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A Radiation-Shielded, Shallow Underground, Long-Coherence Time Quantum Qubit Concept
Ionizing radiation from ambient radioactivity has been shown to reduce the coherence time of superconducting qubits. Qubit performance can be improved by shielding the system from external radiation: operating the system underground to reduce cosmic rays, and use of lead shielding to reduce ambient gammas. Here we present a design for a lead shield optimized for a dilution refrigerator operating in PNNL's Shallow Underground Laboratory. The shield reduces the ambient gamma flux by approximately 99.8%, so that cosmic ray muons dominate the ionizing radiation input to the qubits.
Recent progress in the synthesis of advanced biofuel and bioproducts
Energy is one of the most complex fields of study and an issue that influences nearly every aspect of modern life. Over the past century, combustion of fossil fuels, particularly in the transportation sector, has been the dominant form of energy release. Refining of petroleum and natural gas into liquid transportation fuels is also the centerpiece of the modern chemical industry used to produce materials, solvents, and other consumer goods. In the face of global climate change, the world is searching for alternative, sustainable means of producing energy carriers and chemical building blocks. The use of biofuels in engines predates modern refinery optimization and today represents a small but significant fraction of liquid transportation fuels burnt each year. Similarly, white biotechnology has been used to produce many natural products through fermentation. The evolution of recombinant DNA technology into modern synthetic biology has expanded the scope of biofuels and bioproducts that can be made by biocatalysts. Furthermore, this opinion examines the current trends in this research space, highlighting the substantial growth in computational tools and the growing influence of renewable electricity in the design of metabolic engineering strategies. In short, advanced biofuel and bioproduct synthesis remains a vibrant and critically important field of study whose focus is shifting away from the conversion of lignocellulosic biomass toward a broader consideration of how to reduce carbon dioxide to fuels and chemical products.
Concentric Ring Gas Atomization Die Design for Optimized Particle Production
In partnership with Linde plc (formerly Praxair, Inc.), Ames Laboratory will further develop and commercialize its concentric-ring high pressure gas atomization (CR-HPGA) gas-die technology with a goal to improve the precision of metal powder production in a desired size range and quality. The successful commercialization of this technology could reduce production cost of metal powders and improve reliability of this industrial process, increasing specialty alloy powder availability for new applications and adoption into additive manufacturing and multiple materials sectors. The project scope is to [1] utilize AMES compressible gas flow and melt break-up models to explore a wide swath of parameter space available for the CR-HPGA technology, identifying the most promising gas-die designs for fabrication, [2] verify and optimize the new gas-die design, fabricate it and select preferred operating parameters by gas-only flow imaging and aspiration pressure measurements, down-selecting gas compositions and atomization parameters for full scale testing, [3] perform pilot-scale atomization trials with Al and/or Cu alloys of a new CR-HPGA gas-die at selected parameters, [4] evaluate gas-die performance by comparing resulting powder size distribution and powder quality attributes with equivalent powders made by conventional close-coupled HPGA technology, and [5] assess (through Praxair partnership) reduction in operational costs of new CR-HPGA gas-die through the use of an inert gas recovery/recycling system. In partnership with Praxair, Inc., the team, led by Ames Laboratory senior metallurgist Iver Anderson, intend to demonstrate the performance of an optimized CR-HPGA gas-die design in a pilot scale atomizer and better understand the design and operational controls to increase overall benefits for precision metal powder production.
Enabling Production of Low Carbon Emissions Steel through CO 2 Capture from Blast Furnace Gases at Cleveland Cliffs’ 5 mtpa Steel Plant at Burns Harbor, Indiana
Dastur International Inc. has prepared a DOE-funded Pre-front-end engineering design (Pre-FEED) study to capture up to 2.8 mtpa of CO 2 from the available Blast Furnace (BF) gases at the Burns Harbor steel plant operated by Cleveland Cliffs in Burns Harbor, Indiana. The project consists of an amine solvent based pre-combustion carbon capture system, along with a unique BF gas conditioning process which significantly optimizes the project design and architecture in terms of higher volumes (2.8 mtpa vis-à-vis 1.6 mtpa without conditioning) & concentration (32 vol% in conditioned gas vs. 22% in raw BF gas) of CO 2 , which can be captured using a single train, resulting in reduced size and capital cost of the CO 2 capture island, and improved overall economics on a $\$$/tCO 2 captured basis.
