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All sources NASA NTRS DOE OSTI

DOE OSTI2022

Phase II Development of the Surveillance Test Articles to Improve the Design, Fabrication, and Testing

Advanced reactors, such as the molten salt reactor (MSR), require materials that will withstand harsher environments than the materials used for lower temperature water reactors. Materials used in MSR construction need not only withstand elevated temperature, temperature cycling, and neutron radiation, but must be able to withstand the corrosive molten salt environment. Significant materials research has been driven by the materials needs of the MSR due to the harsh environment and the material data requirements to support licensing. Limited operation experience with MSRs has made this challenging work. Information is very limited on materials degradation due to irradiation, molten salt corrosion, elevated temperature, and the resulting fatigue, creep, and creep-fatigue loading during operation. While efforts are underway to better understand the effects of this harsh environment on construction materials, the Advanced Reactors Technologies (ART) Program has been working to develop materials surveillance test articles that could be used in a materials surveillance program and allow for the collection of information on the materials degradation during plant operation and could support timely licensing of these advanced reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

HP-FLEX: Field demonstration of the semantics-driven configuration of a Model Predictive Control system to make heat pumps flexible

Model Predictive Control (MPC) has demonstrated significant potential for optimizing building operations and enabling demand flexibility. However, the widespread adoption of MPC is hindered by complex manual configuration and commissioning processes that must be conducted by control experts working alongside building operators. These challenges drive up costs and reduce scalability, particularly when technical human resources and building automation systems are limited, such as in small and medium commercial buildings (SMCBs). This paper demonstrates how semantic standards, specifically ASHRAE 223P, can accelerate the adoption of MPC applications for load flexibility in SMCBs. The authors present a replicable control framework, titled “HP-FLEX” that leverages a building’s semantic model to bootstrap the required data configuration for an MPC controller developed for optimizing heat pump systems as flexible grid resources. The semantic model helps streamline the deployment workflow, particularly for site setup, data/control commissioning, and model setup. This integration enhances portability, transferability, and scalability of the HP-FLEX MPC, which has been developed to support MPC-based supervisory HVAC controllers in SMCBs. Additionally, the paper details the required building and thermostat metadata information to enable the HP-FLEX MPC based on field demonstrations. The new workflow was tested in a small commercial building located in California, U.S., and demonstrated a load shifting performance of 9% based on a dynamic pricing signal that varies by the hour. This work provides a practical pathway for transitioning sophisticated building applications from custom to standardized semantic representations, supporting the broader adoption of advanced control strategies like MPC. The framework also establishes a foundation for evolving metadata requirements as applications mature while maintaining compatibility with industry standards.

Paul, Lazlo↗

DOE OSTI2025

HP-FLEX: Field Demonstration of the Semantics-Driven Configuration of a Model Predictive Control System to Make Heat Pumps Flexible

Paul, Lazlo↗

DOE OSTI2023

In vitro continuous protein evolution empowered by machine learning and automation

Directed evolution has become one of the most successful and powerful tools for protein engineering. However, the efforts required for designing, constructing, and screening a large library of variants can be laborious, time-consuming, and costly. With the recent advent of machine learning (ML) in the directed evolution of proteins, researchers can now evaluate variants in silico and guide a more efficient directed evolution campaign. Furthermore, recent advancements in laboratory automation have enabled the rapid execution of long, complex experiments for high-throughput data acquisition in both industrial and academic settings, thus providing the means to collect a large quantity of data required to develop ML models for protein engineering. In this perspective, here we propose a closed-loop in vitro continuous protein evolution framework that leverages the best of both worlds, ML and automation, and provide a brief overview of the recent developments in the field.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Specifications and a Prototype Software to Demonstrate a Data Catalog for Hanford Datasets

Environmental management activities at the Hanford Site produce extensive data about site conditions, contaminants, and cleanup activities. Managing, archiving, and accessing that data requires a high degree of collaboration among site contractors and a high level of awareness by project managers and staff. The Hanford Site has a range of databases (e.g., Hanford Environmental Information System [HEIS]) and their associated user interfaces (e.g., Environmental Dashboard Application [EDA], Virtual Library [VL]), as well as other document management systems (e.g., Integrated Document Management System [IDMS]). However, Hanford lacks a single unified resource to find data (which itself comes in multiple formats) amongst the multiple disparate systems, not to mention ad hoc data not contained in an official repository/database.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

