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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 OSTI2018

ADAPT

The purpose of the ADAPT code is to generate Dynamic Event Trees (DET) using a user specified set of simulators. ADAPT can utilize any simulation tool which meets a minimal set of requirements. ADAPT is based on the concept of DET which uses explicit modeling of the deterministic dynamic processes that take place during a nuclear reactor plant system (or other complex system) evolution along with stochastic modeling. When DET are used to model various aspects of Probabilistic Risk Assessment (PRA), all accident progression scenarios starting from an initiating event are considered simultaneously. The DET branching occurs at user specified times and/or when an action is required by the system and/or the operator. These outcomes then decide how the dynamic system variables will evolve in time for each DET branch. Since two different outcomes at a DET branching may lead to completely different paths for system evolution, the next branching for these paths may occur not only at separate times, but can be based on different branching criteria. The computational infrastructure allows for flexibility in ADAPT to link with different system simulation codes, parallel processing of the scenarios under consideration, on-line scenario management (initiation as well as termination), analysis of results, and user friendly graphical capabilities. The ADAPT system is designed for a distributed computing environment; the scheduler can track multiple concurrent branches simultaneously. The scheduler is modularized so that the DET branching strategy can be modified (e.g. biasing towards the worst-case scenario/event). Independent database systems store data from the simulation tasks and the DET structure so that the event tree can be constructed and analyzed later. ADAPT is provided with a user-friendly client which can easily sort through and display the results of an experiment, precluding the need for the user to manually inspect individual simulator runs.

Reynolds, John↗
DOE OSTI2020

Materials response under hypersonic flow conditions probed by multi-modal diagnostics in a benchtop wind tunnel

In this LDRD “Materials response under hypersonic flow conditions probed by multi-modal diagnostics in a benchtop wind tunnel” a peer-reviewed lab-scale hypersonic wind tunnel (WT) was planned to be assembled in-house, along with time and space resolved and related flow and materials diagnostics to derive high quality measurements and the science that is able to inform the development of our modeling and simulation codes for the hypersonic regime. The baseline capability to achieve Mach 3 flow was scheduled to be completed in Q4 2020, in the first year of the LDRD. However due to the COVID-19 delays and travel restrictions starting in March, 2020 this task could not be completed to carry out the materials response studies in-house, or those planned with external partners that were engaged during this LDRD (NASA Langley, NASA Ames, Texas A&M university, and Virginia Tech). Still, using our hypersonic computational fluid simulation codes, many of the hypersonic components have been designed (for Mach 3, 5, and 7) and procured (or in procurement phase), and a new dedicated hypersonic laboratory was facilitized, 2 WT test sections have been manufactured, and a Mach 3 nozzle was 3D printed for prototyping. In parallel, unique materials diagnostics have been tested under static conditions and fully specified and awaiting validation in our partner facilities. Due to the new hypersonic initiative announced in January, 2020 we proposed a related scope increase in hypersonics research requiring energy interactions and new diagnostics well beyond what could be supported by an LDRD ER. A decision was made to end the current LDRD ER and formally propose a new LDRD SI that leverages the current effort, which will be starting in FY21. As a result of the uniqueness of this LDRD work, our early efforts have been encouraged and well received in the hypersonic research community, and is now part of multiple unsolicited internal and external project proposals expected to be funded in the coming years, which is encouraging to make LLNL a leader in experimentally probing extreme physics of local energetics critical to hypersonics.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Validation Exercise of a Coarse Finite Element Model of Laser Welds