Resilient NdFeB magnet recycling under the impacts of COVID-19 pandemic: Stochastic programming and Benders decomposition
Neodymium-iron-boron (NdFeB) magnets are the most powerful magnets per unit volume sold in the commercial market. Despite the increasing demand for clean energy applications such as electric vehicles and wind turbines, disruptive events including the COVID-19 pandemic have caused significant uncertainties in the supply and demand for NdFeB magnets. Therefore, this study aims to alleviate the risk of supply shortage for NdFeB magnets and the containing critical materials, rare-earth elements (REEs), through the development of a resilient reverse supply chain and logistics network design. We develop scenarios to model the unique impact of the COVID-19 pandemic on the proposed business, incorporating both disruption intensity and recovery rate. We formulate a chance-constrained two-stage stochastic programming model to maximize the profit while guaranteeing the network resiliency against disruption risks. To solve the problem in large-scale instances, we develop an efficient Benders decomposition algorithm that reduces the computational time by 98.5% on average compared to the default CPLEX algorithm. When applied to the United States, the model suggests the optimal facility locations, processing capacities, inventory levels, and material flows for NdFeB magnet recyclers that could meet 99.7% of the demand. To the best of our knowledge, this study is the first to incorporate the impacts of the COVID-19 pandemic to design a resilient NdFeB magnet recycling supply chain and logistics network, leveraging risk-averse stochastic programming.
Decision-Making Under Uncertainty for a Digital Thread-Enabled Design Process
Abstract Digital thread is a data-driven architecture that links together information from all stages of the product lifecycle. Despite increasing application in manufacturing, maintenance/operations, and design related tasks, a principled formulation of analyzing the decision-making problem under uncertainty for the digital thread remains absent. The contribution of this article is to present a formulation using Bayesian statistics and decision theory. First, we address how uncertainty propagates in the product lifecycle and how the digital thread evolves based on the decisions we make and the data we collect. By using these mechanics, we explore designing over multiple product generations or iterations and provide an algorithm to solve the underlying multistage decision problem. We illustrate our method on an example structural design problem where our method can quantify and optimize different types and sequences of decisions, ranging from experimentation, manufacturing, and sensor placement/selection, to minimize total accrued costs.
Microgrid Design Toolkit (MDT) User Guide. Software v1.3.
The Microgrid Design Toolkit (MDT) supports decision analysis for new ("greenfield") microgrid designs as well as microgrids with existing infrastructure. The current version of MDT includes two main capabilities. The first capability, the Microgrid Sizing Capability (MSC), is used to determine the size and composition of a new, grid connected microgrid in the early stages of the design process. MSC is focused on developing a microgrid that is economically viable when connected to the grid. The second capability is focused on designing a microgrid for operation in islanded mode. This second capability relies on two models: the Technology Management Optimization (TMO) model and Performance Reliability Model (PRM).
Evaluating the intrinsic resistance to balling of alloys: A High-throughput physics-informed and data-enabled approach
To date, the vast majority of work on metal additive manufacturing (AM) has been framed in terms of the need to tune processing conditions for a particular AM technology in order to print conventional alloys, oftentimes developed for fabrication methods other than AM. This approach overlooks the fact that historically, many engineering alloy system has been designed with a particular processing route in mind, e.g., ingot metallurgy, powder metallurgy, rapid quenching, etc. There are thus significant opportunities to design alloys specifically for AM. A key challenge is that alloy design requires performance metrics that can be optimized by exploring the alloy chemistry space. Here, we present a study in which we examine how intrinsic thermophysical properties can be used to estimate performance metrics related to the behavior of a solidifying metal droplet under AM-relevant conditions. By identifying these intrinsic properties, it is possible to directly incorporate ‘intrinsic printability’, specifically ‘intrinsic resistance to balling’, into AM-focused alloy design.
