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At least 73 records · Page 4

Testing of Helium Cooled Metal Molybdenum Disk Target System

NorthStar Medical Radioisotopes LLC is planning to produce the important medical radioisotope molybdenum-99 (Mo-99) through a photonuclear reaction on molybdenum-100 (Mo-100). In this approach, multiple thin disks of enriched molybdenum metal will be bombarded with a 40-MeV electron beam. Because enriched Mo-100 is expensive, we intend to use as much beam power as possible to achieve maximum production yield and minimize the size of the target. This requirement leads to very high beam power density (heat deposition in the target), which sets challenging requirements for cooling. Together with scientists at Los Alamos National Laboratory, a team at Argonne National Laboratory has developed and demonstrated a cooling approach using pressurized helium, which allows for efficient heat removal. One of the challenges in this approach is the management of the heat load on the target window. The target window separates the high-pressure helium atmosphere inside the target from the vacuum in the beamline, so it is constantly under stress from differential pressure. Also, the window is cooled only by the helium gas flow from one side, making the window design challenging. High heat deposition in the target disks also imposes a strict requirement on performance of the helium cooling system and thickness of the target disks. The target disks are produced from metal powder via a press-and-sinter process. The resulting disks do not possess as high a tensile strength as solid molybdenum and might not survive the vibration from the high-velocity helium coolant and the high thermal stress from beam heating.

42 ENGINEERING↗

Increasing Diversity in Energy Efficiency Professions: JUMP into STEM

A long-term approach to achieving equity in the energy efficiency industry is to increase the overall diversity of the professionals who work in the field. Building science professionals have a direct impact on energy efficiency policy and programs, as many become leaders within the industry. Buildings are used every day by almost all members of U.S. society, so diversity in building science professionals is critical to better understanding the behaviors and needs of the full spectrum of building occupants. Many groups of people, including minorities and women, are underrepresented in the industry. The industry needs to attract a wide range of current college and university students to careers in building science, so that many perspectives are included in the clean energy transition. The JUMP into STEM student competition addresses this need by inspiring students from diverse backgrounds to use creative ideation to solve real-world building science problems. Student teams - comprising an interdisciplinary mix of majors and underrepresented groups in science, technology, engineering, and math (STEM) - respond to one of three buildings-related challenges. Diversity is encouraged through challenge design, competition guidelines, and evaluation criteria for the submission. Winners of the competition are awarded paid building science internships, a gateway to a career in this field. The competition has an advisory panel of diversity experts and a professor team that includes representation from minority-serving institutions. This paper presents the JUMP into STEM approach to fostering diversity and creative ideation on technical challenges and discusses outcomes from the 2019 and 2020 competitions.

buildings↗

Subtleties of nanophotonic lithium niobate waveguides for on-chip evanescent wave sensing

Thin-film lithium niobate (TFLN) is promising for optical sensing due to its high nonlinearities, but its material properties present unique design challenges. We compare the sensing performance of the fundamental modes on a TFLN waveguide with a fluorescent dye sample. The TM mode has better overlap with the sample, with a 1.4 × greater sample absorption rate versus the TE mode. However, the TM mode also scatters at a 1.4 × greater rate, yielding less fluorescence overall. The TE mode is, therefore, more appropriate for sensing. Our findings have important implications for TFLN-based sensor designs.

optics↗

Folding Coarse-Grained Oligomer Models with PyRosetta

Non-biological foldamers are a promising class of macromolecules that share similarities to classical biopolymers such as proteins and nucleic acids. Currently, designing novel foldamers is a non-trivial process, often involving many iterations of trial synthesis and characterization until folded structures are observed. In this work, we aim to tackle these foldamer design challenges using computational modeling techniques. We developed CG PyRosetta, an extension to the popular protein folding python package, PyRosetta, which introduces coarse-grained (CG) residues into PyRosetta, enabling the folding of toy CG foldamer models. Although these models are simplified, they can help explore overarching physical hypotheses about how oligomers can form. Through systematic variation of CG parameters in these models, we can investigate various folding hypotheses at the CG scale to inform the design process of new foldamer chemistries. In this study, we demonstrate CG PyRosetta’s ability to identify minimum energy structures with a diverse structural search over a range of simple models, as well as two hypothesis-driven parameter scans investigating the effects of side-chain size and internal backbone angle on secondary structures. We are able to identify several types of secondary structures from single- and double-helices to sheet-like and knot-like structures. Here, we show how side-chain size and backbone bond angle both play an important role in the structure and energetics of these toy models. Optimal side-chain sizes promote favorable packing of side chains, while specific backbone bond angles influence the specific helix type found in folded structures.

