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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Benchmarking Autonomous Scattering Experiments Illustrated on TAS

With the advancement of artificial intelligence and machine learning methods, autonomous approaches are recognized to have great potential for performing more efficient scattering experiments. In our view, it is crucial for such approaches to provide thorough evidence about respective performance improvements in order to increase acceptance within a scientific community. Therefore, we propose a benchmarking procedure designed as a cost-benefit analysis that is applicable to any scattering method sequentially collecting data during an experiment. For a given approach, the performance assessment is based on how much benefit, given a certain cost budget, it is able to acquire in predefined test cases. Different approaches thus get a chance for comparison and can make their advantages explicit and visible. Key components of the procedure, i.e., cost measures, benefit measures, and test cases, are made precise for the setting of three-axes spectrometry (TAS) as an illustration. Finally, we discuss neglected aspects and possible extensions for the TAS setting and comment on the procedure’s applicability to other scattering methods. A Python implementation of the procedure to simplify its utilization by interested researchers from the field is also provided.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Missing data in multi-omics integration: Recent advances through artificial intelligence

Biological systems function through complex interactions between various ‘omics (biomolecules), and a more complete understanding of these systems is only possible through an integrated, multi-omic perspective. This has presented the need for the development of integration approaches that are able to capture the complex, often non-linear, interactions that define these biological systems and are adapted to the challenges of combining the heterogenous data across ‘omic views. A principal challenge to multi-omic integration is missing data because all biomolecules are not measured in all samples. Due to either cost, instrument sensitivity, or other experimental factors, data for a biological sample may be missing for one or more ‘omic techologies. Recent methodological developments in artificial intelligence and statistical learning have greatly facilitated the analyses of multi-omics data, however many of these techniques assume access to completely observed data. A subset of these methods incorporate mechanisms for handling partially observed samples, and these methods are the focus of this review. We describe recently developed approaches, noting their primary use cases and highlighting each method's approach to handling missing data. We additionally provide an overview of the more traditional missing data workflows and their limitations; and we discuss potential avenues for further developments as well as how the missing data issue and its current solutions may generalize beyond the multi-omics context.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Inter-Domain Fusion for Enhanced Intrusion Detection in Power Systems: An Evidence Theoretic and Meta-Heuristic Approach

False alerts due to misconfigured or compromised intrusion detection systems (IDS) in industrial control system (ICS) networks can lead to severe economic and operational damage. However, research using deep learning to reduce false alerts often requires the physical and cyber sensor data to be trustworthy. Implicit trust is a major problem for artificial intelligence or machine learning (AI/ML) in cyber-physical system (CPS) security, because when these solutions are most urgently needed is also when they are most at risk (e.g., during an attack). To address this, the Inter-Domain Evidence theoretic Approach for Inference (IDEA-I) is proposed that reframes the detection problem as how to make good decisions given uncertainty. Specifically, an evidence theoretic approach leveraging Dempster–Shafer (DS) combination rules and their variants is proposed for reducing false alerts. A multi-hypothesis mass function model is designed that leverages probability scores obtained from supervised-learning classifiers. Using this model, a location-cum-domain-based fusion framework is proposed to evaluate the detector’s performance using disjunctive, conjunctive, and cautious conjunctive rules. The approach is demonstrated in a cyber-physical power system testbed, and the classifiers are trained with datasets from Man-In-The-Middle attack emulation in a large-scale synthetic electric grid. For evaluating the performance, we consider plausibility, belief, pignistic, and general Bayesian theorem-based metrics as decision functions. To improve the performance, a multi-objective-based genetic algorithm is proposed for feature selection considering the decision metrics as the fitness function. Finally, we present a software application to evaluate the DS fusion approaches with different parameters and architectures.

42 ENGINEERING↗
DOE OSTI2023

Envisioning urban environments resilient to vector-borne diseases: a protocol to study dengue in Vietnam

