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DOE OSTI2021

Experimental Investigation of Polymer Induced Fouling of Heater Tubes in the First-Ever Polymer Flood Pilot on Alaska North Slope

Mineral fouling in heat exchangers has been extensively investigated by researchers in recent times. The oil and gas industry has a long history of fouling issues in production systems as a result of produced fluids treatment. Due to decline in production rates in oilfields new technologies are being developed and field tested in pilots. Polymer flooding is one such technology that involves addition of polymers to injection fluids to enhance oil production. A polymer flood pilot has been set up in the Schrader Bluff viscous oil reservoir at Milne Point field on the Alaska North Slope (ANS). The results from the pilot are encouraging, however a major concern of the operator is the influence of polymer on the production system after breakthrough, especially the fouling in heat exchangers. This study investigates the propensity of polymer fouling on the heater tubes as a function of different variables, with the ultimate goal of determining safe and efficient operating conditions. This work applies a multi-experimental approach to study the severity of polymer-induced fouling in both dynamic and static states of produced fluids as well as studying the stability of polymer solutions at different temperatures. A unique experimental setup was designed and developed in-house to simulate the fouling process on the heating tube. The influence of heating tube skin temperature, tube material, and polymer concentration on fouling tendency was investigated. Each test was run five times with the same tube, and in each run, the freshly prepared synthetic brine and polymer solution was heated from 77°F to 122°F to mimic field-operating conditions. The heating time and fouling amount were recorded for each run. Dynamic Scale Loop (DSL) tests were conducted to study fouling due to polymer at different temperatures (165°F to 350°F) in a dynamic state of fluid flow where the fluids mimic the residence time of fluids in the heat exchanger on the field pilot. Cloud point measurement has also been conducted to find the critical temperature at which the polymer in solution becomes unstable and precipitates out. The morphology and composition of the deposit samples were analyzed by environmental scanning electron microscopy (ESEM) and X-ray diffraction (XRD), respectively. It was found that the presence of polymer in produced fluids would aggravate the fouling issues on both carbon steel and stainless-steel surfaces at all tested skin temperatures. Only higher skin temperatures of 250°F and 350°F could cause polymer-induced fouling issues on the copper tube surface, and the fouling tendency increased with polymer concentration. At the lower skin temperatures of 165°F, no polymer-induced fouling was identified on the copper tube. A critical temperature that is related to the cloud point of the polymer solution was believed to exist, below which polymer-induced fouling would not occur, and only mineral scale was deposited but above which the polymer would aggravate the fouling issue. The cloud point of the tested polymer solution was determined to be between 220°F and 230°F. In the DSL tests it was found that at higher skin temperatures of 250°F and 350°F tube blocking was observed in the DSL tests whereas the tests at 165°F and 200°F did not show any tube blocking in the same time period. These experiments also manifested the influence of cloud point of the solution as deposit rate increased significantly in both carbon steel and stainless-steel tubes when the skin temperature was higher than the solution cloud point. The results of this study have provided guidance to the operator for the field-operations.

Dhaliwal, Anshul↗

DOE OSTI2023

Design optimization of an ethanol heavy-duty engine using design of experiments and bayesian optimization