The objective of this project is to validate low-fidelity models of 304L to 304L stainless steel partial-penetration laser welds for thin sheets. Low-fidelity means that the weld is represented by coarsely meshed element blocks. Here, the hexahedral element size is approx imately half the weld penetration depth. The material behavior of the block is represented by a J2 plasticity model with a Voce hardening function. The source of the data used in this work is an extensive experimental study conducted by Sharlotte Kramer (1528) and published in 2015. Figure 1 shows a cross-section of the weld of interest. The nominal thickness of the sheets is 0.063 in. while the target penetration depth of the weld is in the range of 0.028 to 0.032 in., extending about half the sheet thickness. Uniaxial tension tests provided data for calibration of base material and weld models. Results of two validation geometries were also provided. The principal validation geometry is shown in Fig. 2. It consists of a plate specimen with in-plane dimensions 6 in × 2.875 in loaded in tension. A circular plug with a 1.5 in. diameter was cut from the center of the plate and then welded in place. The details of the welding schedule are given. An important assumption is that the welds in the calibration and validation specimens have similar geometric and material properties as those in the validation tests. The task was to first calibrate models for the base material and the welds and then simulate the validation tests until the point of weld first failure.

36 MATERIALS SCIENCE↗
DOE OSTI2023

LANL/UTEP Metallurgical Science Center of Excellence Planning

LANL seeks research assistance and skills development in three distinct subject areas over the next three years. Individualized scope and detail implementation schedules will be the first deliverable for these subject areas and will represent a joint determination between LANL SMEs and UTEP faculty based on LANL priority (schedule) and UTEP staffing (faculty and student- graduate and undergraduate) and capability of immediate availability per schedule. LANL will endeavor to make summer internships available to UTEP students within these subject areas: Chemical Sciences, Additive Manufacturing, and Metallurgy: casting and solidification, characterization, and metal purification. Unarticulated but generalized scoping tasks follow within each topical area to indicate that students and technicians familiar with these skill sets are of value and interest in LANL workforce pipeline and the tasks that seek to develop these skill sets are intentional on LANL's part. Consequently, LANL will sponsor 2-3 senior (capstone) projects within these subject areas per year over the three year time period within these task areas and within separations technologies, wet chemical analysis, and analytical characterization and quantification as required by all tasks.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Analyses to support waste disposition of SNS Inner Reflector Plug

The Spallation Neutron Source Inner Reflector Plug (IRP) is scheduled for replacement. It reached end of life-time because of moderator neutron poison and decoupler burn-out, which is used for shaping neutron pulses. The replacement of IRP is a complex task due to its location in the area receiving high irradiation, being under significant amount of shielding, and size, which requires removal in segments. In order to support the disposal of spent IRP, waste characterization analyses are performed. These analyses include an accurate estimate of the radionuclide inventory and the residual dose rate. Using this data, the package class is determined for the and appropriate containers for temporary storage and subsequent transport are assigned. Analyses are performed in four steps: a radiation transport analysis of the radiation environment for producing the flux and isotope production information using MCNPX, a multi-cell transmutation analysis based n CINDER90, the generation of decay gamma source terms, and the gamma transport analyses of the decay gammas arriving at residual dose rates again using MCNPX. . Due to the complexity of the geometry of the lower segment, transport analyses for the residual dose rate require elaborate work for the source terms definition.

Popova, Irina I.↗
DOE OSTI2024

Simultaneously improving accuracy and computational cost under parametric constraints in materials property prediction tasks

Abstract Modern data mining techniques using machine learning (ML) and deep learning (DL) algorithms have been shown to excel in the regression-based task of materials property prediction using various materials representations. In an attempt to improve the predictive performance of the deep neural network model, researchers have tried to add more layers as well as develop new architectural components to create sophisticated and deep neural network models that can aid in the training process and improve the predictive ability of the final model. However, usually, these modifications require a lot of computational resources, thereby further increasing the already large model training time, which is often not feasible, thereby limiting usage for most researchers. In this paper, we study and propose a deep neural network framework for regression-based problems comprising of fully connected layers that can work with any numerical vector-based materials representations as model input. We present a novel deep regression neural network, iBRNet, with branched skip connections and multiple schedulers, which can reduce the number of parameters used to construct the model, improve the accuracy, and decrease the training time of the predictive model. We perform the model training using composition-based numerical vectors representing the elemental fractions of the respective materials and compare their performance against other traditional ML and several known DL architectures. Using multiple datasets with varying data sizes for training and testing, We show that the proposed iBRNet models outperform the state-of-the-art ML and DL models for all data sizes. We also show that the branched structure and usage of multiple schedulers lead to fewer parameters and faster model training time with better convergence than other neural networks. Scientific contribution: The combination of multiple callback functions in deep neural networks minimizes training time and maximizes accuracy in a controlled computational environment with parametric constraints for the task of materials property prediction.

