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At least 739 records · Page 41

Universal control of a six-qubit quantum processor in silicon

Future quantum computers capable of solving relevant problems will require a large number of qubits that can be operated reliably. However, the requirements of having a large qubit count and operating with high fidelity are typically conflicting. Spins in semiconductor quantum dots show long-term promise but demonstrations so far use between one and four qubits and typically optimize the fidelity of either single- or two-qubit operations, or initialization and readout. Here, we increase the number of qubits and simultaneously achieve respectable fidelities for universal operation, state preparation and measurement. We design, fabricate and operate a six-qubit processor with a focus on careful Hamiltonian engineering, on a high level of abstraction to program the quantum circuits, and on efficient background calibration, all of which are essential to achieve high fidelities on this extended system. State preparation combines initialization by measurement and real-time feedback with quantum-non-demolition measurements. These advances will enable testing of increasingly meaningful quantum protocols and constitute a major stepping stone towards large-scale quantum computers.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Topology optimization of permanent magnets for stellarators

In this work, we introduce a topology optimization method to design permanent magnets for advanced stellarators. Recent researches show that permanent magnets have great potentials to simplify stellarator coils. We adopt state-of-the-art numerical techniques to determine the presence of magnets in the entire designing space. The FAMUS code is developed and it can design engineering-feasible permanent magnets for general stellarators satisfying the constraints of the maximum material magnetization and explicit forbidden regions. FAMUS has been successfully verified against the previously proposed linear method. Three different permanent magnet designs together with planar TF coils for a half-Tesla NCSX configuration have been obtained for demonstrations. The designs have good accuracy in generating the desired equilibrium and offer considerably large plasma access on the outboard side. The results show that FAMUS is a flexible, advanced numerical tool for future permanent magnet stellarator designs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

An Integrated Framework for Risk Assessment of Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants: Methodology Refinement and Exploration

This report documents activities performed by Idaho National Laboratory (INL) during Fiscal Year (FY) 2023 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk Informed Systems Analysis (RISA) Pathway, digital instrumentation and control (DI&C) risk assessment project. In FY 2019, the RISA Pathway initiated a project to develop a risk assessment strategy for delivering a technical basis to support effective, and secure DI&C technologies for digital upgrades/designs. A risk assessment-informed framework was proposed for this strategy, which aims to (1) provide a best-estimate, risk informed capability to quantitatively estimate the safety margin obtained from plant modernization, especially for safety-related DI&C systems, (2) support and supplement existing risk informed DI&C design guides by providing quantitative risk information and evidence, (3) offer a capability of design architecture evaluation of various DI&C systems, (4) assure the long-term safety and reliability of safety-related DI&C systems, and (5) reduce uncertainty in costs and support integration of DI&C systems in the plant. To achieve these technical goals, the LWRS-developed framework provides a means to address relevant technical issues by: (1) defining a risk informed analysis process for DI&C upgrade that integrates hazard analysis, reliability analysis, and consequence analysis, (2) applying risk informed tools to address common cause failures (CCFs) and quantify corresponding failure probabilities for DI&C technologies, particularly software CCFs, (3) evaluating the impact of digital failures at the component level, system level, and plant level, and (4) providing insights and suggestions on designs to manage the risks, thus to support the development and deployment of advanced DI&C technologies in nuclear power plants (NPPs). Adding diversity within a system or components is the primary means to eliminate and mitigate CCFs, but diversity also increases system complexity and may not address all sources of systematic failures. Optimization of diversity and redundancy applications for the safety-critical DI&C systems remains a challenge. To deal with the technical issues in addressing potential software CCFs in safety-related DI&C systems of NPPs and supporting relevant design optimization, the proposed framework provides: (a) A best-estimate, risk informed capability to address new technical digital issues quantitatively, focusing on software CCFs in safety-related DI&C systems of NPPs; (b) A common and a modularized platform for DI&C designers, software developers, cybersecurity analysts, and plant engineers to predict and prevent risk in the early design stage of DI&C systems; (c) Technical bases and risk informed insights to assist users address the risk informed alternatives for evaluation of CCFs in safety-related DI&C systems of NPPs; and (d) A risk informed tool that offers a capability of design architecture evaluation of various DI&C systems to support system design decisions in diversity and redundancy applications. The research and development efforts of this project in FY 2023 are focused on refining current methods on software CCF modeling and estimation and exploring additional innovative approaches to risk assessment of DI&C systems to enable a more comprehensive and complete assessment of various safety-related DI&C design architectures. The primary audience of this report are DI&C designers, engineers, and probabilistic risk assessment (PRA) practitioners. This includes stakeholders, such as the nuclear utilities and regulators who consider the deployment and upgrade of DI&C systems, DI&C software developers and reviewers, and cybersecurity specialists. It should be noted that all the analyses are performed for the demonstration of the methodology, not for the evaluation of an actual digital control system. Results are obtained based on limited design information and testing data.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Aero-Thermal Characterization of Accelerating and Diffusing Passages Downstream of Rotating Detonation Combustors

