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DOE OSTI2024

Solidification of SRPPF Aqueous Recovery Liquid: Process Disruptions

Savannah River National Laboratory has identified a magnesium oxysulfate grout formulation to solidify the liquid effluent from the Savannah River Plutonium Processing Facility (SRPPF) Aqueous Recovery System (ARS). The formulation uses reactive, light burnt MgO, anhydrous MgSO 4 , and dead burnt MgO and provides good mixability, similar density to the original Portland Cement-based mix, and a leachate pH of 9.39, within the assumed Waste Isolation Pilot Plant (WIPP) brine pH range. The formulation will solidify the effluent using a solid first, lost paddle design, within a 55-gallon drum. A series of seven different process disruptions, or irregular processing during the expected ARS liquid effluent solidification process, were completed to assess the severity of the impact of the process disruption and how the formulation behaves under these conditions. Seven process disruptions were investigated including: No Mixing, Partial Mixing, Compacted/Stratified, Sealed, Overfilled, Underfilled, and Undermixed. The impact on solidification, leachate pH, Er (a Pu/Am surrogate) distribution, and density of the resulting grout were established. Overall, four of the process disruptions, No Mixing, Partial Mixing, Compacted/Stratified, and Overfilled, resulted in basic, unabsorbed liquid that would require additional solidification to be acceptable for WIPP. Most solids generated in this testing met the expected WIPP leachate pH requirements. Solids from the Compacted/Stratified process disruption test, however, produced a leachate pH slightly above the assumed range in the WIPP performance assessment. The distribution of Er for most of the tests was relatively consistent. The No Mixing process disruption test, however, resulted in non-homogeneous Er distribution, indicating that radioactive material would not be evenly distributed throughout this solid’s matrix. The Overfilled test is the only mix with a density slightly below the acceptable value. The Sealed and Overmixed tests produced a solid closest to the standard mix with no unabsorbed liquid, expected leachate pH levels, consistent Er concentrations, and expected density, indicating these process disruptions do not have a negative impact on the final grout form. Based on the other process disruptions, it is critical to have premixed dry materials and a well-defined mixing process to produce a solid, homogeneous magnesium oxysulfate cement, with no unabsorbed liquid. These process disruption tests show that the lack of adequate mixing is fundamental to creating an improperly solidified material that may require remediation.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials Using MALAMUTE

Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy, aims to develop and qualify additively-manufactured materials for nuclear applications. The key challenges to these efforts are the microstructural variabilities observed on the AM products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-through-put experimental and modeling techniques to accelerate the qualification efforts. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the AM process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture the microstructural variabilities is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning modeling capabilities to develop a digital twin for AM that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation for AM materials. The melting and subsequent solidification that occurs during the AM process is a complex phenomenon that requires multiscale multiphysics analysis. Idaho National Laboratory’s (INL) Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for AM materials in an efficient, reliable, and cost-effective way. This work package focuses on understanding the role of process variabilities on the various microstructural characteristics of the AM materials. Microstructures unique to AM materials, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. In fiscal year (FY) 24, we significantly advanced upon our work in the last fiscal year, both on physics-based and ML models. The alloy solidification model available in MOOSE has been extended to incorporate the thermodynamic properties and free energy relevant to 316SS. The model demonstrates the Cr segregation that occurs during solidifcation. It is demonstrated that rate of solidification and solute segregation is primarily influence by the cooling rate dictating the level of freezing. This work captures the microstructural variabilities at the subgrain level that are often missing in the part-scale models. With an aim to connect the microstructural evolution model to realistic process conditions, a reduced order model is developed for predicting the thermal conditions around meltpool from high-fidelity process simulations. Furthermore, machine learning approach is used to accelerate the temperature prediction during the AM process. In the following years, MALAMUTE will be used to connect different aspects of the models and quantitatively predict the microstructural evolution. The developed ML-based surrogate model will consider the process conditions as the input to predict the microstructural features in a cost-effective way. The generated microstructures can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work help identify the key microstructural features at the subgrain level that are significant in property/performance prediction of the AM products. This work will provide inputs to the large-scale process variability models to reevaluate and validate assumptions/simplifications made in the part-scale models. Furthermore, through active learning this work will help identify the data need from both modeling and experimental sides for development of a robust digital twin for AM.