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

The Myth of Fungible FTE: A Quantitative Assessment of Matrixed Resource Allocation

Matrix organizations allow scientific facilities to share specialized personnel across projects, operations, maintenance, and strategic initiatives. Nominal staffing allocations, however, may not capture the schedule consequences of fragmented individual commitments, limited access to specialist groups, and intermittent availability of key decision makers. We developed a stochas- tic, daily-time-step simulation of a hypothetical medium-sized accelerator-facility project com- prising sequential phases and parallel tasks. Each task requires role-specific work measured in FTE-days. Ordinary personnel may be unavailable because they contribute concurrently to other institutional activities, while designated key roles have independently specified daily un- availability probabilities. An organization-wide priority factor scales the number of people from each functional group who can effectively contribute to the project. It is interpreted as a composite proxy for project access and workforce fragmentation across competing commit- ments. We examined project completion time as a function of this factor and Project Lead unavailability using 100 Monte Carlo runs per condition. Increasing priority factor from 0.1 to 1.0 reduced median completion time from 1708.5 days (interquartile range 1681.5–1735.25) to 390 days (interquartile range 379–399). At priority factor = 0.1, increasing Project Lead unavailability from 0.5 to 0.9 increased median completion time from 1713.5 days (interquartile range 1691–1733.25) to 4,417 days (interquartile range 4271.75–4550.5). The model quantifies the commonly expected sensitivity of project schedules to fragmented resource commitments and limited coordination availability. Within this model, the results also indicate a possible threshold regime in which small increases in workforce availability yield only modest sched- ule improvements until sufficient capacity becomes accessible, after which project performance improves sharply. With further validation and calibration, this quantitative framework could support resource-allocation decisions during initial project planning and subsequent schedule rebaselining.

Bai, Mei [SLAC National Accelerator Laboratory (SL↗
DOE OSTI2021

Stochastic Continuous-time Flexibility Scheduling and Pricing in Wholesale Electricity Markets

Large-scale integration of intermittent renewable energy sources (RES) is calling for additional flexibility resources as well as more advanced modeling and optimization techniques to account for the increasing uncertainty and variability in power systems operation. As the RES integration gains momentum, the magnitude and frequency of their variations increase, which may trigger ramping scarcity events in real-time power systems operation. This necessitates revisiting the present definition of power systems flexibility and reserve services to reflect their robustness and adequacy towards sub-interval variations of the load and RES, as well as adjusting the operation models to accommodate the new reserve services. This project took a fundamental approach and aimed at developing continuous-time scheduling and pricing model that accurately models the continuous-time variations of load and RES and efficiently deploys the ramping capability of flexible resources to compensate the sources of variability and uncertainty in the market. In this regard, this project pursued the following goals: Developing stochastic multi-fidelity continuous-time optimization models for scheduling of energy storage (ES) systems and flexible loads in wholesale energy markets; Developing the theory and practices of continuous-time locational marginal pricing for valuating energy storage systems and flexible loads in wholesale energy markets; Developing function space solution approach to convert the proposed stochastic multi-fidelity continuous-time optimization models into tractable mixed-integer linear optimization models; and Defining flexibility reserve as a new type of reserve in markets that would enable ultimate participation of energy storage devices in provision of services to compensate the variability and uncertainty of RES in electricity markets. This project successfully completed all five major tasks defined in the SOPO, and produced 8 high-impact journal papers, 6 conference papers, 3 published U.S. patents, and one web-based software for continuous-time operation optimization of power systems. The application of the proposed flexibility reserve and the stochastic multi-fidelity continuous-time operation scheduling models would modify the forward commitment and schedule of generating units, ES devices and flexible loads, and would line up the resources in such a way that the composition of available resources is better prepared to respond to the sub-hourly variations of the load and renewable resources in real-time operation. Therefore, this project paves the way to sustainable, reliable, and economic integration of renewable energy resources in power system, supporting the progress towards reaching the national targets on energy independence. Even if the proposed models offers a radically different point of view as compared to existing models, it does not alter fundamentally the architecture of power systems operations, nor the complexity of the scheduling problem, so the integration of this project in power systems is extremely practical.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Quantum Alternating Operator Ansatz (QAOA) Phase Diagrams and Applications for Quantum Chemistry