An Overview of Electric Vehicle Load Modeling Strategies for Grid Integration Studies

The adoption of electric vehicles (EVs) has emerged as a solution to reduce greenhouse gas emissions in the transportation sector, which has motivated the implementation of public policies to promote their use in several countries. However, the high adoption of EVs poses challenges for the electricity sector, as it would imply an increase in energy demand and possible impacts on the power quality (PQ) of the power grid. Therefore, it is important to conduct EV integration studies in the power grid to determine the amount that can be incorporated without causing problems and identify the areas of the power sector that will require reinforcements. Accurate EV load patterns are required for this type of study that, through mathematical modeling, reflect both the dynamic behavior and the factors that influence the decision to recharge EVs. This article aims to present an overview of EVs, examine the different factors considered in the literature for modeling EV load patterns, and review modeling methods. EV load modeling methods are classified into deterministic, statistical, and machine learning. The article shows that each modeling method has its advantages, disadvantages, and data requirements, ranging from simple load modeling to more accurate models requiring large datasets.

Computer Science↗

DOE OSTI2024

Accelerating the Discovery of New, Single Phase High Entropy Ceramics via Active Learning

High-entropy ceramics have garnered interest due to their remarkable hardness, compressive strength, thermal stability, and fracture toughness; yet the discovery of new high-entropy ceramics (out of a tremendous number of possible elemental permutations) still largely requires costly, inefficient, trial-and-error experimental and computational approaches. The entropy forming ability (EFA) factor was recently proposed as a computational descriptor that positively correlates with the likelihood that a 5-metal high-entropy carbide (HECs) will form the desired single phase, homogeneous solid solution; however, discovery of new compositions is computationally expensive. If you consider 8 candidate metals, the HEC EFA approach uses 49 optimizations for each of the 56 unique 5-metal carbides, requiring a total of 2744 costly density functional theory calculations. Here, we describe an orders-of-magnitude more efficient active learning (AL) approach for identifying novel HECs. To begin, we compared numerous methods for generating composition-based feature vectors (e.g., magpie and mat2vec), deployed an ensemble of machine learning (ML) models to generate an average and distribution of predictions, and then utilized the distribution as an uncertainty. Here we then deployed an AL approach to extract new training data points where the ensemble of ML models predicted a high EFA value or was uncertain of the prediction. Our approach has the combined benefit of decreasing the amount of training data required to reach acceptable prediction qualities and biases the predictions toward identifying HECs with the desired high EFA values, which are tentatively correlated with the formation of single phase HECs. Using this approach, we increased the number of 5-metal carbides screened from 56 to 15,504, revealing 4 compositions with record-high EFA values that were previously unreported in the literature. Our AL framework is also generalizable and could be modified to rationally predict optimized candidate materials/combinations with a wide range of desired properties (e.g., mechanical stability, thermal conductivity).

36 MATERIALS SCIENCE↗

DOE OSTI2022

Application of sulfur SAD to small crystals with a large asymmetric unit and anomalous substructure

