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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Pu(IV) quantification via visible–near-infrared absorption spectroscopy: tackling interferences using D-optimal design and partial least squares

Here, this study presents a novel analytical approach for quantifying Pu(IV) in glove box environments using fiber-optic-based visible–near-infrared absorption spectroscopy in combination with partial least squares regression (PLSR) and design of experiments. The method addresses significant challenges posed by overlapping spectral features arising from Nd(III), which is a common fission product impurity, and the speciation variability of Pu(IV) nitrato complexes in HNO 3 concentrations ranging from 2.5 to 11 M. A curated training set consisting of data from 20 samples was developed via D-optimal design to enable robust PLSR model calibration for Pu(IV) using the near-infrared band near 1050 nm. The training set was acquired from samples in cuvettes with a 1-cm path length and was used to build the PLSR model. The robustness of the model was validated with data collected using a dip probe with a 1-cm path length and varying Pu(IV) concentrations. The strong performance of the model indicates good model transfer from cuvette to dip probe and highlights the potential for in situ measurements and online monitoring of reactions in a crystallization reactor vessel. The results demonstrate that this combined spectroscopic and chemometric approach can accurately and simultaneously quantify Pu(IV) and HNO 3 , thereby offering a promising tool for real-time monitoring in process environments.

Actinide↗
DOE OSTI2024

Quantification of Hydrogen Isotopes Utilizing Raman Spectroscopy Paired with Chemometric Analysis for Application across Multiple Systems

On-line and real-time analysis of a chemical process is a major analytical challenge that can drastically change the way the chemical industry or chemical research operates. With in situ analyses, new and powerful understanding of chemistry can be gained; however, building robust tools for long-term monitoring faces many challenges that include compensating for instrument drift, instrument replacement, and sensor or probe replacement. Accounting for these changes by recollecting calibration data and rebuilding quantification models can be costly and time consuming. Here, in this study, methods to overcome these challenges are demonstrated with an application of Raman spectroscopy to monitoring hydrogen isotopes with varied speciation within dynamic gas streams. Specifically, chemical data science tools such as chemometric modeling are leveraged along with several examples of calibration transfer approaches. Furthermore, the optimization of instrument and sensors cell parameters for targeted gas phase analyses is discussed. While the particular focus on hydrogen is highly beneficial within the nuclear energy sector, mechanisms built and demonstrated here are widely applicable to optical spectroscopy monitoring in numerous other chemical systems that can be leveraged in other hazardous processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

In-Situ Spatial Mapping of Hydrogen in Yttrium Hydrides at LANSCE (FY23 Version, Rev. 1)

