Search NASA⌕ Search

SEARCH · Search NASA

Results for “integration of the source function”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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

At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Implementation of the D1S Methodology for Shutdown Dose Rate Calculations in the OpenMC Monte Carlo Particle Transport Code

We present an implementation of the direct one-step (D1S) methodology for shutdown dose rate (SDR) calculations in the OpenMC Monte Carlo particle transport code. In addition to being the first fully open-source D1S implementation, it is also the first to require no ad hoc source code or nuclear data library modifications. The code can seamlessly switch between production of prompt and decay photons based on a user input parameter, and the decay data needed for decay photon generation are made available through a depletion chain file, which is already used for OpenMC’s built-in depletion/activation solver. A set of Python functions significantly eases the burden of computing and applying time correction factors needed to properly account for the time dependence of radionuclide activity. To assess the accuracy of the D1S implementation, SDR calculations have been carried out for three problems: a prism of iron irradiated by 14-MeV neutrons, the ITER port plug computational benchmark, and the Frascati Neutron Generator (FNG) ITER dose rate benchmark problem from the Shielding INtegral Benchmark Archive and Database (SINBAD). For each of these problems, comparisons were made to calculations using the rigorous two-step (R2S) method. The results on the iron prism problem illustrate how the D1S method achieves superior spatial resolution compared to the R2S method without the need for spatial discretization of the activation regions. The D1S and R2S results for the ITER port plug benchmark agree well with previously reported results in the literature. While the D1S results are 10% to 15% lower than the R2S results, this may be due to stochastic uncertainty and/or spatial discretization in the R2S calculations. On the FNG dose rate benchmark problem, the D1S method produces dose rate estimates that are within 4% of the dose rates predicted using a cell-based R2S workflow. The D1S estimates of the SDR are also in reasonable agreement with the experimental measurements and show the same basic trends that have been observed in previous works. A qualitative analysis of the execution time and uncertainty for the R2S and D1S workflows suggests that the D1S method would attain a higher figure of merit.

D1S method↗
DOE OSTI2021

A Comprehensive Open-Source R Software For Statistical Metrology Calculations: From Uncertainty Evaluation To Risk Analysis

Whether calibrating equipment or inspecting products on the factory floor, metrology requires many complicated statistical calculations to achieve a full understanding and evaluation of measurement uncertainty and quality. In order to assist its workforce in performing these calculations in a consistent and rigorous way, the Primary Standards Lab at Sandia National Laboratories (SNL) has developed a free and open-source software package for computing various metrology calculations from uncertainty propagation to risk analysis. In addition to propagating uncertainty through a measurement model using the well-known Guide to Expression of Uncertainty in Measurement or Monte Carlo approaches, evaluating the individual Type A and Type B uncertainty components that go into the measurement model often requires other statistical methods such as analysis of variance or determining uncertainty in a fitted curve. Once the uncertainty in a measurement has been calculated, it is usually evaluated from a risk perspective to ensure the measurement is suitable for making a particular conformance decision. Finally, SNL’s software can perform all these calculations in a single application via an easy-to-use graphical interface, where the different functions are integrated so the results of one calculation can be used as inputs to another calculation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Domain Aware Deep-learning Algorithms Integrated with Scientific-computing Technologies (DADAIST)

