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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Computational Tools and Workflows for Quantitative Risk Assessment and Decision Support for Geologic Carbon Storage Sites: Progress and Insights from the U.S. DOE’s National Risk Assessment Partnership

The 2005 Intergovernmental Panel on Climate Change (IPCC) Special Report on CCS raised the profile of CO2 capture and storage (CCS) as an important technology for reducing greenhouse gas (GHG) emissions. CCS is now recognized as a key component of most climate change mitigation scenarios. Since publication of that report the international research, development, and deployment (RD&D) community has advanced key technical aspects, clarified regulatory requirements, explored value chain and infrastructure solutions, and developed incentive paradigms to enable and promote large-scale deployment of CCS. These efforts have included research to better characterize geologic storage resources, to improve injection performance and storage efficiency, to assess and manage subsurface environmental risks, and to advance monitoring technologies to assure system conformance. These efforts have helped to build confidence in the viability of geologic carbon storage (GCS), but stakeholder concerns about long-term risks and liability associated with GCS remain a hurdle to broad acceptance and large-scale deployment of CCS. Since 2010, the U.S. DOE’s National Risk Assessment Partnership (NRAP) – a research collaboration between five contributing national laboratories – has worked to establish and demonstrate methods and tools to quantify and manage the subsurface environmental risks associated with GCS, amidst uncertainty. This work supports the Office of Fossil Energy and Carbon Management Carbon Transport and Storage Program’s goal of advancing safe and secure commercial-scale GCS deployment. To address the technical challenge of simulating the physical response of the GCS site to large-scale CO2 injection, NRAP has adopted an approach that relies on coupling computationally efficient reduced-order and/or data-driven proxy models of important system components (i.e., storage reservoir, sealing caprock, leakage pathways, intermediate formations, overlying groundwater aquifers, and the atmosphere) in integrated assessment framework. That integrated model of the physical system is complemented with fit-for purpose functionality to support site characterization and risk-related decisions. The recently released NRAP Phase II toolset includes the Open-Source Integrated Assessment Model (NRAP-Open-IAM) for evaluation of trends in leakage risk and potential impact, tools to support monitoring design optimization (Designs for Risk Evaluation and Management – DREAM v3.0 and Passive Seismic Monitoring Tool - PSMT), and tools for state of stress evaluation (State-of-Stress Analysis Tool - SOSAT) and forecasting induced seismicity risk. The NRAP team has also released a pair of reports describing conceptual workflows to incorporate physics-based, quantitative risk assessment into many of the design, planning, operation, and closure decisions for GCS projects. An online catalogue highlights published studies where these tools and methods are demonstrated. In this presentation, the utility of these products to assess risks and address key stakeholder questions will be highlighted through examples, and related insights about the safety and security of geologic carbon storage in qualified storage sites will be discussed. The prospect of rapid, large-scale deployment of GCS technology to aggressively reduce anthropogenic CO2 emissions requires careful consideration of interference between multiple commercial-scale storage projects within a basin. Going forward, NRAP is expanding and adapting site-scale risk quantification tools and methods to enable assessment of risks and inform management decisions for basin-scale deployment. Increasingly, this work will leverage next-generation approaches for surrogate modelling, fast prediction, and advanced visualization enabled by machine learning and artificial intelligence to promote virtual learning, scenario evaluation, and augment risk-based decision making.

quantitative risk assessment, geologic carbon stor↗
DOE OSTI2013

Petascale Adaptive CFD (ALCF ESP Technical Report)