This report summarizes the development of neutron imaging capabilities and experimental activities performed at the Los Alamos Neutron Science Center (LANSCE) with the main goal of measuring temperature-driven hydrogen diffusion within bulk-yttrium hydride (YH x ) materials. Yttrium hydride is the leading candidate to serve as a solid neutron moderator in microreactor cores, owing to its high density of hydrogen atoms as well as its superior thermal stability compared to all other metal hydrides. The experimental results and technique developments reported herein support the U.S. Department of Energy Office of Nuclear Energy’s (DOE-NE) Microreactor Program under Technology Maturation. In particular, it addresses the critical need to experimentally validate and verify hydrogen-diffusion models of metal hydrides used in high-temperature microreactor designs by means of high-spatial-resolution neutron imaging. These capabilities were designed to apply large temperature gradients across centimeter-sized YH x pellets to simulate conditions faced in the microreactor environment. In principle, neutron imaging, combined with in-situ sample heating, enables near real-time tracking of hydrogen diffusion in YH x on the sub-millimeter scale. In this report, an overview of neutron imaging methodology and technologies are given in the context of recent spatial measures of hydrogen concentrations in similar metal hydrides. Additionally, the commissioning and operation of a custom-built compact dual-zone furnace is given along with details on three in-situ heating measurements of YH x performed over the 2020 to 2022 LANSCE operation cycles. The aims of these experiments ranged from furnace commissioning, determining sample quality, i.e., hydrogen uniformity via neutron computed tomography, and studying the effects of applied temperature-gradients on YH x pellets. Analyses and results from these neutron imaging measurements are given along with outlooks and guidelines for optimal future hydrogen diffusion measurements. Our conclusions are as follows. Image analyses indicate that centimeter-sized yttrium hydride cylindrical pellets exhibit uniform, whole-body hydrogen desorption and absorption without clear temperature dependence as reflected in the image attenuation at the opposing ends of each sample. This suggests that despite the large magnitude in temperature gradients applied by the furnace heating elements, the sample equilibrates to an unknown intermediate temperature. The origin of this result is likely the combination of short sample length (∼1cm) and use of a TZM can for containment where the latter created a thermal short across the sample. Nevertheless, the results from the most recent measurements indicate that neither significant concentration gradients of hydrogen were formed in centimeter-sized samples through the entire temperature range (25 °C to 950 °C) nor any formed due to temperature gradients on the order of 50 °C/cm up to 700 °C/cm. Furthermore, images from the FY2021 and FY2022 measurements indicate that samples of YH x , fabricated from either the direct hydride or powder metallurgy methods, are highly uniform in their hydrogen concentration to within the measurements’ spatial resolutions. The following questions arise from these latest results: 1) What is the intermediate temperature of the pellets in the TZM cans? 2) How quickly does the temperature equilibrate within the sample? and, 3) Do the observed changes in image attenuation follow known pressure-composition-temperature relations of yttrium hydride?

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Decentralized Distributed Proximal Policy Optimization (DD-PPO) for High Performance Computing Scheduling on Multi-User Systems

Resource allocation in High Performance Computing (HPC) environments presents a complex and multifaceted challenge for job scheduling algorithms. Beyond the efficient allocation of system resources, schedulers must account for and optimize multiple performance metrics, including job wait time and system throughput. Traditional heuristic-based scheduling algorithms increasingly struggle and lack the efficiency needed to meet the demands and address the complexity and scale of modern HPC systems. Consequently, recent research efforts have focused on leveraging advancements in Artificial Intelligence (AI) and Deep Learning (DL), particularly Reinforcement Learning (RL), to develop more adaptable and intelligent scheduling strategies. Previous RL-based scheduling approaches have explored a range of algorithms, from Deep Q-Networks (DQN) to Proximal Policy Optimization (PPO), and more recently, hybrid methods that integrate Graph Neural Networks (GNNs) with RL techniques. However, a common limitation across these methods is their reliance on relatively small datasets, with few methods being evaluated using large-scale, multi-million-job trace datasets representative of real-world HPC workloads. Moreover, existing RL schedulers face scalability issues due to centralized policy updates, which hinder training efficiency and performance when applied to large datasets. This study introduces a novel RL-based scheduler utilizing Decentralized Distributed Proximal Policy Optimization (DD-PPO) algorithm, which supports large-scale distributed training across multiple workers without requiring parameter synchronization at every step. By eliminating reliance on centralized updates to a shared policy, the DD-PPO scheduler enhances scalability, training efficiency, and sample utilization. Experimental validation using a large real-world dataset containing over 11.5 million job traces collected from petascale HPC systems over six years assesses the influence of dataset scale on training effectiveness and compares DD-PPO performance to traditional and advanced scheduling approaches. The experimental results demonstrate improved scheduling performance in comparison to both heuristic-based schedulers and existing RL-based scheduling algorithms.