36 MATERIALS SCIENCE↗

NASA NTRS1981

Assessment of Advanced Coal Gasification Processes

This report represents a technical assessment of the following advanced coal gasification processes: AVCO High Throughput Gasification (HTG) Process; Bell Single-Stage High Mass Flux (HMF) Process; Cities Service/Rockwell (CS/R) Hydrogasification Process; Exxon Catalytic Coal Gasification (CCG) Process. Each process is evaluated for its potential to produce SNG from a bituminous coal. In addition to identifying the new technology these processes represent, key similarities/differences, strengths/weaknesses, and potential improvements to each process are identified. The AVCO HTG and the Bell HMF gasifiers share similarities with respect to: short residence time (SRT), high throughput rate, slagging and syngas as the initial raw product gas. The CS/R Hydrogasifier is also SRT but is non-slagging and produces a raw gas high in methane content. The Exxon CCG gasifier is a long residence time, catalytic, fluidbed reactor producing all of the raw product methane in the gasifier. The report makes the following assessments: 1) while each process has significant potential as coal gasifiers, the CS/R and Exxon processes are better suited for SNG production; 2) the Exxon process is the closest to a commercial level for near-term SNG production; and 3) the SRT processes require significant development including scale-up and turndown demonstration, char processing and/or utilization demonstration, and reactor control and safety features development.

coal gasification↗

NASA NTRSDate not supplied

Introduction of an Agile Systems Engineering Process to the NASA Armstrong Flight Research Center

This paper will provide background on the National Aeronautics and Space Administration (NASA) Aeronautics Research Mission Directorate (ARMD) ARMD Test Data Portal (ATDP) effort along with the rationale that motivated the ATDP team to consider implementing an agile systems engineering (SE) process. The primary motivation for using this new SE process was based on the desire to mitigate a major risk to project success. Since the agile SE process had never been used before at the NASA Armstrong Flight Research Center (AFRC), utilizing this new SE process required a trailblazing effort to introduce it to NASA AFRC leadership. As a result, the ATDP team chose to adhere to the agile SE process in addition to the trusted classical waterfall SE process on ATDP. Through successful execution of this hybrid SE process during Phase I, along with demonstrating the value and rigor of the agile SE process, NASA AFRC leadership ultimately permitted the ATDP project to solely use the agile SE process for developments beyond Phase I. While it is the opinion of the authors that the classical SE process is more effective for most projects at NASA AFRC, some projects may deem that the agile SE process is more advantageous. Although this paper is focused on introducing the agile SE process to NASA AFRC, lessons learned will be valuable to other organizations as well. Details of how the ATDP team implemented the agile SE process during Phase I will be summarized, along with how the team met all required classical SE milestones. Lessons learned throughout Phase I of this effort will also be highlighted.

Daniel S. Jones↗

DOE OSTI2023

CyTRICS Impact-Based Prioritization Process

Cyber Testing for Resilient Industrial Control Systems™ (CyTRICS™) is the Department of Energy’s (DOE’s) program for cybersecurity vulnerability testing, digital subcomponent enumeration, and forensic assessment. CyTRICS leverages best-in-class test facilities and analytic capabilities at six DOE National Laboratories and strategic partnerships with key stakeholders including technology developers, manufacturers, asset owners and operators, and interagency partners. During the program’s development, CyTRICS established a unique methodology for prioritizing digital components within operational technology (OT) and industrial control systems (ICS) in the Energy Sector Industrial Base (ESIB) for cyber vulnerability testing. The CyTRICS prioritization process leverages multiple characteristics of systems, components, and their contextual deployment to calculate a quantification of individual digital components for CyTRICS testing. The initial version of the CyTRICS prioritization process was premised largely upon the impact which could result to an energy sector industrial control system if the digital component under testing was compromised, either through malicious means, faulty engineering, or other modes. CyTRICS has termed this process the “CyTRICS Impact-based Prioritization Process.” This paper describes the factors identified for use in the Impact-based Prioritization process and identifies the rationale for inclusion. During development, three National Laboratories piloted this prioritization process and generated prioritization scores for seven systems. Following the piloting of the process, laboratory subject matter experts (SME) validated that the numerical scores generated by the prioritization process were consistent with their knowledge of the impact that may occur should any of these systems be disrupted. The following document explains how to perform the prioritization process to generate prioritization scores for energy sector systems. After outlining assumptions required to conduct the process, it describes how to identify and elicit data which can be leveraged to evaluate a system and assign numerical values for each factor. The prioritization process uses different weights on different factors; rationale for each weight is included within the paper. Additionally, the paper includes some recommendations for future enhancements to prioritization, including lessons learned from developing and piloting the process. Finally, a comprehensive appendix includes example documents to be leveraged by those looking to execute the prioritization process.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2016