Optimizing FPGA-based Accelerator Design for Large-Scale Molecular Similarity Search (Special Session Paper)
Molecular similarity search has been widely used in drug discovery to rapidly identify structurally similar compounds from large molecular databases. With the increasing size of chemical libraries, there is growing interest in the efficient ac- celeration of large-scale similarity search. Existing works mainly focus on CPU and GPU to accelerate the computation of Tatimoto coefficient in measuring the pairwise similarity between different molecular fingerprints. In this paper, we propose and optimize an FPGA-based accelerator design on exhaustive and approximate search algorithms. On exhaustive search using BitBound & fold- ing, we analyze the similarity cutoff and folding level relationship with search speedup and accuracy, and propose a scalable on- the-fly query engine on FPGAs to reduce the resource utilization and pipeline interval. We achieve a 450 million compounds-per- second processing throughput for a single query engine. On approximate search using hierarchical navigable small world (HNSW), a popular algorithm with high recall and query speed, we propose an FPGA-based graph traversal engine to utilize high throughput register array based priority queue and fine- grained distance calculation engine to increase the processing capability. Experimental results show that the proposed FPGA- based HNSW implementation achieves a 35× speedup than existing works on CPU. To the best of our knowledge, our FPGA- based implementation is the first attempt to accelerate molecular similarity search on FPGA and has the highest performance among existing approaches.
Distribution Substation Planning Toolkit (dsp-toolkit) v1.0
The Distribution Substation Planning Toolkit (DSP Toolkit) is a software suite designed to streamline the planning and optimization of distribution substations. This toolkit offers a comprehensive set of tools and APIs for data curation, short-term electric load forecasting, and weather-sensitive load adjustment, making it an essential resource for utility companies, engineers, and researchers. Features • Data Preprocessing and Curation: Efficiently manage and preprocess large datasets to ensure high-quality input for analysis. • Short-Term Load Forecasting: Utilize data-driven models to predict short-term electric loads accurately. • Weather-Sensitive Modeling: Automatically adjust load forecasts based on weather data to predict future peak demands more precisely. Uses The DSP Toolkit is ideal for planning and optimizing distribution substations, providing a user-friendly interface and comprehensive documentation. It is suitable for both novice and experienced users, facilitating efficient and accurate planning processes. Advantages • Efficiency: Automates complex planning tasks, reducing manual effort and minimizing errors. • Scalability: Handles large datasets and complex models, making it suitable for large-scale projects. • Community and Support: Open-source with active community contributions, ensuring continuous improvement and support. • Extensibility: Easily extendable with custom modules and plugins, allowing users to tailor the toolkit to their specific needs. The DSP Toolkit stands out by offering a robust, flexible, and user-friendly solution for distribution substation planning. Public Abstract
An Innovative Design of High-Temperature, Sensible Molten Salt Thermal Energy Storage Systems With Geopolymer Insulation
This project aims to mitigate the risks of the current molten salt TES tank designs. The goal is to create a more reliable and cost-effective TES by (1) developing a new material solution for internal thermal insulation and (2) by designing a concrete-based tank structure to eliminate metal-related issues (e.g., corrosion, thermomechanical failure, etc.). Firstly, the project aims to engineer a composite refractory insulation material that possesses sufficient thermal insulation properties without significantly sacrificing the resistance to salt permeation and mechanical strength, which is the major trade-off in traditional refractory insulation design. The primary objective is to perform materials design and optimization of the composite refractory to achieve the required thermal and mechanical properties as suitable internal insulation for molten salt TES. Secondly, the project aims to design a concrete-based TES system with conventional low- cost Portland cement serving as the primary tank structure and the aforementioned composite refractory as the internal thermal insulation and salt containment. The objective is to investigate the technical and economical feasibilities of concrete tanks for high- temperature molten salt storage applications.
