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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Final Project Report: Tools for the Development of High Performance Energy Applications and Systems

This project concentrated on various aspects of creating tool infrastructure to make it easier to program large-scale parallel computers. The research conducted during this project is summarized in this report. The complete details of the work are available in the ten publications listed at the end of the report. Many of the concepts created during this project have been incorporated into tools and made available as freely downloadable software (at www.dyninst.org). It also supported the Ph.D. studies of three students.

Hollingsworth, Jeff↗
DOE OSTI2019

Summing Corrections For La-140 On Counter 137 [Slides]

The counting of filters for gamma activity is often done with the sample situated very close or wrapped around the detector. This improves the counting statistics but can increase the probability of summing and affect the calculation of yield. An MCNP model of counter 137 (a 65% HPGe detector) was used to compute the total and peak efficiencies for two sample types, 40 and 20. The type 40 is a long filter strip wrapped in a Marinelli beaker which sits around the detector in an annular fashion. The type 20 is a 8.4 in radius filter, quarter-folded and placed flat on the detector face. A comparison of the peak efficiency computed in MCNP was compared to the measured efficiency to ensure consistency. Using the measured peak efficiency and MCNP-computed total efficiency, the TrueCoinc program was run to estimate the summing values for La-140 peaks. The results showed the summing out of two peaks (487 and 1596 keV) was around 6-8% in for both sample types.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Strengthening Nanotwinned Metals beyond the Hall-Petch Limit

A research partnership program was conducted to study the roles of trace element segregation as a fundamentally new mechanism of grain-boundary and twin-boundary strengthening in nanocrystalline-nanotwinned face-centered-cubic silver metals. The goals were to create new metals that break the decades-old theoretical Hall-Petch strength limit, and to study a new class of super-strong conducting materials. This program combined synergistic experimental and computational studies at the atomic scale, using a wide range of resources and scientific expertise at the University of Vermont, Ames Laboratory and Lawrence Livermore National Laboratory. This research led to a discovery that annealing of nanotwinned silver with trace concentrations of Cu solute atoms (<1.0 wt.%) results in grain sizes and twin spacings well below those previously obtained for silver. These new materials, termed as nanocrystalline-nanotwinned silver, showed a record hardness 42% higher than the Hall-Petch limit in pure nanotwinned silver or stronger metals like copper, with excellent retention of electrical conductivity and microstructure stability at elevated temperature. This research used large-scale hybrid Monte-Carlo/molecular-dynamics atomistic simulations and ab-initio calculations to predict new impurity-segregation behaviors and fundamentally new plastic deformation mechanisms in nanocrystalline-nanotwinned silver metals. A new interatomic potential for atomistic simulation of segregation of Ni impurity in Ag grain-boundaries and plasticity of Ag-Ni alloys was developed.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Probing catalytic activity in defect sites in transition metal oxides and sulfides using cluster models: A combined experimental and theoretical approach

Our research program has combined experimental and computational methods to study a range of cluster models for heterogeneous catalytic materials. The focus of our studies has been transition metal oxide and sulfide clusters in non-traditional oxidation states, and their chemical and physical interactions with water, to model steps in the electrocatalytic or photocatalytic hydrogen evolution reaction (HER). In the closing year of this project, we have also begun to explore addition systems in preparation for future grant proposals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Cost targets to achieve commercially viable thermal storage in buildings