Diesel-fueled engines still hold a large market share in the medium and heavy-duty transportation sector. However, the increase in fossil fuel prices and the strict emission regulations are leading engine manufacturers to seek cleaner alternatives without a compromise in performance. Alcohol-based fuels, such as ethanol, offer a promising alternative to diesel fuel in meeting regulatory demands. Ethanol provides cleaner combustion and lower levels of soot due to its chemical properties, in particular its lower level of carbon content. In addition, the stoichiometric operating conditions of alcohol fueled engines enable the mitigation of NOx emissions in aftertreatment stage. With the promise of retrofitting diesel engines to run on ethanol to reduce emissions, the thermal efficiency of these engines remains the primary optimization target. In order to find the optimal ethanol-fueled engine design that maximizes the thermal efficiency, a large design space needs to be investigated using engineering tools. In this study, previous research by the authors on optimizing the design of a single-cylinder ethanol-fueled engine was extended to explore the design space for a heavy-duty multi-cylinder engine configuration. A heavy-duty engine setup with multiple operating conditions at different engine speeds and loads were considered. A design optimization analysis was performed to identify the potential designs that maximize the indicated thermal efficiency in an ethanol-fueled compression ignition engine. First, a computational fluid dynamics (CFD) model of the engine was validated using experimental data for four drive cycle points. Using a design of experiments (DoE) approach and a parameterized piston bowl geometry, the model was then exercised to explore the relationship among geometric features of the piston bowl and spray targeting angle and indicated thermal efficiency across all tested operating conditions. After evaluating 165~candidate designs, a piston bowl geometry was identified that yielded an increase between 1.3 to 2.2 percentage points in indicated thermal efficiency for all tested conditions, while satisfying the operational design constraints for peak pressure and maximum pressure rise rate. The increased performance was attributed to enhanced mixing that led to the formation of a more homogeneous distribution of in-cylinder temperature and equivalence ratio, higher combustion temperatures, and shorter combustion duration. Finally, a Bayesian optimization (BOpt) analysis was employed to find the optimal piston bowl geometry with a fixed spray injector angle for one of the operating conditions. Using BOpt, a piston candidate was identified that resulted in a 1.9~percentage point increase in thermal efficiency from the baseline design, yet only required 65\% of the design samples investigated using the DoE approach.

Tekgul, Bulut↗

DOE OSTI2020

The black hole weak gravity conjecture with multiple charges

We study the effect of higher-derivative corrections on asymptotically flat, fourdimensional, dyonic black holes in low-energy models of gravity coupled to N U(1) gauge fields. For large extremal black holes, the leading O (1/Q 2 ) correction to the extremality bound is calculated from the most general low-energy effective action containing operators with up to four derivatives. Motivated by the multi-charge generalization of the Weak Gravity Conjecture, we analyze the necessary kinematic conditions for an asymptotically large extremal black hole to decay into a multi-particle state of extremal black holes. In the large black hole regime, we show that the convex hull condition degenerates to the requirement that a certain quartic form constructed from the Wilson coefficients of the fourderivative effective operators, is everywhere positive. Using on-shell unitarity methods, we show that higher-derivative operators are renormalized at one-loop only if they generate local, on-shell matrix elements that are invariant tensors of the electromagnetic duality group U(N). The one-loop logarithmic running of the four-derivative Wilson coefficients is calculated and shown to imply the positivity of the extremality form at some finite value of Q 2 . This result generalizes an argument recently given by Charles [1], and shows that under the given assumptions the multi-charge Weak Gravity Conjecture is not a Swampland criterion.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

A carbon molecular sieve membrane-based reactive separation process for pre-combustion CO 2 capture

In this study, we discuss a hybrid system combining a membrane reactor (MR) and an adsorptive reactor (AR), with the MR's reject stream serving as the AR's feed. We apply this system for the water gas shift (WGS) reaction for H 2 generation and simultaneous CO 2 capture in the context of the Integrated Gas Combined Cycle (IGCC) process for power generation from coal and biomass. This MR-AR system attains a high conversion exceeding equilibrium, produces a pure H 2 product for power generation, and delivers a high-pressure CO 2 stream ready for sequestration. Specifically, in our study we use carbon molecular sieve membranes (CMSMs) and a commercial sour-shift WGS catalyst. Lab experiments were carried-out to determine the membrane characteristics, and the MR performance under IGCC-relevant conditions, i.e., for temperatures up to 250 °C and pressures up to 25 bar, employing a model coal gasifier syngas. The CMSM and the catalyst have displayed robust and stable performance during a long-term run (~750 h of syngas exposure). We evaluated the MR-AR system in multi-cycle runs and it has demonstrated superior performance to that of a conventional packed-bed reactor, producing a high-purity H 2 product directly useable in a turbine for power generation. We conclude from the study, that the CMSM-based MR-AR system is a good candidate technology for environmentally-benign power generation. We are currently constructing a pilot-scale system for field demonstration of the technology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Electrochemical Reduction Selectivity of Crotonaldehyde on Copper