Transmitted primarily by Aedes aegypti (Ae. aegypti) and Aedes albopictus (Ae. albopictus), arboviral diseases pose a major global public health threat. Dengue, chikungunya, and zika are increasingly prevalent in Southeast Asia. Among other arboviruses, dengue and zika are becoming more common in Central and South America. Given human encroachment into previously uninhabited, often deforested areas, to provide new housing in regions of population expansion, conceptualizing built urban environments in a novel way is urgently needed to safeguard against the growing climate change-driven threat of vector-borne diseases. By understanding the spread from a One Health perspective, enhanced control and prevention can be achieved. This is particularly important considering that climate change is likely to significantly impact the persistence of ponded water where mosquitoes breed due to increasing temperature and shifting rainfall patterns with regard to magnitude, duration, frequency, and season. Models can incorporate aquatic mosquito stages and adult spatial dynamics when habitats are heterogeneously available, thereby including dispersal and susceptible-exposed-infected-recovered (SEIR) epidemiology. Coupled with human population distribution (density, locations), atmospheric conditions (air temperature, precipitation), and hydrological conditions (soil moisture distribution, ponding persistence in topographic depressions), modeling has improved predictive ability for infection rates. However, it has not informed interventional approaches from an urban environment perspective which considers the role of ponds/lakes that support green spaces, the density of population that enables rapid spread of disease, and varying micro-habitats for various mosquito stages under climate change. Here, for an example of dengue in Vietnam, a preventive and predictive approach to design resilient urban environments is proposed, which uses data from rapidly expanding metropolitan communities to learn continually. This protocol deploys computational approaches including simulation and machine learning/artificial intelligence, underpinned by surveillance and medical data for validation and adaptive learning. Its application may best inform urban planning in low-middle income countries in tropical zones where arboviral pathogens are prevalent.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Grand challenges in the design, manufacture, and operation of future wind turbine systems

Abstract. Wind energy is foundational for achieving 100 % renewable electricity production, and significant innovation is required as the grid expands and accommodates hybrid plant systems, energy-intensive products such as fuels, and a transitioning transportation sector. The sizable investments required for wind power plant development and integration make the financial and operational risks of change very high in all applications but especially offshore. Dependence on a high level of modeling and simulation accuracy to mitigate risk and ensure operational performance is essential. Therefore, the modeling chain from the large-scale inflow down to the material microstructure, and all the steps in between, needs to predict how the wind turbine system will respond and perform to allow innovative solutions to enter commercial application. Critical unknowns in the design, manufacturing, and operability of future turbine and plant systems are articulated, and recommendations for research action are laid out. This article focuses on the many unknowns that affect the ability to push the frontiers in the design of turbine and plant systems. Modern turbine rotors operate through the entire atmospheric boundary layer, outside the bounds of historic design assumptions, which requires reassessing design processes and approaches. Traditional aerodynamics and aeroelastic modeling approaches are pressing against the limits of applicability for the size and flexibility of future architectures and flow physics fundamentals. Offshore wind turbines have additional motion and hydrodynamic load drivers that are formidable modeling challenges. Uncertainty in turbine wakes complicates structural loading and energy production estimates, both around a single plant and for downstream plants, which requires innovation in plant operations and flow control to achieve full energy capture and load alleviation potential. Opportunities in co-design can bring controls upstream into design optimization if captured in design-level models of the physical phenomena. It is a research challenge to integrate improved materials into the manufacture of ever-larger components while maintaining quality and reducing cost. High-performance computing used in high-fidelity, physics-resolving simulations offer opportunities to improve design tools through artificial intelligence and machine learning, but even the high-fidelity tools are yet to be fully validated. Finally, key actions needed to continue the progress of wind energy technology toward even lower cost and greater functionality are recommended.

17 WIND ENERGY↗
DOE OSTI2026

Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that challenge traditional analysis pipelines. The LSST Dark Energy Science Collaboration (DESC) aims to derive robust constraints on dark energy and dark matter from these data, requiring methods that are statistically powerful, scalable, and operationally reliable. Artificial intelligence and machine learning (AI/ML) are already embedded across DESC science workflows, from photometric redshifts and transient classification to weak lensing inference and cosmological simulations. Yet their utility for precision cosmology hinges on trustworthy uncertainty quantification, robustness to covariate shift and model misspecification, and reproducible integration within scientific pipelines. This white paper surveys the current landscape of AI/ML across DESC's primary cosmological probes and cross-cutting analyses, revealing that the same core methodologies and fundamental challenges recur across disparate science cases. Since progress on these cross-cutting challenges would benefit multiple probes simultaneously, we identify key methodological research priorities, including Bayesian inference at scale, physics-informed methods, validation frameworks, and active learning for discovery. With an eye on emerging techniques, we also explore the potential of the latest foundation model methodologies and LLM-driven agentic AI systems to reshape DESC workflows, provided their deployment is coupled with rigorous evaluation and governance. Finally, we discuss critical software, computing, data infrastructure, and human capital requirements for the successful deployment of these new methodologies, and consider associated risks and opportunities for broader coordination with external actors.