This technical report summarized the contribution of the DADAIST project funded by the Data Model Convergence Initiative via the Laboratory Directed Research and Development (LDRD) investments at Pacific Northwest National Laboratory (PNNL). Specifically, we report the development of the NeuroMANCER (Neural Modules with Adaptive Nonlinear Constraints and Efficient Regularizations), a new open-source Scientific Machine Learning library for formulating and solving parametric constrained optimization problems, physics-informed system identification, and parametric optimal control problems. NeuroMANCER is using differentiable programming to combine modern data-driven models and optimization modeling language into a coherent algorithmic and software framework. NeuroMANCER is a Pytorch-based framework and adopts much of its philosophy focused on research and development, rapid prototyping, and streamlined deployment. Strong emphasis is given to extensibility, interoperability with the PyTorch ecosystem, and quick adaptability to custom domain problems. Neuromancer repository contains a comprehensive library of differentiable modules, including custom activation functions, matrix factorizations, deep learning architectures, neural differential equations, differential equation solvers, implicit layers such as iterative solvers, high-level API for symbolic expressions, API for modeling and control of dynamical systems, and extensive set of tutorial code examples in the form of python scripts and jupyter notebooks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Bio-inspired strategies for next-generation perovskite solar mobile power sources

Smart electronic devices are becoming ubiquitous due to many appealing attributes including portability, long operational time, rechargeability and compatibility with the user-desired form factor. Integration of mobile power sources (MPS) based on photovoltaic technologies with smart electronics will continue to drive improved sustainability and independence. With high efficiency, low cost, flexibility and lightweight features, halide perovskite photovoltaics have become promising candidates for MPS. Realization of these photovoltaic MPS (PV-MPS) with unconventionally extraordinary attributes requires new ‘out-of-box’ designs. Natural materials have provided promising designing solutions to engineer properties under a broad range of boundary conditions, ranging from molecules, proteins, cells, tissues, apparatus to systems in animals, plants, and humans optimized through billions of years of evolution. Applying bio-inspired strategies in PV-MPS could be biomolecular modification on crystallization at the atomic/meso-scale, bio-structural duplication at the device/system level and bio-mimicking at the functional level to render efficient charge delivery, energy transport/utilization, as well as stronger resistance against environmental stimuli (e.g., self-healing and self-cleaning). In this paper, we discuss the bio-inspired/-mimetic structures, experimental models, and working principles, with the goal of revealing physics and bio-microstructures relevant for PV-MPS. Here the emphasis is on identifying the strategies and material designs towards improvement of the performance of emerging halide perovskite PVs and strategizing their bridge to future MPS.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Constraining neutrino-nucleon form factors with charged-current scattering at the Electron-Ion Collider

Next-generation neutrino oscillation experiments such as the Deep Underground Neutrino Experiment require percent-level knowledge of neutrino-nucleon interaction cross sections. The nucleon axial form factor 𝐹 𝐴 ⁡(𝑄 2 ), parametrized by the axial mass 𝑀 𝐴 , is the dominant source of uncertainty in the quasielastic channel, and the parity-violating structure function 𝑥⁢𝐹 3 is poorly constrained on free nucleons. We propose using charged-current (CC) electron-proton scattering at the Electron-Ion Collider (EIC) to address both problems simultaneously. The measurement exploits three key features of the EIC: (1) helicity-selective electron bunches provide in situ electromagnetic background rejection; (2) a longitudinally polarized proton target enables extraction of 𝐹 𝐴 ⁡(𝑄 2 ) through the target-spin asymmetry 𝐴 𝑈⁢𝐿 ; and (3) the 𝑦-distribution leverage in CC deep inelastic scattering (DIS) separates 𝐹 2 and 𝑥⁢𝐹 3 on a free proton, without nuclear corrections. Using a Fisher information analysis at $\sqrt{𝑠}$ =141 GeV with 500 fb −1 of integrated luminosity, we project the Cramér-Rao statistical floor of 𝛿⁢𝑀 𝐴 ≈0.03 GeV (3%). Incorporating first-order realistic detector effects, such as zero-degree calorimeter acceptance, 𝑄 2 smearing (5%), and background noise from helicity subtraction, the projected sensitivity is severely background-limited due to the small signal-to-background ratio (𝑆/𝐵 ≈ 3 ×10 −4 ) in the elastic channel. Achieving competitive sensitivity (𝛿⁢𝑀 𝐴 ≈ 0.14 GeV) would require ∼10 −7 background suppression, 3 orders of magnitude beyond current projections. The CC DIS 𝑦 distribution provides subpercent extraction of 𝑥⁢𝐹$^{𝑊^{−}}_{3}$ over 0.05 < 𝑥 < 0.5, representing the most robust electroweak measurement in the near term.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