Lignocellulosic waste is a potential feedstock for the generation of fuels and commodity chemicals, but existing conversion methods are too cost-intensive to be viable long-term solutions. Electrochemical reductions are promising for decentralized biomass valorization due to their modular scaling and capacity to run intermittently and without high temperatures or pressures. Using crotonaldehyde as a multi-functional model compound for the many partially unsaturated oxygenates found in processed biomass, we here demonstrate the production of butanal, butanol, butene, and butane (variously useful as commodity chemicals and major components of liquified petroleum gas) under ambient conditions by reductive bulk electrolysis with a copper mesh working electrode. We identify an optimum potential for reduced organic production under the reaction conditions and compare product distributions from reductions of intermediate species to further propose branching reaction pathways. Though butanal is typically the most abundant product from crotonaldehyde reduction, most of the butene and butane appear to result from a pathway involving initial reduction of the aldehyde group. We discuss evidence that selectivity is driven by interplay between crotonaldehyde reduction, local pH shifts due to the hydrogen evolution reaction, and changes in site reactivity and availability due to electrode fouling. This demonstration of model electrochemical biomass valorization also serves to inform further exploration into reduction of multi-functional molecules and electrochemical biomass processing in general.

biomass↗

DOE OSTI2023

Rapid, Approximate Multi-Axis Vibration Testing [Thesis]

The aerospace industry uses vibration shaker tables to perform component durability testing. In these tests a component, piece of equipment, or entire system is attached to a shaker table where it is subjected to dynamic excitation. The goal is to understand how the article under test will perform in its service environment without having to run it through its entire service life via field testing. In a vibration test, an aerospace system or component is qualified if it is shown to survive a test meant to replicate its lifetime service conditions. The test is designed based on recorded field data. To develop a test, a system is taken through all of its intended environments, e.g., transportation, launch, and reentry. Acceleration data measured from these environments is then brought back to the lab and imported into a shaker table controller. The controller then drives a vibration test intended to mimic the acceleration conditions experienced by the system or specific components of the system in the field. However, it is often difficult to match the measured field response in a lab test. This is largely due to the test’s boundary conditions and excitation methods. In a lab test, a shaker table is the excitation source. The two most common shaker table types, differentiated by their number of independent degrees of freedom, are single-axis and multi-axis shaker tables. Multi-axis shakers have the ability to reproduce service environments more realistically, as real accelerations inevitably produce multiple degrees of freedom of excitation simultaneously. Figure 1 depicts a generic multi-axis testing setup on a three-axis shaker table. Often multi-axis tests use six-degree-of-freedom (6DOF) shaker tables. Yet multi-axis shakers are not yet common in the aerospace industry due in part to their high cost and the difficulty for shaker controllers to handle the added complexity. Single-axis shaker tables are much more common. They are not as expensive to purchase and have a wide range of control software options.

42 ENGINEERING↗

DOE OSTI2021

Benchmark estimate of the effect of anthropogenic emissions on the ocean surface

Abstract Investigations into the role of anthropogenic emissions in the occurrence of extreme weather often use a method that compares simulations of atmospheric climate models run under a factual scenario of historical boundary conditions observed during the period of the event against simulations run under a counterfactual scenario of what those boundary conditions might naturally have been over that same period in the absence of anthropogenic emissions. A particular requirement for this experiment design is an accurate estimation of ocean surface boundary conditions for use by the counterfactual natural simulations. Here we use output from the CMIP5 multi‐climate‐model archive to develop a robust estimate of sea surface temperatures and sea ice conditions for use in counterfactual natural simulations, intended as a benchmark estimate to facilitate comparison across climate models and across studies. This development includes tests to ensure that the final estimate is stable from year‐to‐year and stable against other perturbations to the methodology, as well as consideration of the strengths and weaknesses in comparison to other available attributable warming estimates. While this estimate is tailored specifically for the International CLIVAR C20C+ Detection and Attribution Project, it can be used by related projects as well.