Aubourg, Eric [APC, Paris] (ORCID:000000025592023X↗
DOE OSTI2025

TRAILS Output Files

Overview This data repository contains ZIP files that store compressed versions of the output of running the WaterPaths utility planning and management tool in the DU Re-Evaluation mode (to download the tool, please see this GitHub repository). The tool was used to simulate the six-utility North Carolina Research Triangle problem. Details on the contents of each ZIP file can be seen below. Data details Temporal range: Weekly data for 2,344 weeks from 2015 to 2060 (45 years). Spatial range: Six water utilities in the North Carolina Research Triangle region (0: Chapel Hil/OWASA, 1: Durham, 2: Cary, 3: Raleigh, 4: Pittsboro, and 5: Chatham) File types: CSV and OUT Different solutions available The solution numbers correspond to the different pathway strategies (henceforth referred to as "solutions") discussed in paper's main and supporting text (abstract and link to the paper here). They are as follows: Sol92: The Durham-focused pathway strategy Sol132: The Raleigh-focused pathway strategy Sol140: The regionally-robust pathway strategy Objectives files These files can be accessed by unzipping solXX_objectives_pathways.zip that contains 1,000 Objectives_RDMXX_solsXX_to_XX.csv files. Each CSV file will consist of a row representing all the objective values for that specific solution, while every six columns represents the reliability, restriction frequency, infrastructure net present value ($ mil), peak financial cost, worst-case cost, and unit cost ($ per MG; in that order) for each of the six utilities. There will be 1,000 such files, denoting the performance of the six utilities across the 1,000 deeply uncertain states of the world (DU SOWs). Pathway files These files can be accessed by unzipping solXX_objectives_pathways.zip that contains 1,000 Pathways_sXX_RDMXX.out file. Each OUT corresponds to the set of infrastructure being triggered in a specific DU SOW, and each file will have the name file will consist of four tab-delimited columns that are described as follows: Realization: The realization in which an infrastructure options being triggered utility: The utility currently triggering infrastructure week: The week in which a specific infrastructure option is being triggered infra.: The infrastructure option being triggered If the OUT file contains only the header line, no infrastructure was triggered for that specific DU SOW. Policies files These files can be obtained by unzipping Policies.zip. Each of the 1,000 CSV files within the unzipped folder will contain weekly water use restriction policies for all 1,000 hydroclimatic realizations within a specific DU SOW. The column structure is as follows: 0rest_m: restriction multiplier for utility 0 (values between 0 and 1) 1rest_m: restriction multiplier for utility 1 (values between 0 and 1) 2rest_m: restriction multiplier for utility 2 (values between 0 and 1) 3rest_m: restriction multiplier for utility 3 (values between 0 and 1) 4rest_m: restriction multiplier for utility 4 (values between 0 and 1) 5rest_m: restriction multiplier for utility 5 (values between 0 and 1) 0transf: transfer volume for utility 0 (in MGD) 1transf: transfer volume for utility 1 (in MGD) 2transf: transfer volume for utility 2 (in MGD) 3transf: transfer volume for utility 3 (in MGD) 4transf: transfer volume for utility 4 (in MGD) 5transf: transfer volume for utility 5 (in MGD) Water Sources files These files can be obtained by unzipping WaterSources_subset.zip. Each of the 100 CSV files within the unzipped folder will contain weekly state variables at each water source for all 1,000 hydroclimatic realizations within a specific DU SOW. The column structure is as follows: Xvolume: available water volume from source X (in MGD) Xs_area: surface area of source X (in ACF) Xdemand: demand drawn from a water source from source X (in MGD) Xup_spill: upstream spillage from source X (in MGD) Xww_inflow: wastewater inflow from source X (in MGD) Xcatch_inflow: upstream catchment inflow to source X (in MGD) Xevap: evaporation multiplier for source X (values between 0 and 1) Xds_spill: downstream spillage from source X (in MGD) X_Y_alloc_cap: the allocated capacity from source X to utility Y (values between 0 and 1) X_Y_alloc_dem: the allocated demand from source X to utility Y (values between 0 and 1) Xtrmt_alloc_Y: the allocated treatment capacity from source X to utility Y (values between 0 and 1) Utilities files These files can be obtained by unzipping Utilities_subset.zip. Each of the 100 CSV files within the unzipped folder will contain weekly state variables at each utility for all 1,000 hydroclimatic realizations within a specific DU SOW. The column structure is as follows: Xst_vol: total available storage volume of utility X (in MG) Xcapacity: total storage capacity of utility X (in MG) Xnet_inf: : net inflow for all storage infrastructure for utility X (in MGD) Xst_rof: short term ROF for utility X (values between 0 and 1) Xst_stor_rof: short-term storage ROF for utility X (values between 0 and 1) Xst_trmt_rof: short-term treatment ROF for utility X (values between 0 and 1) Xlt_rof: long-term ROF for utility X (values between 0 and 1) Xlt_stor_rof: long-term storage ROF for utility X (values between 0 and 1) Xlt_trmt_rof: long-term treatment ROF for utility X (values between 0 and 1) Xrest_demand: restricted demand for utility X (in MGD) Xunrest_demand: unrestricted demand for utility X (in MGD) Xunfulf_demand: unfulfilled demand for utility X (in MGD) Xwastewater: wastewater return for utility X (in MGD) Xtreat_capacity: total treatment capacity for utility X (in MG) Xcont_fund: reserve (contingency) fund balance for utility X Xins_pout: insurance payout for utility X (% annual volumetric revenue) Xins_price: insurance price for utility X (% annual volumetric revenue) Xinfra_npv: infrastructure net present value for utility ($mil) Xst_vol: total available storage volume of utility X (in MG) Xdebt_serv: debt service for utility X (usually once per year if the infrastructure is triggered; % annual volumetric revenue) Xstor_vol: total stored volume (in MGD) Xobs_ann_dem: observed annual demand for utility X (in MGD) Xproj_dem: projected annual demand for utility X (in MGD) Xpv_debt_serv: present value of debt service payments for utility X (% annual volumetric revenue) Xgross_rev: gross revenue for utility X ($mil) Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program.

