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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Tools And Methods to Analyze Plant Outage Schedule and Assist Schedulers in Improving Outage Resilience

Refueling outages of nuclear power plants (NPPs) are considered one of the most critical phases throughout the plant lifetime. In such instances, tens of thousands of activities (e.g., maintenance, surveillance) are performed in a short amount of time (typically 2-3 weeks unless major backfitting or modernization projects are carried out) by a large number of crews (e.g., electricians, mechanics) that are hired as contractors. As a consequence, a plant outage can be expensive not only in terms of costs (e.g., contractor labor, material), but also in terms of loss generation since the plant is taken off the grid during the full outage duration (an indicative metric is about 1.2M$/day of loss of revenue). Thus, there is a continuous need to decrease the economic impact of outages on plant finances. This can be done by: decreasing the frequency of plant outages (e.g., from 18 to 24 months), reducing the time to complete the outage, and reducing the risk of outage delays. The Optimization of Outage Activities project under the Risk Informed Systems Analysis Pathway (RISA) sponsored by Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program focuses on developing tools and methods to support NPPs with outage schedule optimization. The developed tools and methods are designed to analyze plant outage schedule with the goal of identify critical elements in the schedule that might pose a high risk of delays. These methods and tools can be considered resource-centric in the sense that they address outage challenges as a resource optimization problem. In this context, resources are either time and crews; outage delays occurs when either (or both) resources are insufficient to complete the set of tasks assigned at a specific time instant of the outage. This report provides details on how plant resources (time and crews) can be allocated in such a way that delays are minimized. In this respect, two classes of methods have been developed: the first one focuses on the time resource and how variability of the time to complete outage tasks may impact outage delays. The second one integrates available resources to assess when dailies activities should be performed such that the risk of outage delays are minimized.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

3_wise_bears

This is a small set of python scripts and HTML that uses OpenAI API to control large language models (LLMs) working agentically to solve a posed question / problem. There are 3 agents and they work to get it "just right" by taking on various "roles" of friendly and adversarial critics. It repeats a number of times specified by the user, and then writes a report.

DeBardeleben, Nathan Andrew [Los Alamos National L↗
DOE OSTI2025

Existing Methods for Grid Strength Assessment and Role of Hydropower in Future Grids

The power system is undergoing rapid evolution with increasing penetration of inverter-based resources (IBRs), large loads, microgrids, and power electronic devices. Ensuring reliable and stable operation of the modern power grid is a multi-faceted challenge that requires detailed understanding of this complex system. Dynamic stability is a major concern in maintaining the security of power grids as the generation mixes and large loads transitions to include high shares of power electronic devices. The operation of IBRs in regions with low system strength and higher grid impedances has been found to be the main reason behind many of the power system instabilities that manifest themselves in various types of oscillations and interactions that, if not properly addressed and damped, can jeopardize the reliable operation of the power system. Weak grid conditions compound such stability problems, particularly when many IBRs operate in proximity to and connect to weak power grids. Therefore, it is important to assess grid strength for planning, integration and operation of IBRs for a given power system. In this report, we aim to understand and classify existing methodologies for grid strength assessment, along with their limitations and future needs. Further, we aim to utilize the huge untapped potential in utilizing hydro energy resources in addressing some of the pertinent challenges of grid strength and reliable operation of modern power systems. Hydropower, historically valued for its flexibility and dispatchability, now faces new constraints due to reduced share of synchronous machines in the generation mix and seasonal variability of available water resources. Yet, these same plants present untapped potential beyond energy generation - notably, as providers of critical grid services. This report explores how hydropower plants, particularly through operation as synchronous condensers, can play a pivotal role in strengthening the grid amid evolving system dynamics.