Stone, Dáithí A.↗

DOE OSTI2026

HydraGNN v5.0

HydraGNN v5.0 expands the code base into a more portable, scalable, and flexible framework for scientific graph learning, with particular strength in atomistic machine-learning interatomic potentials and large-scale distributed training. The release adds Fully Sharded Data Parallel (FSDP) support alongside existing DDP and DeepSpeed paths, including FSDP-aware checkpointing and optimizer integration, and introduces a configurable multi-precision training workflow supporting FP32, BF16, and FP64 across GPUs and Intel XPUs. For atomistic modeling, HydraGNN v5.0 strengthens its MLIP capabilities through dynamic graph construction at every forward pass, energy-conserving force prediction via automatic differentiation, and per-atom energy loss formulations, while extending EGNN models to properly handle periodic boundary conditions. The release also broadens model expressiveness through graph-level attribute conditioning, adds new multi-task and model-parallel extensions such as MACE support and encoder/decoder branch optimization, and expands application coverage with integrated examples for datasets including OC25, Nabla2-DFT, QCML, Open Polymers 2026, and OPF. In parallel, HydraGNN v5.0 improves production readiness through performance optimizations for large-scale runs, stratified sampling and linear-regression preprocessing utilities, and tested installation scripts for DOE supercomputers including Frontier, Aurora, Perlmutter, and Andes. Overall, the release advances HydraGNN as a robust software platform for scalable graph neural networks across materials science, chemistry, and scientific machine learning workflows

Lupo Pasini, Massimiliano [Oak Ridge National Labo↗

DOE OSTI2024

TEAMER - Extreme Events Modeling for the MARMOK-OWC Wave Energy Converter

Through the TEAMER program, Sandia National Laboratories (SNL) collaborated with IDOM Incorporated to study their MARMOK-Oscillating Water Column (MARMOK-OWC) wave energy conversion device. The study yielded a quantitative understanding of hydrodynamic pressures on the oscillating water column (OWC) device surfaces, the mooring tensions, and the dynamic performance of the device under extreme ocean wave conditions. This project utilized a comprehensive multi-phase Navier-Stokes flow solver with an overset body-fit mesh to predict fluid velocities and hydrodynamic forces on the MARMOK-OWC device. Computational Fluid Dynamics (CFD) analysis were conducted using OpenFOAM. This data includes the OpenFOAM cases (setup and data) to run the extreme events developed during the project. This project is part of the TEAMER RFTS 4 (request for technical support) program.

16 TIDAL AND WAVE POWER↗

DOE OSTI2026

Supervisory Control and Data Acquisition for Electrochemical Separation Experimentation

The Python-based program is a laboratory automation tool designed to control and monitor electrochemical systems. The tool was developed for capacitive deionization (CDI) experiments, but it can be used for any system that requires controlled voltage or current segments and multi-parameter monitoring. The program integrates hardware components to run user-defined experimental parameters, providing operational control of a programmable power supply, peristaltic pump, and data acquisition devices. Currently, the program is structured with a workflow that includes an initialization (or pre-run) phase, a main loop, and a post-experiment stabilization (or post-run) phase. The initialization phase prepares and stabilizes the cell, ensuring that the electrodes and solution reach a baseline state before the experiment begins. The main loop consists of multiple voltage segments that repeat, controlling the experiment while recording key parameters such as time, voltage, current, pH, and conductivity. Finally, the post-experiment stabilization phase allows the system to stabilize after the experiment, returning the cell and solution to equilibrium conditions before ending the sequence. The program is designed with four variations, each tailored to different experimental needs. All variations include both the initialization and post-experiment stabilization stages, which run for a set amount of time, voltage, current, and flow rate before and after the main experiment block. The main loop runs for a set number of cycles, as defined by the user input, and each cycle is composed of 2 or 4 segments. The 4 program variations are described as follows: Program 1: The main program includes 2 segments. Each segment is defined to have a set duration, flow rate, voltage, and current. This program measures conductivity, flow rate, voltage, and current. Program 2: The main program expands Program 1 to include 4 segments. Each segment has a specified duration, flow rate, voltage, and current. Like Program 1, it measures conductivity, flow rate, voltage, and current. Program 3: The main program consists of 2 segments, each defined by time, flow rate, voltage, and current. In addition to conductivity, flow rate, voltage, and current, Program 3 collects pH and temperature data through a 4-channel data acquisition device. Program 4: This program independently controls two channels of a multi-channel power supply simultaneously. While conductivity can only be measured for one cell at a time, the dual-channel control makes it possible to operate two cells simultaneously under different voltage/current conditions. The main program includes 2 segments.For each program, all measurements are automatically logged and integrated into a single Excel output file. Data are displayed in numerical format and plotted, both in real time, to track system performance. A key feature of the program is its ability to synchronize all outputs so that every measurement shares a single timestamp, ensuring accurate alignment of voltage, current, pH, conductivity, and pH data.By combining hardware control, real-time monitoring, and unified data collection, this program significantly reduces manual workload and minimizes errors, making it a reliable platform for researchers, engineers, and laboratory technicians conducting CDI experiments, among other electrochemical tests.