Determining Hamiltonian ground states and energies is a challenging task with many possible approaches on quantum computers. While variational quantum eigensolvers are popular approaches for near term hardware, adiabatic state preparation is an alternative that does not require noisy optimization of parameters. Beyond adiabatic schedules, QAOA is an important method for optimization problems. In this work we modify QAOA to apply to finding ground states of molecules and empirically evaluate the modified algorithm on several molecules. This modification applies physical insights used in classical approximations to construct suitable QAOA operators and initial state. We find robust qualitative behavior for QAOA as a function of the number of steps and size of the parameters, and demonstrate this behavior also occurs in standard QAOA applied to combinatorial search. To this end we introduce QAOA phase diagrams that capture its performance and properties in various limits. In particular we show a region in which non-adiabatic schedules perform better than the adiabatic limit while employing lower quantum circuit depth. We further provide evidence our results and insights also apply to QAOA applications beyond chemistry.

Kremenetski, Vladimir↗
DOE OSTI2025

Bridging Equipment Reliability Data and Risk Informed Decisions in a Plant Operation Context

Industry equipment reliability and asset management programs are essential elements that help ensure the safe and economical operation of nuclear power plants. The effectiveness of these programs is addressed in several industry-developed and regulatory programs. The Risk-Informed Asset Management (RIAM) project is tasked to develop tools in support of the equipment reliability and asset management programs at nuclear power plants. These tools are designed to create a direct bridge between component health/lifecycle data and decision making (e.g., maintenance scheduling and project prioritization). The goal of this article is to provide a guide for specific use cases that the RIAM project is targeting. We have grouped uses cases into three main areas. The first area focuses on the analysis of equipment reliability data with a particular emphasis on condition-based data, such as test/surveillance reports and component monitoring data. The second area focuses on the integration of equipment reliability into system/plant reliability models to determine system/plant health and identify the components that are critical to maintain an operational system. Lastly, the third area manages plant resources, such as maintenance activities and replacement scheduling using optimization methods. Here the primary focus is on supporting typical system engineer decisions regarding maintenance activity scheduling and component aging management. This is performed in a risk-informed context where the term “risk” is broadly constructed to include both plant reliability and economics. This framework combines data analytics tools to analyze equipment reliability data with risk-informed methods designed to support system engineer decisions (e.g., maintenance and replacement schedules, optimal maintenance posture) in a customizable workflow.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Experience in using commercial clouds in CMS

Historically high energy physics computing has been performed on large purpose-built computing systems. In the beginning there were single site computing facilities, which evolved into the Worldwide LHC Computing Grid (WLCG) used today. The vast majority of the WLCG resources are used for LHC computing and the resources are scheduled to be continuously used throughout the year. In the last several years there has been an explosion in capacity and capability of commercial and academic computing clouds. Cloud resources are highly virtualized and intended to be able to be flexibly deployed for a variety of computing tasks. There is a growing interest amongst the cloud providers to demonstrate the capability to perform large scale scientific computing. In this presentation we will discuss results from the CMS experiment using the Fermilab HEPCloud Facility, which utilized both local Fermilab resources and Amazon Web Services (AWS). The goal was to work with AWS through a matching grant to demonstrate a sustained scale approximately equal to half of the worldwide processing resources available to CMS. We will discuss the planning and technical challenges involved in organizing the most IO intensive CMS workflows on a large-scale set of virtualized resource provisioned by the Fermilab HEPCloud. We will describe the data handling and data management challenges. Also, we will discuss the economic issues and cost and operational efficiency comparison to our dedicated resources. At the end we will consider the changes in the working model of HEP computing in a domain with the availability of large scale resources scheduled at peak times.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