Remote Retrieval, Disruption and Processing of Legacy Nuclear Waste - 16179

The UK National Nuclear Laboratory (NNL) is currently undertaking, in conjunction with Sellafield Ltd, a series of high profile trials of robotic equipment on a full scale test facility at their Workington Laboratory in England. These trials are designed to underpin and substantiate the processes proposed for the remote retrieval, disruption and processing of legacy Miscellaneous Beta Gamma Waste (MBGW) from a number of different donor plants on the Sellafield site, including some of the older fuel storage ponds and storage silos. These processes will be conducted inside the new Box Encapsulation Plant (BEP) which is due to be commissioned at Sellafield by 2020. These processes will subsequently enable Sellafield Ltd to safely recover legacy Intermediate Level Waste (ILW) from the older storage facilities on site and process it into a safer form which is more suitable for long term storage and then ultimately final disposal in a Geological Disposal Facility. As part of this legacy waste processing within the BEP plant, MBGW waste items will have to be remotely removed from a variety of different types of import skips. This waste will then be transferred, using robots, into the export container, which is a 3 m{sup 3} box liner, where the processed waste will be flood grouted to produce an encapsulated waste package. As part of this processing, any MBGW items consisting of sealed containers will have to be remotely disrupted before being grout encapsulated. This disruption is in order to (a) release any trapped liquors or gases from the containers and (b) ensure full grout infiltration of the container contents, thus minimising the amount of free liquors and voids within the final waste product. The NNL are conducting this technical work to confirm the viability of the proposed concept, processes and equipment. The developed solution consists of a linear layout of processing equipment with a pair of heavy duty, standard industrial, 'commercially off the shelf' (COTS) robots positioned around a Waste Handling Table. These robots have a 500 kg payload capacity and are operated remotely to handle all of the various types of MBGW waste, including the heavy and bulky items. The robots can be operated in manual tele-operation mode as well as pre-programmed automatic mode and have a range of primary and secondary tooling in order to perform the waste processing and disruption tasks as well as undertake general house-keeping and cleaning duties within the cell. The main tools include two types of hydraulic grab for the main waste recovery and handling tasks and a hydraulic shear for the waste disruption activities, but a number of other tools have also been designed and built for ancillary activities, such as a set of general purpose gripper jaws, drilling tool, bolt removal tool, pump tool, spray tool and a number of different housekeeping tools etc. The robot trials were started in November 2014, and have attracted a significant degree of high level stakeholder interest, including the UK Nuclear Decommissioning Authority (NDA), Office for Nuclear Regulation (ONR), Environment Agency (EA) and Radioactive Waste Management Limited (RWML) witnessing a number of the critical demonstration trials. Robots of the type selected are readily available, robust, reliable and easy to use. The trials have been very successful in proving the retrieval, processing and disruption operations, reducing the burden in the legacy waste facilities. Utilising robots to perform these tasks will lower the probability of producing non-compliant waste packages. In addition the robots can increase the consolidation of incoming waste skips into outgoing 3 m{sup 3} box liners, significantly increasing packing efficiencies and reducing the overall number of export containers produced thus lowering the decommissioning and waste storage/disposal costs. The trials have also enabled an assessment of the optimum way to operate the robots, as well as their reliability and maintainability issues. Engineering work associated with recovering failed robots and removing them from a typical Sellafield plant environment and then subsequently replacing them with functioning robots has also been undertaken and further work is now planned. The success of the trials, completed safely to a very challenging timescale, has enabled the use of robots in BEP to be endorsed as a viable option by the key stakeholders. Following a robust decision making process, this was a clear cut decision. The technical development and testing of the robot technology is currently continuing in order to investigate areas such as robot reliability, failure recoveries, human factors, ease of waste identification, optimisation of throughput rates, nuclearisation modifications etc. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2016

THE INTERMEDIATE NEUTRON-CAPTURE PROCESS AND CARBON-ENHANCED METAL-POOR STARS

Carbon-enhanced metal-poor (CEMP) stars in the Galactic Halo display enrichments in heavy elements associated with either the s (slow) or the r (rapid) neutron-capture process (e.g., barium and europium, respectively), and in some cases they display evidence of both. The abundance patterns of these CEMP- s / r stars, which show both Ba and Eu enrichment, are particularly puzzling, since the s and the r processes require neutron densities that are more than ten orders of magnitude apart and, hence, are thought to occur in very different stellar sites with very different physical conditions. We investigate whether the abundance patterns of CEMP- s / r stars can arise from the nucleosynthesis of the intermediate neutron-capture process (the i process), which is characterized by neutron densities between those of the s and the r processes. Using nuclear network calculations, we study neutron capture nucleosynthesis at different constant neutron densities n ranging from 10{sup 7}–10{sup 15} cm{sup -3}. With respect to the classical s process resulting from neutron densities on the lowest side of this range, neutron densities on the highest side result in abundance patterns, which show an increased production of heavy s -process and r -process elements, but similar abundances of the light s -process elements. Such high values of n may occur in the thermal pulses of asymptotic giant branch stars due to proton ingestion episodes. Comparison to the surface abundances of 20 CEMP- s / r stars shows that our modeled i -process abundances successfully reproduce observed abundance patterns, which could not be previously explained by s -process nucleosynthesis. Because the i -process models fit the abundances of CEMP- s / r stars so well, we propose that this class should be renamed as CEMP- i .