Cycle benefits of rotating detonation engines show up to five percentage points of efficiency gain for low-pressure ratio engines. An optimal integration between the combustor and the turbine needs to be guaranteed to realize this potential gain. The rotating detonation combustor (RDC) exhausts transonic flow with shocks rotating at frequencies ranging from a few to tens of kilohertz depending on the number of present waves. Hence, the turbine design requires precise knowledge of the fluctuations and losses downstream of the combustor. This paper focuses on the quantification of fluctuations and losses for accelerating and diffusing passages. The analysis of the combustor is performed via reactive unsteady Reynolds Averaged Navier-Stokes (URANS) simulations. The unsteady RANS equations are solved via CFD++ from Metacomp with a one-step reaction mechanism for an H2-air mixture. The resolving of the boundary layer is achieved with a structured mesh of around 36 million cells. Inlet pressure of 10 bar and two different back pressures are applied to the combustor to model the interconnection with downstream turbines. Finally, we present and assess a methodology to reduce the computational time to model these passages ten times.

Braun, James↗

Scalable foundation models for numerical simulations on HPC platforms

In recent years, foundation models (FMs) have begun to reshape numerical simulations on high-performance computing (HPC) platforms. These large, pre-trained AI models enable rapid predictions across a broad range of physical domains, including Earth system modeling, fluid dynamics, materials science, as well as complex multi-modal simulations in aerospace engineering and fusion research. By training on diverse datasets, FMs learn intricate relationships and underlying physical behavior while also enabling the quantification of uncertainty in their predictions. This capability allows simulations that once required days of numerical calculation to be completed in minutes (FM inference), supporting real-time design optimization, uncertainty-aware decision making, and more comprehensive exploration of complex scenarios.

AI↗

Design and Characterization of Highly Diffusive Turbine Vanes Suitable for Transonic Rotating Detonation Combustors

In rotating detonation engines the turbine inlet conditions may be transonic with unprecedented unsteady fluctuations. To ensure an acceptable engine performance, the turbine passages must be suited to these conditions. This article focuses on designing and characterizing highly diffusive turbine vanes to operate at any inlet Mach number up to Mach 1. First, the effect of pressure loss on the starting limit is presented. Afterward, a multi-objective optimization with steady RANS simulations, including the endwall and 3D vane design is performed. Compared to previous research, significant reductions in pressure loss and stator-induced rotor forcing are obtained, with an extended operating range and preserving high flow turning. Finally, the influence of the inlet boundary layer thickness on the vane performance is evaluated, inducing remarkable increases in pressure loss and downstream pressure distortion. Employing an optimization with a thicker inlet boundary layer, specific endwall design recommendations are found, providing a notable improvement in both objective functions.