pnnl/mcl-runtime

The Minos Computing Library (MCL) is a task-based programming language and runtime for extremely heterogeneous systems. MCL facilitates writing program for heterogeneous devices and porting applications across different systems and devices. MCL supports asynchronous execution of computing tasks on all available heterogeneous devices, including GPUs, FGPAs, fixed-point accelerators, and AI accelerators. MCL provides a high-level programming interface and automatically and autonomously performs resource management, load balancing, and locality-aware scheduling.

Central, PNNL Developer↗
DOE OSTI2019

Tritium Aging of Regenerated LANA.75

Tritium Aging of Regenerated LANA.75 is a multi-year project. Activities include: tritium aging of a LANA.75 sample regenerated in 2018, regenerating a second tritium-aged LANA.75 sample, and verifying the thermal stability of LANA.75 under regeneration conditions. During FY19, the Task Technical Plan (TTP) addendum providing testing guidance for monitoring tritium aging of the regenerated LANA.75 sample was approved. A desorption isotherm at 150 °C was collected on the regenerated LANA.75 sample. A second desorption isotherm at 150 °C is scheduled to be collected along with additional desorption isotherms at 80 and 120 °C. Fabrication, pressure testing, and leak testing of the high temperature test cell for regeneration of the second tritium-aged LANA.75 sample was completed. Selection of the second LANA.75 sample to be regenerated is anticipated in FY20. Thermal stability testing of LANA.75 material was completed. Hydrogen desorption isotherms at 80, 100 and 120 °C were collected prior to and after a lengthy annealing process (200 hours combined over two weeks) at 750 °C. The isotherms showed similar performances for all temperatures. However, the plateau region for the isotherms after the annealing process were flatter, suggesting that the annealing process was beneficial to the hydride. Sample analyses, including XRD, SEM, ICP and particle size distribution, are ongoing.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Integrating HPC, AI, and Workflows for Scientific Data Analysis: Report from Dagstuhl Seminar 23352

The Dagstuhl Seminar 23352, titled “Integrating HPC, AI, and Workflows for Scientific Data Analysis,” held from August 27 to September 1, 2023, was a significant event focusing on the synergy between High-Performance Computing (HPC), Artificial Intelligence (AI), and scientific workflow technologies. The seminar recognized that modern Big Data analysis in science rests on three pillars: workflow technologies for reproducibility and steering, AI and Machine Learning (ML) for versatile analysis, and HPC for handling large data sets. These elements, while crucial, have traditionally been researched separately, leading to gaps in their integration. The seminar aimed to bridge these gaps, acknowledging the challenges and opportunities at the intersection of these technologies. The event highlighted the complex interplay between HPC, workflows, and ML, noting how ML has increasingly been integrated into scientific workflows, thereby enhancing resource demands and bringing new requirements to HPC architectures, like support for GPUs and iterative computations. The seminar also addressed the challenges in adapting HPC for large-scale ML tasks, including in areas like deep learning, and the need for workflow systems to evolve to leverage ML in data analysis fully. Moreover, the seminar explored how ML could optimize scientific workflow systems and HPC operations, such as through improved scheduling and fault tolerance. A key focus was on identifying prestigious use cases of ML in HPC and understanding their unique, unmet requirements. The stochastic nature of ML and its impact on the reproducibility of data analysis on HPC systems was also a topic of discussion.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