The aerodynamic simulations of this project involve modeling of active flow control based on synthetic jet actuation that has been shown experimentally to produce large-scale flow changes (e.g., re-attachment of separated flow or virtual aerodynamic shaping of lifting surfaces) from micro-scale input. This micro-scale input consists for instance in 0.1 W piezoelectric disk which resonate in a cavity alternately and pushes/pulls out/in the fluid through a small slit to create small-scale vortical structures that interact with, and thereby dramatically alter, the cross flow. This process has yet to be understood fundamentally. Synthetic jet actuators offer the prospect of not only augmenting lift but also other forces and moments in a dynamic and controlled fashion for a range of operating conditions. They have been demonstrated to restore and maintain flow attachment and reduce vibrations in wind turbine blades during dynamic pitch, thereby reducing unsteady loads on gearboxes that are currently the prime failure point. In virtual-shape flight control surfaces for aerial vehicles (including commercial airplanes), conventional control surfaces (e.g., flaps, rudder, etc.) can be augmented or even replaced with active flow control, thus improving their lift-to-drag ratio and/or control power. The goal of the numerical simulations proposed in this project is to provide a complementary and detailed view of the flow interactions at a much higher Reynolds number, approaching engineering application scales for the first time.

Jansen, Kenneth E.↗
DOE OSTI2013

Testing of Optimized Bubbler Configuration for HLW Melter

The principal objective of this work was to determine the glass production rate increase and ancillary effects of adding more bubbler outlets to the current WTP HLW melter baseline. This was accomplished through testing on the HLW Pilot Melter (DM1200) at VSL. The DM1200 unit was selected for these tests since it was used previously with several HLW waste streams including the four tank wastes proposed for initial processing at Hanford. This melter system was also used for the development and optimization of the present baseline WTP HLW bubbler configuration for the WTP HLW melter, as well as for MACT testing for both HLW and LAW. Specific objectives of these tests were to: Conduct DM1200 melter testing with the baseline WTP bubbling configuration and as augmented with additional bubblers. Conduct DM1200 melter testing to differentiate the effects of total bubbler air flow and bubbler distribution on glass production rate and cold cap formation. Collect melter operating data including processing rate, temperatures at a variety of locations within the melter plenum space, melt pool temperature, glass melt density, and melter pressure with the baseline WTP bubbling configuration and as augmented with additional bubblers. Collect melter exhaust samples to compare particulate carryover for different bubbler configurations. Analyze all collected data to determine the effects of adding more bubblers to the WTP HLW melter to inform decisions regarding future lid re-designs. The work used a high aluminum HLW stream composition defined by ORP, for which an appropriate simulant and high waste loading glass formulation were developed and have been previously processed on the DM1200.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

Wetland Water Cooling Partnership: The Use of Constructed Wetlands to Enhance Thermoelectric Power Plant Cooling and Mitigate the Demand of Surface Water Use

Through the Phase I study segment of contract #DE-NT0006644 with the U.S. Department of Energy’s National Energy Technology Laboratory, Applied Ecological Services, Inc. and Sterling Energy Services, LLC (the AES/SES Team) explored the use of constructed wetlands to help address stresses on surface water and groundwater resources from thermoelectric power plant cooling and makeup water requirements. The project objectives were crafted to explore and develop implementable water conservation and cooling strategies using constructed wetlands (not existing, naturally occurring wetlands), with the goal of determining if this strategy has the potential to reduce surface water and groundwater withdrawals of thermoelectric power plants throughout the country. Our team’s exploratory work has documented what appears to be a significant and practical potential for augmenting power plant cooling water resources for makeup supply at many, but not all, thermoelectric power plant sites. The intent is to help alleviate stress on existing surface water and groundwater resources through harvesting, storing, polishing and beneficially re-using critical water resources. Through literature review, development of conceptual created wetland plans, and STELLA-based modeling, the AES/SES team has developed heat and water balances for conventional thermoelectric power plants to evaluate wetland size requirements, water use, and comparative cooling technology costs. The ecological literature on organism tolerances to heated waters was used to understand the range of ecological outcomes achievable in created wetlands. This study suggests that wetlands and water harvesting can provide a practical and cost-effective strategy to augment cooling waters for thermoelectric power plants in many geographic settings of the United States, particularly east of the 100th meridian, and in coastal and riverine locations. The study concluded that constructed wetlands can have significant positive ancillary socio-economic, ecosystem, and water treatment/polishing benefits when used to complement water resources at thermoelectric power plants. Through the Phase II pilot study segment of the contract, the project team partnered with Progress Energy Florida (now Duke Energy Florida) to quantify the wetland water cooling benefits at their Hines Energy Complex in Bartow, Florida. The project was designed to test the wetland’s ability to cool and cleanse power plant cooling pond water while providing wildlife habitat and water harvesting benefits. Data collected during the monitoring period was used to calibrate a STELLA model developed for the site. It was also used to inform management recommendations for the demonstration site, and to provide guidance on the use of cooling wetlands for other power plants around the country. As a part of the pilot study, Duke Energy is scaling up the demonstration project to a larger, commercial scale wetland instrumented with monitoring equipment. Construction is expected to be finalized in early 2014.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2014