Multiscale design of nonlinear materials using a Eulerian shape optimization scheme
Motivated by recent advances in manufacturing, the design of materials is the focal point of interest in the material research community. One of the critical challenges in this field is finding optimal material microstructure for a desired macroscopic response. This work presents a computational method for the mesoscale-level design of particulate composites for an optimal macroscale-level response. The method relies on a custom shape optimization scheme to find the extrema of a nonlinear cost function subject to a set of constraints. Three key “modules” constitute the method: multiscale modeling, sensitivity analysis, and optimization. Multiscale modeling relies on a classical homogenization method and a nonlinear NURBS-based generalized finite element scheme to efficiently and accurately compute the structural response of particulate composites using a nonconformal discretization. A three-parameter isotropic damage law is used to model microstructure-level failure. An analytical sensitivity method is developed to compute the derivatives of the cost/constraint functions with respect to the design variables that control the microstructure's geometry. The derivation uncovers subtle but essential new terms contributing to the sensitivity of finite element shape functions and their spatial derivatives. Several structural problems are solved to demonstrate the applicability, performance, and accuracy of the method for the design of particulate composites with a desired macroscopic nonlinear stress-strain response.
Rheo-Structural Spectroscopy: Fingerprinting the In Situ Response of Fluids to Arbitrary Flow Fields
The objectives of this project were to develop new sample environments, measurement methodologies and associated modeling tools for characterizing the structural response to arbitrarily complex processing flows using small angle scattering, and to apply these new tools for understanding the fundamental physics governing the structuring of anisotropic particulate and polymeric materials under flow histories and conditions relevant to industrial processing flows. The research resulted in the development and implementation of a new sample environment, the fluidic four roll mill (FFoRM), for in situ small angle neutron and X-ray scattering (SANS/SAXS) measurements. These measurements are capable of generating large data sets that “fingerprint” how a complex fluid responds to a wide range of flow histories involving time variations in deformation type and rate. New modeling tools were developed to extract detailed microstructural information from such data sets, including orientation distribution functions and interparticle correlation functions, as well as reduced-order parametric descriptors of these high-dimensional functions that can be used to readily map, visualize and interpret a fluid’s structural response to its flow history. These new tools were applied to a range of model materials involving elongated particle suspensions in order to provide new insights into the physics of how flow couples with orientational and structural order in complex flows, particularly under non-dilute conditions for which no accurate theories currently exist. Using these investigations, we elucidated a number of new insights into the fundamental phenomena driving such process-structure-property relationships. These findings provide guidance for the further development of rheological models, and ultimately can inform the rational and model-based design of flow processes to achieve optimized orientational ordering that is key to the properties and function of a wide range of energy-relevant materials.
Gallium Arsenide Semiconductor Opening Switches: Enabling Nanosecond High Powering Pulsed Systems
The goal of this work awas to investigate design manufacturing semiconductor opening switches (SOS) in both silicon and gallium arsenide (GaAs). Solid-state opening switches are critical components for pulsed power systems and applications in directed energy, dielectric wall accelerators, and novel semiconductor manufacturing techniques. Under this funding, we have developed silicon SOS designs that suppresses an unwanted prepulse, increases the peak output voltage by about 10 percent, and reduces the pulse rise time by ~ 4x compared with more conventional profiles. Through this effort we have improved device fabrication and bonding techniques. Additionally, work on this the GaAs opening switch has defined a unique application space that these devices are suited for. GaAs opening switches have short risetimes and pulse widths compared to silicon devices, however the short carrier lifetime of GaAs makes the circuit design more challenging than in silicon. For high PRF operation, the lifetime of the GaAs is an advantage compared to silicon. TCAD simulations of GaAs devices have been used to optimize a GaAs profile. Additionally, we have designed pulsers with sub 50ns reserve pump times and tested GaAs COTs PIN diodes in them.