36 MATERIALS SCIENCE↗

Introduction to the Demonstration of Pyroshock Response Prediction Methods Using NASA ShockSat Data Virtual Collection

Mechanical shocks, particularly pyroshocks, are environments that all spacecraft and launch vehicles experience. Pyroshocks can be challenging design environments for space hardware because these events are often high frequency, high intensity transients. Furthermore, since design decisions must be made relatively early in the design phase, understanding, and accurately defining pyroshock excitation and the loads that it imparts on components is important to ensure the high reliability required of spacecraft and rockets. Here, general guidelines about shock transmission through spacecraft structures are provided in NASA and MIL handbooks and standards, but these are relatively broad guidelines, and recent studies have shown that more accurate and less-conservative predictions are needed. However, predicting shock response accurately is still a challenge because of the high frequencies in a pyroshock excitation.

Babuska, Vit [Sandia National Laboratories (SNL-NM↗

The SENSEI Generic In Situ Interface: Tool and Processing Portability at Scale [Book Chapter]

One key challenge when doing in situ processing is the investment required to add code to numerical simulations needed to take advantage of in situ processing. Such instrumentation code is often specialized, and tailored to a specific in situ method or infrastructure. Then, if a simulation wants to use other in situ tools, each of which has its own bespoke API [4], then the simulation code team will quickly become overwhelmed with having a different set of instrumentation APIs, one per in situ tool or method. In an ideal situation, such instrumentation need happen only once, and then the instrumentation API provides access to a large diversity of tools. In this way, a data producer’s instrumentation need not be modified if the user desires to take advantage of a different set of in situ tools. The SENSEI generic in situ interface addresses this challenge, which means that SENSEI-instrumented codes enjoy the benefit of being able to use a diversity of tools at scale, tools that include Libsim, Catalyst, Ascent, as well as user-defined methods written in C++ or Python. SENSEI has been shown to scale to greater than 1M-way concurrency on HPC platforms, and provides support for a rich and diverse collection of common scientific data models. Furthermore, this chapter presents the key design challenges that enable tool and processing portability at scale, some performance analysis, and example science applications of the methods.

Bethel, E. Wes↗

Oversizing and Part-Load Problems

Oversizing, the common engineering practice of specifying devices with capacity exceeding the actual load requirement, is a widespread practice across virtually all building technologies end-use categories, including HVAC, electrical systems, lighting, appliances, and plug loads. This practice, driven by factors like design uncertainty, institutional pressures, and risk aversion, results in wasted capital investment, control difficulties, and excessive energy consumption due to inefficient part-load operation. Part-load operation, where devices run below maximum capacity, is the dominant operating mode in most energy systems and presents a complex design challenge. Solutions to match output to load fall into three broad categories: constraining the output, adjusting the device’s internal behavior, and linking output to energy storage or other waste-heat reuse applications. The energy implications of part-load are critical, as efficiency often drops sharply as load decreases across a wide diversity of devices. To quantify the extent of this problem, we derive a dimensionless Part-Load Metric (PLM) based on device efficiency and its frequency distribution of operating hours at various output levels. The PLM quantifies the deviation of a device's actual efficiency from its maximum design efficiency. This metric also serves as a measure of "capital inefficiency," enabling engineers to compare the impact of different part-load solutions and providing a unified framework for evaluating performance across various devices and systems.

Meier, Alan↗

Machined phononic crystals to block high-order Lamb waves and crosstalk in through-metal ultrasonic communication systems

For systems that require complete metallic enclosures (e.g., containment buildings for nuclear reactors), it is impossible to access interior sensors and equipment using standard electromagnetic techniques. A viable way to communicate and supply power through metallic barriers is the use of elastic waves and ultrasonic transducers, introducing several design challenges that must be addressed. Specifically, the use of multiple communication channels on the same enclosure introduces an additional mechanism for signal crosstalk between channels: guided waves propagating in the barrier between channels. This work numerically and experimentally investigates a machined phononic crystal to block MHz Lamb wave propagation between ultrasonic communication channels, greatly reducing wave propagation and the resulting crosstalk voltage. Blind grooves are machined into one or both sides of a metallic barrier to introduce a periodic unit cell, greatly altering the guided wave dispersion in the barrier. Numerical simulations are used to determine a set of groove geometries for testing, and experiments were performed to characterize the wave-blocking performance of each design. The best-performing design was tested using piezoelectric transducers bonded to the barrier, showing a 14.4 dB reduction in crosstalk voltage. Overall, the proposed periodic grooving method is a promising technique for completely isolating ultrasonic power/data transfer systems operating in a narrow frequency range.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