Enhanced Severe Transient Analysis for Prevention Technical Program Plan

This document outlines the development of a high fidelity, best estimate nuclear power plant severe transient simulation capability that will complement or enhance the integral system codes historically used for licensing and analysis of severe accidents. As with other tools in the Risk Informed Safety Margin Characterization (RISMC) Toolkit, the ultimate user of Enhanced Severe Transient Analysis and Prevention (ESTAP) capability is the plant decision-maker; the deliverable to that customer is a modern, simulation-based safety analysis capability, applicable to a much broader class of safety issues than is traditional Light Water Reactor (LWR) licensing analysis. Currently, the RISMC pathway’s major emphasis is placed on developing RELAP-7, a next-generation safety analysis code, and on showing how to use RELAP-7 to analyze margin from a modern point of view: that is, by characterizing margin in terms of the probabilistic spectra of the “loads” applied to systems, structures, and components (SSCs), and the “capacity” of those SSCs to resist those loads without failing. The first objective of the ESTAP task, and the focus of one task of this effort, is to augment RELAP-7 analyses with user-selected multi-dimensional, multi-phase models of specific plant components to simulate complex phenomena that may lead to, or exacerbate, severe transients and core damage. Such phenomena include: coolant crossflow between PWR assemblies during a severe reactivity transient, stratified single or two-phase coolant flow in primary coolant piping, inhomogeneous mixing of emergency coolant water or boric acid with hot primary coolant, and water hammer. These are well-documented phenomena associated with plant transients but that are generally not captured in system codes. They are, however, generally limited to specific components, structures, and operating conditions. The second ESTAP task is to similarly augment a severe (post-core damage) accident integral analyses code with high fidelity simulations that would allow investigation of multi-dimensional, multi-phase containment phenomena that are only treated approximately in established codes.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

Experimental Design for Hanford Low-Activity Waste Glasses with High Waste Loading

This report discusses the development of an experimental design for the initial phase of the Hanford low-activity waste (LAW) enhanced glass study. This report is based on a manuscript written for an applied statistics journal. Appendices A, B, and E include additional information relevant to the LAW enhanced glass experimental design that is not included in the journal manuscript. The glass composition experimental region is defined by single-component constraints (SCCs), linear multiple-component constraints (MCCs), and a nonlinear MCC involving 15 LAW glass components. Traditional methods and software for designing constrained mixture experiments with SCCs and linear MCCs are not directly applicable because of the nonlinear MCC. A modification of existing methodology to account for the nonlinear MCC was developed and is described in this report. One of the glass components, SO 3 , has a solubility limit in glass that depends on the composition of the balance of the glass. A goal was to design the experiment so that SO 3 would not exceed its predicted solubility limit for any of the experimental glasses. The SO 3 solubility limit had previously been modeled by a partial quadratic mixture model expressed in the relative proportions of the 14 other components. The partial quadratic mixture model was used to construct a nonlinear MCC in terms of all 15 components. In addition, there were SCCs and linear MCCs. This report describes how a layered design was generated to (i) account for the SCCs, linear MCCs, and nonlinear MCC and (ii) meet the goals of the study. A layered design consists of points on an outer layer, and inner layer, and a center point. There were 18 outer-layer glasses chosen using optimal experimental design software to augment 147 existing glass compositions that were within the LAW glass composition experimental region. Then 13 inner-layer glasses were chosen with the software to augment the existing and outer-layer glasses. The experimental design was completed by a center-point glass, a Vitreous State Laboratory glass, and replicates of the center point and Vitreous State Laboratory glasses.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Bottom Fixed Platform Dynamics Models Assessing Surface Ice Interactions for Transitional Depth Structures in the Great Lakes: FAST8 – IceDyn