Valentino, Lauren [Argonne National Laboratory (AN↗

DOE OSTI2021

Predicting Characteristics of the Water Cycle From Scaling Relationships

Over multi-decadal time scales, assuming that changes in subsurface water storage are negligible, the continental precipitative water flux, P, can be divided into two principal components, Q (run-off, including soil infiltration and groundwater recharge) and ET (evapotranspiration). Taking into account a broadly applied Budyko's phenomenology to describe the relationship of ET/P as a function of PET/P, where PET is the potential evapotranspiration, we propose a theoretical framework for predicting characteristics of the water cycle from scaling relationships. In this framework, the ecosystem net primary productivity is expressed in terms of soil formation and vegetation growth, which is mathematically optimized with respect to the water partitioning, generating directly the value ET/P. Here, the mathematical optimization is based on the general ecological principle that dominant ecosystems tend to be those that, for any given conditions, maximize conversion of atmospheric carbon to biomass. It is shown that application of the results of mathematical optimization to water-limited ecosystems is possible by applying the optimization only to a vegetation covered portion of the surface. For energy-limited ecosystems, the optimization can be applied only to a portion of the precipitation equal to PET, assuming that the remaining P simply runs off. We use theoretical and actual values of plant root fractal dimensionalities, d f , to predict ranges of ET/P as a function of PET/P for 0 ≤ PET/P ≤ 1 and compare with annual and multi-decadal means of ET/P. By comparing the developed approach with a large amount of data collected from the literature, we demonstrate its successful applications to both water- and energy-limited systems.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Machine learning surrogates for ion energy–angle distributions in thermal and RF plasma sheaths

Ion energy–angle distributions (IEADs) at material surfaces are a critical input for plasma–material interaction (PMI) studies in fusion devices, yet they are computationally expensive to obtain using particle-in-cell (PIC) simulations. In this work, we develop a machine learning surrogate based on a deep deconvolutional neural network (DDeCNN) trained on large databases generated with the hPIC2 code. The surrogate is capable of reconstructing IEADs from sheath parameters for both thermal and radio-frequency (RF) plasmas, including cases with multiple ion species. Across thousands of test cases, the model achieves high accuracy, with over 97 % of predictions classified as good or average based on standard error metrics (MAE, MSE, L2). Even in the more challenging RF and multi-species regimes, the surrogate reliably captures the multi-peak structure of PIC results. Once trained, the surrogate produces IEADs in milliseconds on a common workstation, yielding speedups of six to seven orders of magnitude compared with running a full PIC simulation. This computational gain enables dense parameter scans and direct coupling of IEAD predictions with PMI and erosion models on whole-device scales in fusion-relevant conditions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