WRF-ELM v1.0: a regional climate model to study land–atmosphere interactions over heterogeneous land use regions

Abstract. The Energy Exascale Earth System Model (E3SM) Land Model (ELM) is a state-of-the-art land surface model that simulates the intricate interactions between the terrestrial land surface and other components of the Earth system. Originating from the Community Land Model (CLM) version 4.5, ELM has been under active development, with added new features and functionality, including plant hydraulics, radiation–topography interaction, subsurface multiphase flow, and more explicit land use and management practices. This study integrates ELM v2.1 with the Weather Research and Forecasting (WRF; WRF-ELM) model through a modified Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) framework, enabling affordable high-resolution regional modeling by leveraging ELM's innovative features alongside WRF's diverse atmospheric parameterization options. This framework includes a top-level driver for variable communication between WRF and ELM and Earth System Modeling Framework (ESMF) caps for the WRF atmospheric component and ELM workflow control, encompassing initialization, execution, and finalization. Importantly, this LILAC–ESMF framework demonstrates a more modular approach compared to previous coupling efforts between WRF and land surface models. It maintains the integrity of ELM's source code structure and facilitates the transfer of future developments in ELM to WRF-ELM. To test the ability of the coupled model to capture land–atmosphere interactions over regions with a variety of land uses and land covers, we conducted high-resolution (4 km) WRF-ELM ensemble simulations over the Great Lakes region (GLR) in the summer of 2018 and systematically compared the results against observations, reanalysis data, and WRF-CTSM (WRF coupled with the Community Terrestrial Systems Model). In general, the coupled WRF-ELM model has reasonably captured the spatial distribution of surface state variables and fluxes across the GLR, particularly over the natural vegetation areas. The evaluation results provide a baseline reference for further improvements in ELM in the regional application of high-resolution weather and climate predictions. Our work serves as an example to the model development community for expanding an advanced land surface model's capability to represent fully-coupled land–atmosphere interactions at fine spatial scales. The development and release of WRF-ELM marks a significant advancement for the ELM user community, providing opportunities for fine-scale regional representation, parameter calibration in coupled mode, and examination of new schemes with atmospheric feedback.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

NONPARAMETRIC EMPIRICAL BAYES ESTIMATION THROUGH DECONVOLUTION IN PROBABILISTIC RISK ANALYSIS

The advent of Markov Chain Monte Carlo (MCMC) simulations and availability of open source software made full hierarchical Bayesian analysis tractable and a popular choice in parameter estimation for Probabilistic Risk Assessment (PRA). However, despite its theoretical attractiveness, the hierarchical Bayesian analysis is not without its problems. Two of the most prominent practical difficulties in applying hierarchical Bayes analysis in practice to analyze source-to-source variability, for example, is its sensitivity to the selection of the first-stage prior and dependence of the rate of convergence on the selection of the first-stage prior. The first-stage prior in hierarchical Bayesian analysis is usually assumed having a parametric form, often conjugate to the likelihood function (aleatory model). To facilitate convergence for hierarchical Bayes models, the first-stage prior is frequently set through empirical Bayes estimate, which can use available historical data to find the parameters of the first-stage prior. The paper discusses a new nonparametric empirical Bayes estimation and compare it to several well-known parametric estimates such as method of moments and maximum likelihood. The new nonparametric technique exploits the prior predictive distribution as integral equation and proceeds to solve it with respect to prior assuming the sampling data distribution and aleatory model are available. The discussion covers topics such as selection of aleatory model for future data, selection of regularization parameter, and density estimation for historical data. For comparison, the paper is using contrived as well as real-world data from utilities. Utilities data represent failure data with Poisson distribution used for aleatory model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