The application of sulfur single-wavelength anomalous dispersion (S-SAD) to determine the crystal structures of macromolecules can be challenging if the asymmetric unit is large, the crystals are small, the size of the anomalously scattering sulfur structure is large and the resolution at which the anomalous signals can be accurately measured is modest. Here, as a study of such a case, approaches to the SAD phasing of orthorhombic Ric-8A crystals are described. The structure of Ric-8A was published with only a brief description of the phasing process [Zeng et al. (2019), Structure , 27 , 1137–1141]. Here, alternative approaches to determining the 40-atom sulfur substructure of the 103 kDa Ric-8A dimer that composes the asymmetric unit are explored. At the data-collection wavelength of 1.77 Å measured at the Frontier micro-focusing Macromolecular Crystallography (FMX) beamline at National Synchrotron Light Source II, the sulfur anomalous signal strength, |Δ ano |/σΔ ano ( d ′′/sig), approaches 1.4 at 3.4 Å resolution. The highly redundant, 11 000 000-reflection data set measured from 18 crystals was segmented into isomorphous clusters using BLEND in the CCP 4 program suite. Data sets within clusters or sets of clusters were scaled and merged using AIMLESS from CCP 4 or, alternatively, the phenix.scale_and_merge tool from the Phenix suite. The latter proved to be the more effective in extracting anomalous signals. The HySS tool in Phenix , SHELXC / D and PRASA as implemented in the CRANK 2 program suite were each employed to determine the sulfur substructure. All of these approaches were effective, although HySS , as a component of the phenix.autosol tool, required data from all crystals to find the positions of the sulfur atoms. Critical contributors in this case study to successful phase determination by SAD included (i) the high-flux FMX beamline, featuring helical-mode data collection and a helium-filled beam path, (ii) as recognized by many authors, a very highly redundant, multiple-crystal data set and (iii) the inclusion within that data set of data from crystals that were scanned over large ω ranges, yielding highly isomorphous and highly redundant intensity measurements.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Accelerated Materials Deployment in Advanced Nuclear Power Plants

The purpose of this report is to begin the development of a maximally efficient process for licensing and deploying new materials in Advanced Non-Light-Water Reactors (ANLWRs). Some new materials that are to be used in some new plants are seen as possibly introducing risks, because our understanding of those new materials’ behavior in the conditions generated by some novel plant designs is less complete than our understanding of the behavior of materials with long use histories in existing designs. In these cases, an approved code/standard or a code case to support the use of these materials in the novel design’s safety case may not exist for the regulator to utilize as part of the licensing determination. This circumstance creates the potential for an extremely long licensing process for new designs using new materials. The present strategy is to show how to manage these risks proactively, in such a way as to permit licensing decisions to be made in a timely manner, based on this risk management process. The present report outlines the gaps in the current codes to support deployment and use of novel materials and begins the development of the necessary risk management framework that is focused on the subject materials issues; it is based on risk-informed in-service surveillance practices, carried out in such a way as to compensate for current limitations in our state of knowledge. This development will enable licensing and deployment of the subject materials, conditional on the proactive surveillance process to be established. While this report is occasioned by limitations in our knowledge of certain materials issues that may arise in advanced designs, in-service surveillance is always done in order to compensate for a lack of knowledge: if we knew that components were not already failed and not trending toward failure, we would not perform surveillance, even in current-generation plants (except that prescriptive requirements would force us to do so). What is different about the surveillance program discussed here is that the issues are newer and the relevant experience base is less complete, so the surveillance presently contemplated may need to measure new things and/or measure them more often than has been traditional for surveillance coupons. The present report is devoted to the risk management framework and applies American Society of Mechanical Engineers Boiler and Pressure Vessel Code Section XI, Division [1] to establish the structure of a protocol for carrying out the necessary surveillance. These documents are generic: they do not tell us how often to surveille, or what to surveille, or what to measure, but rather how to determine those things, given certain technical inputs. The Regulatory Development R&D Program [2] is currently developing the companion supporting technical basis for the materials surveillance technology that, when completed and validated, can be used by owner/operator and NRC to implement a materials degradation management program for ANLWRs. This report also outlines salient points of discussion, positive potential outcomes, and potential concerns from industry and the USNRC. These aspects of the report intend to inform future work to develop a proposed technical process for adoption by the industry and endorsement by the USNRC to allow developers to propose a risk informed and conservative approach for the use of materials where operating experience/data and codes and standards may not exist for use of a novel material in an operating reactor environment. Additionally, such a technology could be leveraged to potentially reduce part of the upfront materials data requirements from ongoing long-term materials testing so that early action on license application could be undertaken by NRC, in parallel with the continuation of long-term data collection. This could accelerate the schedule for a first-of-a-kind ANLWR deployment or a nth-of-a-kind new materials insertion for established ANLWR designs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Full-Scale Soft-Sensor Implementations Enable WRRF Hybrid Digital-Twins