AI↗
DOE OSTI2023

Correlated Trajectory Uncertainty for Adaptive Sequential Decision Making

One of the great challenges with decision making tasks on real world systems is the fact that data is sparse and acquiring additional data is expensive. In these cases, it is often crucial to make a model of the environment to assist in making decisions. At the same time, limited data means that learned models are erroneous, making it just as important to equip the model with good predictive uncertainties. In the context of learning sequential decision making policies, these uncertainties can prove useful for informing which data to collect for the greatest improvement in policy performance \citep{mehta2021experimental, mehta2022exploration} or informing the policy about unsure regions of state and action space to avoid during test time \citep{yu2020mopo}. Additionally, assuming that realistic samples of the environment can be drawn, an adaptable policy can be trained that attempts to make optimal decisions for any given possible instance of the environment \citep{ghosh2022offline, chen2021offline}. In this work, we examine the so-called ``probabilistic neural network'' (PNN) model that is ubiquitous in model-based reinforcement learning (MBRL) works. We argue that while PNN models may have good marginal uncertainties, they form a distribution of non-smooth transition functions. Not only are these samples unrealistic and may hamper adaptability, but we also assert that this leads to poor uncertainty estimates when predicting multiple step trajectory estimates. To address this issue, we propose a simple sampling method that can be implemented on top of pre-existing models.We evaluate our sampling technique on a number of environments, including a realistic nuclear fusion task, and find that, not only do smooth transition function samples produce more calibrated uncertainties, but they also lead to better downstream performance for an adaptive policy.