To create long-term solutions for offshore wind turbines in a variety of environmental conditions, CAE tools are needed to model the design-driving loads that interact with an offshore wind turbine system during operation. This report describes our efforts in augmenting existing CAE tools used for offshore wind turbine analysis with a new module that can provide simulation capabilities for ice loading on the system. This augmentation was accomplished by creating an ice-loading module coupled to FAST8, the CAE tool maintained by the NREL for simulating land-based and offshore wind turbine dynamics. The new module includes both static and dynamic ice loading that can be applied during a dynamic simulation of the response of an offshore wind turbine. The ice forces can be prescribed, or influenced by the structure’s compliant response, or by the dynamics of both the structure and the ice floe. The new module covers ice failure modes of spalling, buckling, crushing, splitting, and bending. The supporting structure of wind turbines can be modeled as a vertical or sloping form at the waterline. The Inward Battered Guide Structure (IBGS) foundation designed by Keystone Engineering for the Great Lakes was used to study the ice models coupled to FAST8. The IBGS foundation ice loading simulations in FAST8 were compared to the baseline simulation case without ice loading. The ice conditions reflecting those from Lake Huron at Port Huron and Lake Michigan at North Manitou were studied under near rated wind speed of 12 m/s for the NREL 5-MW reference turbine. Simulations were performed on ice loading models 1 through 4 and ice model 6 with their respective sub-models. The purpose of ice model 5 is to investigate ice loading on sloping structures such as ice-cones on a monopile and is not suitable for multi-membered jacketed structures like the IBGS foundation. The key response parameters from the simulations, shear forces and moments from the tower base and IBGS foundation base, were compared. Ice models 1 and 6 do not significantly affect the tower fore-aft shear and moment. However, ice model 2 (dynamic analyses), model 3 (random ice loading), and model 4 (multiple ice failure zone loading) show increased effect on the tower fore-aft shear and moment with significant effect from ice model 3.1. In general ice loading creates large reaction forces and moments at the base of the IBGS foundation; the largest occurred in model 1.1 (steady creep ice indentation loading) followed by model 3.1 (random creep ice indentation loading). In general the power production from the ice loading cases had little deviation from the baseline case without ice loading. For ultimate limit state (ULS), ice model 1.1 ice and 3.1 appear to be the ice most critical models to consider at an early stage of design. Ice model 4 is an important tool for assessing structural fatigue.

17 WIND ENERGY↗
DOE OSTI2016

Estimating the National Carbon Abatement Potential of City Policies: A Data-Driven Approach