Grasa, Sergio↗

Artificial Intelligence for Autonomous Molecular Design: A Perspective

Domain-aware artificial intelligence has been increasingly adopted in recent years to expedite molecular design in various applications, including drug design and discovery. Recent advances in areas such as physics-informed machine learning and reasoning, software engineering, high-end hardware development, and computing infrastructures are providing opportunities to build scalable and explainable AI molecular discovery systems. This could improve a design hypothesis through feedback analysis, data integration that can provide a basis for the introduction of end-to-end automation for compound discovery and optimization, and enable more intelligent searches of chemical space. Several state-of-the-art ML architectures are predominantly and independently used for predicting the properties of small molecules, their high throughput synthesis, and screening, iteratively identifying and optimizing lead therapeutic candidates. However, such deep learning and ML approaches also raise considerable conceptual, technical, scalability, and end-to-end error quantification challenges, as well as skepticism about the current AI hype to build automated tools. To this end, synergistically and intelligently using these individual components along with robust quantum physics-based molecular representation and data generation tools in a closed-loop holds enormous promise for accelerated therapeutic design to critically analyze the opportunities and challenges for their more widespread application. This article aims to identify the most recent technology and breakthrough achieved by each of the components and discusses how such autonomous AI and ML workflows can be integrated to radically accelerate the protein target or disease model-based probe design that can be iteratively validated experimentally. Taken together, this could significantly reduce the timeline for end-to-end therapeutic discovery and optimization upon the arrival of any novel zoonotic transmission event. Our article serves as a guide for medicinal, computational chemistry and biology, analytical chemistry, and the ML community to practice autonomous molecular design in precision medicine and drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗

Simulation-Based Design and Optimization of Waste Heat Recovery Systems

This fact sheet features an R&D project that will develop a modeling platform that can quantify the value of a district energy system and its potential for waste heat recovery. The new software analysis platform will evaluate and optimize district energy systems to better utilize low-temperature waste heat from nearby commercial and industrial buildings. The platform will help project developers and engineers easily quantify the potential value and cost savings of community energy systems for both producers and consumers of waste heat.

Combined heat and power, CHP, District energy, Was↗

Reinforcement learning-based design of shape-changing metamaterials

During the last decade, artificially architected materials have been designed to obtain properties unreachable by naturally occurring materials, whose properties are determined by their atomic structure and chemical composition. In this work, we implement a new reinforcement learning (RL) method able to rationally design unique metamaterial structures at the nano-, micro-, and macroscale, which change shape during operational conditions. As an example, we apply this method to design nanostructured silicon anodes for Li-ion batteries (LIBs). The RL model is designed to apply different actions and predict change during operational conditions. The multi-component reward function comprises an increase in the total storage capacity of the resulting battery electrode and structural parameters, such as the minimum distance between the individual components of the nanostructure. Upon experimental validation using a polymer-based 3D printing technique, we expect that the newly discovered structures improve the current Si-based LIB anodes state-of-the-art by almost three times and almost ten times the current commercial LIB based on a graphitic anode. Furthermore, this RL-based optimization method opens up vast design space for other responsive metamaterials with tailored properties and pre-programmed structural transformation.

25 ENERGY STORAGE↗

CCM vs. CRM Design Optimization of a Boost-derived Parallel Active Power Decoupler for Microinverter Applications

Single-phase inverter or rectifier systems often make use of an auxiliary active power decoupler (APD) to balance the mismatch between steady DC power and fluctuating AC power. This paper deals with efficiency and size optimization of a parallel boost-type APD circuit for PV microinverter applications. Specifically, design of an eGaNFET-based, 400 W APD circuit, employing planar inductor and operating in either continuous conduction mode (CCM) or critical conduction mode (CRM) is considered. Available design variables including inductance value, inductor core geometry, capacitor voltage, switching frequency, and modulation scheme (CCM vs. CRM) are explored to identify Pareto-optimal configurations, which can achieve low California Energy Commission (CEC) efficiency drop while also reducing the footprint area of the inductor. The theoretical study predicts that the optimal CRM design can achieve 37% reduced inductor size, while operating with similar efficiency drop, compared to the optimal CCM design. Experimental results, obtained using two separate 40 V, 400 W hardware prototypes for CCM and CRM, are presented to verify the analyses.