To mitigate the variation in demand on the electric grid, thermal energy storage (TES) is an alternative to electric batteries or installing new peaking power plants. Stakeholders and policy makers across the United States have expressed interests in promoting TES, as demonstrated by the US Department of Energy’s Grid-Interactive Efficient Buildings program and the efforts of various state legislatures. However, the cost value provided by TES are unclear. If reliable cost benefits were determined, stakeholders would have a clearer picture of the financial returns that can be gained from their investment in TES. In this report, EnergyPlus was used to perform whole-building simulations for two residential buildings in Indianapolis and Atlanta. The HVAC system in both buildings were equipped with phase change material TES. The TES tank was charged in off-peak hours and discharged in peak hours to perform load shifting. First, the economic value implied by existing time-of-use (TOU) rates offered by utility companies was analyzed via whole-building simulation. Second, existing demand reduction (DR) incentives sourced from 3 different electrical grid administrators (i.e., California, Texas, and New England region) were surveyed to determine their implied value. Lastly, the economic value implied by different types of deferred peak power plants were reviewed. The full value of TES to the entire society consists of value to the utility, OEMs, facility installers, and other stakeholders. This report focuses on the value to the utility with emphasis on the deferred capital of peak power plant. The value from the deferred capital of peak power plant is manifested to the customer in the form of demand reduction program and Time-of-Use utility rate program. In this report, an initial proxy of the value of TES is made by assuming the deferred capital cost of power plant is the full value to reduce peak demand. Three levels of financial value of TES systems were assessed. Two are currently available to some residential customers: (1) the benefit from TOU pricing alone and (2) the benefit from TOU pricing in combination with DR incentive programs. The third level was computed as the full cost of deferred capital cost of peaking power plants. This represents the potential value that could be gained by the utilities or conceivably be offered to consumers.

25 ENERGY STORAGE↗
DOE OSTI2022

Excited State Phenomena in Correlated Nanostructures: Transition Metal Oxide Clusters and Nanocrystals

The primary goal of our research program was to apply advanced computational techniques to predict electronic properties of molecular species. These include transition metal monoxide and dioxide clusters and negatively charged copper oxide clusters which are important systems for their utilization in a variety of technological applications. The enhanced electron-electron interactions inherent in these systems make it challenging to model their excited state properties within a parameter-free approach. The excited state properties in question include the interaction of light with these molecules which results in ejection of electrons from the system, or the absorption of light by the molecular system resulting in a molecule in a higher-energy state. Our overall goal was to investigate the impact of various computational and theoretical approaches on such electronic and optical excitations in these systems. Comparing several variants of theoretical approaches with available experimental data, and high-level quantum chemistry computations helped us identify the level of theory and approximations that are needed to achieve a reasonable balance between accuracy and computational demand.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Connecting Minds: AI Use Cases to Bridge Power Systems and Large Language Models for Practical Applications

Recent advances in artificial intelligence (AI) and development of large language models (LLMs) present the opportunity to develop a new generation of power systems applications. In contrast with early power system AI applications based on structured numerical data, LLMs offer unique capabilities to perform logical reasoning using text documents, unstructured data, and application programming interface (API) calls to computational software. This paper seeks to bridge the knowledge gap between power systems engineers and LLM developers through a crosscutting explanation of use cases, characteristics, requirements, practical considerations from the perspectives of both LLM capabilities and industry needs. Specific focus is given to applications that can be realistically deployed by electric utilities. After introducing the architecture of LLMs and unique challenges of the power systems domain, this paper proposes twenty representative LLM applications grouped into categories of 1) power system operations, 2) asset management, 3) system planning and analytics, and 4) energy management and protection systems. Five use cases are presented within each category with descriptions of the motivation, objectives, approaches, example inputs / outputs, and benefits of each use case.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

AI Applications to Physics Experiments at Jefferson Lab

We survey how AI/ML is being deployed across Jefferson Lab's experimental and accelerator programs. In EPSCI, Hydra applies computer vision to automate real-time data-quality monitoring across all four experimental halls, replacing manual inspection of hundreds to thousands of histograms per shift. AIEC (AI Experiment Controls) uses ML to stabilize drift chamber gains and is now part of standard CEBAF production running, while AI Optimized Polarization (AIOP) targets autonomous control of polarized targets and photon beam angular alignment. In CASA, cavity fault classification models identify faulted cavities and trip types from waveform data with ~85% and ~78% agreement to labeled data, respectively, and are deployed in production; a separate effort applies LLMs and hybrid search to make the CEBAF operations logbook AI-ready. QCD-focused work includes transformer- and GAN-based generative models for particle-level event simulation, with distributed GAN training scaling studies on Polaris. Additional efforts span ML-on-FPGA for the EIC and a new Data Science Department coordinating anomaly detection, uncertainty quantification, and HPC-scalable ML lab-wide. Collectively, these projects illustrate AI's growing role in improving efficiency across JLab's nuclear physics mission.