13 HYDRO ENERGY↗
DOE OSTI2025

An Optimization-Based Law of Mass Action Precipitation/Dissolution Model

Rare earth elements (REE) and many other critical minerals are necessary for the manufacturing of modern everyday technologies, including microchips, batteries and electric motors. Recovery of these materials typically involves aqueous systems which can be modeled as chemical equilibrium problems. One common method for solving these problems is the law of mass action approach (LMA), where a system of non-linear equations involving the equilibrium constants is solved. However, despite being theoretically simple, these problems are in practice very difficult to solve. Currently, the use of iterative heuristics based on saturation indices to decide on which species and reactions to include in the calculations is the state of the art to arrive at a solution. Here, we present an optimization-based alternative to solve chemical equilibria problems involving precipitation/dissolution reactions without the need for such heuristics. Our approach is first validated against the LMA software MINTEQ and PHREEQC for a number of case studies, and then applied to a novel REE recovery process reported in the literature. Overall, our approach was found to have close agreement with MINTEQ and PHREEQC, and we were able to successfully replicate the reported yield and purity for the published REE process.

42 ENGINEERING↗
DOE OSTI2026

A Discrete Hankel Transform Approach to Nuclear Data Processing for Fusion Applications

This study introduces advancements to the numerical solutions employed in the processing of nuclear data for fusion applications. It leverages the convolution theorem and Fourier transform techniques to enhance computational efficiency and broaden applicability. Building upon a previously reported discrete Hankel transform approach for Doppler broadening, this work refines the solution of convolution integrals central to these applications. The methodology provides a general and unified framework for evaluating any convolution operation, regardless of whether the underlying problem involves temperature effects in nuclear reactions. The applicability to the nuclear data processing for fusion is demonstrated by deriving the convolution integrals for some of the fusion-related quantities. As before, the convolution operation utilizes a Gaussian-based kernel; however, the discrete Hankel transform of order $𝛼$ = $\frac{1}{2}$ is now applied to the forward Fourier transform of the nonkernel argument, rather than the inverse Fourier transform. This modification eliminates the need for the integration of the nonkernel, cross section–based function, which is a step that posed challenges for certain pointwise cross-section representations. It also removes the requirement for cross-section linearization. Optimized for graphics processing unit architectures, the approach significantly improves computational performance. These advancements are currently under evaluation as the foundation for the next-generation thermonuclear data file processing codes being developed at Lawrence Livermore National Laboratory.

Nuclear science and engineering↗
DOE OSTI2024

News Media Framing of Suicide Circumstances and Gender: Mixed Methods Analysis

Background: Suicide is a leading cause of death worldwide. Journalistic reporting guidelines were created to curb the impact of unsafe reporting; however, how suicide is framed in news reports may differ by important characteristics such as the circumstances and the decedent’s gender. Objective: This study aimed to examine the degree to which news media reports of suicides are framed using stigmatized or glorified language and differences in such framing by gender and circumstance of suicide. Methods: We analyzed 200 news articles regarding suicides and applied the validated Stigma of Suicide Scale to identify stigmatized and glorified language. We assessed linguistic similarity with 2 widely used metrics, cosine similarity and mutual information scores, using a machine learning–based large language model. Results: News reports of male suicides were framed more similarly to stigmatizing (P<.001) and glorifying (P=.005) language than reports of female suicides. Considering the circumstances of suicide, mutual information scores indicated that differences in the use of stigmatizing or glorifying language by gender were most pronounced for articles attributing legal (0.155), relationship (0.268), or mental health problems (0.251) as the cause.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Improving Cyber Situational Understanding