SeeSnake: Radiological Characterisation of Complex Nuclear Spaces - 16162

Decommissioning of retired nuclear plant can be a difficult task for many reasons. A number of locations around the world have active cells awaiting decommissioning without a clear understanding of the internal structure and possible locations of radiological sources. Furthermore, many nuclear plant drawings and models do not reflect the 'as built' dimensions, and precise data required for safe and cost effective decommissioning is not readily available. This presents decommissioning teams with significant challenges when attempting to plan and execute their decommissioning programs. Due to the complex nature and constrained environments typical of legacy nuclear plant, it is often not possible to deploy traditional remote handling/inspection technologies. Even where possible, identifying the correct sensor, or suite of sensors, to characterize the environment is problematic. To address this common issue Create Technologies Ltd (Createc) and OC Robotics collaborated on SeeSnake, a project aimed at leveraging radiometric source mapping with 3D scanning and confined space remote handling. Createc's imaging and sensing systems have strong capabilities in the nuclear industry, led by their N-Visage products, as do OC Robotics' snake-arm robots as a remote deployment platform for confined and hazardous spaces. The snake-arm robot enables access through small penetrations with the ability to navigate internal structures and other obstacles without support from the environment; whilst multiple re-positioning of the N-Visage sensor, integrated with the snake-arm system, enables a thorough 3D map to be created. The SeeSnake project demonstrates the integrated systems ability to capture valuable radiation, visual and positional data enabling the characterization of a complex active environment. To mature the concept into an integrated, functional technology the SeeSnake partners overcame a number of challenges before demonstrating to key stakeholders in the UK nuclear industry. This paper describes these challenges and the solutions implemented, along with the results from early system trials and stakeholder demonstrations. It also addresses the next steps for the SeeSnake technology and identifies a number of applications which will may benefit from its exploitation. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Molecular dynamics investigation of hexagonal boron nitride sputtering and sputtered particle characteristics

The sputtering of hexagonal boron nitride (h-BN) by impacts of energetic xenon ions is investigated using a molecular dynamics (MD) model. The model is implemented within an open-source MD framework that utilizes graphics processing units to accelerate its calculations, allowing the sputtering process to be studied in much greater detail than has been feasible in the past. Integrated sputter yields are computed over a range of ion energies from 20 eV to 300 eV, and incidence angles from 0° to 75°. Sputtering of boron is shown to occur at energies as low as 40 eV at normal incidence, and sputtering of nitrogen at as low as 30 eV at normal incidence, suggesting a threshold energy between 20 eV and 40 eV. The sputter yields at 0° incidence are compared to existing experimental data and are shown to agree well over the range of ion energies investigated. The semi-empirical Bohdansky curve and an empirical exponential function are fit to the data at normal incidence, and the threshold energy for sputtering is calculated from the Bohdansky curve fit as 35 ± 2 eV. These results are shown to compare well with experimental observations that the threshold energy lies between 20 eV and 40 eV. It is demonstrated that h-BN sputters predominantly as atomic boron and diatomic nitrogen, and the velocity distribution function (VDF) of sputtered boron atoms is investigated. The calculated VDFs are found to reproduce the Sigmund-Thompson distribution predicted by Sigmund's linear cascade theory of sputtering. The average surface binding energy computed from Sigmund-Thompson curve fits is found to be 4.5 eV for ion energies of 100 eV and greater. This compares well to the value of 4.8 eV determined from independent experiments.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

The Hi-GAL compact source catalogue – II. The 360° catalogue of clump physical properties