Cities are increasingly taking actions such as building code enforcement, urban planning, and public transit expansion to reduce emissions of carbon dioxide in their communities and municipal operations. However, many cities lack the quantitative information needed to estimate policy impacts and prioritize city actions in terms of carbon abatement potential and cost effectiveness. This report fills this research gap by providing methodologies to assess the carbon abatement potential of a variety of city actions. The methodologies are applied to an energy use data set of 23,458 cities compiled for the U.S. Department of Energy City Energy Profile tool. The analysis develops a national estimate of the carbon abatement potential of realizable city actions in six specific policy areas encompassing the most commonly implemented city actions. The results of this analysis suggest that, in aggregate, cities could reduce nationwide carbon emissions by about 210 million metric tons of carbon dioxide (MMT CO2) per year in a 'moderate abatement scenario' by 2035 and 480 MMT CO2/year in a 'high abatement scenario' by 2035 through these common actions typically within a city's control in the six policy areas. The aggregate carbon abatement potential of these specific areas equates to a reduction of 3%-7% relative to 2013 U.S. emissions. At the city level, the results suggest the average city could reduce carbon emissions by 7% (moderate) to 19% (high) relative to current city-level emissions. In the context of U.S. climate commitments under the 21st session of the Conference of the Parties (COP21), the estimated national abatement potential of the city actions analyzed in this report equates to about 15%-35% of the remaining carbon abatement necessary to achieve the U.S. COP21 target. Additional city actions outside the scope of this report, such as community choice aggregation (city-level purchasing of renewable energy), zero energy districts, and multi-level governance strategies, could significantly augment the carbon abatement contributions of city actions toward national climate targets. The results suggest that cities may play a pivotal role in progress toward national climate targets. In addition to providing carbon and emissions estimates, this report estimates the national net economic impacts of policies for which cost and benefit data are available. Impact metrics include employment, worker earnings, and gross domestic product (GDP). For the policy areas studied, the economic analysis demonstrates that city carbon abatement may be achieved with only minimal and generally slightly positive economic impacts. Employment impacts range from 0.04% to 0.13% of U.S, employment during implementation and zero to 0.1% thereafter. GDP estimates show net impacts of 0.02% to 0.07% of GDP during implementation and impacts from -0.02% to zero thereafter. This report quantitatively demonstrates the material impact of a limited set of local policy areas on national carbon abatement potential. The magnitude of estimated carbon reductions from city policies, 3%-7% of national emissions by 2035, suggests an important role for city-led actions in reaching U.S. climate goals. Multi-level governance at the city, state, and national levels could augment the carbon abatement potential of city actions and make cities a key component of long-term U.S. climate strategies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

PIMS: Memristor-Based Processing-in-Memory-and-Storage.

Continued progress in computing has augmented the quest for higher performance with a new quest for higher energy efficiency. This has led to the re-emergence of Processing-In-Memory (PIM) ar- chitectures that offer higher density and performance with some boost in energy efficiency. Past PIM work either integrated a standard CPU with a conventional DRAM to improve the CPU- memory link, or used a bit-level processor with Single Instruction Multiple Data (SIMD) control, but neither matched the energy consumption of the memory to the computation. We originally proposed to develop a new architecture derived from PIM that more effectively addressed energy efficiency for high performance scientific, data analytics, and neuromorphic applications. We also originally planned to implement a von Neumann architecture with arithmetic/logic units (ALUs) that matched the power consumption of an advanced storage array to maximize energy efficiency. Implementing this architecture in storage was our original idea, since by augmenting storage (in- stead of memory), the system could address both in-memory computation and applications that accessed larger data sets directly from storage, hence Processing-in-Memory-and-Storage (PIMS). However, as our research matured, we discovered several things that changed our original direc- tion, the most important being that a PIM that implements a standard von Neumann-type archi- tecture results in significant energy efficiency improvement, but only about a O(10) performance improvement. In addition to this, the emergence of new memory technologies moved us to propos- ing a non-von Neumann architecture, called Superstrider, implemented not in storage, but in a new DRAM technology called High Bandwidth Memory (HBM). HBM is a stacked DRAM tech- nology that includes a logic layer where an architecture such as Superstrider could potentially be implemented.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Technology Applicable to Remote Hazardous Constituents and Waste Stream Grouping

Originally, Rocky Flats transuranic (TRU) waste was managed from waste streams that were composed of similar chemical constituents and were generated from identical processes. Early waste processing at Idaho National Laboratory (INL) maintained the Rocky Flats record until INL began generating their own item description codes (IDCs). Ultimately TRU waste management at INL was driven to combine wastes from all IDCs that were assigned by Rocky Flats to a summary category group regardless of whether the chemical constituents in the waste were known or not. At this point, the Waste Isolation Pilot Plant (WIPP) certified program requirements to process the TRU waste according to waste streams was nearly eliminated. In April 2018, operations in the Sludge Repackage Project (SRP) area were halted by a radiological materials release accident in which drums containing waste experienced a loss of containment after over-pressurization occurred. Four SRP drums, repackaged in the Accelerated Retrieval Project (ARP) V area, had an adverse, delayed chemical reaction event occur when prohibited materials from one container were mixed with chemically-incompatible materials from other containers in the repackaging process. This incident was the direct result of processing unknowns under a protocol where the components of the waste were added to incompatible materials resulting in a run-away reaction. After a Difficult Waste Team (DWT) and Los Alamos National Laboratory – Carlsbad Operations (LANL-CO) subject matter expert (SME) visit in September 2019, it was determined by the team that all of the work observed on the September visit was performed upstream of the certified program and that the Visual Examination (VE) and other methods typically used by the certified program were being used for Acceptable Knowledge (AK) augmentation. This augmentation was needed in order to determine what the constituents in the waste might be, and is based primarily on collections of VE results. The waste at INL that is being processed in what is considered to be the Priority 2 (yellow) and Priority 3 (red) lists either needs AK to be completed before certified characterization can occur or is waiting to be processed because the waste has been mixed with unknown wastes resulting in a lack of information that could have impact of the ability to certify and ship the waste to the WIPP.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Modeling Systematic Discrepancies in Nuclear-Data Measurements with Machine Learning

