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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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This poster will discuss model-driven setup of CEBAF for the 12GeV era, focusing on the elegant Download Tool (eDT). eDT is a new operator tool that generates magnet design setpoints for various machine energies and pass configurations. eDT was developed in the effort towards a process for reducing machine configuration time and reproducibility by way of an accurate accelerator model.
Subsurface bacteria including sulfate reducing bacteria (SRB) reduce soluble U(VI) to insoluble U(IV) with subsequent precipitation of UO 2 . We have shown that SRB reduce U(VI) to nanometer-sized UO 2 particles (1-5 nm) which are both intra- and extracellular, with UO 2 inside the cell likely physically shielded from subsequent oxidation processes. We evaluated the UO 2 nanoparticles produced by Desulfovibrio desulfuricans G20 under growth and non-growth conditions in the presence of lactate or pyruvate and sulfate, thiosulfate, or fumarate, using ultrafiltration and HR-TEM. Results showed that a significant mass fraction of bioreduced U (35-60%) existed as a mobile phase when the initial concentration of U(VI) was 160 µM. Further experiments with different initial U(VI) concentrations (25 - 900 M) in MTM with PIPES or bicarbonate buffers indicated that aggregation of uraninite depended on the initial concentrations of U(VI) and type of buffer. It is known that under some conditions SRB-mediated UO 2 nanocrystals can be reoxidized (and thus remobilized) by Fe(III)-(hydr)oxides, common constituents of soils and sediments. To elucidate the mechanism of UO 2 reoxidation by Fe(III) (hydr)oxides, we studied the impact of Fe and U chelating compounds (citrate, NTA, and EDTA) on reoxidation rates. Experiments were conducted in anaerobic batch systems in PIPES buffer. Results showed EDTA significantly accelerated UO 2 reoxidation with an initial rate of 9.5 M day-1 for ferrihydrite. In all cases, bicarbonate increased the rate and extent of UO 2 reoxidation with ferrihydrite. The highest rate of UO 2 reoxidation occurred when the chelator promoted UO 2 and Fe(III) (hydr)oxide dissolution as demonstrated with EDTA. When UO 2 dissolution did not occur, UO 2 reoxidation likely proceeded through an aqueous Fe(III) intermediate as observed for both NTA and citrate. To complement to these laboratory studies, we collected U-bearing samples from a surface seep at the Rifle field site and have measured elevated U concentrations in oxic iron-rich sediments. To translate experimental results into numerical analysis of U fate and transport, a reaction network was developed based on Sani et al. (2004) to simulate U(VI) bioreduction with concomitant UO 2 reoxidation in the presence of hematite or ferrihydrite. The reduction phase considers SRB reduction (using lactate) with the reductive dissolution of Fe(III) solids, which is set to be microbially mediated as well as abiotically driven by sulfide. Model results show the oxidation of HS– by Fe(III) directly competes with UO 2 reoxidation as Fe(III) oxidizes HS– preferentially over UO 2 . The majority of Fe reduction is predicted to be abiotic, with ferrihydrite becoming fully consumed by reaction with sulfide. Predicted total dissolved carbonate concentrations from the degradation of lactate are elevated (log(pCO 2 ) ~ –1) and, in the hematite system, yield close to two orders-of-magnitude higher U(VI) concentrations than under initial carbonate concentrations of 3 mM. Modeling of U(VI) bioreduction with concomitant reoxidation of UO 2 in the presence of ferrihydrite was also extended to a two-dimensional field-scale groundwater flow and biogeochemically reactive transport model for the South Oyster site in eastern Virginia. This model was developed to simulate the field-scale immobilization and subsequent reoxidation of U by a biologically mediated reaction network.
Advanced energy systems based on molten salt technologies have gained increasing interest, and their design and construction are likely going to involve the use of materials of dissimilar chemistries. With dissimilar materials being used, galvanic coupling induced corrosion acceleration is one of the mechanisms believed to be an issue for the deployment of molten salt systems. However, non-galvanic interaction mechanisms such as activity gradient mass transport have also been theorized to represent a potential challenge. Unfortunately, the two effects have often been compounded in the literature, such that it is still unclear which degradation mechanisms are at play in these systems. In the following study, electrochemical measurements are used to predict the effect that galvanic coupling would have on dissimilar materials submerged in the same molten salt medium. Dissimilar material salt exposure tests are then performed to investigate whether galvanic coupling or activity gradient driven processes account for the levels of materials degradation observed. Finally, these separate effect electrochemical and corrosion experiments uniquely elucidate the degradation mechanisms in dissimilar materials systems.