ABSTRACT We present the 360° catalogue of physical properties of Hi-GAL compact sources, detected between 70 and 500 $\mu$m. This release not only completes the analogous catalogue previously produced by the Hi-GAL collaboration for −71° ≲ ℓ ≲ 67°, but also meaningfully improves it because of a new set of heliocentric distances, 120 808 in total. About a third of the 150 223 entries are located in the newly added portion of the Galactic plane. A first classification based on detection at 70 $\mu$m as a signature of ongoing star-forming activity distinguishes between protostellar sources (23 per cent of the total) and starless sources, with the latter further classified as gravitationally bound (pre-stellar) or unbound. The integral of the spectral energy distribution, including ancillary photometry from λ = 21 to 1100 $\mu$m, gives the source luminosity and other bolometric quantities, while a modified blackbody fitted to data for $\lambda \ge 160~\mu$m yields mass and temperature. All tabulated clump properties are then derived using photometry and heliocentric distance, where possible. Statistics of these quantities are discussed with respect to both source Galactic location and evolutionary stage. No strong differences in the distributions of evolutionary indicators are found between the inner and outer Galaxy. However, masses and densities in the inner Galaxy are on average significantly larger, resulting in a higher number of clumps that are candidates to host massive star formation. Median behaviour of distance-independent parameters tracing source evolutionary status is examined as a function of the Galactocentric radius, showing no clear evidence of correlation with spiral arm positions.

Elia, Davide↗
DOE OSTI2021

Phylogenetic occurrence of the phenylpropanoid pathway and lignin biosynthesis in plants

The phenylpropanoid pathway serves as a rich source of metabolites in plants and provides precursors for lignin biosynthesis. Lignin first appeared in tracheophytes and has been hypothesized to have played pivotal roles in land plant colonization. In this review, we summarize recent progress in defining the lignin biosynthetic pathway in lycophytes, monilophytes, gymnosperms, and angiosperms. In particular, we review the key structural genes involved in p-hydroxyphenyl-, guaiacyl- and syringyl-lignin biosynthesis across plant taxa and consider and integrate new insights on major transcription factors, such as NACs and MYBs. We also review insight regarding a new transcriptional regulator, 5-enolpyruvylshikimate-3-phosphate (EPSP) synthase, canonically identified as a key enzyme in the shikimate pathway. We use several case studies, including EPSP synthase, to illustrate the evolution processes of gene duplication and neo-functionalization in lignin biosynthesis. This review provides new insights into the genetic engineering of the lignin biosynthetic pathway to overcome biomass recalcitrance in bioenergy crops.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Cost Analysis of Wind and Uranium from Seawater Acquisition symbiotic Infrastructure using Shell Enclosures