High Throughput Data-Driven Design of Laser-Crystallized 2D MoS 2 Chemical Sensors: A Demonstration for NO 2 Detection

High throughput characterization and processing techniques are becoming increasingly necessary to navigate multivariable, data-driven design challenges for sensors and electronic devices. For two-dimensional materials, device performance is highly dependent upon a vast array of material properties including the number of layers, lattice strain, carrier concentration, defect density, and grain structure. In this work, laser crystallization was used to locally pattern and transform hundreds of regions of amorphous MoS 2 thin films into 2D 2H-MoS 2 . Here a high throughput Raman spectroscopy approach was subsequently used to assess the process-dependent structural and compositional variations for each illuminated region, yielding over 6000 distinct nonresonant, resonant, and polarized Raman spectra. The rapid generation of a comprehensive library of structural and compositional data elucidated important trends between structure–property processing relationships involving laser-crystallized MoS 2 , including the relationships between grain size, grain orientation, and intrinsic strain. Moreover, extensive analysis of structure/property relationships allowed for intelligent design and evaluation of major contributions to device performance in MoS 2 chemical sensors. In particular, it is found that NO 2 sensor performance is strongly dependent on the orientation of the MoS 2 grains relative to the crystal plane.

36 MATERIALS SCIENCE↗

Integrated design of aluminum-enriched high-entropy refractory B2 alloys with synergy of high strength and ductility

Refractory high-entropy alloys (RHEAs) are promising high-temperature structural materials. Their large compositional space poses great design challenges for phase control and high strength-ductility synergy. The present research pioneers using integrated high-throughput machine learning with Monte Carlo simulations supplemented by ab initio calculations to effectively navigate phase selection and mechanical property predictions, developing single-phase ordered B2 aluminum-enriched RHEAs (Al-RHEAs) demonstrating high strength and ductility. These Al-RHEAs achieve remarkable mechanical properties, including compressive yield strengths up to 1.7 gigapascals, fracture strains exceeding 50%, and notable high-temperature strength retention. They also demonstrate a tensile yield strength of 1.0 gigapascals with a ductility of 9%, albeit with B2 ordering. Furthermore, we identify valence electron count domains for alloy ductility and brittleness with the explanation from density functional theory and provide crucial insights into elemental influence on atomic ordering and mechanical performance. The work sets forth a strategic blueprint for high-throughput alloy design and reveals fundamental principles governing the mechanical properties of advanced structural alloys.

Science & Technology - Other Topics↗

Scaling Superconducting Quantum Computers with Chiplet Architectures

Fixed-frequency transmon quantum computers (QCs) have advanced in coherence times, addressability, and gate fidelities. Unfortunately, these devices are restricted by the number of on-chip qubits, capping processing power and slowing progress toward fault-tolerance. Although emerging transmon devices feature over 100 qubits, building QCs large enough for meaningful demonstrations of quantum advantage requires overcoming many design challenges. For example, today’s transmon qubits suffer from significant variation due to limited precision in fabrication. As a result, barring significant improvements in current fabrication techniques, scaling QCs by building ever larger individual chips with more qubits is hampered by device variation. Severe device variation that degrades QC performance is referred to as a defect. Here, we focus on a specific defect known as a frequency collision. When transmon frequencies collide, their difference falls within a range that limits two-qubit gate fidelity. Frequency collisions occur with greater probability on larger QCs, causing collision-free yields to decline as the number of on-chip qubits increases. As a solution, we propose exploiting the higher yields associated with smaller QCs by integrating quantum chiplets within quantum multi-chip modules (MCMs). Yield, gate performance, and application-based analysis show the feasibility of QC scaling through modularity. Our results demonstrate that chiplet architectures, relative to monolithic designs, benefit from average yield improvements ranging from 9.6 – 92.6 × for ≲5 qubit machines. In addition, our simulations explore the design space of chiplet systems and discover configurations that demonstrate average two-qubit gate infidelity reductions that are at best 0.815 × their monolithic counterpart. Lastly, we observe that carefully-selected modular systems achieve fidelity improvements on a range of benchmark circuits.

quantum architecture↗

Phase stability and tensorial thermal expansion properties of single to high-entropy rare-earth disilicates