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Assessment of advanced solvent-based post-combustion CO 2 capture processes using a bi-objective optimization technique

We report the optimized performance of two advanced CO 2 capture processes is compared to that of a monoethanolamine (MEA) baseline for a gas-powered CO 2 capture retrofit of an existing coal-fired facility. The advanced temperature-swing processes utilize piperazine and mixed-salt solvents. The mixed-salt treatment involves the use of ammonia for CO 2 absorption and potassium carbonate primarily to control ammonia slip. The processes are represented in terms of energy duty requirements within a modular heat integration code developed for CO 2 capture modeling and optimization. The model includes a baseload coal plant, a gas-fired subsystem containing gas turbines and a heat recovery steam generator (HRSG), and a CO 2 capture facility. A formal bi-objective optimization procedure is applied to determine the design (e.g., detailed HRSG components and pressure levels, gas turbine capacity, CO 2 capture capacity) and time-varying operations of the facility to simultaneously maximize net present value (NPV) and minimize total capital requirement (TCR), while meeting a maximum CO 2 emission intensity constraint. For a realistic scenario constructed using historical data, optimization results indicate that both advanced processes outperform MEA in both objectives, and the mixed-salt process in turn outperforms the piperazine process. Specifically, for the scenario considered, the base case mixed-salt process achieves 16% greater NPV and 14% lower TCR than the MEA process, and 10% greater NPV and 5% lower TCR than the piperazine process. A five-case sensitivity study of the mixed-salt process indicates that it is competitive with the piperazine process and consistently outperforms the MEA process.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

A review of separation processes proposed for advanced fuel cycles based on technology readiness level assessments

The results of an international review of separation processes for spent nuclear fuel (SNF) recycling in future closed fuel cycles are reported. This study was made by the Expert Group on Fuel Recycling Chemistry (EGFRC) organised by the Nuclear Energy Agency (NEA) of the Organisation for Economic Co-operation and Development (OECD). A unique feature of this study was that processes were classified according to a hierarchy of separations aimed at different elements within spent fuel (uranium; uranium-plutonium co-recovery; minor actinides; high heat generating radionuclides) and also the Head-end processes, used to prepare the SNF for chemical separation, were included. Separation processes covered both wet (hydrometallurgical) and dry (pyro-chemical) processes. A bespoke methodology suitable for assessing the technological maturities of separation processes for SNF recycling was then designed based on the well-established Technology Readiness Level (TRL) scale but adapted for spent nuclear fuel separations. The innovative feature of this was the use of a matrix approach that enabled information on both the scale of testing and materials used in testing to be combined in the evaluation of the TRL. TRLs for the Head-end, hydrometallurgical and pyrochemical processes that had been reviewed by the study have been evaluated. It is clear that, whilst more R&D is needed in most cases to raise TRLs, suitable options exist to enable the recovery of all actinide elements plus the HHRs by wet or dry processes, even up to the extraordinarily difficult isolation of americium alone. The technology readiness levels (TRL) for most processes fall in the ‘proof of principle’ range with TRLs between 4 and 6, although this is an evaluation at the system level and some gaps in the individual process steps may still exist that strictly would reduce the overall TRL. That is, flowsheets have been proposed for processes and tested with active materials under increasingly relevant conditions, through to initial hot tests with small quantities of SNF. The results of this work were shown to be comparable to a parallel European study that evaluated TRLs for minor actinide partitioning processes.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2019

Novel techniques for constraining neutron-capture rates relevant for r -process heavy-element nucleosynthesis