At current consumption rates, global conventional reserves of uranium, estimated to be 7.6 million tonnes, could be depleted in a little over a century. As these reserves decrease, extraction of uranium is expected to shift to lower quality sites, resulting in higher extraction costs and greater environmental impact. Fortunately, the ocean contains approximately 4.5 billion tonnes of uranium, as uranyl ions at low concentrations of about 3 ppb. Uranium from seawater is a widely studied topic due to the long term security it can assure the nuclear fuel cycle. Additionally, harvesting uranium from seawater could reduce some of the environmental impacts associated with the recovery of land-based uranium. Seawater uranium is meant to create a cost ceiling that provides economic stability and potential savings, not to act as a direct competitor to terrestrial uranium. A recent review of uranium recovery technologies by found uranium adsorption by chelating polymers to be the most promising in terms of adsorbent capacity, environmental footprint, and cost. In this technology, chelating polymers are deployed in seawater and remain submerged until the amount of captured uranium approaches the adsorption capacity. Then, an elution bath is used to strip metal ions, such as uranium, off the adsorbent polymer. A polymer may be immersed in a number of elution baths before it is regenerated by an alkali wash to free its functional groups, allowing for the reuse of the polymer. The output from the elution process undergoes purification and precipitation typical for mined uranium to produce yellowcake. Offshore systems for the extraction of uranium from seawater have been developed since the early 2000's. The system currently studied by a nationwide consortium of national laboratory and university partners utilizes continuous adsorbent polyethylene fibers that are braided around a porous polypropylene float which can be made into long lengths. However, because this deployment scheme requires the adsorbent be brought to a mother-ship for the elution process and redeployed afterward, it has significant practical and economic deployment challenges. Detailed economic analysis have identified the adsorbent production and mooring as the most expensive components of the recovery process. Based on these results, proposed an alternative deployment method that is attached to an offshore wind turbine and continuously takes the adsorbent, that is fabricated into a belt, from the ocean through an elution process and before submerging it back into the sea. However, it has been found that adsorbents with high tensile strength and durability often have very low uranium adsorption properties. Hence, the Wind and Uranium from Seawater Acquisition symbiotic Infrastructure (WUSABI) previously studied, which requires the adsorbent into a belt held in tension, could face difficulties in an ocean environment. Instead, the design, further referred to in this paper as WUSABI-Koosh, utilized a two-part system to decouple the mechanical and chemical needs of an adsorbent for seawater harvesting of uranium. In these designs, the uranium adsorbent material with high adsorbent capacity is enclosed in a hard permeable outer shell with sufficient mechanical strength and durability for use in an offshore environment and chemical resilience against elution treatments. The integration of the design of a uranium harvesting system into an offshore wind turbine tower is pursued because the development of offshore wind or uranium harvesting by themselves bears a high capital cost for the structures, but if the mooring function can be shared, the overall cost for each will be lower. Furthermore, initial design analysis and prototyping of these symbiotic systems has proven they are technically feasible. This paper presents preliminary cost analysis for comparison to uranium recovery via alternative deployment schemes.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2025

Engineering modular enzyme assembly: synthetic interface strategies for natural products biosynthesis applications

Covering: 2020 to 2025Natural products remain indispensable sources of therapeutic and bioactive compounds, yet traditional discovery strategies are constrained by compound rediscovery. Modular biosynthetic enzymes, such as type I polyketide synthases (PKSs) and type A non-ribosomal peptide synthetases (NRPSs), offer promising platforms for combinatorial biosynthesis owing to their programmable architectures. However, practical implementation is frequently limited by inter-modular incompatibility and domain-specific interactions. This review highlights recent advances in modular enzyme assembly enabled by synthetic interfaces-including cognate docking domains, synthetic coiled-coils, SpyTag/SpyCatcher, and split inteins-which function as orthogonal, standardized connectors to facilitate post-translational complex formation. These interfaces support rational investigations into substrate specificity, module compatibility, and pathway derivatization as well as general enzyme clustering applications beyond PKS and NRPS systems. Synthetic interfaces can be integrated with computational tools to support a more systematic and scalable framework for modular enzyme engineering by providing predictive insights into domain compatibility and interface design. These approaches within iterative design-build-test-learn workflows can accelerate the programmable assembly of biosynthetic systems and expand the accessible chemical space for natural products.

Kim, Gahyeon↗
DOE OSTI2019

XScan: An Integrated Tool for Understanding Open Source Community-Based Scientific Code

Many scientific communities have adopted community-based models that integrate multiple components to simulate whole system dynamics. The community software projects’ complexity, stems from the integration of multiple individual software components that were developed under different application requirements and various machine architectures, has become a challenge for effective software system understanding and continuous software development. The paper presents an integrated software toolkit called X-ray Software Scanner (in abbreviation, XScan) for a better understanding of large-scale community-based scientific codes. Our software tool provides support to quickly summarize the overall information of scientific codes, including the number of lines of code, programming languages, external library dependencies, as well as architecture-dependent parallel software features. The XScan toolkit also realizes a static software analysis component to collect detailed structural information and provides an interactive visualization and analysis of the functions. We use a large-scale community-based Earth System Model to demonstrate the workflow, functions and visualization of the toolkit. We also discuss the application of advanced graph analytics techniques to assist software modular design and component refactoring.