14 SOLAR ENERGY↗

Automated Design-for-Reliability of a Power Electronics Module

The design of a power electronics module is a multiphysics problem and involves electrical, thermal, and reliability targets and constraints. Although the electrical and thermal design consider multiple aspects such as materials, geometry, and energy losses, reliability design is often limited to the selection of the attachment materials and electrical interconnect types. In this paper, we incorporate reliability metrics in the design phase by investigating the impact of package geometry on the thermomechanical behavior of the die-attachment material. To this end, we conducted thermal and power cycling simulations of a commercial six-pack power module with silicon carbide devices to compute junction temperature and strain energy density per cycle, respectively. We performed multiple simulations with different geometric dimensions and established a correlation between input features and output variables using subspace-based dimension reduction. The machine learning-based dimension reduction method serves as a surrogate model, which can be employed to identify the optimal module design from a thermal and reliability standpoint.

design-for-reliability↗

Development of a clostridia-based cell-free system for prototyping genetic parts and metabolic pathways

Gas fermentation by autotrophic bacteria, such as clostridia, offers a sustainable path to numerous bioproducts from a range of local, highly abundant, waste and low-cost feedstocks, such as industrial flue gases or syngas generated from biomass or municipal waste. Unfortunately, designing and engineering clostridia remains laborious and slow. The ability to prototype individual genetic part function, gene expression patterns, and biosynthetic pathway performance in vitro before implementing designs in cells could help address these bottlenecks by speeding up design. Unfortunately, a high-yielding cell-free gene expression (CFE) system from clostridia has yet to be developed. Here in this paper, we report the development and optimization of a high-yielding (236 ± 24 μg/mL) batch CFE platform from the industrially relevant anaerobe, Clostridium autoethanogenum. A key feature of the platform is that both circular and linear DNA templates can be applied directly to the CFE reaction to program protein synthesis. We demonstrate the ability to prototype gene expression, and quantitatively map aerobic cell-free metabolism in lysates from this system. We anticipate that the C. autoethanogenum CFE platform will not only expand the protein synthesis toolkit for synthetic biology, but also serve as a platform in expediting the screening and prototyping of gene regulatory elements in non-model, industrially relevant microbes.

59 BASIC BIOLOGICAL SCIENCES↗

ADAM: A web platform for graph-based modeling and optimization of supply chains

Modeling and optimization are essential tasks that arise in the analysis and design of supply chains (SCs). SC models are essential for understanding emergent behavior such as transactions between participants, inherent value of products exchanged, as well as impact of externalities (e.g., policy and climate) and of constraints. Unfortunately, most users of SC models have limited expertise in mathematical optimization, and this hinders the adoption of advanced decision-making tools. Here, in this work, we present ADAM, a web platform that enables the modeling and optimization of SCs. ADAM facilitates modeling by leveraging intuitive and compact graph-based abstractions that allow the user to express dependencies between locations, products, and participants. ADAM model objects serve as repositories of experimental, technology, and socio-economic data; moreover, the graph abstractions facilitate the organization and exchange of models and provides a natural framework for education and outreach. Here, we discuss the graph abstractions and software design principles behind ADAM, its key functional features and workflows, and application examples.

97 MATHEMATICS AND COMPUTING↗

Description of FY25 Theory and Simulation Performance Target: Development of an integrated modeling framework for fusion reactor design and assessment

The urgency to deliver fusion power is growing now more than ever, with increasing pressure for both public programs and private companies to meet milestones timelines and overcome significant remaining technical challenges to ensure growth of a nascent fusion industry in time to meet rapidly growing clean energy demands. With incredible advancements in computation and years of investment in fusion model development and validation, integrated modeling is poised to fill a key role in accelerating the timeline to a fusion pilot plant (FPP). Future fusion pilot plants will operate in regimes far beyond current experience, and device design will rely on physics-based prediction and extrapolation. Many concepts will also rely on simulation to assess safety (shielding, tritium management, materials activation and lifetimes), economics and scalability before the decision to build. Importantly, integrated simulation can be used to reveal and solve the complexities of system integration that may otherwise not be apparent in physical components or models developed in isolation. New experimental test facilities that produce relevant conditions to validate and resolve key technical challenges for various subsystems (materials, blankets, fuel cycle, etc.) have been repeatedly called for by the fusion community but are not yet realized. Integrated modeling has an important role in identifying realistic load conditions (thermal, electromagnetic, plasma, neutron and photon loads, etc.) and defining the components and experiments for these test facilities in order to ensure meaningful validation that sufficiently reduces modeling uncertainties and technical risk for the full integrated reactor. The Fusion REactor Design and Assessment (FREDA) SciDAC project is building a component-based integrated modeling framework & data structure to enable self-consistent, multi-fidelity, iterative optimization workflows for the fusion reactor design process. FREDA aims to shorten the time to viable designs by providing a set of flexible workflows to support the various stages of the design process using an integrated model hierarchy, ranging from the simple analytic descriptions to the highest fidelity, theory-based plasma and engineering modeling developed by the fusion and fission communities. These tools are expected to be needed for timely support of FPP design in the milestone program and in the FIRE collaboratives. The plasma simulation backbone of FREDA is IPS-FASTRAN with newly developed coupled Core-Edge Pedestal-SOL (CESOL) workflows, which is being extended to the far-SOL region up to the plasma facing components. FREDA incorporates the FERMI engineering modeling suite and will enable self-consistent evaluation of the thermal shields, limiters, blanket, magnets, and other surrounding structures with predictions of temperatures, erosion, dpa, activation, tritium generation and transport, creep, corrosion, material degradation, etc. Parametric generation of 3D CAD enables rapid iteration of component geometry in response to plasma and loading specifications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Helium recovery system at IB3A