Offline Reinforcement Learning↗
DOE OSTI2024

Sub-pilot-scale Production of High-Value Products from U.S. Coals

Investigators from the University of Utah, University of Wyoming and Marshall University pursued a program to study the conversion of raw coal to high-value products of carbon fiber and silicon carbide. Team members also developed an initial framework for a data portal that can incorporate laboratory data on coal processing and product quality, and also work with tools for machine learning for data analysis, data visualization and economic assessment. Experimental R&D efforts focused on the conversion of raw coal to coal tar and other byproducts, and the resulting tar intermediates were upgraded to form anisotropic and isotropic pitch materials. These pitch materials were produced from coal using both thermal (pyrolysis) and chemical (mild solvolysis liquefaction) decomposition of raw coal. Four different coals were studied: Utah bituminous coal (Sufco), Wyoming PRB coal (Black Thunder), Illinois bituminous coal (Illinois #6), and West Virginia bituminous coal (Flying Eagle). Both metallurgical-grade coking coals and lower-grade steam coals were investigated, and controlled secondary gas-phase reactions were used during a two-stage pyrolysis process to induce cracking and condensation reactions among the pyrolytic tar species. This approach successfully improved the performance of the lower grade coals for yielding pitch materials, with properties more consistent with a commercial-grade pitch that had previously demonstrated success for quality carbon fiber production. The use of waste plastic materials was also studied, to help improve physical and chemical characteristics of the intermediate tars and final pitch product; in particular, for lowering the pitch softening point to an acceptable level for melt spinning carbon fiber. Mild solvolysis liquefaction was also used as a method for producing pitch for carbon fiber production. As expected, significantly higher pitch yields were obtained using this approach, and waste plastic materials were also successfully used to reduce pitch softening point to an acceptable level. The plastic materials were also utilized to create a solvent for the mild solvolysis process, and this plastic-derived solvent was shown to provide results consistent with more expensive commercial chemical solvents, and could thus avoid the need for costly recovery and recycle of a liquefaction solvent. Additional experimental R&D focused on the production of silicon carbide (β-SiC) from the residual char byproduct from pitch production, and also on the production of carbon fiber from the anisotropic pitch. SiC was successfully synthesized using a mixture of residual char and sandstone at a ratio of 1:1. Reaction temperature and residence time were optimized and yielded a product purity of 81%. For carbon fiber production, the most successful pitch samples were obtained from the mild solvolysis liquefaction approach, combined with the use of a plastic (HDPE)-derived solvent. Fiber properties improved over time as laboratory fiber production methodologies improved, and final yields of carbon fiber were obtained with a diameter of 12.14 ± 1.10 um, Modulus of 173.73 ± 15.25 GPa, and Tensile Strength of 1.04 ± 0.10 GPa. A proof-of-concept Modern Community Research Data Portal (MCRDP) was developed and deployed for coal and coal-derived pitch characterization, with the full support of (i) remote web-based access, (ii) distributed analysis, (iii) interactive visualization and exploration, (iv) shared and long-term data access, (v) advanced query capabilities and (vi) real-time collaboration. The Coal to Products Data Portal “coaltoproducts.org” provides researchers with space to store and share data within a project, tools for analyzing and understanding data for scientific investigation, and the ability to publish data to the broader community for reproducibility. The portal leverages the Material Commons 2.0 (MC) platform developed by the Center for PRedictive Integrated Structural Materials Science (PRISMS) of the University of Michigan, to achieve long-term longevity of data collections and, more importantly, collaborative science. A number of data visualization tools were also assessed and implemented for interrogating the experimental and modeling data. The machine learning portion of this project analyzed datasets from two different coal conversion processes performed on a diverse set of coal samples from both the coal pyrolysis experiments and the solvent liquefaction experiments. The work was initiated by exploring standard regression models on the pyrolysis data, aiming to understand the impact of sample characteristics and processing conditions on key product metrics. Over the course of the project, the focus expanded to include a variety of machine learning tools, delving into both supervised and unsupervised learning methods. Models tested on the pyrolysis data included linear, ridge, lasso, elastic-net, Gaussian process, random forest regression, and AutoSklearn, and the approach was continually refined to enhance predictive accuracy and model interpretability. Similar techniques were applied to the liquefaction data with an additional focus on feature engineering. Along with mesophase content, additional outputs of interest were the pitch yield, softening point, and QI content. Insights derived from these analyses are crucial in determining the factors influencing the quality and yield of coal-derived products. As the work progressed, the research evolved from foundational model comparisons to analyses of random forests, decision paths, and feature importance scores. A thorough market analysis was performed to examine the prospects of coal-based carbon fibers. The best opportunities for coal come from its lower and more stable price relative to petroleum, particularly for subbituminous coals, which is the primary advantage that a coal refinery may have over a petroleum refinery. Before a commercial CTP production facility can be modeled, however, several things need to be understood regarding the nature of the would-be coal refinery. These include the technology to be deployed, the size of facility, the volume(s) of co-product(s), and the waste and emissions profile of the plant. The volume of co-products and waste may be substantial and will require separate market analysis to ensure viability. In the near-term, the importance of coal tar pitch, in the form of carbon pitch, to the aluminum and steel industries is likely to overshadow the alternative use of this material as an input for carbon fiber. The importance of steel and aluminum in building materials, and the need for carbon materials in their manufacturing, will ensure that demand for these products remains for the long run. In addition, carbon fiber may also be the best substitute for steel and aluminum well into the future. While society will eventually be able to shift production of much of its electricity needs to renewables, it will not be able to shift away from fossil fuels for production of high-strength construction and vehicular materials. Demand for carbon fiber is expected to increase quickly, but the volume of carbon fiber and the amount of coal that would be needed to produce even a sizeable share of this market may still be relatively small compared to current coal production. Thus, other coal-based products like graphene, graphite, carbon foams, resins, and carbon-based building products will play important roles in sustaining coal production as coal-fired power generation continues to decline.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Physics-Informed Evolutionary Strategy Based Control for Mitigating Delayed Voltage Recovery

Here, in this work we propose a novel data-driven, real-time power system voltage stability control method based on the physics-informed guided meta evolutionary strategy (ES). The main objective is to quickly provide an adaptive control strategy to secure system voltage stability. The problem is challenging due to the high-dimensional feature of the power system model and the fast-changing and uncertain nature of power system operation scenarios. To this end, a model-free and derivative-free guided ES method is applied. The method is further combined with a meta-learning strategy to make the learnt control policy automatically adapted to unseen operation conditions and fault scenarios, which is highly desired for real-time emergency control. Last but not least, physical knowledge is embedded in the above method through a trainable action mask technique to rule out unnecessary load shedding actions for better learning and control performance. Case studies on the IEEE 300-bus system and comparisons with other state-of-the-art benchmark methods verify the superiority of the proposed physics-informed guided meta ES method in realizing fast and adaptive power system voltage stability control.