Abstract Machine learning models with uncertainty quantification have recently emerged as attractive tools to accelerate the navigation of catalyst design spaces in a data-efficient manner. Here, we combine active learning with a dropout graph convolutional network (dGCN) as a surrogate model to explore the complex materials space of high-entropy alloys (HEAs). We train the dGCN on the formation energies of disordered binary alloy structures in the Pd-Pt-Sn ternary alloy system and improve predictions on ternary structures by performing reduced optimization of the formation free energy, the target property that determines HEA stability, over ensembles of ternary structures constructed based on two coordinate systems: (a) a physics-informed ternary composition space, and (b) data-driven coordinates discovered by the Diffusion Maps manifold learning scheme. Both reduced optimization techniques improve predictions of the formation free energy in the ternary alloy space with a significantly reduced number of DFT calculations compared to a high-fidelity model. The physics-based scheme converges to the target property in a manner akin to a depth-first strategy, whereas the data-driven scheme appears more akin to a breadth-first approach. Both sampling schemes, coupled with our acquisition function, successfully exploit a database of DFT-calculated binary alloy structures and energies, augmented with a relatively small number of ternary alloy calculations, to identify stable ternary HEA compositions and structures. This generalized framework can be extended to incorporate more complex bulk and surface structural motifs, and the results demonstrate that significant dimensionality reduction is possible in thermodynamic sampling problems when suitable active learning schemes are employed.
The Department of Energy, Office of Science operates world-leading facilities for experimental, observational, and computational science. DOE supercomputing facilities will reach performance at the scale of ExaFLOPs in the coming years, enabling new vistas of scale and precision for large scale simulations and data analysis. Experimental scientific facilities are undergoing similar upgrades that will lead to higher data rates and correspondingly larger computational demands, and will increase the need for near-real-time processing and resilient support for more complex workflows. A transformation of science is underway, with workloads at supercomputing facilities increasingly driven by this explosion of data from instruments and experimental facilities, as well as the accelerating use of Artificial Intelligence (AI) as a tool for scientific discovery. A seamless integration of computing, networking, instruments, and experimental facilities is required to support these emerging workloads and open up a new frontier of U.S. leadership in scientific discovery. We propose to accomplish this by providing frictionless access to the ASCR supercomputing facilities. We describe our vision of combining the power of ASCR supercomputers and networking infrastructure into an integrated scalable fabric, available to end user scientists via interfaces that aim to automate and simplify access to high performance computing systems. This will enable unprecedented computational science capabilities for experimental and observational facilities, and will create new opportunities to combine large simulations and modeling with experimental facility data analysis. This blueprint for creating an integrated network of computational and experimental facilities will provide an enriched discovery environment and open doors for new scientific communities to access the DOE’s world-leading computing and networking capabilities.
The MBX software provides an advanced platform for molecular dynamics simulations, leveraging state-of-the-art MB-pol and MB-nrg data-driven many-body potential energy functions. Developed over the past decade, these potential energy functions integrate physics-based and machine-learned many-body terms trained on electronic structure data calculated at the "gold standard" coupled-cluster level of theory. Recent advancements in MBX have focused on optimizing its performance, resulting in the release of MBX v1.2. While the inherently many-body nature of MB-pol and MB-nrg ensures high accuracy, it poses computational challenges. MBX v1.2 addresses these challenges with significant performance improvements, including enhanced parallelism that fully harnesses the power of modern multicore CPUs. In conclusion, these advancements enable simulations on nanosecond time scales for condensed-phase systems, significantly expanding the scope of high-accuracy, predictive simulations of complex molecular systems powered by data-driven many-body potential energy functions.
In the state of Leidenfrost boiling, liquid drops are observed to vibrate in a variety of modal patterns. Theories are presented which predict the frequency of oscillation and show that the observed model patterns of drops correspond to the minimum energy oscillatory excitation state. High-speed photographic techniques were used to record these motions and substantiate the theories. An incipient temperature was also found for water drops in film boiling below which free oscillations do not exist. In addition to these oscillations, photographic sequences are presented which show that wave motion can exist along the circumference of the drop. Following the study of free oscillations, the system was mounted on a shaker table and the drop subjected to a range of forced frequencies and accelerations.
In the state of Leidenfrost boiling, liquid drops are observed to vibrate in a variety of modal patterns. Theories are presented which predict the frequency of oscillation and show that the observed modal patterns of drops correspond to the minimum energy oscillatory excitation state. High-speed photographic techniques were used to record these motions and substantiate the theories. An incipient temperature was also found for water drops in film boiling below which free oscillations do not exist. In addition to these oscillations, photographic sequences are presented which show that wave motion can exist along the circumference of the drop. Following the study of free oscillations, the system was mounted on a shaker table and the drop subjected to a range of forced frequencies and accelerations.
