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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 OSTI2023

Characterizing Reactor Operations from Realistic Simulated Environmental Samples: Combining High-Performance Computing and Data Analytics

Environmental sampling is a common technique employed by inspectors and facility operators in nuclear safeguards, proliferation detection, and process monitoring contexts. Interpreting measurements performed on samples or collections of samples and ensuring the information extracted is accurate and precise is difficult. To date, these analyses have relied on simulated data to enable systematic studies; however, these models are inherently limited by the fidelity of the models and the implicit spatial averaging of isotopic composition or other signatures of interest. To advance this capability, we have refined the spatial discretization and expanded the range of physics in the simulation codes we use to perform reactor simulations and depletion calculations. This allows us to generate data that are more representative of real environmental samples, especially for the length scale of the isotopic composition and associated variation. Accordingly, these new data allow a more realistic assessment of traditional and new data analytic analysis methods. Here we present motivation for developing reactor simulations using high-performance computing methods and resources, impacts of these new simulations on our assessment of data analysis and interpretation methods, and initial results of developing and systematically testing data analytic methods designed to overcome the challenges expected of real-world samples. We also quantify the performance of these analyses using defensible statistical methods.

Dayman, Ken J.↗
DOE OSTI2020

Results of the International Energy Agency Bioenergy Round Robin on the Analysis of Heteroatoms in Biomass Liquefaction Oils

A round robin study evaluating the analysis of biomass liquefaction oils (BLOs) from fast pyrolysis and hydrothermal liquefaction (HTL) was performed, involving fifteen laboratories in seven countries in order to assess the current status of analytical techniques for the determination of nitrogen, sulfur, and chlorine content in BLOs and evaluate potential differences in origin (i.e. fast pyrolysis vs HTL). The BLOs were produced from a range of feedstocks including pine, mixed softwoods, forest residues, micro-algae, miscanthus, and wheat straw to cover a variety in nitrogen, sulfur, and chlorine content and speciation. Nine samples were distributed, comprised of eight separate BLOs and one blind duplicate produced by five producers. The samples were analyzed for water, carbon, hydrogen, nitrogen, sulfur, and chlorine content. No analytical test method was mandated; laboratories were encouraged to utilize whichever method they determined would be most applicable, relying on the existing body of BLO literature as a guide. The results of this round robin study are presented in this paper. The results of the carbon, hydrogen, and water measurements as reference analyses had relative standard deviations (2.9%, 3.5, and 5.6%, respectively) that were comparable to those found in past round robin studies on fast pyrolysis bio-oil. The analysis of nitrogen, sulfur, and chlorine showed higher levels of variability. Laboratories mostly chose the same method for water, carbon, hydrogen and nitrogen determination whereas there were a variety of methods chosen for sulfur and chlorine determination. The results suggest that specific analytical methods for the determination of nitrogen, sulfur, and chlorine should be further refined to ensure reproducible and accurate results for BLO analysis due to their importance on emissions, material selection, and catalyst activity.

Biomass, IEA Bioenergy, pyrolysis, hydrothermal li↗
DOE OSTI2022

Systems and methods for data analytics for virtual energy audits and value capture assessment of buildings

A system may provide virtual energy audits of one or more target buildings. The system may retrieve weather data and energy usage data specific to a given target building from a weather server and a utility server, respectively. The system may store predefined building characteristics corresponding to the given target building in local memory. Based on the weather data, energy usage data, and/or predefined building characteristics, the system may generate one or more building markers that characterize the energy usage and efficiency of the given target building. Building efficiency diagnostics and energy conservation prognostics may be generated based on the building markers and may be sent by the system to be displayed via a user interface of a client device. The energy conservation prognostics may include one or more energy conservation measure recommendations and corresponding predicted cost/energy savings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

2.3.4.100 - Lignin Utilization

Lignin depolymerization to aromatic monomers is a primary route for myriad lignin valorization strategies. To date, there are many strategies able to cleave aryl-ether linkages in lignin, but the lignin polymer, in both its native and processed forms, contains a substantial fraction of refractory carbon-carbon linkages between aromatic units, which typically limits aromatic monomer yields to -30-40 wt% or lower, depending on the feedstock. To that end, the Lignin Utilization (LigU) project addresses the critical challenge of lignin depolymerization catalysis with emphasis on C-C bond cleavage. Being able to achieve cost-effective C-C bond catalysis in lignin depolymerization would enable a substantial increase in accessible aromatic monomer yields from lignin. Among the catalysis strategies that have been investigated in the LigU project, we have made substantial progress in the use of autoxidation catalysis, inspired by the industrial conversion of p-xylene to terephthalic acid, for C-C bond cleavage in lignin. Using multiple substrates, we have demonstrated that autoxidation catalysis can produce mixtures of bio-available aromatic monomers for conversion to exemplary bioproducts, such as cis,cis-muconic acid, in collaboration with the Biological Lignin Valorization project. Prior to FY23, the LigU project also included lignin analytical chemistry method development, lignin analytics for BETO-funded projects, and model compound syntheses, which will also be presented.