42 ENGINEERING↗
DOE OSTI2021

Towards developing multiscale-multiphysics models and their surrogates for digital twins of metal additive manufacturing

Artificial intelligence (AI) embedded within digital models of manufacturing processes can be used to improve process productivity and product quality significantly. The application of such advanced capabilities particularly to highly digitalized processes such as metal additive manufacturing (AM) is likely to make those processes commercially more attractive. AI capabilities will reside within Digital Twins (DTs) which are living virtual replicas of the physical processes. DTs will be empowered to operate autonomously in a diagnostic control capacity to supervise processes and can be interrogated by the practitioner to inform the optimal processing route for any given product. The utility of the information gained from the DTs would depend on the quality of the digital models and, more importantly, their faster-solving surrogates which dwell within DTs for consultation during rapid decision-making. In this article, we point out the exceptional value of DTs in AM and focus on the need to create high-fidelity multiscale-multiphysics models for AM processes to feed the AI capabilities. We identify technical hurdles for their development, including those arising from the multiscale and multiphysics characteristics of the models, the difficulties in linking models of the subprocesses across scales and physics, and the scarcity of experimental data. We discuss the need for creating surrogate models using machine learning approaches for real-time problem-solving. We further identify non-technical barriers, such as the need for standardization and difficulties in collaborating across different types of institutions. We offer potential solutions for all these challenges, after reflecting on and researching discussions held at an international symposium on the subject in 2019. Here, we argue that a collaborative approach can not only help accelerate their development compared with disparate efforts, but also enhance the quality of the models by allowing modular development and linkages that account for interactions between the various sub-processes in AM. A high-level roadmap is suggested for starting such a collaboration.

36 MATERIALS SCIENCE↗
DOE OSTI2024

In Situ X-ray Scattering Reveals Coarsening Rates of Superlattices Self-Assembled from Electrostatically Stabilized Metal Nanocrystals Depend Nonmonotonically on Driving Force

Self-assembly of colloidal nanocrystals (NCs) into superlattices (SLs) is an appealing strategy to design hierarchically organized materials with promising functionalities. Mechanistic studies are still needed to uncover the design principles for SL self-assembly, but such studies have been difficult to perform due to the fast time and short length scales of NC systems. To address this challenge, we developed an apparatus to directly measure the evolving phases in situ and in real time of an electrostatically stabilized Au NC solution before, during, and after it is quenched to form SLs using small-angle X-ray scattering. By developing a quantitative model, we fit the time-dependent scattering patterns to obtain the phase diagram of the system and the kinetics of the colloidal and SL phases as a function of varying quench conditions. The extracted phase diagram is consistent with particles whose interactions are short in range relative to their diameter. We find the degree of SL order is primarily determined by fast (subsecond) initial nucleation and growth kinetics, while coarsening at later times depends nonmonotonically on the driving force for self-assembly. We validate these results by direct comparison with simulations and use them to suggest dynamic design principles to optimize the crystallinity within a finite time window. The combination of this measurement methodology, quantitative analysis, and simulation should be generalizable to elucidate and better control the microscopic self-assembly pathways of a wide range of bottom-up assembled systems and architectures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Phase I Final Technical Report on Energy-Efficient Reconfigurable Universal Accelerator Interconnect

It is well known that application specific computing systems, optimally designed and configured for a given workload, offer much higher energy-efficiency and throughput than general purpose systems. In modern computing systems, heterogeneous computing systems have emerged that exploit the energy and performance benefits of combining various different domain-specific processor architectures. Application domains such as high-performance computing and machine learning now process terabyte-sized data sets, requiring enormous processing and memory resources. These applications have very high-power consumption due to bottlenecks in the electrical interconnection between processing units. This project aims to reduce both communication energy and latency by integrating universally available accelerators with silicon photonics. It also aims to increase system throughput by exploiting emerging technologies in silicon photonic reconfigurable interconnects; this will allow the system to balance itself in real time to accommodate changes in workloads and data flows.