The rapid-neutron capture process (r process) is identified as the producer of about 50% of elements heavier than iron. This process requires an astrophysical environment with an extremely high neutron flux over a short amount of time ( seconds), creating very neutron-rich nuclei that are subsequently transformed to stable nuclei via beta decay. In 2017, one site for the r process was confiimed: the advanced LIGO and advanced Virgo detectors observed two neutron stars merging, and immediate follow-up measurements of the electromagnetic transients demonstrated an "afterglow" over a broad range of frequencies fully consistent with the expected signal of an r process taking place. Although neutronstar mergers are now known to be r-process element factories, contributions from other sites are still possible, and a comprehensive understanding and description of the r process is still lacking. One key ingredient to large-scale r-process reaction networks is radiative neutron-capture ( n , γ ) rates, for which there exist virtually no data for extremely neutron rich nuclei involved in the r process. Due to the current status of nuclear-reaction theory and our poor understanding of basic nuclear properties such as level densities and average g-decay strengths, theoretically estimated ( n , γ ) rates may vary by orders of magnitude and represent a major source of uncertainty in any nuclear-reaction network calculation of r-process abundances. Here in this review, we discuss new approaches to provide information on neutron-capture cross sections and reaction rates relevant to the r process. In particular, we focus on indirect, experimental techniques to measure radiative neutron-capture rates. While direct measurements are not available at present, but could possibly be realized in the future, the indirect approaches present a first step towards constraining neutron-capture rates of importance to the r process.

(n, γ,) cross sections↗

DOE OSTI2015

A novel digital pulse processing architecture for nuclear instrumentation

The field of nuclear instrumentation covers a wide range of applications, including counting, spectrometry, pulse shape discrimination and multi-channel coincidence. These applications are the topic of many researches, new algorithms and implementations are constantly proposed thanks to advances in digital signal processing. However, these improvements are not yet implemented in instrumentation devices. This is especially true for neutron-gamma discrimination applications which traditionally use charge comparison method while literature proposes other algorithms based on frequency domain or wavelet theory which show better performances. Another example is pileups which are generally rejected while pileup correction algorithms also exist. These processes are traditionally performed offline due to two issues. The first is the Poissonian characteristic of the signal, composed of random arrival pulses which requires to current architectures to work in data flow. The second is the real-time requirement, which implies losing pulses when the pulse rate is too high. Despite the possibility of treating the pulses independently from each other, current architectures paralyze the acquisition of the signal during the processing of a pulse. This loss is called dead-time. These two issues have led current architectures to use dedicated solutions based on re-configurable components like Field Programmable Gate Arrays (FPGAs) to overcome the need of performance necessary to deal with dead-time. However, dedicated hardware algorithm implementations on re-configurable technologies are complex and time-consuming. For all these reasons, a programmable Digital pulse Processing (DPP) architecture in a high level language such as Cor C++ which can reduce dead-time would be worthwhile for nuclear instrumentation. This would reduce prototyping and test duration by reducing the level of hardware expertise to implement new algorithms. However, today's programmable solutions do not meet the need of performance to operate online and not allow scaling with the increase in the number of measurement channel. That is why an innovative DPP architecture is proposed in this paper. This architecture is able to overcome dead-time while being programmable and is flexible with the number of measurement channel. Proposed architecture is based on an innovative execution model for pulse processing applications which can be summarized as follow. The signal is not composed of pulses only, consequently, pulses processing does not have to operate on the entire signal. Therefore, the first step of our proposal is pulse extraction by the use of dedicated components named pulse extractors. The triggering step can be achieved after the analog-to-digital conversion without any signal shaping or filtering stages. Pileup detection and accurate pulse time stamping are done at this stage. Any application downstream this step can work on adaptive variable-sized array of samples simplifying pulse processing methods. Then, once the data flow is broken, it is possible to distribute pulses on Functional Units (FUs) which perform processing. As the date of each pulse is known, they can be processed individually out-of-order to provide the results. To manage the pulses distribution, a scheduler and an interconnection network are used. pulses are distributed on the first FU which is not busy without congesting the interconnection network. For this reason, the process duration does not result anymore in dead-time if there are enough FUs. FUs are designed to be standalone and to comprises at least a programmable general purpose processor (ARM, Microblaze) allowing the implementation of complex algorithms without any modification of the hardware. An acquisition chain is composed of a succession of algorithms which lead to organize our FUs as a software macro-pipeline, A simple approach consists in assigning one algorithm per FU. Consequently, the global latency becomes the worst latency of algorithms execution on FU. Moreover, as algorithms are executed locally - i.e. on a FU - this approach limits shared memory requirement. To handle multichannel, we propose FUs sharing, this approach maximize the chance to find a non-busy FU to process an incoming pulse. This is possible since each channel receive random event independently, the pulse extractors associated to them do not necessarily need to access simultaneously to all Computing resources at the same time to distribute their pulses. The major contribution of this paper is the proposition of an execution model and its associated hardware programmable architecture for digital pulse processing that can handle multiple acquisition channels while maintaining the scalability thanks to the use of shared resources. This execution model and associated architecture are validated by simulation of a cycle accurate architecture SystemC model. Proposed architecture shows promising results in terms of scalability while maintaining zero dead-time. This work also permit the sizing of hardware resources requirement required for a predefined set of applications. Future work will focus on the interconnection network and a scheduling policy that can exploit the variable-length of pulses. Then, the hardware implementation of this architecture will be performed and tested for a representative set of application.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2015