Effective cybersecurity operations require the ability to analyze large amounts of information to assess security risks and formulate defensive strategies against adversaries. This has become more complex in recent years as the sprawl and interconnectivity of devices grows through implementation of virtualization, cloud computing, and Internet of Things (IoT). The amount of data and analysis required for effective cybersecurity command and control decisions far exceeds humans’ capacity to perform manually. We characterize the analysis problem as cyber situational understanding. The research presented to improve cyber situational understanding focuses on vulnerability analysis and threat intelligence. Regarding vulnerabilities, entities must analyze and plan work for between thousands and tens of thousands of software vulnerabilities annually. Entities heavily use network firewalls to limit vulnerability exposure. As a result, some of these vulnerabilities permit exposure to adversarial exploitation, whereas others are inaccessible and therefore present negligible risk of exploitation. Distinguishing between high and low risk software vulnerabilities requires a deep understanding of the vulnerability, network firewall protection, and characteristics of the targeted device. This problem is solved by extracting network service features from vulnerability data features using both machine-learning and natural language processing. Then, the network firewall topology is parsed to determine which vulnerabilities are reachable by adversaries. Ultimately, a state-based safety analysis ascertains which vulnerabilities are unsafe. A related vulnerability analysis problem occurs in cybersecurity operations when associating an entity’s hardware and software assets to public vulnerability databases. Assets often reveal hardware and software through installation artifacts and network service identification, and entities store these artifacts in inventory databases. However, software and hardware vendors apply a standard Common Platform Enumeration (CPE) naming convention when publicly reporting vulnerabilities. Associating these two datasets often requires many hours to days of manual inspection. The proposed solution automates the mapping approach of human analysts using fuzzy matching techniques, natural language processing, and, ultimately, machine learning to present a small set of recommendations for mapping the two datasets. The result significantly reduces human analysis time and reduces the occurrence of false positives in vulnerability notifications. Finally, cyber threat intelligence (CTI) requires associating cyber observable artifacts, such as IP addresses, URIs, and file hashes, with cyber threat tactics, techniques, and procedures. Unfortunately, most CTI data is compartmentalized across multiple organizations and cannot be shared due to the legal and reputational risk with cyber threat being associated with the entity. The approach to solving this problem inovlves using a distributed ledger with anonymous token spending and authentication. This allows a consortium of semi-trusted entities to share the workload of curating CTI for a threat sharing community’s cooperative benefit.

Huff, Philip↗
DOE OSTI2025

Low-symmetry polymorph of GaP upends bonding paradigms of metallic high-pressure III–V compounds

Here, the pressure-induced polymorphism of binary octet compounds has long been considered a settled problem although the possible atomic disordering of some phases remains a puzzling observation. Taking GaP as a case study, we conclude, through x-ray microdiffraction and first-principles calculations, that its high-pressure metallic phase (previously reported as being disordered) adopts in fact an ordered base-centered monoclinic structure previously unknown in this class of compounds. The formation of layered patterns with variable degrees of interlayer dimerization, as observed in GaP, marks a paradigm shift of our understanding of ordering in octet high-pressure phases which calls for a more extensive re-examination. A rich polymorphism with fine tuning of chemical and physical properties can be envisioned.

36 MATERIALS SCIENCE↗
DOE OSTI2025

MATHUSLA: An External Long-Lived Particle Detector to Maximize the Discovery Potential of the HL-LHC

We present the current status of the MATHUSLA (MAssive Timing Hodoscope for Ultra-Stable neutraL pArticles) long-lived particle (LLP) detector at the HL-LHC, covering the design, fabrication and installation at CERN Point 5. MATHUSLA40 is a 40 m-scale detector with an air-filled decay volume that is instrumented with scintillator tracking detectors, to be located near CMS. Its large size, close proximity to the CMS interaction point and about 100 m of rock shielding from LHC backgrounds allows it to detect LLP production rates and lifetimes that are one to two orders of magnitude beyond the ultimate reach of the LHC main detectors. This provides unique sensitivity to many LLP signals that are highly theoretically motivated, due to their connection to the hierarchy problem, the nature of dark matter, and baryogenesis. Data taking is projected to commence with the start of HL-LHC operations. We summarize the new 40m design for the detector that was recently presented in the MATHUSLA Conceptual Design Report, alongside new realistic background and signal simulations that demonstrate high efficiency for the main target LLP signals in a background-free HL-LHC search. We argue that MATHUSLA's uniquely robust expansion of the HL-LHC physics reach is a crucial ingredient in CERN's mission to search for new physics and characterize the Higgs boson with precision.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Improving unfolding and systematic uncertainty estimation using generative diffusion networks (Final Technical Report)