2.5D/3D integration↗
DOE OSTI2026

Single-Particle Isotopic Analysis Using the Liquid Sampling-Atmospheric Pressure Glow Discharge Microplasma Coupled to an Orbitrap Mass Spectrometer

Isotope ratio (IR) determinations on individual particles can provide valuable information relative to the sample, including processing and dating, which could be valuable to geological and nuclear material analysis communities. In comparison to “bulk” measurements, in which all particle information is lost and homogenized within a sample, “particle” measurements allow for fingerprinting and can provide unique insights related to the sample’s nano- and micro- compositions. Furthermore, the complex nature of particles, with potential isobaric interferences from either the matrix or other elements within the same particle, poses a significant analytical challenge for conventional mass spectrometry platforms employed for elemental analysis due to their limited mass resolution. Presented here is the first demonstration of an ultrahigh resolution method for single particle (SP) isotopic analysis using the liquid sampling-atmospheric pressure glow discharge (LS-APGD) microplasma ionization source coupled to an Orbitrap mass spectrometer and FTMS Booster X2T acquisition/processing system. For proof of concept, well-characterized CeO 2 microparticles with a nominal diameter of 1.0 μm were analyzed at a mass resolution of ∼330,000. Important instrumental parameters were optimized to enable the detection of single particles. The 142 Ce/ 140 Ce ratio of individual particles and the population average were determined and compared to an SP inductively coupled plasma time-of-flight mass spectrometry (ICP-TOF-MS) analysis. The isotopic accuracy and precision were determined by two methods and found to be in good agreement with the SP-ICP-TOF-MS method, with the linear regression slope method providing a more accurate ratio, showing a ∼1.4% relative difference from the ratio determined from a particle digest. The encouraging performance of the method was further supported by a determined detection limit of 2.9 fg ( 142 Ce). The developed method is anticipated to overcome many of the challenges posed by isobaric interferences encountered in conventional mass spectrometry techniques when analyzing real-world samples for particle isotopic composition.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Multi-Functional Distributed Fiber Sensors for Pipeline Monitoring and Methane Detections. Final Report

As an abundant and cheap fossil energy source, natural gas has become a significant energy supply to support the United States’ economy. However, the large-scale extraction and utilization of natural gas also impose significant challenges on methane leakage. This problem is exacerbated by aging gas utility delivery systems, including interstate high-pressure pipelines, storage, and transmission facilities. This project aims to develop a cost-effective fiber optical sensing method that can perform multi-parameter real-time measurements of natural gas pipelines across long interrogation distances up to 100 km with 1-meter spatial resolution. This sensing tool can evaluate overall pipeline efficiency and reduce methane emissions for mid-stream methane infrastructures. To accomplish this objective, research and development efforts funded by this project have resulted in the following accomplishments: This project successfully has developed new functional sensory polymer materials using Metal-Organic Frameworks (MOFs) that can be coated on optical fiber through the reel-to-reel coating process. Functional polymer-coated optical fibers can perform sensitive methane detection through evanescence wave interaction and strain-based measurements to achieve 1% detection sensitivities. The new sensors fibers support both distributed measurements and multiplexed fiber sensors array for multi-point measurements. This project developed and optimized a new multi-core optical fiber that supports simultaneous and distributed measurements of strain and temperatures with 1-meter spatial resolutions across up to 100-km interrogation distance. This new fiber, combined with sensory polymercoated fiber, could perform both distributed temperature and methane detections. This project developed a new artificial intelligence big data algorithm approach that can effectively analyze high-resolution data harnessed by distributed fiber sensors to protect natural gas pipelines against external threats and detect internal defects induced by corrosion. Working with our industry partner, this project developed new optical fibers that support fiber sensor fabrications through polymer coating after the fibers are drawn. These new fibers eliminate the need for direct sensor fabrication when the fiber is fabricated on a fiber draw tower, which drastically expands fiber sensors' applicability. This research project has significantly advanced the distributed fiber sensing technology. It will dramatically increase the applicability and adaptability of distributed fiber sensors for a wide array of applications in energy, sustainability, and environmental science, including structural health monitoring of natural gas pipelines, oil infrastructures, hydrogen facilities, and environmental monitoring of carbon storage sites, water supply systems, and others.