The growing demand for sustainable cryogenic operations at Fermilab has underscored the need to improve helium management, particularly at the Industrial Building 3A (IB3A) test facility. IB3A characterizes and tests superconductors, cables, and coils for projects such as the HL-LHC AUP and Mu2e, yet currently relies on 500 L Dewars whose boil-off is vented to atmosphere, wasting a critical, non-renewable resource and increasing the cost of testing. A project is therefore under way to link IB3A to an existing purification and liquefaction station in a neighboring building through a dedicated pipeline. Captured helium will be transferred, purified, reliquefied, and returned for reuse, cutting losses and operating costs. This paper details the first two project phases: "Design and Engineering" and "Procurement and Installation." The design phase finalizes pipeline specifications, establishes flow-control requirements, and resolves integration challenges with existing cryogenic infrastructure. The procurement and installation phase covers material sourcing, pipeline construction, and deployment of control and monitoring systems to assure reliable, efficient operation. Key technical hurdles-route optimization, pressure drop mitigation, and interface compatibility-are discussed alongside implemented solutions. Implementing the pipeline and upgrading IB3A will dramatically reduce helium consumption and therefore testing cost, strengthening Fermilab's capacity to support frontier science far into the future.

Porwisiak, D. [Fermilab; Wroclaw Tech. U.]↗

Pilot-Scale Testing of an Integrated Circuit for the Extraction of Rare Earth Minerals and Elements from Coal and Coal Byproducts Using Advanced Separation Technologies