The high level trigger and express data production at STAR

To meet the demands of the Beam Energy Scan phase-II (BES-II) program, the STAR experiment at the Relativistic Heavy Ion Collider (RHIC) developed a dual real-time framework consisting of a High Level Trigger (HLT) and an Express Data Production system (xProduction). The HLT operates online within the Data Acquisition (DAQ) chain on a dedicated multi-core CPU cluster with the option to offload compute-intensive kernels to Xeon Phi coprocessors. It uses parallelized algorithms, such as the Cellular Automaton (CA) Track Finder, to perform rapid tracking, vertexing, and event filtering. This allows it to select events of interest in real time and provide immediate feedback on detector and beam conditions. In contrast, the xProduction workflow runs concurrently and independently of the DAQ loop. It applies near offline-quality calibration and reconstruction within hours of data collection. The xProduction input is the express data stream, whose content can be enriched by HLT trigger/priority selections under DAQ/HLT resource constraints, and it uses the STAR calibration/conditions framework, incorporating online calibration/QA information when available. This enables early preliminary physics analysis, including the reconstruction of rare signals, such as hyperons and hypernuclei. It also provides collaboration-wide access to analysis-ready datasets. Together, the HLT and xProduction systems form a complementary architecture: the HLT performs online event selection while the xProduction chain delivers high-quality results within a short amount of time. This integrated framework has enabled the prompt reconstruction of the $^5_Λ$ He hypernucleus with high statistical significance and the efficient processing of hundreds of millions of heavy-ion collision events. In conclusion, its demonstrated scalability and robustness establish a model for future high-luminosity experiments requiring both online event filtering and rapid access to analysis-quality data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Robustness of Deep Learning Classification to Adversarial Input on GPUs: Asynchronous Parallel Accumulation Is a Source of Vulnerability

The ability of machine learning (ML) classification models to resist small, targeted input perturbations—known as adversarial attacks—is a key measure of their safety and reliability. We show that floating-point non associativity (FPNA) coupled with asynchronous parallel programming on GPUs is sufficient to result in misclassification, without any perturbation to the input. Additionally, we show that this misclassification is particularly significant for inputs close to the decision boundary and that standard adversarial robustness results may be overestimated up to 4.6 when not considering machine-level details. We first study a linear classifier, before focusing on standard Graph Neural Network (GNN) architectures and datasets used in robustness assessments. We develop a novel black-box attack using Bayesian optimization to discover external workloads that can change the instruction scheduling which bias the output of reductions on GPUs and reliably lead to misclassification. Motivated by these results, we present a new learnable permutation (LP) gradient-based approach to learning floating-point operation orderings that lead to misclassifications. The LP approach provides a worst-case estimate in a computationally efficient manner, avoiding the need to run identical experiments tens of thousands of times over a potentially large set of possible GPU states or architectures. Finally, using instrumentation-based testing, we investigate parallel reduction ordering across different GPU architectures under external background workloads, when utilizing multi-GPU virtualization, and when applying power capping. Our results demonstrate that parallel reduction ordering varies significantly across architectures under the first two conditions, substantially increasing the search space required to fully test the effects of this parallel scheduler-based vulnerability. These results and the methods developed here can help to include machine-level considerations into adversarial robustness assessments, which can make a difference in safety and mission critical applications.

Shanmugavelu, Sanjif [Maxeler Technologies, a Groq↗

DOE OSTI2024

T3CO (Transportation Technology Total Cost of Ownership) Open Source [SWR-21-54]

T3CO (Transportation Technology Total Cost of Ownership), is open source software for modeling total cost of ownership for commercial vehicles with advanced powertrains. T3CO is a modeling framework for determining geospatially and temporally optimized total cost of ownership (TCO) for vehicle powertrain technologies. T3CO runs NREL's FASTSim™ software for a representative set of operating conditions to minimize TCO based on vehicle parameters that affect purchase and operating costs (e.g., fuel/electricity consumption, asset depreciation, opportunity costs associated with charging time) while simultaneously ensuring that firm performance constraints (e.g. zero-to-sixty time, gradeability) are satisfied. T3CO will enable the user to control which powertrain parameters are used in optimizing TCO, and these parameters will be modified by a multi-objective optimization (MOO) algorithm to identify a Pareto-optimal solution set. The optimization algorithm will be modular so that users can choose from many different MOO options or insert their own user-defined optimization tool. NREL T3CO Homepage: https://www.nrel.gov/transportation/t3co.html PyPI package: https://pypi.org/project/t3co/

Lustbader, Jason↗

DOE OSTI2025

REBOUND: Reverse Engineering Bidirectional Outflow Under Non-Equilibrium Diffusion