Mei, Xinxin [Thomas Jefferson National Accelerator↗
DOE OSTI2022

Koopman Model Predictive Control for Eco-Driving of Automated Vehicles

In this paper, we develop a data-driven process for building a model predictive control (MPC) for eco-driving of automated vehicles. The process involves performing system identification in which the non-linear vehicle dynamics model is approximated by the Koopman operator, a linear predictor of higher state-dimension, in a data-driven framework. This approach allows us to formulate the eco-driving problem in a constrained quadratic program that leads to a computationally fast MPC. The MPC is then implemented as a closed-loop control of an electric vehicle in numerical simulations for demonstration.

autonomous vehicle↗
DOE OSTI2021

Bioinformatic teaching resources - for educators, by educators - using KBase, a free, user-friendly, open source platform

Over the past year, biology educators and staff at the Department of Energy Systems Biology Knowledgebase (KBase) initiated a collaborative effort to develop a curriculum for bioinformatics education. KBase is a free and easily accessible data science platform that integrates many bioinformatics resources into a graphical user interface built upon reproducible analysis notebooks. KBase held conversations with college and high school instructors to understand how KBase could potentially support their educational goals. These conversations morphed into a working group of biological and data science instructors that adapted the KBase platform to their curriculum needs, specifically around concepts in Genomics, Metagenomics, Pangenomics, and Phylogenetics. The KBase Educators Working Group developed modular, adaptable, and customizable instructional units. Each instructional module contains teaching resources, publicly available data, analysis tools, and markdown capability to tailor instructions and learning goals for each class. The online user interface enables students to conduct hands-on data science research and analyses without requiring programming skills or their own computational resources (these are provided by KBase). Alongside these resources, KBase continues to work with instructors, supporting the development of additional curriculum modules. For anyone new to the platform, KBase, and the growing KBase Educators Organization, provides a community network, accompanied by community-sourced guidelines, instructional templates, and peer support to use KBase within a classroom whether virtual or in-person.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Fused Global-Local Economic Model Predictive Control for Real-Time Eco-Optimal Control of a Heavy Duty Truck

This paper introduces a fused global-local economic model predictive control (MPC) with varying time steps for maximizing the fuel economy of a heavy-duty truck, where global MPC is performed over a long horizon using cloud-based dynamic programming (DP), and local MPC is performed on-board the vehicle using sequential quadratic programming (SQP). Because of the computational complexity of DP, an in-depth analysis of how the discretization levels, time constant, and horizon length influence the energy savings and computational complexity of online DP has been performed. The fused optimization technique has been evaluated over different route profiles, accounting for traffic, using a medium-fidelity proprietary Volvo model. Results show 4-7% fuel savings, as compared to a baseline model.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Detecting Anomalous Computation with RNNs on GPU-Accelerated HPC Machines

This paper presents a workload classification framework that discriminates illicit computation from authorized workloads on GPU-accelerated HPC systems. As such systems become more and more powerful, they are exploited by attackers to run malicious and for-profit programs that typically require extremely high computing ability to be successful. Our classification framework leverages the distinctive signatures between illicit and authorized workloads, and explore machine learning methods to learn the workloads and classify them. The framework uses lightweight, non-intrusive workload profiling to collect model input data, and explores multiple machine learning methods, particularly recurrent neural network (RNN) that is suitable for online anomalous workload detection. Evaluation results on three generations of GPU machines demonstrate that the workload classification framework can tell apart the illicit authorized workloads with a high accuracy of over 95%.