matsim-agents v1.0

matsim-agents is a multi-agent AI framework for atomistic materials simulation and discovery. It orchestrates large language models (LLMs), machine-learned interatomic potentials (MLIPs), and DFT codes into a single agentic loop running on laptops and DOE leadership-class supercomputers. MULTI-AGENT ORCHESTRATION A LangGraph state machine with three nodes: a Planner that converts a natural-language research objective into structured tasks; an Executor that dispatches atomistic tools and loops until the queue is empty; and an Analyst that summarizes results into a human-readable report. State is checkpointed after every step and human-in-the-loop gates can be inserted at any edge. HYPOTHESIS-DRIVEN DISCOVERY CHAT An interactive REPL (matsim-agents chat) that couples LLM dialogue with atomistic simulation. Chemical formulas are automatically detected in conversation turns and trigger a full crystal-phase exploration: structure generation → relaxation → stability scoring → result injection back into the conversation, creating a closed hypothesis-refinement loop. CRYSTAL PHASE ENUMERATION Given a composition, the phase explorer enumerates prototypes by stoichiometry: elemental (fcc/bcc/hcp/sc/diamond), binary 1:1 (rocksalt/CsCl/zincblende/ wurtzite/fluorite/rutile), ternary 1:1:3 (cubic perovskite), ternary 1:2:4 (perovskite + spinel), quaternary 1:1:2:6 (Fm-3m double perovskite). 2-D prototypes (graphene, h-BN, MoS2 2H/1T) and multilayer stacking are also supported via --include-2d and --num-layers. SUPERCELL GENERATION AND SITE DECORATION Auto-tiling to a minimum atom count (--min-atoms), explicit NxNxN tiling (--supercell), symmetry-distinct site decorations (--n-orderings), and isotropic lattice-scale sweeps (--lattice-scales) for volume bracketing. MLFF RELAXATION AND STABILITY SCORING HydraGNN (multi-headed GNN) drives structure relaxation via ASE with FIRE, BFGS, or BFGSLineSearch. Stability output: delta-E/atom ranking across phases and a max-residual-force dynamical-stability proxy. Other MLIPs (MACE, NequIP, Orb) can be plugged in through the same interface. DFT BACKENDS Quantum ESPRESSO pw.x and VASP 6.6 are first-class labellers. Both have validated GPU builds and SLURM/PBS launchers for three DOE platforms: Frontier (AMD MI250X, ROCm), Aurora (Intel PVC, oneAPI), Perlmutter (NVIDIA A100, CUDA). QE produces ~100 binaries (pw.x, ph.x, epw.x, ...). VASP supports scf, relax, vc-relax, and vc-relax-shape run types. ACTIVE-LEARNING LOOP matsim-agents al run CONFIG.yaml drives an iterative HydraGNN-DFT loop: MD generates candidates → ensemble/MC-dropout uncertainty selects the most informative → DFT labels them in parallel inside one allocation → dataset grows → HydraGNN retrains → repeat. DFT backend is a single YAML toggle (dft.backend: vasp | qe). LLM-generated seed structures are supported (no curated POSCAR library needed). Config uses ${VAR}, ${VAR:-default}, ${VAR:?msg} shell-style substitution for cross-user/cross-site portability. LLM BACKENDS Ollama (local, default), vLLM (HPC multi-GPU serving), OpenAI, Anthropic, HuggingFace Transformers+Accelerate. Selected at runtime via flag or env var with no code changes. HPC PORTABILITY Same Python entry points run on Frontier (ROCm 7.2), Aurora (oneAPI), and Perlmutter (CUDA 12). DFT and ML stacks are never co-loaded in the same shell; they couple through the scheduler and filesystem. Advanced multi-node launchers (serve, discovery-chat, single-relaxation, active-learning, QE warm-start) are provided for all three platforms. CODABENCH COMPETITION BUNDLE A self-contained benchmark: 159 atomistic test structures across 11 material classes, 5 tasks (formation energy, forces, ML relaxation, AI-DFT relaxation, phase stability ranking), public/private leaderboard split (30/70), and four ready-to-run baselines: MACE-MP-0, HydraGNN, UMA, AllScAIP.