This final technical report summarizes the key accomplishments on the unfolding using diffusion model project, a DOE award received by PI Pierre-Hugues Beauchemin at Tufts University. This project main goal was to investigate the potential of diffusion models for unfolding experimental High Energy Physics data from detector effects while controlling systematics uncertainties. The project accomplished its goals by completing the following objectives: 1) Performing an object-by-object, event-by-event unfolding of various kinematic distributions reconstructed from detector data in HEP in a way that keeps correlations between unfolded observables while demonstrating competitive performance compared to standard algorithms used in the field; 2) Address the generalization problem by developing an unfolding algorithm capable to correctly infer the underlying distributions of observables and processes never seen before, while controlling the dominant theoretical uncertainties affecting the process, therefore increasing the effectiveness, the precision, and the applicability of the developed algorithm; 3) Understand the theoretical foundations between the developed algorithm so to extend it to applications beyond experimental HEP, for broader benefits to the society. This report provides an overview of the accomplishments related to each of these key objectives.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Supporting ARPA-E Power Grid Optimization (Final Report)

Pacific Northwest National Laboratory (PNNL), Arizona State University (ASU), Georgia Institute of Technology (Georgia Tech), Los Alamos National Laboratory (LANL), National Renewable Energy Laboratory (NREL), Texas A&M University (TAMU), The University of Texas at Austin (UT), and the University of Wisconsin-Madison (UW-M) supported the ARPA-E Grid Optimization (GO) Competition by providing a common problem formulation, data format, datasets, evaluation mechanism, scoring, rules, and results that resulted in the awarding of $\$9.24$ million dollars to teams from academia, industry, and national labs for solving three sets of increasingly difficult non-linear, security- constrained AC Optimal Powerflow (AC-OPF) optimization problems in order to increase the efficiency of the US Electric Grid. It is estimated that a 1% increase in efficiency can save $\$1$ billion. Current industry practices typically use a linear DC model (DC-OPF) in order solve the OPF problem within the time constraints of the operation schedule. The GO Competition challenges the best power engineers, mathematicians, and computer scientists to make possible operational decisions based on accurate physical models. To accomplish this, the GO Competition created a series of Challenges and funded teams to produce the best solver. Challenge 1 was to solve the security constrained Alternating Current Optimal Power Flow (ACOPF) problem. Challenge 2 extended that to by adding adjustable transformer tap ratios, phase shifting transformers, switchable shunts, price-responsive demand, ramp rate constrained generators and loads, and fast-start unit commitment (UC). Furthermore, Challenge 2 was a maximization problem while Challenge 1 was a minimization problem. While Challenge 3 was being developed, the entrants were invited to find better solutions to the Challenge 2 synthetic datasets with no restrictions on time, hardware, or algorithms. The Challenge 2 solutions turned out to be very good. Challenge 3 expanded the Challenge 2 problem further by using multiperiod dynamic markets, including advisory models for extreme weather events, day-ahead markets, and the real-time markets with an extended look-ahead. These problems included active bid-in demand and topology optimization. Together the Challenges used nearly 30 million CPU hours. Since each team was working on the same problem, using the same data, and running on the same hardware, fair comparisons could be drawn as to the best solver. The datasets were varied enough, however, that the best solver for one dataset was not necessarily the best at another, so cumulative scores were used. The process was managed by the PNNL maintained website https://GOCompetition.energy.gov, where Entrants could find information about the problem, the data, the rules, submit their solver for evaluation, and see the scores of all the competing teams on a Leaderboard. Interest was world-wide but only American teams were eligible for prizes. The Competition has produced 34 journal articles 115 papers and been cited over 500 times in the literature, including 12 dissertations (4 from foreign countries; Columbia (2), Germany, and Italy) and 3 from the DOE ExaScale project. Software developed by Pearl Street Technologies for Challenges 1 and 2 is now deployed by Southwest Power Pool (SPP) and Midcontinent Independent Service Operator (MISO). Other teams have received inquiries from venture capitalists. Google DeepMind has thanked the Competition for making the datasets developed for the Competition public. They are using it to train machine learning models. The larger datasets have billions of unknowns to be solved for, but only a small percent matter in the final solution. Knowing what unknowns are important can dramatically speedup the solution.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Aligned carbon nanotubes for 1,000x improvement in computing (CRADA Final Report)