03 NATURAL GAS↗
DOE OSTI2024

Decarbonization of heat pump dual fuel systems using a practical model predictive control: Field demonstration in a small commercial building

In the transition from fossil fuel to electrified heating, a concerning trend is emerging in certain regions of the US. Owners of buildings with gas-based systems leave them in place after adding heat pumps (HPs). Existing control solutions for these hybrid (dual fuel) systems are rudimentary and fall short of realizing the full carbon reduction potential of these systems. Model predictive control (MPC) is often regarded as the benchmark for achieving optimal control in integrated systems. However, in the case of small-medium commercial buildings (SMCBs), the control and communication infrastructure required to facilitate the implementation of such advanced controls is often lacking. This paper presents a field implementation of easy-to-deploy MPC for a dual fuel heating system consisting of HPs and a gas-fired furnace (GF) for SMCBs. The control system is deployed on an open-source middleware platform and utilizes low-cost sensor devices to be used for real SMCBs without major retrofits. Here, we demonstrated this MPC in a real office building with 5 HPs and 1 GF for 2 months. The test results showed that MPC reduced 27% of cost while completely eliminating GF usage by shifting 23% of the thermal load from occupied-peak time to non-occupied-non-peak times.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

RADAMS: Resilient and adaptive alert and attention management strategy against Informational Denial-of-Service (IDoS) attacks

Attacks exploiting human attentional vulnerability have posed severe threats to cybersecurity. In this work, we identify and formally define a new type of proactive attentional attacks called Informational Denial-of-Service (IDoS) attacks that generate a large volume of feint attacks to overload human operators and hide real attacks among feints. Here, we incorporate human factors (e.g., levels of expertise, stress, and efficiency) and empirical psychological results (e.g., the Yerkes-Dodson law and the sunk cost fallacy) to model the operators’ attention dynamics and their decision-making processes along with the real-time alert monitoring and inspection. To assist human operators in dismissing the feints and escalating the real attacks timely and accurately, we develop a Resilient and Adaptive Data-driven alert and Attention Management Strategy (RADAMS) that de-emphasizes alerts selectively based on the abstracted category labels of the alerts. RADAMS uses reinforcement learning to achieve a customized and transferable design for various human operators and evolving IDoS attacks. The integrated modeling and theoretical analysis lead to the Product Principle of Attention (PPoA), fundamental limits, and the tradeoff among crucial human and economic factors. Experimental results corroborate that the proposed strategy outperforms the default strategy and can reduce the IDoS risk by as much as 20%. Besides, the strategy is resilient to large variations of costs, attack frequencies, and human attention capacities. We have recognized interesting phenomena such as attentional risk equivalency, attacker’s dilemma, and the half-truth optimal attack strategy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Machine learning-enhanced MPC for demand flexibility in small commercial buildings: An experimental study

Small- and medium-sized commercial buildings (SMCBs) represent the majority of U.S. commercial building stock and a significant share of peak electricity demand, yet they often lack centralized building automation systems, representing a significant untapped resource for urban energy management. This infrastructure gap makes advanced control implementation challenging, limiting the potential for widespread demand flexibility. Model Predictive Control (MPC) has shown strong potential for load shifting, peak demand reduction, and cost savings, but its effectiveness is hindered by unmeasured disturbances such as internal heat gains. This paper presents a Hybrid MPC framework that integrates a physics-based gray-box building thermal model, identified using a lumped disturbance (LD) approach, with a machine learning (ML) model for forecasting unmeasured disturbances. The hybrid approach is designed for buildings with multiple individually controlled heat pump and thermostat pairs, common in SMCBs, and aims to optimize coordinated scheduling of multiple heat pumps under dynamic electricity pricing while respecting comfort constraints. The methodology is validated through both simulations of case study buildings and experimental studies at a highly-instrumented test facility. Simulation results show that the Hybrid MPC achieves substantial load shifting and peak demand reduction, approaching the performance of an ideal MPC with perfect disturbance knowledge, and outperforming a conventional MPC without disturbance forecasting. In experiments, the Hybrid MPC reduced daily HVAC energy costs by 8.7%, peak-price time load (load shifting) by 41.7%, and peak demand by 29.2% compared to baseline control, demonstrating comparable benefits to the 11.6% cost savings, 42.9% load shifting, and 23.2% peak reduction of the ideal MPC. These results demonstrate that the proposed hybrid modeling approach can significantly improve MPC performance in real-world SMCB applications without requiring additional disturbance measurements.