Digital twins (DTs) are on the rise in the wastewater treatment industry and, with ever-increasinguse of online data in more complex applications, it is bringing us towards a digital revolution withinwater resource recovery facilities (WRRFs). An integral data requirement is dynamic real-timeinfluent concentrations. However, few WRRFs have the ability to deploy sensors to generate therequired dynamic influent concentrations. This paper presents an innovative approach to generatedynamic influent profiles with a soft sensor primarily using bioreactor airflow rates and DT models.It has been implemented on three different full-scale facilities and demonstrate it is feasible toretrieve 15-minute interval diurnal influent concentrations. This study overcomes a key bottleneckin DT applications - the lack of realistic high-resolution influent profiles - fostering the developmentof DTs. The fact that the data used in this study are commonly available ensures wide applicabilityfor most WRRFs and prepares them for the advanced controls that digital twins can unlock.

Johnson, Bruce↗

DOE OSTI2024

PV Generation and Load Forecasting for Adjuntas PR Community Microgrids

Existing frameworks to forecast time-series photovoltaic (PV) output power and consumer load for microgrid operations and controls assume a near-continuous availability of real-time input features from the field assets such as PV inverters, energy meters, and weather station. These incoming data points are used to periodically retrain models and update forecast snapshots over a moving horizon window, be it one hour-ahead, one-day ahead, or one-week ahead. However, such frameworks are not resilient to disruptions in data availability caused by losses in communications between the field sensors and data loggers. Hence, there is a need for programs that assume no availability of real-time microgrid asset data and still make reliable forecasts that can be used for decision-making. Such programs would be apt to function in extreme weather events such as hurricanes and would use lightweight recursive time-series models to independently forecast solar irradiance and ambient temperature, then compute PV power from those forecasts, as well as independently forecast consumer load. The codebase performs forecasting for the scenario of when the microgrid does not have a reliable access to forecasts or real-time observations of solar irradiance (I) and ambient temperature (AT) and load (Load) to be able to adequately forecast, in real-time, the PV power production or a business' load. In this case, using historical values of PV power and load, a univariate forecasting of generation and consumption are respectively made. The use-case in particular has two sub-scenarios: one, a normal 7-day ahead forecast where the unavailability of real-time data is assumed due to infrastructure issues such as loss of communication or sensor maintenance or service downtimes. Whereas a hurricane-caused unavailability of real-time data requires a second model trained specifically on historical hurricane days to be able to capture the extreme day behavior of generation in particular, and load if applicable. A gradient boosted regression tree comprises an ensemble of additive models that map between the input of historical values (be it irradiance, temperature, or load) and their corresponding output forecasts of a given horizon such that the individual learner predictions are summed up over the total number of such learners in the ensemble to produce an aggregate forecast. A weighting mechanism is applied to the training data in each iteration, where actual and forecast values are compared to penalize incorrect forecasts by increasing the weight and reducing it to reward correct forecasts. The code's benefits are that it: (a) accounts for a contingency where communication loss renders newly measured real-time data unavailable for model tuning and snapshot updates; (b) presents blind forecasting that recursively determines the next time-step value in a horizon using the forecast of the same attribute from a prior step; and (c) employs lightweight models that, once trained, can reliably generalize for different horizons, which make them suitable for enhancing the resilience of field microgrids prone to extreme events that encounter disruptions to data availability.

Sundararajan, Aditya [Oak Ridge National Laborator↗

DOE OSTI2022

NPD Classification Tools – User Guide NPD Explorer and NPDamCAT Apps

The existing infrastructure at non-powered dams (NPDs) presents a variety of opportunities from generating electricity and economic value to the myriad services they provide. However, it also presents a significant challenge because aging structures must be maintained, and changes to the ecosystems and river systems by NPDs must be managed. The various stakeholders interested in these opportunities and challenges require varying information about NPDs; in many cases, interest in NPDs can extend across the entire population of dams. Even when interest is more narrowly focused on an individual dam or a small subset of dams, understanding how these dams relate to the broader context of NPD infrastructure can be important. As noted in TM 2021/2155, “Each NPD has unique characteristics describing its design, operation, environmental impacts, social impacts, and economic potential. The large number of dams, the diversity of interests related to dams, the variety of dam characteristics, and the types of data required to describe dams all pose major challenges to an analysis of the entire dam population." This user guide describes two web-based tools that facilitate exploration of dams from a variety of perspectives: the NPD Explorer and the NPD Custom Analysis and Taxonomy (NPDamCAT). These tools facilitate access to information about dams and help users interact with the information, making small- to large-scale analyses more convenient for a broad set of stakeholders.