Nuclear data and their associated co-variances are constantly being reevaluated as techniques improve and as new experimental data, as well as nuclearmodel developments, emerge. A standard technique used to evaluate mean values in nuclear data, and their associated covariances, is the generalized linear least squares (GLLS) method. Aligning with recent efforts to incorporate measurement features into nuclear data evaluation, we augment GLLS by including a linear term which attempts to predict potential systematic discrepancies in experimental data as related to the measurement features. Due to the general nature of this augmentation, we are able to apply this evaluation to three key observables: neutroninduced fission cross sections, the average prompt neutron multiplicity, and the prompt-fission neutron spectrum of ²³⁹Pu.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

GANpiler

Focal Area(s): Rather than augment or replace physical models with machine learning, we instead preserve the existing models and augment the underlying code with faster surrogate models created by generative adversarial networks (GAN). To leverage the performance optimization, we also propose a runtime system and user interfaces that allow prediction and tracking of accumulated error as well as dynamic, per-process decision making as to which model (if any) to use for each iteration. Science Challenge: This proposal sits at the nexus of two hard problems. First, climate models based on machine learning will be, by their nature, difficult to trust once their predictions begin diverging from the consensus. Second, compilers and hardware have been making only incremental performance gains for decades. GPGPUs have provided a welcome performance boost for codes that can take advantage of them, but there is no similar technology on the horizon to provide the next performance leap.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Surface Roughness Effects on Heat Transfer in Additively Manufactured sCO 2 Cycle Heat Exchangers

An experimental study was performed to consider the impact of surface roughness on heat transfer and pressure drop to supercritical carbon dioxide (sCO 2 ) in additively manufactured channels. All tests were performed in the Heat Exchange and Experimental Testing (HEET) rig at the U.S. Department of Energy’s (DOE) National Energy Technology Laboratory (NETL) in Morgantown, West Virginia. Four test articles were considered. The first was a hydrodynamically smooth, drawn tube. The other three were additively manufactured channels with square and rectangular cross sections, which were characterized by sand-grain roughness to hydraulic diameter ratios spanning 0.0029 to 0.0073. Friction factors were determined by measuring the tube mass flow rate and pressure drop. The tube side heat transfer coefficient was measured using the Wilson plot technique. It was found that the friction factor results were 8% greater than the Colebrook correlation. Greater deviation was observed between the heat transfer results and the correlations. The Gnielinski correlation overpredicted the experimental points by nominally 30% and the Norris correlation overpredicting the experimental points by nominally 25%. A thermal performance factor was formed from the friction factor augmentation and Nusselt number augmentation results. These results indicated a 10% improvement in heat duty for a heat exchanger constructed utilizing a tube with ϵ/D_h =0.0073 relative to a heat exchanger utilizing a smooth, conventional tube.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Trajectory Optimization via Unsupervised Probabilistic Learning On Manifolds