Zheng, Weijian↗
DOE OSTI2025

Enhancing fire emissions inventories for acute health effects studies: integrating high spatial and temporal resolution data

Daily fire progression information is crucial for public health studies that examine the relationship between population-level smoke exposures and subsequent health events. Issues with remote sensing used in fire emissions inventories (FEI) lead to the possibility of missed exposures that impact the results of acute health effects studies. This paper provides a method for improving an FEI dataset with readily available information to create a more robust dataset with daily fire progression. High temporal and spatial resolution burned area information from two FEI products are combined into a single dataset, and a linear regression model fills gaps in daily fire progression. The combined dataset provides up to 71% more PM 2.5 emissions, 69% more burned area, and 367% more fire days per year than using a single source of burned area information. The FEI combination method results in improved FEI information with no gaps in daily fire emissions estimates. The combined dataset provides a functional improvement to FEI data that can be achieved with currently available data.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2020

Fiberboard Properties and Degradation Rates for Storage of 9975 Shipping Package in KAC (2020 Status Report)

Savannah River Site (SRS) stores plutonium materials within model 9975 shipping packages in the K-Area Complex (KAC). The 9975 shipping package consists of a 35 gallon stainless steel drum, Celotex fiberboard insulation, lead shield, and primary and secondary containment vessels. The 9975 shipping package design, performance, and analysis for safe transport of radioactive material are described in the Safety Analysis Report for Packaging (SARP). Celotex fiberboard provides three safety functions: thermal insulation to limit internal temperature during a fire, criticality control, and resistance to package crushing. The fiberboard material must retain its dimensions and density within certain ranges to provide the required impact resistance, criticality control, and fire resistance. The SRS Surveillance Program monitors material performance to establish a basis for service life and ensures the continued integrity of 9975 packages. Fiberboard samples, taken from multiple fiberboard assemblies fabricated from cane and softwood fiberboard, have been conditioning in elevated temperature environments since 2005. The samples are periodically examined to monitor thermal, mechanical, and physical properties, and assess degradation trends. Fiberboard properties of interest that are evaluated to demonstrate acceptable long-term performance include dimensional stability, density, compressive strength, thermal conductivity, and specific heat capacity. Duplicate samples from multiple package sources have been tested to identify the range of variability in fiberboard properties and degradation rates. Baseline and long-term testing of fiberboard material properties have been reported previously; reference 6 summarized experimental results of cane and softwood fiberboard through May 2019 and presented degradation models for the measured properties. This report presents the cumulative data collected through June 2020 and the corresponding updated aging models.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Development of Analysis Methods that Integrate Numeric and Textual Equipment Reliability Data