Abstract not provided
Data centers (DCs) serve as critical infrastructure for powering the growth and evolution of AI. Next-generation AI DCs present unique challenges in thermal management driven by unprecedented computational demands. This paper provides a comprehensive summary of key stakeholder perspectives on technology gaps, infrastructure requirements, test bed needs, emerging opportunities, and preliminary solutions related to thermal management for AI DCs. It establishes six strategic pillars of thermal management for next generation AI DC: reliability, deployability, efficiency, resilience, measurability, and valorization. The discussion spans a range of critical topics, including advanced cooling technologies, thermal strategies for emerging modular and edge DCs, system-level optimization and control frameworks, infrastructure planning and grid integration designs, benchmarking approaches, and pathways for waste heat recovery and reuse. The proposed research, development, and demonstration efforts are aimed at accelerating the deployment of AI DCs while ensuring energy efficiency, reliability, safety, and regulatory compliance.
A reformation in energy is underway to replace fossil fuels with renewable sources, driven by the development of new, robust, and multi-functional materials. High-entropy materials (HEMs) have emerged as promising candidates for various green energy applications, having unusual chemistries that give rise to remarkable functionalities. This review examines recent innovations in HEMs, focusing on hydrogen generation/storage, fuel cells, batteries, semiconductors/electronics, and catalysis—where HEMs have demonstrated the ability to outperform state-of-the-art materials. We present new master plots that illustrate the superior performance of HEMs compared to conventional systems for hydrogen generation/storage and heat-to-electricity conversion. We highlight the role of computational methods, such as density functional theory and machine learning, in accelerating the discovery and optimization of HEMs. The review also presents current challenges and proposes future directions for the field. We emphasize the need for continued integration of modeling, data, and experiments to investigate and leverage the underlying mechanisms of the HEMs that are powering progress in sustainable energy.
Programmable accelerators have become commonplace in modern computing systems. Advances in programming models and the availability of massive amounts of data have created a space for massively parallel accelerators capable of maintaining context for thousands of concurrent threads resident on-chip. These threads are grouped and interleaved on a cycle-by-cycle basis among several massively parallel computing cores. One path for the design of future supercomputers relies on an ability to model the performance of these massively parallel cores at scale. The SST framework has been proven to scale up to run simulations containing tens of thousands of nodes. A previous report described the initial integration of the open-source, execution-driven GPU simulator, GPGPU-Sim, into the SST framework. This report discusses the results of the integration and how to use the new GPU component in SST. It also provides examples of what it can be used to analyze and a correlation study showing how closely the execution matches that of a Nvidia V100 GPU when running kernels and mini-apps.
Programmable accelerators have become commonplace in modern computing systems. Advances in programming models and the availability of unprecedented amounts of data have created a space for massively parallel accelerators capable of maintaining context for thousands of concurrent threads resident on-chip. These threads are grouped and interleaved on a cycle-by-cycle basis among several massively parallel computing cores. One path for the design of future supercomputers relies on an ability to model the performance of these massively parallel cores at scale. The SST framework has been proven to scale up to run simulations containing tens of thousands of nodes. A previous report described the initial integration of the open-source, execution-driven GPU simulator, GPGPU-Sim, into the SST framework. This report discusses the results of the integration and how to use the new GPU component in SST. It also provides examples of what it can be used to analyze and a correlation study showing how closely the execution matches that of a Nvidia V100 GPU when running kernels and mini-apps.
Microtubules (MTs) constitute the largest components of the eukaryotic cytoskeleton and play crucial roles in various cellular processes, including mitosis and intracellular transport. The property allowing MTs to cater to such diverse roles is attributed to dynamic instability, which is coupled to the hydrolysis of GTP (guanosine-5'-triphosphate) to GDP (guanosine-5'-diphosphate) within the β-tubulin monomers. Understanding the equilibrium dynamics and the structural features of both GDP- and GTP-complexed MT tips, especially at an all-atom level, remains challenging for both experimental and computational methods because of their dynamic nature and the prohibitive computational demands of simulating large, many-protein systems. This study employs the “equation-free” multiscale computational method to accelerate the relaxation of all-atom simulations of MT tips toward their putative equilibrium conformation. Using large MT lattice systems (14 protofilaments × 8 heterodimers) comprising ~21-38 million atoms, we applied this multiscale approach to leapfrog through time and nearly double the computational efficiency in realizing relaxed all-atom conformations of GDP- and GTP-complexed MT tips. Commencing from an initial 4 μs unbiased all-atom simulation, we interleave coarse projective “equation-free” jumps with short bursts of all-atom molecular dynamics simulation to realize an additional effective simulation time of 1.875 μs. Our 5.875 μs of effective simulation trajectories for each system expose the subtle yet essential differences in the structures of MT tips as a function of whether β-tubulin monomer is complexed with GDP or GTP, as well as the lateral interactions within the MT tip, offering a refined understanding of features underlying MT dynamic instability. Furthermore, the approach presents a robust and generalizable framework for future explorations of large biomolecular systems at atomic resolution.