Artificial Intelligence↗
DOE OSTI2021

Domain Aware Techniques in AI: A Survey

An open literature survey of domain aware methods for next generation artificial intelligence as part of a workshop for DNN, R&D

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Development of a Deep Learning Model for Predicting the Drag Coefficients of Spherical and Non-Spherical Particles,

There is yet to be a well-established drag model for non-spherical particles required in a particle-laden flow that could cover a wide range of sphericities. This talk will explore the development of a general drag model for non-spherical particles by applying deep learning using available experimental data available in the literature. The integration of several raw experimental measurements from different sources and research directions allows the training of robust Artificial Intelligence and Machine Learning (ML) models. Neural networks are an ML approach inspired by the inner biological workings of the brain. This work aims to develop a Deep Neural Network (DNN) that predicts drag coefficient values with the ability to adapt appropriately to unseen data. Given the limited number of data points available and the variance found within the data collected from various sources, challenges may arise when looking to train the model. Our study tests and implements various model regularization techniques and assesses different loss and activation functions for the proposed DNN. The proposed model considers a broader range of features other than sphericity and Reynold number. These features include density ratio, solid volume fraction, lengthwise and crosswise sphericity, and more. Furthermore, we present the features that play a significant role in predicting different drag coefficients through feature importance. Within the investigated parameter ranges in this study, the following conclusions can be achieved and summarized below: • An improved drag coefficient model can be developed by considering more features such as, aspect ratio, lengthwise sphericity, crosswise sphericity, and density ratio. • DNN model can predict better results compared to traditional methods using MAE metric. • The proposed model addresses data challenges such as limited data and extreme data points through expanded feature-set and regularization. • Three major features that mostly affect the drag coefficient were identified from a feature importance analysis.

Presa-Reyes, Maria↗
DOE OSTI2021

Keymaker

Explore the source record for details and available documents.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Design and Optimization of a Gas-Cooled, Airfoil Fin Microchannel Heat Exchanger

High-performance microchannel heat exchangers are needed to supply heat for power conversion for nuclear microreactors. An airfoil fin microchannel design, constructed of Alloy 617 with helium as the working fluid, was analyzed and optimized using a design of experiments with artificial intelligence and machine learning techniques. The use of airfoil fins offers the potential to reduce pressure drop across the heat exchanger, as compared to other types of channel configurations. A framework for topology optimization of airfoil fin PCHEs has been developed that can be readily extended to different fin sizes and shapes, as well as different inlet and operating conditions, materials of construction, and working fluids. An optimization procedure was developed that employs computational fluid dynamics for a set of design points identified using Latin hypercube sampling. STAR-CCM+ was used to analyze a simplified two-channel configuration where five parameters were varied – inlet angle, fin scale, extent of staggering, transverse and longitudinal pitches. Two methods were compared for generating surrogate models – a 5D polynomial and a regression neural network. A response surface approximation was created from the surrogate models and input to a genetic algorithm. The genetic algorithm identified a set of optimal points on the Pareto front. The optimal geometry was found across six channel Reynolds numbers ranging from 1000 to 5000 to analyze how varying inlet conditions affects the optimal design. A set of optimal designs that maximizes heat transfer and minimizes pressure drop was identified, and a thermal stress analysis was performed on the optimal design. This work has developed a digital framework for the expedient topology design and evaluation of PCHE designs for gas-cooled microreactor applications. Correlations for the Nusselt number and Darcy friction factor were developed that can be useful for thermal hydraulic analyses using system codes. A thermal stress analysis was conducted and a brief discussion of the status of code cases of PCHEs for nuclear applications is given. Testing and thermomechanical modeling is needed to facilitate future code compliance of PCHEs for high pressure and high temperature applications.