Lupo Pasini, Massimiliano [Oak Ridge National Labo↗
DOE OSTI2018

Ion Exchange Column Thermal Modeling: Prevention of Process Effluent Boiling - 18577

The Savannah River Site treats and disposes of legacy high-level waste, which resulted from 60 years of nuclear material production, medical research, and other nuclear related work. Complex mixtures of soluble and insoluble solids occupy tanks of which the South Carolina Department of Health and Environmental Control recognizes as the single largest environmental risk state-wide. The risk is reduced through removal, treatment, and solidification of decontaminated salt waste. Presently, SRS decontaminates the soluble salt component of the waste, a solution containing actinides and cesium, via implementation of the Actinide Removal Process and the Modular Caustic Side Solvent- Extraction Unit. Caustic solvent extraction is set to increase in scale by late 2018, with startup of the newly constructed Salt Waste Processing Facility. In conjunction with Salt Waste Processing Facility startup, Savannah River Remediation has been tasked by the Department of Energy to demonstrate modular ion exchange technology. The Tank Closure Cesium Removal project aims to demonstrate the feasibility of modular ion exchange processes for future Department of Energy applications. The Tank Closure Cesium Removal system is scheduled to begin operation in mid-2018. Dissolved salt solution, or influent, will be fed through a filter and ion exchange columns in lead-lag configuration, producing effluent. The effluent is fed to a separate high-level waste tank, for future disposition at Salt-stone Production Facility. Once the demonstration project is complete, the loaded ion exchange columns will be purged, dried, and placed in interim safe storage, awaiting final disposition. The ion exchange media selected for the Tank Closure Cesium Removal system is Honeywell UOP IONSIV{sup C} R-9120. The media is a prime candidate for separation of cesium from radioactive high-level waste. UOP R-9120{sup C} is inorganic and has high capacity, selectivity, and affinity for cesium. During operation, influent is fed to the ion exchange columns, contacting UOP R-9210{sup C} packed within the media vessel. Contact initiates loading of isotopes, and given ample time the solid phase (ion exchange media) and solution achieve thermodynamic equilibrium. UOP R-9120{sup C} is a prime candidate for separation of cesium and dissolved fission products. Although the properties of UOP R-9120{sup C} are favorable for separation, the same properties lead to accumulation of a heat source in the ion exchange bed. Cesium-137 and its daughter product, barium-137 m, generate a clear majority of heat (i.e. ∼ 94%). During operation salt solution flows through each column and transports heat from the ion exchange bed. Once operation is terminated, the effluent contained within the porous bed volume becomes essentially static. In the absence of engineering controls, the heat generated may boil the effluent. Rapid vaporization of aqueous salt solution would lead to bed over pressurization. Over pressurization and an associated release of high-level waste would result in adverse impacts to human health and the environment. This paper focuses on modeling the heat transfer associated with the Tank Closure Cesium Removal system ion exchange column. The model evaluates the maximum heat generation and the associated radionuclide loadings that prevent boiling within of the media vessel, under no flow conditions. A safety significant control, maintaining loadings less than the established loading limit ensures associated impacts to human health and the environment are prevented. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