In this article, we present an end-to-end workflow for superconducting qubit readout that embeds codesigned neural networks into the quantum instrumentation control kit (QICK). Capitalizing on the custom firmware and software of the QICK platform, which is built on Xilinx radiofrequency system-on-chip field-programmable gate arrays (FPGAs), we aim to leverage machine learning (ML) to address critical challenges in qubit readout accuracy and scalability. The workflow utilizes the hls4ml package and employs quantization-aware training to translate ML models into hardware-efficient FPGA implementations via user-friendly Python application programming interfaces. We experimentally demonstrate the design, optimization, and integration of an ML algorithm for single transmon qubit readout, achieving 96% single-shot fidelity with a latency of 32.25 ns and less than 16% FPGA lookup table resource utilization. Our results offer the community an accessible workflow to advance ML-driven readout and adaptive control in quantum information processing applications.
The Z-pinch phase of a dense plasma focus (DPF) heats the plasma by rapid compression and accelerates ions across its intense electric fields, producing neutrons through both thermonuclear and beam-target fusion. Driver characteristics have empirically been shown to affect performance, as measured by neutron yield per unit of stored energy. We are exploring the effect of driver characteristics on DPF performance using particle-in-cell (PIC) simulations of a kJ scale DPF. In this work, our PIC simulations are fluid for the run-down phase and transition to fully kinetic for the pinch phase, capturing kinetic instabilities, anomalous resistivity, and beam formation during the pinch. The anode-cathode boundary is driven by a circuit model of the capacitive driver, including system inductance, the load of the railgap switches, the guard resistors, and the coaxial transmission line parameters. It is known that the driver impedance plays an important role in the neutron yield: first, it sets the peak current achieved at pinch time; and second, it affects how much current continues to flow through the pinch when the pinch inductance and resistance suddenly increase. Here we show from fully kinetic simulations how total neutron yield depends on the impedance of the driver and the distributed parameters of the transmission circuit. Direct comparisons between the experiment and simulations enhance our understanding of these plasmas and provide predictive design capability for neutron source applications.
Many of the most important and hardest-to-solve problems related to the synthesis, performance, and aging of materials involve diffusion through the material or along surfaces and interfaces. These diffusion processes are driven by motions at the atomic scale, but traditional atomistic simulation methods such as molecular dynamics are limited to very short timescales on the order of the atomic vibration period (less than a picosecond), while macroscale diffusion takes place over timescales many orders of magnitude larger. We have completed an LDRD project with the goal of developing and implementing new simulation tools to overcome this timescale problem. In particular, we have focused on two main classes of methods: accelerated molecular dynamics methods that seek to extend the timescale attainable in atomistic simulations, and so-called 'equation-free' methods that combine a fine scale atomistic description of a system with a slower, coarse scale description in order to project the system forward over long times.
Here, nanomaterials must be systematically designed to be technologically viable. Driven by optimizing intermolecular interactions, current designs are too rigid to plug in new chemical functionalities and cannot mitigate condition differences during integration. Despite extensive optimization of building blocks and treatments, accessing nanostructures with the required feature sizes and chemistries is difficult. Programming their growth across the nano-to-macro hierarchy also remains challenging, if not impossible. To address these limitations, we should shift to entropy-driven assemblies to gain design flexibility, as seen in high-entropy alloys, and program nanomaterial growth to kinetically match target feature sizes to the mobility of the system during processing. In tis report, following a micro-then-nano growth sequence in ternary composite blends composed of block-copolymer-based supramolecules, small molecules and nanoparticles, we successfully fabricate high-performance barrier materials composed of more than 200 stacked nanosheets (125 nm sheet thickness) with a defect density less than 0.056 µm -2 and about 98% efficiency in controlling the defect type. Contrary to common perception, polymer-chain entanglements are advantageous to realize long-range order, accelerate the fabrication process (<30 min) and satisfy specific requirements to advance multilayered film technology. This study showcases the feasibility, necessity and unlimited opportunities to transform laboratory nanoscience into nanotechnology through systems engineering of self-assembly.