Rare-earth elements (REEs) are essential for electronics, renewable energy, and defense technologies. However, the current supply of REEs relies on mining concentrated in a few countries and energy-intensive separations. DOE’s Basic Energy Sciences (BES) program has launched a grand challenge which aims to ensure a sustainable supply of critical REEs by developing innovative and environmentally friendly separation methods. As an alternative to costly and harmful traditional methods, the Non-Equilibrium Transport Driven Separations (NETS) initiative has created a microfluidic Y-channel co-flow method that applies external fields to exploit magneto- and electrohydrodynamic effects for separating dilute REE ions from complex feedstocks. Computational fluid dynamics (CFD) studies have identified a few operating conditions with promising ion selectivity and separation efficiency. However, challenges remain regarding Y-channel versatility across feedstocks and accurate incorporation of physical phenomena into CFD models. In this work, we develop a multi-fidelity modelling approach which integrates experimental results with CFD simulation to build a surrogate model for the dependence of separation efficiency to variation of design parameters. The surrogate model enables a reinforcement learning (RL) method to adaptively launch CFD and experimental runs, improving model fidelity around optimal Y-channel parameters.

36 MATERIALS SCIENCE↗

DOE OSTI2021

High pressure, high flow rate batch mixing apparatus for high throughput experiments

An automated, high pressure, high flow rate batch mixing apparatus has been designed and constructed for rapid, stable, and repeatable mixing of multiple gases and vapors. The apparatus operates as an intermittent batch mixer with cycles of topping off fresh mixture to maintain pressure in an accumulator tank until consumed in an experimental apparatus. At high duty cycles, the apparatus can also function at steady state. This style of mixing is suitable for experiments such as high repetition rate shock tubes and other devices with intermittent flow demands. It is compact and portable, facilitating use in locations such as synchrotron light sources. The entire apparatus is heated to permit the mixing of vapors from species with low volatilities. The apparatus is fully automated and runs for extended periods with the only intervention being to refresh reagent supplies. Here, the accuracy and repeatability of the apparatus were verified by periodic gas sampling and analysis with gas chromatography. Multi-component mixtures spanning a wide range of complexity, dilution, and volatility of constituents have been prepared. The compositions of the majority of the mixture were found to be stable over several filling cycles, repeatable, and with the proper calibration of set-point conditions, accurate. Challenges were encountered preparing a mixture from multi-component liquids, and potential solutions are discussed.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Irradiation performance of a U-7Mo in Al-Si matrix dispersion full-size fuel plate assembly

The Korea Atomic Energy Research Institute (KAERI) is leading the Ki-Jang Research Reactor (KJRR) project with the intent to develop a new reactor for medical isotope production and other nuclear research purposes. The KJRR core is designed to use high density fuel system where uranium alloyed with 7 wt% molybdenum (U-7Mo) particles are dispersed in a matrix of aluminum alloyed with 5 wt% silicon (Al-5Si) and clad in aluminum alloy 6061 (Al-6061) to form fuel plates. KAERI developed a fabrication facility to construct KJRR fuel assemblies and partnered with the Idaho National Laboratory (INL) to irradiate a full-size fuel assembly, with 21 total fuel plates, in the Advanced Test Reactor (ATR). Irradiation testing and subsequent Post Irradiation Exam (PIE) campaigns were performed successfully over a multi-year project. Monte Carlo neutronic calculations, coupling with a depletion code, were performed based on ATR’s as-run power history which showed that the highest power plate (plate 20) reached 83.1 % end-of-life (EOL) local burnup based on initial 235 U content. Additionally, finite element thermal modeling was performed based as-run power history which showed a beginning-of-life (BOL) peak local heat flux of 184 W/cm 2 . No anomalous fuel performance was observed during the irradiation and target test conditions were achieved. PIE showed favorable performance of the fuel assembly regarding all important phenomena. This paper describes the KJRR fuel assembly irradiation conditions and PIE data to support the conclusion that it performed well, without evidence of unexpected or problematic fuel performance, within an irradiation test designed to bound the KJRR design environment.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