The primary objective of this project was to develop and demonstrate an integrated pilot-scale circuitry for recovering high-value rare earth elements (REEs) from coal and coal byproducts. The target performance was to produce a mixed REE product with content of at least two percent by weight on a dry mass basis in a cost-effective and environmentally benign manner. During the first nine months of the project period (Phase 2 Budget Period 2), pilot plant construction was completed including all field site startup activities such as permitting, engineering design, procurement/bidding, unit fabrication, site construction, equipment installation, module assembly, safety training, and circuit shakedown. During the remaining 21 months of project period (Phase 2 Budget Period 3), detailed field-testing activities were performed including feedstock sample collection and preparation, exploratory testing, circuit modification, detailed parametric study, and performance optimization. A detailed techno-economic analysis was performed based on the pilot plant testing findings which provided various scenarios for REE production. The project successfully accomplished the proposed target performance by producing mixed rare earth oxide (REO) with greater than 90% purity by weight in a continuous pilot scale operation from two distinctly different coarse refuse materials (i.e., West Kentucky No. 13 and Fire Clay coal seams), and at least three secondary sources (i.e., heap leach process and naturally formed acid mine drainage system). Project partners included the University of Kentucky, Virginia Tech, West Virginia University, Alliance Coal, Blackhawk Mining, Mineral Refining Company, and Mineral Separation Technologies. The pilot scale test facility was constructed at a former mining complex owned by Alliance Natural Resource Partners (Alliance Coal). The site was rehabilitated to accommodate the equipment installation, construction and fabrication, electrical power requirement, water line management and containment. The process units constructed and installed included X-ray sorting unit, crushing and grinding unit, physical separation unit, acid leaching unit, solvent extraction unit, and wastewater management unit. A rare earth mineral concentration unit was constructed as a standalone unit for flexible operation. A detailed environmental assessment and control plan was carried out to identify and quantify any potential impacts of the pilot-scale processing circuitry on the human and eco-system health and well-being. Corresponding mitigation strategies and control measures were provided. A conceptual flowsheet was developed to effectively remove thorium and uranium from high purity rare earth oxide mix or any potential radionuclide enriched stream. The two distinct feedstock materials were secured from the Blackhawk Mining Complex in eastern Kentucky where the Fire Clay (Hazard No. 4) seam is processed. The West Kentucky No. 13 (Baker) coarse refuse material was collected from an active process stream at an Alliance coal preparation plant located in western Kentucky. Characterization analysis indicated that both of feed materials generated from the two sources contained >300 ppm of TREEs on a dry whole mass basis which met the requirements for a qualified feed stock. The two feedstocks were further upgraded using a dual x-ray sorter to prepare the feed material for hydrometallurgical circuit. Thermal treatment on feed material prior to leaching was found to: 1) improve the leaching recovery of REEs, 2) increase the leaching kinetics, and 3) allow the leaching reaction to occur at lower acidity. Roasting at 600°C was selected as the pre-treatment condition for both West Kentucky No. 13 and Fire Clay coarse refuse material. Over 40% of leaching recovery was achieved by roasting West Kentucky No. 13 material having a top particle size of 3 mm in the pilot scale operation using 1.2M sulfuric acid leaching at 75OC. Initial pilot scale testing involved continuous operation of the pilot plant for 94 hours. The leaching unit was operated at solid-to-liquid ratio of 1 to 10 (w/v) using 0.5M sulfuric acid solution at a temperature of 75°C. The continuous solvent extraction circuit utilized rougher and cleaner units with DEHPA and TBP as the extractants. An innovative stripping circuit was developed to accumulate the REE concentration in the stripping solution to a level above 600 ppm. A bleed stream from the recycled strip solution was treated using oxalic acid precipitation which produced a high grade rare earth oxalate. The oxalate product was roasted to remove the oxalate which produced a rare earth oxide product having a purity greater 90%. Due to high concentrations of contaminant ions in the pregnant leach solution (PLS), a modified flowsheet was developed that involved pre-concentration of the REEs using multiple stages of precipitation and redissolution. The advantage of this process was improved removal of contamination before the downstream purification process and a significant cost reduction relative to the circuit that utilized the solvent extraction process. The modified circuitry included processes involving leaching, multistage precipitation, redissolution, and oxalate