iDDS: intelligent distributed dispatch and scheduling for workflow orchestration

The intelligent distributed dispatch and scheduling (iDDS) service is a versatile workflow orchestration system designed for large-scale, distributed scientific computing. iDDS extends traditional workload and data management by integrating data-aware execution, conditional logic, and programmable workflows, enabling automation of complex and dynamic processing pipelines. Originally developed for the ATLAS experiment at the large hadron collider, iDDS has evolved into an experiment-agnostic platform that supports both template-driven workflows and a Function-as-a-Task model for Python-based orchestration. This paper presents the architecture and core components of iDDS, highlighting its scalability, modular message-driven design, and integration with systems such as PanDA and Rucio. We demonstrate its versatility through real-world use cases: fine-grained tape resource optimization for ATLAS, orchestration of large Directed Acyclic Graph (DAG) workflows for the Rubin Observatory, distributed hyperparameter optimization for machine learning applications, active learning for physics analyses, and AI-assisted detector design at the electron–ion collider. By unifying workload scheduling, data movement, and adaptive decision-making, iDDS reduces operational overhead and enables reproducible, high-throughput workflows across heterogeneous infrastructures. We conclude with current challenges and future directions, including interactive, cloud-native, and serverless workflow support.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Personnel Certifications for the Design, Installation and Maintenance of Photovoltaic Systems. Final Technical Report

The North American Board of Certified Energy Practitioners (NABCEP) is the most respected, well-established, and widely recognized national certification organization for professionals in the field of renewable energy. NABCEP’s certifications were created to facilitate consumer protection, improve quality assurance, and help employers judge the skills of existing or potential employees. The certifications also allow individuals to differentiate themselves from others in their field and advance their careers while fostering increased recognition of the profession. Since NABCEP’s founding in 2002, all of its certification exams have been administered in paper-and-pencil format twice per year at 30 exam sites around the United States. The biannual test administration and relatively low number of exam sites make it challenging for many qualified renewable energy professionals to take NABCEP’s certification exams. The rigorous nature of the exam, which leads to many candidates failing the exam on their first and sometimes second attempts, combined with the low number of annual test administrations means that anyone who fails will need to wait another six months for the chance to retake the exam. These factors limited the potential growth of NABCEP’s certification programs and the resultant quality assurance within the renewable energy industry. Thanks to a generous grant by the Department of Energy’s Solar Training and Education for Professional (STEP) program, NABCEP recently began updating its Photovoltaic (PV) Installation Professional Exam and developing three new specialty certifications in PV design, PV installation, and PV commissioning & maintenance. One of the primary objectives of this STEP-funded project was to develop the exams using a computer-based testing (CBT) model to increase the number of exam administrations and available exam sites for NABCEP’s certification exams. To successfully complete this project, NABCEP partnered with Castle Worldwide, Inc. With more than 25 years of experience in test development, test administration, analysis of examinations, and has a staff of 70 experienced personnel, NABCEP was confident that Castle’s team was more than qualified to help NABCEP make the transition to CBT examinations. NABCEP and Castle Worldwide jointly developed a project management plan that included key deliverables, scope management, a work breakdown structure, roles and responsibilities, and a project schedule with milestones. The project schedule was carefully monitored to ensure that activities along the critical path were not delayed and, when deemed necessary, the project was crashed to add resources so that activities could occur in parallel. NABCEP and Castle Worldwide staff met weekly to discuss any potential schedule variances and activities which might delay the closing of the project. NABCEP also recruited 46 subject matter experts (SMEs) to work with Castle’s staff on this project. NABCEP staff, SME’s, and Castle staff worked together to develop the Job Task Analyses that form the basis for the certification exams, create new examination forms, conduct passing score studies, and complete a psychometrical analysis of potential exam items. NABCEP’s SMEs utilized Castle’s item development platform during face-to-face meetings, webinars, and remote item authoring to write, review, and approve exam items. Castle also used their software to psychometrically analyze existing exam items to confirm the reliability and validity of the NABCEP’s certification exams. On January 3rd, 2018, NABCEP’s began offering its PV Installation Professional certification exam via CBT. The exam was available at Castle’s nearly 600 exam sites in the United States. A large number of sites will help to ensure better access for solar professionals in rural and remote areas, which drives down the implicit costs of taking the certification exams. Thanks to the STEP grant and the robust partnership between NABCEP and Castle Worldwide, Inc., personnel certifications will be much more accessible and affordable for renewable energy professionals.

14 SOLAR ENERGY↗