13 HYDRO ENERGY↗

DOE OSTI2022

A high-throughput skim-sequencing approach for genotyping, dosage estimation and identifying translocations

The development of next-generation sequencing (NGS) enabled a shift from array-based genotyping to directly sequencing genomic libraries for high-throughput genotyping. Even though whole-genome sequencing was initially too costly for routine analysis in large populations such as breeding or genetic studies, continued advancements in genome sequencing and bioinformatics have provided the opportunity to capitalize on whole-genome information. As new sequencing platforms can routinely provide high-quality sequencing data for sufficient genome coverage to genotype various breeding populations, a limitation comes in the time and cost of library construction when multiplexing a large number of samples. Here we describe a high-throughput whole-genome skim-sequencing (skim-seq) approach that can be utilized for a broad range of genotyping and genomic characterization. Using optimized low-volume Illumina Nextera chemistry, we developed a skim-seq method and combined up to 960 samples in one multiplex library using dual index barcoding. With the dual-index barcoding, the number of samples for multiplexing can be adjusted depending on the amount of data required, and could be extended to 3,072 samples or more. Panels of doubled haploid wheat lines ( Triticum aestivum , CDC Stanley x CDC Landmark), wheat-barley ( T . aestivum x Hordeum vulgare ) and wheat-wheatgrass ( Triticum durum x Thinopyrum intermedium ) introgression lines as well as known monosomic wheat stocks were genotyped using the skim-seq approach. Bioinformatics pipelines were developed for various applications where sequencing coverage ranged from 1 × down to 0.01 × per sample. Using reference genomes, we detected chromosome dosage, identified aneuploidy, and karyotyped introgression lines from the skim-seq data. Leveraging the recent advancements in genome sequencing, skim-seq provides an effective and low-cost tool for routine genotyping and genetic analysis, which can track and identify introgressions and genomic regions of interest in genetics research and applied breeding programs.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

New frontiers in wind-wildlife monitoring systems

Effective minimization of negative effects of wind energy on wildlife is an iterative process whereby direct observations of wildlife effects inform and validate mitigation strategies. Yet, the full implementation of this adaptive management has been hindered by a lack of appropriate data. The accurate, high-resolution data required exceeds the capacity of most current monitoring approaches (human observers or monitoring technologies applied in isolation). Current applications of monitoring technologies struggle to harness their full potential by failing to capitalize on opportunities for integration with additional technologies and/or by having limited temporal and spatial resolution. At the emergence of this new frontier of wildlife monitoring, we review the elements of a robust wind-wildlife monitoring system and highlight sensor fusion principles that facilitate effective implementation and integration of multiple monitoring technologies. We also illustrate how sensor fusion solutions can generate high resolution data on collision and displacement effects on terrestrial wildlife across complex spatial and temporal scales.

17 WIND ENERGY↗

DOE OSTI2021

Analyzing Mass Spectrometry Imaging Data of 13C-Labeled Phospholipids in Camelina sativa and Thlaspi arvense (Pennycress) Embryos

The combination of 13C-isotopic labeling and mass spectrometry imaging (MSI) offers an approach to analyze metabolic flux in situ. However, combining isotopic labeling and MSI presents technical challenges ranging from sample preparation, label incorporation, data collection, and analysis. Isotopic labeling and MSI individually create large, complex data sets, and this is compounded when both methods are combined. Therefore, analyzing isotopically labeled MSI data requires streamlined procedures to support biologically meaningful interpretations. Using currently available software and techniques, here we describe a workflow to analyze 13C-labeled isotopologues of the membrane lipid and storage oil lipid intermediate―phosphatidylcholine (PC). Our results with embryos of the oilseed crops, Camelina sativa and Thlaspi arvense (pennycress), demonstrated greater 13C-isotopic labeling in the cotyledons of developing embryos compared with the embryonic axis. Greater isotopic enrichment in PC molecular species with more saturated and longer chain fatty acids suggest different flux patterns related to fatty acid desaturation and elongation pathways. The ability to evaluate MSI data of isotopically labeled plant embryos will facilitate the potential to investigate spatial aspects of metabolic flux in situ.