BIOMASS FUELS↗
DOE OSTI2025

Chelation ion chromatography as an automated, and cost-effective analytical technique for REE determination: method development and applications

Rare earth elements (REEs), as critical minerals, have important uses in modern energy and technologies, yet are vulnerable to potential supply chain disruptions. To establish domestic REE supply chain, efficient REE detection methods for resource characterization and mineral processing will be needed to accelerate innovations for domestic REE recovery. This study developed a rapid, novel, and cost-effective for REE detection method using ion chromatography (IC) for aqueous samples. Various REE-targeted eluent gradients and post-column agent compositions were tested on the chelation ion chromatography (CIC) with UV-vis detector for optimal separation and quantification of REEs within approximately 20 min. The single-channel pump to deliver the post-column solution to UV-vis detector was replaced with a 4-channel gradient pump, to increase operation and maintenance efficiencies. After method optimization, resulting calibration curves for more than ten REEs achieved high coefficients of determination (R2>0.999) and low relatively standard deviations (below 3.24%), demonstrating sub-ppm level detection limits (0.0897 to 0.1149 mg/L). The reliability of the CIC method was validated through comparison with inductively coupled plasma mass spectrometry (ICP-MS), showing strong agreement in REE recovery from certified standards. The impact of metal ions and salts on REE recovery using CIC was also systematically investigated. CIC consistently exhibited reliable performance in the presence of salt solutions such as NaCl and Na₂SO₄ (up to 10,000 mg/L). Our study also found the presence of high concentrations of Al ions (at 10,000 mg/L) significantly influenced REE determination, and elevated concentrations of Ca ions affected the recovery of specific REEs, including La, Ce, and Pr. The CIC method was further tested on REE-containing eluents from solvent extraction tests out of fly ash leachates. REE detection from these real processing fluids were reported to achieve 90% to 100% recovery rate from our IC method, compared to ICP-MS results. This study underscores the potential of CIC as a reliable and efficient alternative for REE determination in complex matrices. It also highlights the importance of minimizing select interfering metal ions in solutions to ensure accurate results. The REE CIC method presents a promising, low-maintenance, salt-tolerant, and cost-effective alternative to traditional analytical methods for REE analysis.

detection of rare earth elements (REE)↗
DOE OSTI2020

The Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: exploring the halo occupation distribution model for emission line galaxies

We study the modelling of the halo occupation distribution (HOD) for the eBOSS DR16 emission line galaxies (ELGs). Motivated by previous theoretical and observational studies, we consider different physical effects that can change how ELGs populate haloes. We explore the shape of the average HOD, the fraction of satellite galaxies, their probability distribution function (PDF), and their density and velocity profiles. Our baseline HOD shape was fitted to a semi-analytical model of galaxy formation and evolution, with a decaying occupation of central ELGs at high halo masses. We consider Poisson and sub/super-Poissonian PDFs for satellite assignment. We model both Navarro–Frenk–White and particle profiles for satellite positions, also allowing for decreased concentrations. We model velocities with the virial theorem and particle velocity distributions. Additionally, we introduce a velocity bias and a net infall velocity. We study how these choices impact the clustering statistics while keeping the number density and bias fixed to that from eBOSS ELGs. The projected correlation function, w p , captures most of the effects from the PDF and satellites profile. The quadrupole, ξ 2 , captures most of the effects coming from the velocity profile. We find that the impact of the mean HOD shape is subdominant relative to the rest of choices. Here, we fit the clustering of the eBOSS DR16 ELG data under different combinations of the above assumptions. The catalogues presented here have been analysed in companion papers, showing that eBOSS RSD+BAO measurements are insensitive to the details of galaxy physics considered here. These catalogues are made publicly available.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

MethodOpt: a Shiny-based graphical user interface for multivariate optimization of sampling and analytical instrumentation