This report investigates the use of unsupervised probabilistic learning techniques for the analysis of hypersonic trajectories. The algorithm first extracts the intrinsic structure in the data via a diffusion map approach. Using the diffusion coordinates on the graph of training samples, the probabilistic framework augments the original data with samples that are statistically consistent with the original set. The augmented samples are then used to construct conditional statistics that are ultimately assembled in a path-planing algorithm. In this framework the controls are determined stage by stage during the flight to adapt to changing mission objectives in real-time. A 3DOF model was employed to generate optimal hypersonic trajectories that comprise the training datasets. The diffusion map algorithm identfied that data resides on manifolds of much lower dimensionality compared to the high-dimensional state space that describes each trajectory. In addition to the path-planing worflow we also propose an algorithm that utilizes the diffusion map coordinates along the manifold to label and possibly remove outlier samples from the training data. This algorithm can be used to both identify edge cases for further analysis as well as to remove them from the training set to create a more robust set of samples to be used for the path-planing process.

42 ENGINEERING↗
DOE OSTI2022

Low-Cost Preform and Molding Processes

The entry cost for prototyping a composite component for manufacture using automated, high rate processes is prohibitively expensive in many cases, especially for small business, where tooling costs may be several $100k. Discussions with industry also indicate that many small companies, tier 1 and 2 suppliers, have an interest to mold composite parts but do not want to deal with the capital cost, material handling issues, and labor associated with dry fiber preforming operations. While the molders may locate near the end user for logistics reasons, it may be more cost effective for the performer to remain regional and invest in capital equipment to support preform automation, thus keeping costs to a minimum. This project was designed to explore and demonstrate several options to meet these industry needs. Dry fiber preforming approaches were evaluated which allow for low pressure resin infusion, single sided tooling options such a vacuum assisted resin transfer molding (VARTM) or low pressure resin transfer molding (RTM-light). Unlike sheet molding compound, SMC compression molding where typical molding pressures of 1000 psi are required to push material into the desired location; positioning of a dry fiber preform into the desired location on the tool allows for low molding pressures of 10-50 psi. Lower molding pressures allow for use of low cost, additive fabrication of polymeric tooling. Polymeric tooling is suitable for rapid part prototyping and limited production. Dry fiber preforming approaches evaluated included use of commercial chopped strand mat, robotic chopper gun deposition, and continuous fiber preform augmentation using tailored fiber placement (TFP). Use of chopped strand mat does not require a robotic deposition method, however a cutting table is generally required and there is typically 20-30% scrap generation. While various fiber areal weights are available, the preform is not readily optimized for minimal fiber use or weight savings. In contrast, a robotic chopper gun approach allows for localized deposition where fiber is required to meet structural requirements. The robotic method is highly automated and minimizes fiber scrap, however the capital cost of the equipment and engineering labor for programming can result in higher preform cost compared to chopped strand mat in certain cases depending on preform complexity. Dry fiber preforming using the robotic chopper gun method allows for creation of three dimensional forms. This approach may be ideal for molding in-house, or if the preforms stack together densely to allow for efficient shipping. Applications evaluated for this program considered trade-off between fabrication of a fully 3D preform versus production of a flat preform which is designed to readily drape into the final desired shape. Such a preform design greatly simplifies robotic programming and requires no specialized tooling. The flat preforms are easily stacked and shipped to the final molding location. Flat preforms are much easier to augment with TFP continuous fiber to provide local reinforcement. The demonstration and evaluation of these preforming and tooling methods were completed on three component applications. The first application was a battery box cover for an electric vehicle which was highly three dimensional. The second demonstrator article was comprised of complex contours and was used to demonstrate the use of TFP and RTM-light molding process. The third demonstration article was the roof of an operator’s cab for large construction equipment. The roof is relatively flat however it is comprised of complex changes in thickness which clearly demonstrate the advantage of robotic chopper gun approach as compared to using numerous preform layers of chopped strand mat. The cost trades for the various preforming methods are summarized to help guide the reader as to preforming method considerations. Finally, these demonstrations all used glass fiber roving. A fourth, exploratory task was added to evaluate the ability to make preforms using Zoltek’s carbon fiber split tow roving. We were able to adapt the chopper gun to make flat preforms for laminate testing, but further development effort would be required to make suitable preforms.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Argonne Leadership Computing Facility: 2021 Operational Assessment Report