As part of the Cyclotron Road program, the Aligned Carbon team worked to improve and bring to market their technology to manufacture carbon nanotubes (CNTs) for integrated circuits. There are multiple applications which can benefit from the use of CNT based transistors in integrated circuits, including high performance computing, RF data transmission, and biosensing. Each of these applications faces a problem of poor starting CNT material to come to market. Aligned Carbon offers material with the highest ceiling of performance based around a fully aligned, uniform, on-wafer growth of CNTs and our proprietary purification process to remove metallic CNTs leaving only the semi-conducting ones behind. Further, Aligned Carbon has been guided by scalable manufacturing processes that will fit seamlessly into the integrated circuit foundry model of chip fabrication.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Selective electrified polyethylene upcycling by pore-modulated pyrolysis

Plastic waste is a increasing problem, accumulating in landfills and the environment. Pyrolysis is a promising and industrially relevant approach for transforming plastic waste into value-added chemicals. However, the selectivity and yield of traditional plastic pyrolysis are poor, with products featuring broad molar mass distributions. Here we report a highly selective, energy-efficient and catalyst-free pyrolysis method that can upcycle plastic into value-added chemicals via pore-modulated pyrolysis. Using a Joule-heated carbon column, we demonstrate the pivotal role of the reactor’s graded porous structure in decreasing the polydispersity of the reaction intermediates, enabling high product selectivity and yield. The decreasing pore size of the reactor modulates the mass transport in an apparent gating effect—preventing high-molar-mass species from exiting the reactor before sufficient pyrolysis has occurred. Using polyethylene as a model reactant, we demonstrate a high yield of 65.9 ± 5.2% and up to 80.8% selectivity toward value-added aviation fuel precursor (C8–C18 hydrocarbons) without the use of any catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Ristra Project FY23 L2 Milestone Report, Rev.1: MRT #8541: Multiphysics Scaling on EAS-3

The findings of this report were used to close out the ATDM milestone MRT# 8541, which was designed to demonstrate readiness of ATDM multiphysics codes for mission-relevant work on ATS-4, El Capitan. To this end, the closure criteria were to run a 3D shaped charge problem at scale up to 50% of the El Capitan early-access system, RZVernal (AMD Trento CPUs and AMD MI-250X GPUs), demonstrate scalability, and document challenges with the software stack and environment. LANL’s approach to this milestone was to test our modular software capability by developing an entirely new code, Moya, built upon our FleCSI framework. The physics capability and the GPU infrastructure needed for the shaped charge problem on GPUs was added to Moya, and the required calculations were performed at scale. Moya showed good scaling without any fine-tuning of GPU kernels; there is still significant room for performance enhancements, especially for the Legion backend. Tied up in this L2 milestone was a closeout of KPP-3s for the ECP ST Projects at LANL; this material will be covered in a separate document.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Orbital Debris IC Report

We started a simulation campaign in support of our ongoing plasma signatures of small orbital debris project SINTRA program funded by IARPA. This project aims to identify plasma signatures generated by the interaction of small orbital debris (>10cm) with space plasma that can be used for detection, tracking and eventually, develop technologies to remove these small but lethal objects. The small orbital debris problem is increasing exponentially threaten the current and future use of space.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Polymer Macrocycles: A novel topology to control dynamics of rubbery materials (Final Report)