Method optimization is an important step in producing useful data in various experimental settings involving the use of sampling and analytical instrumentation, such as gas-chromatography mass-spectrometry or other analytical techniques. However, traditional optimization techniques often lack the sophistication of more modern optimization techniques developed in areas of applied mathematics. A graphical user interface has been developed that implements a multivariate, multi-objective optimization technique for spectra-generating sampling and analytical instrumentation, which saves substantial time and resources compared to the more traditional approaches to method development.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Electric Distribution System Planning with DERs - High-level Assessment of Tools and Methods

This document addresses analytical tools and methods to support electric distribution system planning with distributed energy resources (DERs) and grid modernization, drawing on leading-edge research and examples from around the United States. In this report, tools include computer models and software, as well as other analytical aids and practices that support distribution system planning. At its core, distribution system planning is about supporting investment decisions. Enhanced and integrated distribution system planning allows utilities to move from a passive reactive role to smart proactive planning with respect to DERs. Distribution system planning can increase transparency around utility distribution system investments before they are brought to the regulatory body for cost recovery, can support deferring traditional infrastructure investments, and can provide information and incentives to customers and third parties about where DERs can help the grid. A growing number of states are beginning to consider a comprehensive distribution system planning process that addresses the costs and benefits of DERs, reflects more choices coming from the customer/third-party domains, is integrated with resource and transmission planning, and incorporates planning scenarios that reflect the dynamic and changing nature of the electricity system

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Measuring photometric redshifts for high-redshift radio source surveys

With the advent of deep, all-sky radio surveys, the need for ancillary data to make the most of the new, high-quality radio data from surveys like the Evolutionary Map of the Universe (EMU), GaLactic and Extragalactic All-sky Murchison Widefield Array survey eXtended, Very Large Array Sky Survey, and LOFAR Two-metre Sky Survey is growing rapidly. Radio surveys produce significant numbers of Active Galactic Nuclei (AGNs) and have a significantly higher average redshift when compared with optical and infrared all-sky surveys. Thus, traditional methods of estimating redshift are challenged, with spectroscopic surveys not reaching the redshift depth of radio surveys, and AGNs making it difficult for template fitting methods to accurately model the source. Machine Learning (ML) methods have been used, but efforts have typically been directed towards optically selected samples, or samples at significantly lower redshift than expected from upcoming radio surveys. This work compiles and homogenises a radio-selected dataset from both the northern hemisphere (making use of Sloan Digital Sky Survey optical photometry) and southern hemisphere (making use of Dark Energy Survey optical photometry). We then test commonly used ML algorithms such as k-Nearest Neighbours (kNN), Random Forest, ANNz, and GPz on this monolithic radio-selected sample. We show that kNN has the lowest percentage of catastrophic outliers, providing the best match for the majority of science cases in the EMU survey. We note that the wider redshift range of the combined dataset used allows for estimation of sources up to z = 3 before random scatter begins to dominate. When binning the data into redshift bins and treating the problem as a classification problem, we are able to correctly identify ≈ 76% of the highest redshift sources—sources at redshift z > 2.51 —as being in either the highest bin (z > 2.51) or second highest (z = 2.25).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Deep Learning enabled spectral energy conversion for in situ exposure measurements

A detector-specific deep learning (DL) approach is presented for spectra-to-exposure conversion using large-format sodium iodide (NaI(Tl)) detectors deployed for in situ environmental radiation measurements in emergency response scenarios. Accurate determination of exposure from NaI spectra is challenging due to poor energy resolution, partial energy absorption, and the strong sensitivity of traditionally deployed analytical conversion methods to calibrated source geometry and pre-deployment assumptions. Here, to address these limitations, a multi-layer perceptron model was trained on a hybrid in situ /Monte Carlo dataset constructed to span a broad range of photon energies, spatial extents, and realistic deployment variability, representative of general in situ emergency response conditions. The DL model was evaluated against commonly fielded analytical approaches under matched simulation conditions, including a single-factor method, a G-function method, and a modeled pressurized ion chamber (PIC) baseline. This study was intentionally computational in scope to enable controlled, like-for-like comparisons between conversion techniques while minimizing confounding real-world variability. Comparison to the modeled PIC provides contextual benchmarking and is not intended as a field inter-comparison with deployed instruments. Across the evaluated 20 keV to 3 MeV energy range, the DL approach consistently exhibited higher accuracy and reduced variance relative to the analytical methods against a deterministically calculated exposure. This may indicate improved robustness to spectral complexity without reliance on source-, geometric-, or spectral region-specific optimization. While results do not represent real-world validation, the presented work demonstrates that deep learning may effectively learn the nonlinear detector response-to-exposure relationship for asymmetric NaI(Tl) detectors and offers a promising pathway for improving in situ exposure estimation using spectroscopic systems already integrated into initial real-time emergency response operations.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2020