This Operational Assessment Report describes how the Argonne Leadership Computing Facility (ALCF) met or exceeded every one of its goals for calendar year (CY) 2021 as an advanced scientific computing center. In CY 2021, the ALCF operated its production resource, Theta, an Intel-based Cray XC40 system (11.7-petaflops) augmented with 24 NVIDIA DGX A100-based nodes (3.9-petaflops) that supports diverse workloads, integrating data analytics with artificial intelligence (AI) training and learning in a single platform. In 2021, we began deploying Polaris, our newest 40- petaflops system, and augmented this powerful testbed system with an additional 28 nodes to support the integration of real-time experiments and HPC resources. We also deployed our two largest storage systems yet, named Grand and Eagle, that will bring new services to our users and will power data-driven research for years to come. Last year, Theta delivered a total of 20.8 million node-hours to 16 Innovative and Novel Computational Impact on Theory and Experiment (INCITE) projects and 7.2 million node-hours to ASCR Leadership Computing Challenge (ALCC) projects (32 awarded during the 2020–2021 ALCC year and 17 awarded during the 2021–2022 ALCC year), as well as substantial support to Director’s Discretionary (DD) projects (5.5 million node-hours). As Table ES.1 shows, Theta performed exceptionally well in terms of overall availability (95.1 percent), scheduled availability (99.4 percent), and utilization (98.1 percent; Table 2.1). As of the submission date of this document, ALCF’s user community has published 249 papers in high-quality, peer-reviewed journals and technical proceedings. At the 2021 International Conference for High Performance Computing, Networking, Storage and Analysis (SC’21), Argonne researchers won two HPCwire Readers’ Choice Awards and were part of a Gordon Bell Prize finalist team recognized for developing an AI-enabled, multi-resolution simulation framework for studying complex biomolecular machines. Their framework was used to observe the SARS-CoV-2 replication-transcription machinery in action, by directly integrating experimental data. ALCF also provided a comprehensive program of high-performance computing (HPC) support services to help our community make productive use of the facility’s diverse and growing collection of resources. We are now entering the exascale era, with exascale machines being planned for national laboratories across the country, including Aurora at Argonne National Laboratory (Argonne) in 2023. ALCF researchers have been leading and guiding numerous strategic activities that will push the boundaries of what’s possible in computational science and engineering and allow us to deliver science on day one.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Interface Problem Formulation Improvements with Application to Nuclear Fuel Performance Analysis

The U.S. Department of Energy’s Nuclear Energy Advanced Modeling and Simulation Program aims to develop predictive capabilities by applying computational methods to the analysis and design of advanced reactor and fuel cycle systems. This program has been providing engineering scale support for the development of BISON, a high-fidelity and high-resolution fuel performance tool. This report documents new developments and robustness improvements in mechanical and thermal (gap heat transfer) contact formulations. The improvements range from the migration of industrial level (“assessment”) nuclear fuel model setups to the usage of mortar formulations, the addition of frictional contact to one-dimensional layered representations of fuel and cladding components, and the addition of the Petrov-Galerkin approach to dual mortar, which improves performance on curved, relatively coarse meshes. In addition, the Lagrange-multiplier enforcement of mechanical mortar contact constraints has been extended to two additional types of enforcement: penalty and augmented Lagrange-Uzawa. We show that the latter approach yields the same interface results as dual mortar in the Multiphysics Object-Oriented Simulation Environment, with the advantage of not worsening the condition number of the system matrix—thereby enabling the use of some general implementations of iterative preconditioners, at the expense of additional system evaluations (i.e., augmentations).

42 ENGINEERING↗
DOE OSTI2025

INTEGRATE – Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements

The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project developed a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. Training data was obtained from improved turbulence and transition models for RANS and hybrid RANS/LES solvers with machine-learned physics-based data-augmented corrections and then using the resulting neural-network(s) augmented RANS model to run thousands of 2-D and 3-D CFD simulations.

17 WIND ENERGY↗