Temperature limitations in nickel-base superalloys have resulted in the emergence of SiC-based ceramic matrix composites as a viable replacement for gas turbine components in aviation applications. Higher operating temperatures allow for reduced fuel consumption but present a materials design challenge related to environmental degradation. Rare-earth disilicates (RE 2 Si 2 O 7 ) have been identified as coatings that can function as environmental barriers and minimize hot component degradation. In this work, single- and multiple-component rare-earth disilicate powders were synthesized via a sol-gel method with compositions selected to exist in the monoclinic C 2/m phase (β phase). Phase stability in multiple cation compositions was shown to follow a rule of mixtures and the C 2/m phase could be realized for compositions that contained up to 25% dysprosium, which typically only exists in a triclinic, P $\bar1$, phase. All compositions exhibited phase stability from room temperature to 1200°C as assessed by X-ray diffraction. The thermal expansion tensors for each composition were determined from high-temperature synchrotron X-ray diffraction and accompanying Rietveld refinements. It was observed that ytterbium-containing compositions had larger changes in the α 31 shear component with increasing temperature that led to a rotation of the principal axes. Principal axes rotation of up to 47° were observed for ytterbium disilicate. The results suggest that microstructure design and crystallographic texture may be essential future avenues of investigation to ensure thermo-mechanical robustness of rare-earth disilicate environmental barrier coatings.

36 MATERIALS SCIENCE↗

The Artificial Scientist: in-Transit Machine Learning of Plasma Simulations

Large-scale simulations or scientific experiments produce petabytes of data per run. This poses massive challenges for I/O and storage when scientific analysis workflows are run manually offline. Unsupervised deep learning-based techniques to extract patterns and non-linear relations from these large amounts of data provide a way to build scientific understanding from raw data, reducing the need for manual pre-selection of analysis steps, but require exascale compute and memory to process the full dataset available. In this paper, we demonstrate a heterogeneous streaming workflow in which plasma simulation data is streamed directly to a Machine Learning (ML) application training a model on the simulation data in-transit, completely circumventing the capacity-constrained filesystem bottleneck. This workflow employs openPMD to provide a high level interface to describe scientific data and also uses ADIOS2, to transfer volumes of data that exceed the capabilities of the filesystem. We employ experience replay to avoid catastrophic forgetting in learning from this non-steady state process in a continual manner and adapt it to improve model convergence while learning in-transit. As a proof-of-concept, we approach the ill-posed inverse problem of predicting particle dynamics from radiation in a particle-incell (PIConGPU) simulation of the Kelvin-Helmholtz instability (KHI). We detail hardware-software co-design challenges as we scale PIConGPU to full Frontier, the Top-1 system as of June 2024 Top500 list.

Kelling, Jeffrey [Helmholtz-Zentrum Dresden Rossen↗

Structural and Optical Properties of High Entropy (La,Lu,Y,Gd,Ce)AlO 3 Perovskite Thin Films

Mixtures of Ce-doped rare-earth aluminum perovskites are drawing a significant amount of attention as potential scintillating devices. However, the synthesis of complex perovskite systems leads to many challenges. Designing the A-site cations with an equiatomic ratio allows for the stabilization of a single-crystal phase driven by an entropic regime. This work describes the synthesis of a highly epitaxial thin film of configurationally disordered rare-earth aluminum perovskite oxide (La 0.2 Lu 0.2 Y 0.2 Gd 0.2 Ce 0.2 )AlO 3 and characterizes the structural and optical properties. The thin films exhibit three equivalent epitaxial domains having an orthorhombic structure resulting from monoclinic distortion of the perovskite cubic cell. An excitation of 286.5 nm from Gd 3+ and energy transfer to Ce 3+ with 405 nm emission are observed, which represents the potential for high-energy conversion. These experimental results also offer the pathway to tunable optical properties of high-entropy rare-earth epitaxial perovskite films for a range of applications.

36 MATERIALS SCIENCE↗

Hierarchical Gaussian process-based Bayesian optimization for materials discovery in high entropy alloy spaces

Bayesian optimization (BO) is a powerful and data-efficient method for iterative materials discovery and design, particularly valuable when prior knowledge is limited, underlying functional relationships are complex or unknown, and the cost of querying the materials space is significant. Traditional BO methodologies typically utilize conventional Gaussian Processes (cGPs) to model the relationships between material inputs and properties, as well as correlations within the input space. However, cGP-BO approaches often fall short in multi-objective optimization scenarios, where they are unable to fully exploit correlations between distinct material properties. Leveraging these correlations can significantly enhance the discovery process, as information about one property can inform and improve predictions about others. Here, this study addresses this limitation by employing advanced kernel structures to capture and model multi-dimensional property correlations through multi-task (MTGPs) or deep Gaussian Processes (DGPs), thus accelerating the discovery process. We demonstrate the effectiveness of MTGP-BO and DGP-BO in rapidly and robustly solving complex materials design challenges that occur within the context of complex multi-objective optimization over FCC FeCrNiCoCu high entropy alloy (HEA) spaces, where traditional cGP-BO approaches fail. Furthermore, we highlight how the differential costs associated with querying various material properties can be strategically leveraged to make the materials discovery process more cost-efficient.