Pengfei, Zou↗
DOE OSTI2020

Matrix Completion Using Alternating Minimization for Distribution System State Estimation: Preprint

This paper examines the problem of state estimation in power distribution systems under low-observability conditions. The recently proposed constrained matrix completion method which combines the standard matrix completion method and power flow constraints has been shown to be effective in estimating voltage phasors under low-observability conditions using single-snapshot information. However, the method requires solving a semidefinite programming (SDP) problem, which becomes computationally infeasible for large systems and if multiple-snapshot (time-series) information is used. This paper proposes an efficient algorithm to solve the constrained matrix completion problem with time-series data. This algorithm is based on reformulating the matrix completion problem as a bilinear (non-convex) optimization problem, and applying the alternating minimization algorithm to solve this problem. This paper proves the summable convergence of the proposed algorithm, and demonstrates its efficacy and scalability via IEEE 123-bus system and a real utility feeder system. This paper also explores the value of adding more data from the history in terms of computation time and estimation accuracy.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2020

Actinide Mixing Optimization Solver

The Actinide Mixing Optimization Solver (AMOS) is a set of programs written in MATLAB that computes theoretical mixes of different uranium, plutonium, thorium, and other actinide materials given a list of isotopic reference materials. Constraints are applied depending on the desired product and a mixture is computed to meet the specified requirements. The optimal mixture consists of a product that uses the lowest possible number of initial actinide materials and has the lowest cost. AMOS is currently applied to particular reference material requests from the sponsoring agency (NNSA) on behalf of the IAEA.

Baldwin, Aaron Taylor↗
DOE OSTI2024

Solving the Grid Optimization Competition Challenge 3 Problem

The Grid Optimization Competition Challenge 3 Problem posed a multiperiod security-constrained unit commitment problem with base-case AC power flow. The problem formulation includes binary unit commitment decisions, nonlinear AC power flow and balance, dispatchable loads, and linearized contingency real power flow, among other features. This talk will present a modified consensus ADMM algorithm, which splits the problem into mixed-integer linear and nonlinear components, as a heuristic solution method for this large-scale mixed integer nonlinear program. We will present some computational results from the competition for our implementation and reflect on the challenges of participating the grid optimization competition.

AC power flow↗
DOE OSTI2012

Notes from the Unit Commitment Linear Programming Project

The Unit Commitment Problem (UCP) is a computational bottleneck in electrical grid optimization. The problem bears a close resemblance to a similar problem in High Performance Computing: the Dynamic Volt age/Frequency Scheduling (DVFS) problem. In my earlier work I successfully tranformed the DVFS into a closely related problem amenable to a linear programming solution and was thus able to schedule problem instances several orders of magnitude larger than any that had been at tempted before. This technical report details my unsuccessful efforts to derive a similar transformation for the UCP. The problem proved impossible not because generators were either on or off, but because the state of an earlier timeslice constrains the states of latter timeslices in ways that cannot be sufficiently well approximated for useful solutions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Polyhedral Relaxations for Optimal Pump Scheduling of Potable Water Distribution Networks

The classic pump scheduling or optimal water flow (OWF) problem for water distribution networks (WDNs) minimizes the cost of power consumption for a given WDN over a fixed time horizon. In its exact form, the OWF is a computationally challenging mixed-integer nonlinear program (MINLP). It is complicated by nonlinear equality constraints that model network physics, discrete variables that model operational controls, and intertemporal constraints that model changes to storage devices. To address the computational challenges of the OWF, this paper develops tight polyhedral relaxations of the original MINLP, derives novel valid inequalities (or cuts) using duality theory, and implements novel optimization-based bound tightening and cut generation procedures. The efficacy of each new method is rigorously evaluated by measuring empirical improvements in OWF primal and dual bounds over 45 literature instances. The evaluation suggests that our relaxation improvements, model strengthening techniques, and a thoughtfully selected polyhedral relaxation partitioning scheme can substantially improve OWF primal and dual bounds, especially when compared with similar relaxation-based techniques that do not leverage these new methods.

bound tightening↗
DOE OSTI2022

Distinguished honors for LANL ASC program associates: E.O. Lawrence Awards and Laboratory Fellow Appointments

LANL ASC program associates have been honored as recipients of highly competitive scientific achievement awards. Luis Chacon and Dana Dattelbaum received DOE E.O. Lawrence awards in 2021 and 2020, respectively. Tim Germann, Lin Yin, Ricardo Lebensohn and Hui Li were four of nine LANL Laboratory Fellows appointed for membership in 2022. Each of these awardees has been an influential technical leader in computational sciences impacting both the ASC program and international scientific communities.

97 MATHEMATICS AND COMPUTING↗