Automated Data Review of Analytical Laboratory Results at Los Alamos National Laboratory - 20299

Newport News Nuclear BWXT-Los Alamos, LLC (N3B) collects samples in support of the U.S. Department of Energy's (DOE) Office of Environmental Management (EM) Los Alamos Legacy Cleanup Contract (LLCC). N3B receives and reviews over 1.6 million sample data points annually in support of various ongoing environmental monitoring and remediation projects of the LLCC. N3B must demonstrate and document that reported external analytical laboratory data produced for the LLCC are of sufficient quality to fulfill their intended purpose and to support defensible decision making as described in EPA QA/G4 Guidance for the Data Quality Objectives Process 1994. In 2018, N3B assumed management of the LLCC along with the Environmental Information Management (EIM) database that contains all historical and current environmental data associated with the LLCC. The entire EIM database is shared between N3B, Triad National Security, LLC (Triad), and New Mexico Environment Department (NMED). These three parties jointly manage the database, its configuration, and changes / updates. All environmental data that are entered into EIM are updated and available, on a daily basis, in the linked public database Intellus New Mexico (Intellus). The quality and defensibility of the environmental data generated from sampling activities is a key component of an effective remediation process. Providing quality data is accomplished through a data assessment process that includes examination, verification, and validation. Examination is the assessment of completeness of the deliverables, identification of any reporting errors, and determining the usability of the data based on the laboratory's evaluation of its data as described in the case narrative received with the data. Verification consists of an evaluation of the Electronic Data Deliverables (EDD) data report to determine the extent to which the external analytical laboratories met method and contract-specific quality control and reporting requirements. Validation consists of determining the data quality and the extent to which the external analytical laboratories accurately and completely reported all sample and quality control results and satisfied all contract requirements. EIM contains an automatic Data Validation Module which performs automated data review (DVM ADR). DVM ADR is a tool to assist in the validation process. When DVM ADR is used in conjunction with manual examination of sample data packages, the combination of the two will meet and exceed the requirements of verification. N3B recognized an opportunity for process improvement, focusing on DVM ADR configuration and enhancements in EIM. Testing EIM's configuration provided proof of the DVM ADR's capabilities and flexibility to accurately perform routine data checks based on analytical methods and regulatory requirements. In addition, the DVM ADR module was improved through enhancements for all analytes, particularly upgrades for radiochemistry data. Extensive testing of the DVM ADR module occurred using EDDs from actual laboratory analyses on the EIM testing site. During this process, N3B manipulated EDD information to verify that the actual outcomes matched the expected outcomes. The results of this testing were shared with the database architects, and configuration improvements were identified to address these results. During this process, N3B identified that the radiochemical DVM ADR capabilities were underutilized, and so enhanced the DVM ADR functionality with respect to radioanalytical assessment. N3B environmental data uploads to Intellus on a daily basis from EIM, once the analytical data undergoes examination and verification. As such, it is important to have a high level of confidence in the quality and defensibility of the data. The process of manual examination, along with the DVM ADR, in conjunction with full validation of a percentage the data specified through the Data Quality Objectives greatly increases efficiency of data review and confidence level of the quality of the data, and gives the project managers, governmental offices, and the public expedited access to high-quality data. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

The force explosion condition is consistent with spherically symmetric CCSN explosions

ABSTRACT One of the major challenges in core-collapse supernova (CCSN) theory is to predict which stars explode and which collapse to black holes. The analytic force explosion condition (FEC) shows promise in predicting which stars explode in that the FEC is consistent with CCSN simulations that use the light-bulb approximation for neutrino heating and cooling. In this follow-up manuscript, we take the next step and show that the FEC is consistent with the explosion condition when using actual neutrino transport in gr1d simulations. Since most 1D simulations do not explode, to facilitate this test, we enhance the heating efficiency within the gain region. To compare the analytic FEC and radiation-hydrodynamic simulations, this manuscript also presents a practical translation of the physical parameters. For example: we replace the neutrino power deposited in the gain region, Lντg, with the net neutrino heating in the gain region; rather than assuming that $\dot{M}$ is the same everywhere, we calculate $\dot{M}$ within the gain region; and we use the neutrino opacity at the gain radius. With small, yet practical modifications, we show that the FEC predicts the explosion conditions in spherically symmetric CCSN simulations that use neutrino transport.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Uncertainty propagation and sensitivity analysis for constrained optimization of nuclear waste vitrification