precipitation followed by roasting of the oxalate product. The circuit produced a mixed REO that was 92.96% pure from the initial test. A detailed parametric test plan was carried out which involved varying key parameters including solids feed rate, acid flowrate, acid concentration, multistage precipitation pH, redissolution pH, oxalate precipitation dosage and pH. The response variables included REE recovery, contaminant recovery, REO product grade and overall chemical consumption. Test results indicated that the acid-to-solid ratio is the key parameter to leaching efficiency as performance deteriorated with an increase in solids concentration. The optimal pH determined for REE precipitation and redissolution was 6.5 and 2.5, respectively. Additional tests were conducted to further improve the flowsheet. Recirculating a portion of the PLS to the feed of the leach tanks improved the leaching performance by lowering the pH of the leaching system and reducing the contamination recovery by shortening the residence time. Moreover, the removal of Al prior to REE precipitation significantly reduced the oxalic acid consumption in the oxalate precipitation circuit. The modified circuit produced over 90% grade REO by weight from both West Kentucky No. 13 and Fire Clay coarse refuse material in pilot scale continuous test programs. A case analysis model was developed to project the REE and major contaminants concentration in each PLS stream based on the leaching condition, pH cut point, and oxalic acid dosage. A correlation was established using empirical and semi-empirical models. Using the models, chemical consumption required for each stage was predicted based on the projected performance of the hydrometallurgy circuit. After identifying the optimum conditions, validation tests were carried out for the treatment of both West Kentucky No. 13 and Fire Clay coarse refuse materials in the pilot plant. The actual circuit performance and chemical consumptions were very close to the model predictions. Other than the two coarse refuse sources, several secondary feedstocks were also tested in the pilot plant facility. A “heap leach” system was constructed using the coarse refuse material generated from cleaning the West Kentucky No. 13 seam coal. Using the two stage SX rougher and cleaner circuit, a concentrate with a grade >90% REO was produced while recovering >97% of the REEs from the heap leach PLS. Naturally generated acid mine drainage (AMD) from West Kentucky No.13 mine was processed using the multistage precipitation circuit in the pilot plant in a test conducted for a period of 32 hours. The final grade of the mix RE oxide produced from the AMD was 90.84% with an overall circuit recovery of 64%. The primary source of REE was the selective precipitation steps involving iron and aluminum rejection. The hydrophobic-hydrophilic separation (HHS) process was proven to effectively recover coal from fine waste materials. For REM recovery, the HHS process was able to produce concentrates at grades of approximately 1.8% REE on an ash basis; however, recovery values were typically low, <10%, under the optimal conditions determined in the laboratory-scale testing. Staged testing of the pilot-scale HHS process for coal recovery and semi-continuous laboratory testing for REM testing showed that a total concentration ratio of more than 15x was observed for the REM recovery process. A circuit simulation package was developed for REE extraction and purification using a spreadsheet-based platform (Microsoft Excel). The REESim circuit simulation package is configured to track the mass and volume flows of components passing through a series of unit operations specified and configured by the user. The mass rates can then be utilized by the user to determine important performance indicators such as product mass yields, concentrate purity levels, element-by-element recoveries, and so forth. The techno-economic analysis showed that the roasting and leaching operations were the most expensive capital items, each contributing approximately 30% to the total capital cost. One notable contributor to the high production costs was the low REE recovery observed in the pilot scale trials. The product basket price was shown to have a strong influence on the economic viability of the scenarios, with the scandium price being the most significant influencer. Operating cost was shown to be extremely sensitive to REE recovery, REE feed grade, and leaching acid consumption. An analysis of ten different scenarios for a 500 t/h commercial operation revealed that three were economically favorable, producing internal rates of return varying from 27.7% to 33.1% and payback periods of 4 to 5 years. The project successfully developed and demonstrated a process to recover REEs from coal and coal byproducts in a pilot-plant operation which consistently produced over 90% grade REO mix from varies types of feedstocks. Commercialization analysis showed that the technology readiness level successfully achieved TRL 6 at the end of the project and demonstrated the need and the potential for scaling the process to further advance the technologies toward the goal of providing a domestic supply of REEs at a commercial scale.