SU-D-BRD-07: Evaluation of the Effectiveness of Statistical Process Control Methods to Detect Systematic Errors For Routine Electron Energy Verification

Purpose: To evaluate the ability of statistical process control methods to detect systematic errors when using a two dimensional (2D) detector array for routine electron beam energy verification. Methods: Electron beam energy constancy was measured using an aluminum wedge and a 2D diode array on four linear accelerators. Process control limits were established. Measurements were recorded in control charts and compared with both calculated process control limits and TG-142 recommended specification limits. The data was tested for normality, process capability and process acceptability. Additional measurements were recorded while systematic errors were intentionally introduced. Systematic errors included shifts in the alignment of the wedge, incorrect orientation of the wedge, and incorrect array calibration. Results: Control limits calculated for each beam were smaller than the recommended specification limits. Process capability and process acceptability ratios were greater than one in all cases. All data was normally distributed. Shifts in the alignment of the wedge were most apparent for low energies. The smallest shift (0.5 mm) was detectable using process control limits in some cases, while the largest shift (2 mm) was detectable using specification limits in only one case. The wedge orientation tested did not affect the measurements as this did not affect the thickness of aluminum over the detectors of interest. Array calibration dependence varied with energy and selected array calibration. 6 MeV was the least sensitive to array calibration selection while 16 MeV was the most sensitive. Conclusion: Statistical process control methods demonstrated that the data distribution was normally distributed, the process was capable of meeting specifications, and that the process was centered within the specification limits. Though not all systematic errors were distinguishable from random errors, process control limits increased the ability to detect systematic errors using routine measurement of electron beam energy constancy.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2025

EV Charging Infrastructure Energization An Overview of Approaches for Simplifying and Accelerating Timelines to Processing EV Charging Load Service Requests

The United States has seen significant growth in electric vehicle (EV) adoption, leading to increased demand for EV charging infrastructure. Over the past decade, EV charging infrastructure site developers, site hosts, and electric distribution utilities have navigated the process to integrate chargers onto the electric grid. Site developers and site hosts have raised the alarm that the integration process for high-powered EV charging projects does not meet the needs of the EV market for timeliness or cost. High-powered charging stations typically require a load service request or an agreement with the local utility to connect to the grid. The process of energizing a new high-powered charging site can be complex and time-consuming, often taking up to 2 years. This timeline is the result of current utility energization processes having been designed for construction projects that take longer to build (i.e., buildings). The specific challenges stem from various factors, including compartmentalization in application processes, the integration of EV charging process approvals with other distributed energy resources (DERs), and the need to ensure grid reliability. The energization process needs to evolve to meet the growing demand for high-powered EV charging. This white paper compiles information gathered through various conversations with key stakeholders, including utilities, utility regulators, EV charging operators, site developers, and authorities having jurisdiction (AHJ) as well as through an extensive literature review. This document identifies the challenges and provides potential solutions to streamline the process of connecting EV charging infrastructure to the power grid in the United States, serving as a starting point for future conversations around these solutions. The solutions noted in this white paper require collaborative efforts among utilities, regulators, and EV charging infrastructure developers to streamline the grid connection process for EV charging infrastructure. They are broadly organized into four areas: 1. Increase data access and transparency: Develop automated load service request tools, integrate hosting capacity and load service request analyses, incorporate EV adoption forecasts, and provide transparency on the processing queue. 2. Improve energization processes and timing: Create fast-track options based on prescreening criteria, provide flexibility or phased approvals in the load service request/interconnection process, build internal knowledge within utilities about EV charging technologies, and provide standardized workforce training. 3. Promote economic efficiency: Right size distribution components to accurately reflect the load requirements of EV charging infrastructure, make proactive investments in grid infrastructure based on EV adoption forecasts and growth projections, and consider energy equity and environmental justice factors such as equitable access to EV charging when planning infrastructure. 4. Improve grid reliability and resilience: Use load management/power control systems (PCS) at EV charging stations, adopt and implement harmonized standards for communication protocols and information models between the EV charging and grid control infrastructure, and address cybersecurity considerations by implementing robust security measures and standards for EV charging infrastructure—with particular emphasis on clarifying the security requirements for the interface to the grid. The objective of the solutions proposed in this white paper is to accelerate the timeline and decrease costs associated with connecting EV charging infrastructure to the grid. Electric utilities, utility regulators, EV charging infrastructure developers, and site hosts will first need to understand which solutions are available in their service territory, and if warranted, which combination of solutions would support their specific needs. Through the successful implementations of solutions at scale detailed here, industry will demonstrate a new and innovative ecosystem where timely deployment and energization of EV charging infrastructure with greater grid resiliency and reliability is a reality.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials using MALAMUTE

The Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy aims to develop and qualify additively manufactured materials for nuclear applications. One key challenge to this is the microstructural variability observed in the additively manufactured products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-throughput experimental and modeling techniques to accelerate qualification. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the additive manufacturing process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture microstructural variability is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning models to develop a digital twin for additive manufacturing that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation. The melting and subsequent solidification that occurs during the additive process is a complex phenomenon that requires multiscale multiphysics analysis. This work package focuses on understanding the role of process variabilities on the unique microstructural characteristics of additively manufactured materials. Microstructural features at the subgrain level, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. Idaho National Laboratory’s Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for additively manufactured materials in an efficient, reliable, and cost-effective way. This work focuses on capturing the microstructural variabilities at the subgrain level that are often missing in the part-scale models. In fiscal year 2025, we significantly advanced upon our work in the last fiscal year, in terms of the predictive capabilities of the physics-based and ML models, by adding the capabilities to capture subgrain-level micro-segregation during solidification using phase-field model and to predict the time-dependent dynamics of the AM process through the MOGPAR model. The alloy solidification model in MOOSE incorporates the thermodynamic properties and free energy relevant to 316 stainless steel. The model demonstrates the Cr and Ni segregation that occurs during solidification, including that the rate of solidification. The microstructural evolution model is connected to the process conditions via the surrogate model developed in this work. This enables predictions of the final microstructure in conjunctions with the manufacturing process. This work supports AMMT's rapid qualification goals by laying the foundation for an efficient and cost-effective model establishing the PSPP correlation for AM. The generated microstructures and predicted micro-segregation can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work helps to identify the key microstructural features at the subgrain level that are significant in property and performance predictions of additively manufactured components. This work will also provide inputs to the large-scale process variability models to reevaluate and validate assumptions and simplifications made in the part-scale models. Furthermore, through active learning this work can help identify the data need from both modeling and experimental sides for development of a robust digital twin for additive manufacturing and accelerate the AMMT's qualification efforts.

36 - MATERIALS SCIENCE↗

DOE OSTI2026

Solar Heat for Industrial Processes: Integration with Chemical Reactors

The integration of solar thermal systems with chemical reactors has been proposed as part of a larger effort to develop and deploy solar heat for industrial processes (SHIP) technologies. A strong motivation for SHIP processes and technologies is the potential for high thermal efficiency coupled with low-cost thermal energy storage (TES) which can enable commercial deployment of such systems. While there are different ways to categorize SHIP technologies, one important such distinction is between directly irradiated systems and indirect off-sun process driven by a SHIP system. While directly irradiated systems can provide high thermal efficiencies and high fluxes, they usually require complex engineering solutions due to the need for redesigning the established processes and unit operations. In most cases, it is also more challenging to couple such a process to a TES system, losing some of the benefits of SHIP. On the other hand, using a SHIP system to drive an industrial process off-sun can allow better integration with existing process chains, easier TES capabilities, and potential for more applications fitting a specific SHIP technology. However, the integration of SHIP systems with the industrial processes is not fully explored in detail, especially in the case of high-temperature processes such as reforming, cracking, cement manufacturing, and iron/steelmaking. Many of these systems require heating fluxes of >50 kW/m^2, supplied via combustion of hydrocarbons in a fire box and benefitting from radiative heat transfer between the flue gases and the reaction zones. As such, using SHIP systems for such processes is more complex than providing the same thermal input in the form of a heat transfer medium (HTM) entering the reactor, kiln, or furnace. Moreover, in case convective heat transfer using SHIP is envisioned, for example using supercritical CO2 as the HTM from a particle receiver, the thermal integration might be more challenging than initially envisioned: lower heat transfer coefficients and limited approach temperature might require large flow rates, causing a mismatch between the process thermal requirements and the thermal capacity of the SHIP system. In addition, even if the heat exchange between SHIP and reactor is effective, there is still a cold leg HTM at the reaction temperature or slightly below it. Chemical plants usually include a set of heat exchangers, heat recovery steam generators, and even power generation units - in a tightly integrated design - to recover the flue gases which are eventually vented. With SHIP systems mostly operating on a closed HTM loop, bottoming the cold leg is crucial. In this talk, we will present different modeling results for a variety of syngas production reactions, using catalytic and chemical looping processes, and discuss some of the challenges and design considerations for off-sun chemical reactors using SHIP systems.