Abstract The vitrification of high‐level waste (HLW) by heating a mixture of glass‐forming chemicals (GFCs) with the waste can be improved using a constrained optimization problem. This study explores how different uncertainty propagation (UP) methods implemented with the optimization process can affect the glass formulation of nuclear waste glasses. UP is the effort of propagating uncertain inputs through a system to understand and quantify output distributions. Uncertainty intervals are crafted from output distributions to inform the optimization algorithm. UP is often implemented with Monte Carlo (MC) sampling for large nonlinear systems, which can be difficult to implement within a constrained optimization algorithm that requires derivative information. Other UP methods often used for optimization under uncertainty (OUU) can be designed to work within an established constrained optimization framework. Methods of UP are evaluated in this study including iterative sampling approaches, first‐order approximations, and surrogate modeling with machine learning (ML). A method of dimensional reduction based on global sensitivity analysis is introduced to support the UP methods for the large dimensionality of the problem. Analytical UP methods able to achieve similar optimums 10 times faster than the baseline MC approach, and produce 93.9% similar output distributions are reported.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Snap-Through Buckling Pressure Prediction of Spherical Caps: A Comparison of Analytical, Implicit, and Explicit Methods

Snap-through buckling is a nonlinear and dynamic instability that occurs in curved shell structures such as domes, pressure vessels, and aerospace panels. Unlike classical linear buckling, it involves a sudden transition between equilibrium states caused by geometric nonlinearity and rapid strain-to-kinetic energy conversion (Timoshenko & Gere, 1961; Budiansky & Roth, 1962). This study investigates the snap-through behavior of thin spherical caps using both implicit and explicit solvers in ANSYS Workbench. While implicit analysis accurately captures quasi-static response, it struggles with convergence near instability. In contrast, explicit LS-DYNA simulation naturally handles the nonlinear dynamic event with minimal tuning and computational cost.

42 ENGINEERING↗
DOE OSTI2023

Electrophoretic device and method to separate and detect analyte ions

This invention is directed to an inexpensive, miniaturized, portable, low-power device and method for electrophoretic separation and electrochemical detection of an analyte, including different isotopes of the same element. The invention replaces a conventional or microfabricated capillary electrophoresis tube with a microchip comprising an array of parallel electrophoretic separation nanotubes or aligned hollow channels fabricated in a porous substrate.

Klavetter, Kyle C.↗
DOE OSTI2021

Methods, systems, and media for detecting the presence of an analyte

In accordance with some embodiments, methods, systems, and media for detecting the presence of are provided. In some embodiments, a method of detecting an analyte is provided, the method comprising: capturing an image of liquid crystals; determining one or more features based on the brightness of the pixels in the image; providing the one or more features to a trained support vector machine, wherein the support vector machine was trained using images captured of other liquid crystals when exposed to a first analyte and the other liquid crystals exposed to a second analyte; and receiving an indication from the support vector machine indicating whether the liquid crystals have been exposed to the first analyte.

Abbott, Nicholas L.↗
DOE OSTI2020

SIERRA Multimechanics Module: Aria User Manual - Version 4.56

Aria is a Galerkin finite element based program for solving coupled-physics problems described by systems of PDEs and is capable of solving nonlinear, implicit, transient and direct-to-steady state problems in two and three dimensions on parallel architectures. The suite of physics currently supported by Aria includes thermal energy transport, species transport, and electrostatics as well as generalized scalar, vector and tensor transport equations. Additionally, Aria includes support for manufacturing process ows via the incompressible Navier-Stokes equations specialized to a low Reynolds number (Re < 1) regime. Enhanced modeling support of manufacturing processing is made possible through use of either arbitrary Lagrangian-Eulerian (ALE) and level set based free and moving boundary tracking in conjunction with quasi-static nonlinear elastic solid mechanics for mesh control. Coupled physics problems are solved in several ways including fully-coupled Newtons method with analytic or numerical sensitivities, fully-coupled Newton-Krylov methods and a loosely-coupled nonlinear iteration about subsets of the system that are solved using combinations of the aforementioned methods. Error estimation, uniform and dynamic h-adaptivity and dynamic load balancing are some of Arias more advanced capabilities.

97 MATHEMATICS AND COMPUTING↗