Optimizing Cornell’s future geothermal district heating performance through systems engineering and simulation
Cornell University’s intention to lower its carbon footprint has motivated this engineering evaluation of using low-temperature geothermal energy to supply heat to the campus district energy system. Optimal selection and operation of heat pumps can significantly improve system performance. To support this analysis, we model the quantitative relationships between heat pump configurations and source flow, supply and distribution temperatures, facility heating design options, output heat generation, and carbon abatement outcomes. A systems approach is used for analysis, troubleshooting, and improvement of the model. The result of this effort is a dynamic tool that appropriately responds to hourly thermal demand and communicates energy response for techno-economic analysis. Simulations indicate that a single well-pair in the local Basement Contact Zone reservoir can satisfy almost 68% of annual thermal demand at nearly 20 MWth average capacity while remaining financially competitive compared to conventional heating with levelized cost of heating (LCOH) as low as $4.55/MMBTU ($15.53/MWh th ). A direct-use scheme with heat pump augmentation provided more carbon abatement and 50 to 150% more annual heat supply than one without augmentation. Exploration of three and four well-pair scenarios prove the capability to achieve 50 MW th baseload heating capacity with competitive LCOH and significant carbon reductions.
Unveiling the influence of selective-area-regrowth interfaces on local electronic properties of GaN p-n junctions for efficient power devices
Here, we report correlated nanoscale mapping of the structure, composition, and properties of regrown GaN p-n junctions to identify how etching and non-planar regrowth processes limit diode performance via the introduction of unintentional dopants and defect states. p-GaN was selectively regrown in n-GaN trenches with SiO 2 masks of variable mask-to-trench-width ratio. Dilute Al layers were periodically introduced during regrowth as markers of the growth interface. Correlated nanoscale mapping of doping, conductivity, and dopant complexes was achieved with atom probe tomography (APT), scanning spreading resistance microscopy (SSRM), and cathodoluminescence (CL) spectroscopy, respectively. The Al marker layers, detected by APT, enabled reconstruction of the faceted growth interface and correlation of the dopant concentration with position and time. The p-GaN growth rate is proportional to the mask-to-trench width ratio while the dopant incorporation rate is invariant. At trench edges, magnesium incorporation is suppressed, and oxygen incorporation enhanced, due to preferential incorporation on the semi-polar growth surface, leading to compensation and less abrupt p-n junctions; the SiO 2 mask is a source of oxygen. Residual etch damage below the regrowth interface induces n-type and p-type conductivity, creating leakage pathways. The non-uniform Mg incorporation is driven by crystal anisotropy and is thus inherent to non-planar regrowth, but can be mitigated by engineering the regrowth interface and process parameters. The unprecedented integration of spatially resolved mapping of dopants, impurities, conductivity, and carrier type is a powerful approach to discriminating distinct factors that limit the performance of regrown diodes, enabling the rational optimization of process and device design.
Effect of 3D-printed surface textures on wear mechanism in 3-body abrasion of soil
This study systematically investigates the enhancement of wear resistance in 3D printed surface textures through both experimental and theoretical approaches. Three distinct surface morphologies (Smooth Surface, Surface with uniformly distributed Pits, and Surface with uniformly distributed Bumps) were fabricated using High-Impact Polystyrene, where the meso-scale textures were precisely controlled through the 3D printing process. Wear behavior was evaluated using a 3-body wear tester in an abrasive particle environment, analyzing the influence of surface textures under various operating conditions. Systematic wear tests revealed that optimally designed surface textures achieved a remarkable 77 % reduction in wear compared to the worst-performing sample. The wear mechanisms were comprehensively characterized through weight loss measurements, Scanning Electron Microscopy (SEM), and Energy Dispersive Spectroscopy (EDS) analyses, elucidating the surface morphology changes and their interaction with wear particles. Notably, the study identified how the geometric characteristics of surface textures influence the movement of wear particles and the distribution of contact stresses. Discrete element method simulations corroborated the experimental findings, providing theoretical validation for the enhanced wear resistance of the optimal structure. The high correlation between simulated wear patterns and experimental results validates the reliability of the proposed design methodology. In conclusion, these results demonstrate that 3D printed surface texturing offers a cost-effective and scalable approach to significantly improve wear resistance in engineering applications, presenting a practical alternative to conventional, high-cost surface engineering methods.