01 COAL, LIGNITE, AND PEAT↗

Design of Novel Hot Gas Component for Gas Turbine Engines Enabled by Materials and Additive Manufacturing Process Development

This CRADA project was the result of a project award under FOA-DOE-0001980. The overarching FOA project team consisted of researchers from Carpenter Technology Corporation (CTC), Solar Turbines Incorporated (Solar), Pennsylvania State University (PSU), University of California-Santa Barbara (UCSB), and Oak Ridge National Laboratory (ORNL). Evaluations were conducted on two high-γ’ superalloys that were designed by CTC and the UCSB. One alloy named GammaPrint-700 (GP-700) is a cobalt-base superalloy. The other alloy named GammaPrint-1100 (GP-1100) is a nickel-base (Ni-base) superalloy. PSU provided expertise and experimental testing of the thermal performance of AM micro-cooling architectures. ORNL provided expertise with the AM superalloy materials characterization and AM processing science. Solar provided turbine component design expertise. The focus of this CRADA report is to document the efforts between ORNL and CTC towards the development of superalloys designed for AM. The project goal was to use an AM processable high-temperature superalloy and design for Additive Manufacturing (DfAM) techniques to design an efficient turbine component (i.e. a turbine tip shoe) with enhanced cooling features that can only be fabricated through additive manufacturing (AM). The efficiencies of existing combined heat and power (CHP) engines are capped by both component design and materials limitations. However, AM of a tip shoe component from a γ’strengthened superalloy offers the design flexibility to increase the efficiency and power of an industrial gas turbine. This project brought about advancements in the DfAM tip shoe design space and in the area of high temperature superalloys processable through laser powder bed fusion (LPBF) AM. State of art computation design tools were utilized to optimize unique cooling features into a tip shoe component design. A two-prong materials development approach was taken to support development of the AM tip shoe geometry. The first approach centered on investigating the processability and the appropriate process science for the industry standard high-γ’ nickel-base (Ni-base) superalloy Mar-M247. This superalloy is typically cast and considered non-weldable by traditional welding standards. In the course of this work, the alloy was not deemed feasible for process scale-up due to significant cracking issues during printing. The second approach focused on the development and evaluation of a novel cobalt-base superalloy, GammaPrint™-700 (GP-700 and a Ni-base superalloy, GammaPrint™-1100 (GP-1100) designed to mitigate the significant AM processing issues with Mar-M247. The processability of these two alloys were investigated through electron beam melting (EBM) binder-jet AM (BJAM), and LPBF as a risk mitigation for manufacturability. To be considered a candidate material for down-selection to proceed to fullscale AM tip shoe engine testing trials, the high temperature creep rupture strength was required to achieve at a minimum, a Larsen Miller Parameter (LMP) increase of 10.9% over the baseline material LPBF AM Hastelloy X.

36 MATERIALS SCIENCE↗

Design of Novel Hot Gas Component for Gas Turbine Engines Enabled by Materials and Additive Manufacturing Process Development

This CRADA project was the result of a project award under FOA-DOE-0001980. The overarching FOA project team consisted of researchers from Carpenter Technology Corporation (CTC), Solar Turbines Incorporated (Solar), Pennsylvania State University (PSU), University of California-Santa Barbara (UCSB), and Oak Ridge National Laboratory (ORNL). Evaluations were conducted on two high-γ’ superalloys that were designed by CTC and the UCSB. One alloy named GammaPrint-700 (GP-700) is a cobalt-base superalloy. The other alloy named GammaPrint-1100 (GP-1100) is a nickel-base (Ni-base) superalloy. PSU provided expertise and experimental testing of the thermal performance of AM micro-cooling architectures. ORNL provided expertise with the AM superalloy materials characterization and AM processing science. Solar provided turbine component design expertise. The focus of this CRADA report is to document the efforts between ORNL and CTC towards the development of superalloys designed for AM. The project goal was to use an AM processable high-temperature superalloy and design for Additive Manufacturing (DfAM) techniques to design an efficient turbine component (i.e. a turbine tip shoe) with enhanced cooling features that can only be fabricated through additive manufacturing (AM). The efficiencies of existing combined heat and power (CHP) engines are capped by both component design and materials limitations. However, AM of a tip shoe component from a γ’strengthened superalloy offers the design flexibility to increase the efficiency and power of an industrial gas turbine. This project brought about advancements in the DfAM tip shoe design space and in the area of high temperature superalloys processable through laser powder bed fusion (LPBF) AM. State of art computation design tools were utilized to optimize unique cooling features into a tip shoe component design. A two-prong materials development approach was taken to support development of the AM tip shoe geometry. The first approach centered on investigating the processability and the appropriate process science for the industry standard high-γ’ nickel-base (Ni-base) superalloy Mar-M247. This superalloy is typically cast and considered non-weldable by traditional welding standards. In the course of this work, the alloy was not deemed feasible for process scale-up due to significant cracking issues during printing. The second approach focused on the development and evaluation of a novel cobalt-base superalloy, GammaPrint™-700 (GP-700 and a Ni-base superalloy, GammaPrint™-1100 (GP-1100) designed to mitigate the significant AM processing issues with Mar-M247. The processability of these two alloys were investigated through electron beam melting (EBM) binder-jet AM (BJAM), and LPBF as a risk mitigation for manufacturability. To be considered a candidate material for down-selection to proceed to full-scale AM tip shoe engine testing trials, the high temperature creep rupture strength was required to achieve at a minimum, a Larsen Miller Parameter (LMP) increase of 10.9% over the baseline material LPBF AM Hastelloy X.

99 GENERAL AND MISCELLANEOUS↗