36 MATERIALS SCIENCE↗

Coal-derived graphene foam and micron-sized silicon composite anodes for lithium-ion batteries

Silicon-based materials demonstrate significant potential as lithium-ion batteries (LIBs) anode, but their expansion and degradation present engineering design challenges for commercial application. In this study, a porous three-dimensional (3D) graphene and micron-sized silicon composite anode (Si@G foam) was synthesized using humic acid (HA) derived from coal as a graphene precursor. In-situ formation of graphene structure through reducing HA was confirmed by Raman spectra. The reduced HA (rHA) shows similar electrical conductivity compared to the commercial conductive carbon. SEM images depict a 3D skeleton of coal-derived graphene, with silicon particles distributed on the 3D graphene foam's internal surface. The Si@G composite-anode displays a good reversible capacity of ~656 mAh/g at a current density of 50 mA/g, as well as a high-rate capability of ~433 mAh/g at a current density of 800 mA/g, and outstanding cycling stability –89.8% capacity retention after 300 cycles, which is significantly higher than that of other foam structures. During lithiation and de-lithiation, the graphene foam serves as a matrix of electrical conductors and a volume expansion support for silicon. This 3D graphene network will be beneficial for developing advanced silicon-based anodes for high-performance LIBs.

25 ENERGY STORAGE↗

Integral and Separate Effects Test Facilities To Support Water Cooled Small Modular Reactors: A Review

This study reviews previous experimental facilities and test programs relevant to water-cooled reactor system design and analysis to meet regulatory compliances. This study aims to find the best solution for designing the required experiments, obtaining necessary test data, and verifying the developed computer code/models to support the new reactor design and development while minimizing cost and time while leveraging experiences from previous facilities to minimize. Nuclear reactor licensing requires supportive design, analysis, and experimental results to ensure the safety of the full-scale prototype reactor in regular operation, as well as during postulated accident scenarios. These reactor design analyses are generally performed using system codes and other associated simulation tools that require assessment, verification and validation using an appropriate experimental dataset. Experimental facilities used for reactor system safety analysis and system code assessments are categorized into integral effects test (IET) and separate effects test (SET) facilities. Further, these IET and SET experiments and studies use geometrically scaled systems to reproduce the prototype system behavior at a reasonable cost, albeit with some scaling-related distortions. The design challenge of these model facilities is to identify and minimize scaling distortions while reproducing the most important operational phenomena in steady-state operation and in postulated accident scenarios. Lessons learned from previous experimental facilities, models, and correlations can support the development of new multipurpose, scaled, hybrid, integrated, and modular experimental facilities for advanced light water-cooled small modular reactors (SMRs). Successful operation of these facilities can significantly reduce upfront reactor development and demonstration costs and time to deployment.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Structure Sensitive Reaction Kinetics of Chiral Molecules on Intrinsically Chiral Surfaces

Enantiospecific heterogeneous catalysis utilizes chiral surfaces to resolve enantiomers via structure sensitive surface chemistry. The catalyst design challenge is the identification of chiral surface structures that maximize enantiospecificity. Herein, we develop data driven models for the enantiospecificity of tartaric acid reactions on chiral Cu(hkl) R&S surfaces. Measurements of enantiospecific rate constants were obtained by using curved Cu(hkl) R&S surfaces that enable kinetic measurements on hundreds of chiral surface orientations. One model uses feature vectors derived from generalized coordination numbers to capture the local structure around Cu atoms exposed by the Cu(hkl) R&S surfaces. The second model introduces the use of chiral cubic harmonic functions to capture the symmetry constraints of the face-centered cubic Cu structure. The model using 58 generalized coordination numbers has a fitting error similar to that of the model using only 5 cubic harmonic functions. The two models predict maxima in the enantiospecificity on surfaces with very similar surface orientations. The models developed in this work are applicable for any enantiospecific reaction happening on any chiral material with a cubic lattice structure, opening the way to understanding the surface structure sensitivity of the enantiospecific reaction kinetics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