One of the current challenges in polymer physics and engineering is to understand the behavior of circular macromolecules. Though such molecules have been made by dilute solution ring closure methods in the past, such procedures have been limited to relatively small, unentangled rings as well as to small sample quantities. Initial studies were also hampered by issues surrounding purity of the rings, though this problem has been somewhat overcome by the use of liquid chromatography at critical conditions (LCCC) methods that separate rings and linear chains. Furthermore, in spite of the continued progress a full understanding of ring dynamics remains elusive. The present work has addressed multiple aspects of the behavior of ring molecules by using a novel synthetic route: reversible radical recombination redox polymerization (R3P) to produce poly(3,6-dioxa-1,8-octanedithiol) (PolyDODT) molecules. The synthesis conditions were chosen to produce linear (LDODT), cyclic (RDODT) and linear-cyclic mixtures (LRDODT) in order to investigate dilute solution and rheological responses of the materials. Importantly, the R3P synthesis makes larger quantities and higher molecular weights than do the dilute solution ring closure methods and also has the potential to be scaled to industrial quantities, thus becoming commercializable. We emphasize two important outcomes of the work. The first is that the rheological investigation shows that very large molecular weight the PolyDODT rings permitted the investigation of ring dynamics from glassy towards terminal flow for molecular sizes corresponding to approximately 300 entanglements of the linear counterpart. At the same time, these very large sized rings could be diluted in a solvent in order to reduce the molecular entanglement density to less than unity. The results demonstrate that ring viscosity follows a Rouse-like linear dependence on molecular weight or entanglement number to approximately 15 entanglements. This range of "Rouse-like" response is greater than what was achieved in all prior works where synthesis conditions have limited the molecular sizes achievable. After the entanglement number $Z_w$ increases beyond approximately 15, we find that the viscosity-molecular weight scaling undergoes a rapid change from the Rouse-like $η\sim{Z}^1_w$ to a very strong power-law scaling that is approximately $η\sim{Z}^{5.8}_w$. This aspect of the work strongly suggests that prior investigations on highly pure rings have invariably been performed on unentangled systems simply because ring molecules have very high entanglement thresholds. The results are further supported by plateau modulus measurements that show that in the "Rouse-like" viscosity regime, for the RDODT samples there is a corresponding lack of a rubbery entanglement plateau and once entanglement coupling is seen, the rubbery plateau also appears in the dynamic modulus data. Furthermore, upon appropriate scaling, we show that the unentangled polyDODT rings (i.e., the diluted high molecular weight rings) show virtually identical dynamics (upon appropriate scaling to account for the chain dilutions) to the dynamics of similarly unentangled LCCC fractionated polystyrene rings.

36 MATERIALS SCIENCE↗
DOE OSTI2024

On the convergence of the fixed point method for solving neutron transport alpha eigenvalue problems

It was shown that the Fixed Point Method (also known as the Rayleigh Quotient Method) is several times faster than the Critical Search Method for solving neutron transport alpha eigenvalue problems. It was also shown that the Fixed Point Method is able to determine the alpha eigenvalues of sub-critical systems that are beyond the reach of the Critical Search Method. Despite these significant advances, the Fixed Point Method remains an unproven algorithm. Here, this report provides a proof.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Observing Quantum Measurement Collapse as a Learnability Phase Transition

During a quantum measurement, superpositions of states with different observable properties probabilistically collapse into one with a sharp value of the measured observable. In macroscopic quantum systems, this collapse arises via a continuous measurement-induced phase transition (MIPT) at a critical value of the strength of interaction with the measurement apparatus. MIPTs lie outside established paradigms for equilibrium or nonequilibrium critical phenomena and delineate distinct, stable dynamical and computational phases of matter. Quantum computers enable programmable simulation of the interaction of a measurement apparatus with a dynamical quantum system, to explore MIPT phenomena over a range of system sizes while retaining quantum coherence. Yet, existing experimental protocols rely on fundamentally nonscalable postselection techniques or direct classical simulation of quantum circuits. Here, we report the scalable observation of finite-size scaling evidence for an observable-sharpening MIPT in monitored quantum circuits in a chain of Yb + 171 ions in Quantinuum’s H1-1 trapped-ion quantum processor. By leveraging an equivalent description as a statistical physics problem, we implement scalable classical algorithms to infer the value of the measured observable from a single experimental shot. This technique enables a truly scalable protocol to observe observable-sharpening MIPTs in generic classes of circuits that cannot be directly classically simulated and also provides enhanced means to detect and suppress errors in the quantum simulation. Published by the American Physical Society 2024

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