation result on the natural gas pipeline model demonstrates the advantages of SAS in accuracy and computational efficiency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Examination of Semi-Analytical Solution Methods in the Coarse Operator of Parareal Algorithm for Power System Simulation

With continuing advances in high-performance parallel computing platforms, parallel algorithms have become powerful tools for development of faster than real-time power system dynamic simulations. In particular, it has been demonstrated in recent years that parallel-in-time (Parareal) algorithms have the potential to achieve such an ambitious goal. Here, the selection of a fast and reasonably accurate coarse operator of the Parareal algorithm is crucial for its effective utilization and performance. This paper examines semi-analytical solution (SAS) methods as the coarse operators of the Parareal algorithm and explores performance of the SAS methods to the standard numerical time integration methods. Two promising time-power series-based SAS methods were considered; Adomian decomposition method and Homotopy analysis method with a windowing approach for improving the convergence. Numerical performance case studies on 10-generator 39-bus system and 327-generator 2383-bus system were performed for these coarse operators over different disturbances, evaluating the number of Parareal iterations, computational time, and stability of convergence. All the coarse operators tested with different scenarios have converged to the same corresponding true solution (if they are convergent) and the SAS methods provide comparable computational speed, while having more stable convergence to the true solution in many cases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Visual Analytics of Multivariate Networks With Representation Learning and Composite Variable Construction

Multivariate networks are commonly found in real-world data-driven applications. Uncovering and understanding the relations of interest in multivariate networks is not a trivial task. This article presents a visual analytics workflow for studying multivariate networks to extract associations between different structural and semantic characteristics of the networks (e.g., what are the combinations of attributes largely relating to the density of a social network?). The workflow consists of a neural-network-based learning phase to classify the data based on the chosen input and output attributes, a dimensionality reduction and optimization phase to produce a simplified set of results for examination, and finally an interpreting phase conducted by the user through an interactive visualization interface. A key part of our design is a composite variable construction step that remodels nonlinear features obtained by neural networks into linear features that are intuitive to interpret. We demonstrate the capabilities of this workflow with multiple case studies on networks derived from social media usage and also evaluate the workflow with qualitative feedback from experts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Cyber-Physical System Implementation for Manufacturing With Analytics in the Cloud Layer

Effective and efficient modern manufacturing operations require the acceptance and incorporation of the fourth industrial revolution, also known as Industry 4.0. Traditional shop floors are evolving their production into smart factories. To continue this trend, a specific architecture for the cyber-physical system is required, as well as a systematic approach to automate the application of algorithms and transform the acquired data into useful information. This work makes use of an approach that distinguishes three layers that are part of the existing Industry 4.0 paradigm: edge, fog, and cloud. Each of the layers performs computational operations, transforming the data produced in the smart factory into useful information. Trained or untrained methods for data analytics can be incorporated into the architecture. A case study is presented in which a real-time statistical control process algorithm based on control charts was implemented. The algorithm automatically detects changes in the material being processed in a computerized numerical control (CNC) machine. The algorithm implemented in the proposed architecture yielded short response times. The performance was effective since it automatically adapted to the machining of aluminum and then detected when the material was switched to steel. The data were backed up in a database that would allow traceability to the line of g-code that performed the machining.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Analytical Modeling of Metal Foam Composite Phase Change Materials (PCM) in Thermal Energy Storage Using Asymptotic Analysis

The use of phase change materials (PCMs) for thermal energy storage can release or absorb a significant amount of latent heat during the freezing or melting process, offering a higher energy storage density. One of the main drawbacks of PCMs is their low thermal conductivity, resulting in poor thermal performance. Recent research has attempted to enhance heat transfer and increase the thermal conductivity of PCMs, including the use of metal foams. However, modeling the metal foam composite PCM using conventional methods is computationally expensive. This paper proposes an asymptotic solution for a Stefan-like problem subject to a convective boundary for outward solidification in a hollow cylinder, capable of predicting the freeze-melt cycle of the metal foam composite PCM. Specifically, three temporal regimes and four spatial layers are considered in the asymptotic analysis for each phase change process. The thermal conductivity is calculated by a theoretical three-dimensional tetrakaidecahedron model, while other thermophysical properties are obtained using the method of volume averaging. The results are verified with numerical data and validated against experimental data in the literature. The presented analytical modeling framework could have the potential to be applied to other types of composite PCMs with considerably lower computational costs compared with conventional methods.

analytical model↗
DOE OSTI2020

Analytic Insights into the Neutronic Characteristics of Neutron Moderators from MCNP Calculations

In the course of re-processing the ENDF/B VIII.0 thermal scattering law data, formulas and data checking insights were developed from MCNP calculations into the neutronic characteristics of moderators. Infinite medium test calculations were carried out in MCNP as part of the verification effort for the ACE formatted thermal scattering files. Edit results from MCNP were combined with derived results to formulate several equations which give analytic insights into the neutronic behavior of moderators.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Analytical Functions for 200 West Pump-and-Treat SCADA Sensor Data