14 SOLAR ENERGY↗

DOE OSTI2022

How Can Construction Process Simulation Modeling Aid the Integration of Lean Principles in the Factory-Built Housing Industry?

New and existing factories that produce and deliver factory-built housing can benefit from construction process simulation modeling to explore the integration of Lean principles in their operations. Construction process simulation modeling provides digital or virtual recreations of the real-world factory environments to visualize, quantify, analyze, and optimize their underlying behavior, including factory productivity, material flow, labor dynamics, bottlenecks, and work scope. One of the key benefits of process simulation modeling is the ability to create and compare "what-if" scenarios, including integrating Lean principles such as reducing waste (for example, transportation, waiting), line balancing, and just-in-time concepts. In general, three process simulation methods are widely used: discrete event simulation (DES), agentbased modeling (ABM), and system dynamics (SD). Myriad process simulation software also is available, but depending on the industry, complexity of the system, and purposes of the simulation, some software might be more appropriate. Similar to how computer-aided design (CAD) software such as AutoCAD and Rhinoceros enable building design of modular or factory-built housing, process simulation modeling software such as jStrobe, ProModel, and AnyLogic can enable factory design of new and existing factories to deliver modular affordable housing at scale, as opposed to traditional site-built construction. Software with DES capabilities can help generate a process model that is a logical representation of resources and activities in a factory. Software with CAD-DES integration can leverage product-process data integration to help spatially visualize a DES model of the factory in the CAD environment. Software with multimethod simulation capabilities, widely used in the manufacturing industry, brings together DES, ABM, and SD in a single platform that allows visualization, quantification, analyses, and optimization at varying data fidelities. Near-real-time data from an existing factory can be directly plugged into multimethod simulation software so that the construction process simulation model is a near-accurate representation of the real-world factory conditions. This report provides insights into the use of simulation as an aid to integrate Lean concepts in factories, including guidelines for selecting the appropriate process simulation modeling method and software. These insights have been developed as part of ongoing process simulation modeling research, development, and demonstration projects at the U.S. Department of Housing and Urban Development, the U.S. Department of Energy, and the National Renewable Energy Laboratory focused on how process simulation models can enable better integration of resilience, energy efficiency, and low-carbon design strategies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2019

Techno-economic optimization of shale gas to dimethyl ether production processes via direct and indirect synthesis routes

The shale gas to dimethyl ether process via direct synthesis route with a syngas H2/CO=1 has a higher dimethyl ether yield than that via indirect synthesis route. However, the direct synthesis route involves various energy and capital-intensive processes, such as the complicated dimethyl ether separation process, and the CO2 recycle and comprssion process. In this study, a techno-economic analysis of the shale gas to dimethyl ether process via direct and indirect synthesis routes is performed using Aspen Process Economic Analyzer based on the rigorous process model developed in Aspen Plus. Effects of different CO2 removal technologies and dimethyl ether synthesis technologies from syngas are evaluated. Both, direct and indirect plants are optimized in Aspen Plus equation-oriented environment using the rigorous process models. The effects of key design parameters (i.e. H2/CO in the syngas) as well as investment parameters (i.e. prices of raw materials, products, and utilities, plant scale) on the process economics as been evaluated. The direct dimethyl ether synthesis route processing a syngas with H2/CO =1 and using the Rectisol process for CO2 capture has the highest overall thermal efficiency on the lower heat value basis is found to be much lower than the current U.S. market diesel price indicating the feasibility of the process.

Mevawala, Chirag↗

DOE OSTI2026

Opportunities and challenges in process modeling and simulation of electrochemical systems

Electrochemical technologies have garnered intense interest in both academic research and industrial applications due to their potential to increase energy efficiency and reduce carbon footprint. However, electrochemical process fundamentals have been absent from the chemical process simulators available to millions of chemical engineers worldwide. To expedite process research and development of electrochemical technologies in the chemical industry, it is imperative that essential electrochemical process fundamentals be incorporated into process simulators to support modeling and simulation of electrochemical processes. Here, this study examines three process fundamentals key to the research and development of electrochemical processes: electrochemical reaction kinetics, electrolyte thermodynamics, and heat and mass transfer. It further illustrates application of these process fundamentals with a case study modeling an electrochemical process for the conversion of acrylonitrile to adiponitrile. The modeling example highlights the roles of applied voltage on reaction rates, and electron flow rates on the performance of electrochemical processes. It suggests that the applied voltage and electron flow rates are two unique concepts that should be included in simulators for electrochemical processes.

09 BIOMASS FUELS↗