Within the Light Water Reactor Sustainability (LWRS) program, the Risk-Informed Systems Analysis (RISA) Pathway is performing collaborative research on the development and deployment of technologies designed to assist operating nuclear power plants (NPPs) to reduce operating costs improve plant reliability and availability. One of the RISA research areas is focusing on the development of methods and tools designed to optimize plant operations (e.g., maintenance/replacement schedules, optimal maintenance postures for plant structures, systems, and components [SSCs]) in a manner that is more cost effective than current approaches and makes better use of available SSC health data. The Risk-Informed Asset Management (RIAM) project targets this research area by creating a direct bridge between component equipment reliability (ER) data and system engineer decision making regarding maintenance activity scheduling and component aging management. In this respect, one challenge that NPP system engineers are facing is that the amount of ER data being continuously generated is not only extremely large in size, but it comes in different forms: textual (e.g., condition or maintenance reports) and numeric (e.g., generated by monitoring systems). All these data elements provide them with valuable insights and information regarding: 1) the discovery of anomalous behaviors or degradation trends, 2) the identification of the possible causes behind such behaviors/trends, and 3) the prediction of their direct consequences. However, several challenges have proved to be roadblocks to this process. While some of these challenges are technical in nature (i.e., data are often distributed over several physical servers/databases), others are conceptual in nature: data elements come in different formats (e.g., numeric or textual), and measured values have different scales (e.g., vibration spectra and oil temperature). The activities performed by the RIAM project during FY23 directly tackles the need to simultaneously integrate the analysis of ER data in all its forms, numeric and textual. Note that such task has never been performed before due to the complexity of the systems under consideration but, most importantly, because of the technical challenges behind the harmonization of ER data formats and the lack of adequate computational methods to analyze them. Our approach borrows ideas and concepts from the medical field where integration of several data sources is vital to assist medical practitioners to perform correct diagnosis and indicate optimal treatments. In our view a NPP asset is equivalent to a patient in a medical context. The main difference is the complexity of a human body is a magnitude more complex when compared to typical assets commonly present in NPPs (e.g., centrifugal pumps, or motor operated valves). This simplifies our first requirement when analyzing heterogenous ER data formats: to put data into “context”. Context is here intended as the additional piece of information that is needed by ER data analysis tools to understand what these data elements are referring to, i.e., which king of knowledge they are generating. In our context, this knowledge can be translated into models that capture the form and functional architecture of assets/systems, their dependencies, and how they interact. These models actually emulate the knowledge that that NPP system engineers possess about assets and systems; this is their key of success when analyzing ER data, their challenge is ability to handle large amount of data. Here, we employ model-based system engineering (MBSE) models of systems and assets to represent and capture their architecture and functional, i.e. cause-effect, relations. Then, ER data elements are processed by identifying first of all which elements of the developed MBSE elements they are referring to. For numeric ER data this task is fairly easy since it is possible to precisely pinpoint what MBSE elements the corresponding sensor are observing (e.g., bearing temperature of a centrifugal pump). Task is much harder for textual data since the information contained in issue or maintenance reports needs to “be understood” by a computational tool. Here we called this process as “knowledge extraction”. Once again, we borrow the experience in the medical field where methods to extract knowledge from textual data have been developed in the past decade. The missing element for us is the availability of a complete dictionary of NPP related concepts (in addition to the MBSE models presented earlier) that can put “text into context”. In FY23, such dictionary has been developed along with all the computational elements required for knowledge extraction. Lastly, once numeric and textual ER data elements have been processed and “understood”, then the last step is the discovery of possible cause-effect relations among them. This is performed by observing if a logical connection through the MBSE models exists, and if the

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Modular Integrated System for Carbon-Neutral Methanol Synthesis Using Direct Air Capture and Carbon-Free Hydrogen Production

This study investigates the development and economic analysis of a modular integrated system for carbon-neutral methanol synthesis, leveraging direct air capture (DAC) and solid oxide electrolysis cells (SOEC) for carbon dioxide and hydrogen production, respectively. The proposed system integrates a novel building-based DAC process, functionalized solid sorbents, and low-energy SOEC technology, aiming to minimize operational and capital costs. A comparison between the base case system (1,000 t methanol/year) and a scaled-up model (14,758 t methanol/year) reveals significant improvements in efficiency and economic feasibility. The scaled-up system achieves a levelized cost of methanol (LCOM) of $740/t, a 7.5% reduction compared to that of conventional DAC-based systems, while utilizing existing building HVAC infrastructure for air handling. Detailed sensitivity analyses were conducted, evaluating the effects of plant capacity and air flow rate on the LCOM, demonstrating the scalability of the building-based DAC system. The cradle-to-gate life cycle analysis shows that the proposed process using renewable-sourced electricity achieves a 38% reduction in greenhouse gas (GHG) emission compared to reported values of green methanol production technologies that use a conventional DAC and a conventional methanol synthesis catalyst. When fossil-sourced electricity is used in the proposed process, it leads to about a 37.5% reduction in GHG emission in comparison to reported values for conventional methanol production technologies using steam methane reforming technology and fossil-sourced electricity.

alcohols↗