Historical operations at the U.S. Department of Energy’s Hanford Site included disposal of waste fluids to the subsurface in the 200 West Area on the Hanford Central Plateau. Subsequent infiltration of fluids has resulted in groundwater contamination with carbon tetrachloride, nitrate, uranium, technetium-99, and other contaminants. A pump-and-treat (P&T) system, with an extraction/injection well network and an aboveground treatment plant, was implemented as part of interim and final remedies in the 200 West area. The HYPATIA single-page web application (part of the SOCRATES suite) is being developed to provide access to and analysis of chemistry and treatment facility sensor data for this 200 West P&T system. For the web application, analytical algorithms were developed to perform summing, differencing, smoothing, outlier detection, change-point detection, mass flow rate, and injectivity calculations on the data. Candidate algorithms were identified and tested, with the best-performing algorithms then assembled for implementation in HYPATIA. Because HYPATIA is hosted on the Amazon Web Services (AWS) cloud computing platform, algorithms were implemented in a back-end AWS Lambda function that can be called by the HYPATIA front end. The Lambda function applies the requested data processing to specified data via functions written in R, Python, and JavaScript. Development, testing, and review of the data analysis algorithms was completed under an NQA-1 quality program. This new HYPATIA functionality will provide information to support site decisions regarding P&T system performance and optimization.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

An Analytical Model of Erosive Wear of BioMass Comminution Components

An analytical erosion model that relates critical process parameters (speed and impingement angle) to critical material attributes of inorganic mineral species in feedstock (density, size, aspect ratio) and substrate (hardness, toughness, and fatigue ductility) was applied to model wear in pretreatment milling operations. Results of the model were compared to experimental measurements of wear produced using the Accelerated Wear Test (AWT) rig at Idaho National Laboratory (INL). Results showed that there is good agreement between predicted and measured performance, and that a quality-by-design (QbD) tool can be developed to predict component reliability based on scientific engineering principles in lieu of trial-and-error approaches.

09 BIOMASS FUELS↗
DOE OSTI2022

MIS Shelf-Life Final Report for Plutonium Oxide Item CAN92 (SSR147) from Rocky Flats Analytical Laboratory Operations

A plutonium/uranium oxide material from the Material Identification and Surveillance (MIS) Program inventory has been studied to determine the gas generation and corrosion behavior in a storage environment. Sample CAN92 represents plutonium/uranium oxides stored in 3013 containers. The material originated in the analytical laboratory at Rocky Flats. This study followed over time the gas pressure and composition of a sample with nominally 0.5 wt% water in a sealed container with an internal volume scaled to 1/400th of the volume of a 3013 container. Gas compositions had been measured periodically over 5 ½ years. The maximum observed gas pressure from gas generation was 117 kPa. The increase over the initial pressure of 77.5 kPa was largely due to the generation of nitrogen and carbon dioxide with minor amounts of hydrogen and nitrous oxide. The internal components of SSR147 were inspected 6.5 years after it was removed from the array. The inside surfaces of the inner bucket exposed to the original sample for 1,937 days had an etched appearance and general corrosion. No pitting was observed on the inner bucket that held the sample.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Development of Analytical Method for Measuring U and Pu Particles by Laser Ablation MC-ICP-MS for the NWAL

This project will develop methodology to identify and analyze U and Pu containing particles using laser ablation MC-ICP-MS. The proposed work will involve: i) setup and optimization of the LA-MC-ICP-MS system, ii) testing and validation by analysis of QC standards, iii) designing analytical protocols for single particles, and iv) developing a data processing system. Ultimately, this project will output a detailed operating procedure documenting the experimental techniques and data processing routines required to perform particle identification and analysis by laser ablation MC-ICP-MS.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2023

Determination of Total, Organic, and Inorganic Carbon and Total Nitrogen in Biological Cultures and Liquid Fraction Process Samples: Laboratory Analytical Procedure (LAP)

This Laboratory Analytical Procedure (LAP) describes the quantitative determination of total organic carbon, inorganic carbon in whole suspended biological samples and in the cell-free supernatant, by combustion and CO 2 detection with a non-dispersive infrared (NDIR) detector. This allows for distinct reporting of soluble and insoluble organic and inorganic carbon. In addition, this procedure covers the quantitative determination of soluble nitrogen in the cell-free supernatant.

09 BIOMASS FUELS↗
DOE OSTI2024

Enabling Efficient Surveillance, Control, and Automation of Geothermal Operations with Advanced Predictive Analytics

Automation and control of geothermal energy production and operations require reliable and efficient predictive tools. While physics-based simulation offers a comprehensive tool for predicting energy production performance in geothermal systems, predicting the behavior of geothermal reservoirs involves complex multi-physics processes with coupling effects, highly uncertain input parameters and subsurface descriptions. Moreover, building, running, and integrating simulation models into standard model calibration and optimization workflows entail significant technical and computational efforts. An emerging alternative to physics-based simulation is data-driven predictive analytics models that have gained popularity in energy industry. In this report, we develop novel predictive models for integration into real-time fault diagnosis and model predictive control algorithms to improve the efficiency of energy production operations in geothermal reservoirs. The report includes two major research Thrust Areas, that is, the surface power plant and the subsurface reservoir.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Real Time Phasor Analytics (RTPA) and RTPA-SCR System Strength Online Tool

This presentation showcases the Real-Time Phasor Analytics (RTPA) framework for monitoring inertia and assessing system strength in power grids. RTPA is an open-source tool designed to standardize access to data from Power Management Units (PMUs) and Phasor Data Concentrators (PDCs). It facilitates real-time connectivity to multiple PDCs in accordance with the IEEE C37.118-2 standard and supports asynchronous data stream integration. Additionally, RTPA can simulate a PDC server streaming C37.118-2 data and provides Python bindings for seamless interaction with the framework, eliminating the need for direct Rust programming.

24 POWER TRANSMISSION AND DISTRIBUTION↗