metabolomics↗

DOE OSTI2021

Review of Neutron Uncertainty Types

The assessment of neutron data requires understanding the uncertainty. The uncertainty of the final quantities of interest, neutron multiplication and mass, is a combination of many other uncertainties because it is a combination of many other variables. The equations for the values of neutron multiplication and mass are quite complex. The relations between the many contributing variables and their uncertainties are complicated leading to complications in combining them. Additionally, some are statistical in nature, while others are systematic. In an effort to better understand and quantify the total uncertainty in neutron multiplication and SNM mass, they have been subdivided into four categories: setup, detector response, nuclear data, and object characterization. This paper discusses the four categories and what types of uncertainties are in each of them. This effort attempts to generalize this process for any neutron detector, but many of our examples show the Next Generation Multiplicity Detector (MC-15).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Phase equilibria of advanced technology uranium silicide-based nuclear fuel

The phases in uranium-silicide binary system were evaluated in regards to their stabilities, phase boundaries, crystal structures, and phase transitions. The results from this study were used in combination with a well assessed literature to optimize the U-Si phase diagram using the CALPHAD method. A thermodynamic database was developed, which could be used to guide nuclear fuel fabrication, could be incorporated into other nuclear fuel thermodynamic databases, or could be used to generate data required by fuel performance codes to model fuel behavior in normal or off-normal reactor operations. The U 3 Si 2 and U 3 Si 5 phases were modeled using the Compound Energy Formalism model with 3 sublattices to account for the variation in composition. The crystal structure used for the USi phase was the tetragonal with an I4/mmm space. Above 450°C, the U 3 Si 5 phase was modeled. The composition of the USi 2 phase was adjusted to USi 1.84 . The calculated invariant reactions and the enthalpy of formation for the stoichiometric phases were in agreement with experimental data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes

Uncertainty propagation in complex engineering systems often poses significant computational challenges related to modeling and quantifying probability distributions of model outputs, as those emerge as the result of various sources of uncertainty that are inherent in the system under investigation. Gaussian Processes regression (GPs) is a robust meta-modeling technique that allows for fast model prediction and exploration of response surfaces. Multi-fidelity variations of GPs further leverage information from cheap and low fidelity model simulations in order to improve their predictive performance on the high fidelity model. In order to cope with the high volume of data required to train GPs in high dimensional design spaces, a common practice is to introduce latent design variables that are typically projections of the original input space to a lower dimensional subspace, and therefore substitute the problem of learning the initial high dimensional mapping, with that of training a GP on a low dimensional space. Here in this paper, we present a Bayesian approach to identify optimal transformations that map the input points to low dimensional latent variables. The \projection" mapping consists of an orthonormal matrix that is considered a priori unknown and needs to be inferred jointly with the GP parameters, conditioned on the available training data. The proposed Bayesian inference scheme relies on a two-step iterative algorithm that samples from the marginal posteriors of the GP parameters and the projection matrix respectively, both using Markov Chain Monte Carlo (MCMC) sampling. In order to take into account the orthogonality constraints imposed on the orthonormal projection matrix, a Geodesic Monte Carlo sampling algorithm is employed, that is suitable for exploiting probability measures on manifolds. We extend the proposed framework to multi-fidelity models using GPs including the scenarios of training multiple outputs together. We validate our framework on three synthetic problems with a known lower-dimensional subspace. The benefits of our proposed framework, are illustrated on the computationally challenging aerodynamic optimization of a last-stage blade for an industrial gas turbine, where we study the effect of an 85-dimensional shape parameterization of a three-dimensional airfoil on two output quantities of interest, specifically on the aerodynamic efficiency and the degree of reaction

42 ENGINEERING↗