Demand Flexibility↗
DOE OSTI2024

Distributed Machine Learning Workflow with PanDA and iDDS in LHC ATLAS

Machine Learning (ML) has become one of the important tools for High Energy Physics analysis. As the size of the dataset increases at the Large Hadron Collider (LHC), and at the same time the search spaces become bigger and bigger in order to exploit the physics potentials, more and more computing resources are required for processing these ML tasks. In addition, complex advanced ML workflows are developed in which one task may depend on the results of previous tasks. How to make use of vast distributed CPUs/GPUs in WLCG for these big complex ML tasks has become a popular research area. In this paper, we present our efforts enabling the execution of distributed ML workflows on the Production and Distributed Analysis (PanDA) system and intelligent Data Delivery Service (iDDS). First, we describe how PanDA and iDDS deal with large-scale ML workflows, including the implementation to process workloads on diverse and geographically distributed computing resources. Next, we report real-world use cases, such as HyperParameter Optimization, Monte Carlo Toy confidence limits calculation, and Active Learning. Finally, we conclude with future plans.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Radsource Mr: Mixed Reality Planning Tool For Radioactive Recovery

The RadSource MR system leverages Meta Quest 3's advanced mixed reality capabilities to create a comprehensive spatial planning platform for end-of-life sealed radioactive source recovery operations. The application utilizes the Quest 3's high-resolution passthrough cameras and spatial mapping algorithms to generate accurate 3D environmental models. Core technical components include: (1) Real-time spatial measurement algorithms calculating distances, angles, slopes, and surface areas with sub-centimeter accuracy; (2) Virtual object placement system allowing users to position digital representations of recovery equipment (trailers, containment vessels, protective barriers) within the real environment; (3) Voice recording and annotation system for hands-free documentation in protective equipment; (4) 3D mesh capture and storage capabilities for post-operation analysis and regulatory documentation. (5) Procedure documentation is available for viewing in Mixed Reality, providing an innovative and convenient way to access the information during pre-visit and inspection activities. (6) Support for screen capture for the view for real world and virtual objects together to use it later for planning. The system integrates computer vision techniques for environmental understanding, spatial mathematics for precise measurements, and human-computer interaction principles optimized for hazardous environment operations. Data persistence allows teams to save and share planning sessions across multiple stakeholders while maintaining operational security requirements.

Khadka, Rajiv [Idaho National Laboratory (INL), Id↗
DOE OSTI2022

Deep Learning At Depth: Estimating subsurface parameters from geophysical monitoring data

Geophysical imaging techniques are a non-invasive way to image the subsurface and understand both subsurface solid (rock/soil) and fluid property distributions and their evolution in time. Inversions of the geophysical data, such as Electrical Resistance Tomography (ERT) data, are solved to estimate the subsurface property distributions, such as conductivity, and many inversion techniques smooth out sharp gradients in rock or fluid property distributions. Sharp gradients in subsurface properties tend to be present in situations with complex subsurface structures, which are common in many subsurface applications. We have successfully demonstrated that it is possible to inform, or constrain, inversions with neural networks trained on synthetic data with complex subsurface structures. Initial results suggest this process may be optimizable to yield property distributions that better represent the true property distributions than the same inversion process without the neural network constraint. Future work would optimize the neural network performance for this application and then apply the synthetic-data trained neural network to real data to understand the utility and performance of this technique for real data sets.

47 OTHER INSTRUMENTATION↗