42 ENGINEERING↗
DOE OSTI2022

Apparatus and amendment of wind turbine blade impact detection and analysis

A multisensory system provides both temporal and spatial coverage capacities for auto-detection of bird collision events. The system includes an apparatus having a first circuitry to capture and store a series of images or video of a blade of a wind turbine; and a memory to store the images from the first circuitry. The apparatus also has one or more sensors to continuously sense vibration of the blade or for acoustic recordings; and a second circuitry to analyze the sensor data stream and/or the series of images or video to identify a cause of the vibration and to trigger the camera(s). A communication interface transmits data from the second circuitry to another device, wherein the second circuitry applies artificial intelligence or machine learning to control sensitivity of the one or more sensors.

Johnston, Matthew↗
DOE OSTI2024

Smart Pixels: In-pixel AI for on-sensor data filtering

We present a smart pixel prototype readout integrated circuit (ROIC) designed in CMOS 28 nm bulk process, with in-pixel implementation of an artificial intelligence (AI) / machine learning (ML) based data filtering algorithm designed as proof-of-principle for a Phase III upgrade at the Large Hadron Collider (LHC) pixel detector. The first version of the ROIC consists of two matrices of 256 smart pixels, each 25$\times$25 $\mu$m$^2$ in size. Each pixel consists of a charge-sensitive preamplifier with leakage current compensation and three auto-zero comparators for a 2-bit flash-type ADC. The frontend is capable of synchronously digitizing the sensor charge within 25 ns. Measurement results show an equivalent noise charge (ENC) of $\sim$30e$^-$ and a total dispersion of $\sim$100e$^-$ The second version of the ROIC uses a fully connected two-layer neural network (NN) to process information from a cluster of 256 pixels to determine if the pattern corresponds to highly desirable high-momentum particle tracks for selection and readout. The digital NN is embedded in-between analog signal processing regions of the 256 pixels without increasing the pixel size and is implemented as fully combinatorial digital logic to minimize power consumption and eliminate clock distribution, and is active only in the presence of an input signal. The total power consumption of the neural network is $\sim$ 300 $\mu$W. The NN performs momentum classification based on the generated cluster patterns and even with a modest momentum threshold, it is capable of 54.4% - 75.4% total data rejection, opening the possibility of using the pixel information at 40MHz for the trigger. The total power consumption of analog and digital functions per pixel is $\sim$ 6 $\mu$W per pixel, which corresponds to $\sim$ 1 W/cm$^2$ staying within the experimental constraints.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Improving Reliability of Large Language Models for Nuclear Power Plant Diagnostics Technical Presentation

Large Language Models (LLMs) struggle out of the box when answering factually about detailed questions, especially in domains that are sparsely represented in their training data. This causes hallucinations and reduces reliability making it difficult for them to be used in practice. This work shows that using RAG techniques can improve factual accuracy and reliability, allowing for the application of LLMs in specialized areas, even when those areas that aren’t extensively covered in their initial training.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

SIVB's 2024 In Vitro Biology Meeting Proceedings

SIVB's 2024 World Congress on In Vitro Biology took place in Saint Louis, Missouri, from June 8th to 12th, 2024. The conference featured renowned speakers from academic and non-academic institutions who will present recent advancements in critical areas like plant transformation, genome editing, synthetic biology, advanced breeding technologies, cellular agriculture, future food sources, chromosome engineering, epigenetics, artificial intelligence, and machine learning. The Society for In Vitro Biology (SIVB) has always considered the education and professional development of young researchers as an integral component of its mission. The 2024 World Congress program, along with SIVB’s student initiatives, was customized to foster scientific growth and professional development among students and young scientists empowering them in their professional journeys. The recording of the DOE supported "Single Cell RNA Sequencing" workshop was made publicly available at https://youtu.be/A0UnuYwefwg for easy retrieval and reference of all information shared during the live event, thereby increasing accessibility and knowledge transfer. Their are 14 articles in the proceedings and the full list of files is located at https://link.springer.com/journal/11626/volumes-and-issues/60-1/supplement.

10 SYNTHETIC FUELS↗