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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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NASA NTRS2024

Helium Conservation by Diffusion Limited Purging of Liquid Hydrogen Tanks

Recently, the new 1.2-million-gallon liquid hydrogen (LH 2 ) storage tank at the Kennedy Space Center Launch Complex 39 was purged, replacing nitrogen with helium, using a piston purge process. This purging approach had been used on Space Shuttle External Tanks (ET) in the past, but not on one of the large LH 2 tanks. The result was a huge success, potentially saving more than 1 million cubic feet of helium and significant labor. To better understand this result, a diffusion-based purge model was developed. The model predictions accurately match the helium concentration data, providing understanding of the helium savings and suggesting how to better optimize the purging of this large tank for future operations. The model was then applied to the piston purging of the Space Shuttle ETs, showing that these purges were not optimal, but that the amount of wasted helium was not excessive and that additional helium might have been saved by small changes to the process. The Space Shuttle Program (SSP) has ended, but these results are applicable to the purging of future large cylindrical LH 2 tanks. Finally, data have been obtained on inerting the Space Launch System (SLS) Core Stage (CS) LH 2 tank, replacing hydrogen with helium after an aborted launch. This is a more complicated case, but the model predicts that substantial helium might be saved by modifying the current purging process.

Helium Conservation↗
DOE OSTI2024

Learning model combining convolutional deep neural network with a self-attention mechanism for AC optimal power flow

Alternating current optimal power flow (OPF) analysis is critical for efficient and reliable operation of power systems. For large systems or repetitive computations, the traditional methods such as the direct and gradient methods, or non-traditional methods, such as the genetic algorithm and simulating annealing, are time-consuming and unsuitable for real-time computing. The work in this paper proposes a novel framework to obtain the optimal solution of power flow in real-time using a combination of convolutional neural networks and a self-attention mechanism. All parameters of the power networks are rearranged in an image-like shape of a multi-channel image where each channel is a two-dimensional matrix. The proposed approach is adaptive with every input size of power systems as well as frequent variations of network topologies without intervention to the framework core. The encompassment of all power system contexts in which all parameters of internal elements, generation costs, and topology information are included, contributes to the higher accuracy of inference compared to other current machine-learning-based OPF-solving methods. Besides, the proposed framework established on ubiquitous platforms is effortlessly integrated into current infrastructures of power systems, and the great efficiency along with the computation speed may serve as a critical point for practical implications, such as enabling faster decision-making during real-time operations, predicting system contingencies, and remedial actions based on an offline pre-trained model. Furthermore, this supervised learning process is applied to the dataset of four case studies of meshed power systems: the IEEE 5-bus system (IEEE-5), the IEEE 30-bus system (IEEE-30), the IEEE 39-bus system (IEEE-39), and the IEEE 57-bus system (IEEE-57) to prove the efficacy of the proposed method.

42 ENGINEERING↗
DOE OSTI2024

Ternary complexes of isopentenyl phosphate kinase from Thermococcus paralvinellae reveal molecular determinants of non‐natural substrate specificity

Abstract Isopentenyl phosphate kinases (IPKs) have recently garnered attention for their central role in biocatalytic “isoprenol pathways,” which seek to reduce the synthesis of the isoprenoid precursors to two enzymatic steps. Furthermore, the natural promiscuity of IPKs toward non‐natural alkyl‐monophosphates (alkyl‐Ps) as substrates has hinted at the isoprenol pathways' potential to access novel isoprenoids with potentially useful activities. However, only a handful of IPK crystal structures have been solved to date, and even fewer of these contain non‐natural substrates bound in the active site. The current study sought to elucidate additional ternary complexes bound to non‐natural substrates using the IPK homolog fromThermococcus paralvinellae(TcpIPK). Four such structures were solved, each bound to a different non‐natural alkyl‐P and the phosphoryl donor substrate/product adenosine triphosphate (ATP)/adenosine diphosphate (ADP). As expected, the quaternary, tertiary, and secondary structures ofTcpIPK closely resembled those of IPKs published previously, and kinetic analysis of a novel alkyl‐P substrate highlighted the potentially dramatic effects of altering the core scaffold of the natural substrate. Even more interesting, though, was the discovery of a trend correlating the position of two α helices in the active site with the magnitude of an IPK homolog's reaction rate for the natural reaction. Overall, the current structures ofTcpIPK highlight the importance of continued structural analysis of the IPKs to better understand and optimize their activity with both natural and non‐natural substrates.

Biochemistry & Molecular Biology↗
NASA NTRS2015

Open-Source Conceptual Sizing Models for the Hyperloop Passenger Pod

Hyperloop is a new mode of transportation proposed as an alternative to California's high speed rail project, with the intended benefits of higher performance at lower overall costs. It consists of a passenger pod traveling through a tube under a light vacuum and suspended on air bearings. The pod travels up to transonic speeds resulting in a 35 minute travel time between the intended route from Los Angeles and San Francisco. Of the two variants outlined, the smaller system includes a 1.1 meter tall passenger capsule traveling through a 2.2 meter tube at 700 miles per hour. The passenger pod features water-based heat exchangers as well as an on-board compression system that reduces the aerodynamic drag as it moves through the tube. Although the original proposal looks very promising, it assumes that tube and pod dimensions are independently sizable without fully acknowledging the constraints of the compressor system on the pod geometry. This work focuses on the aerodynamic and thermodynamic interactions between the two largest systems; the tube and the pod. Using open-source toolsets, a new sizing method is developed based on one-dimensional thermodynamic relationships that accounts for the strong interactions between these sub-systems. These additional considerations require a tube nearly twice the size originally considered and limit the maximum pod travel speed to about 620 miles per hour. Although the results indicate that Hyperloop will need to be larger and slightly slower than originally intended, the estimated travel time only increases by approximately five minutes, so the overall performance is not dramatically affected. In addition, the proposed on-board heat exchanger is not an ideal solution to achieve reasonable equilibrium air temperatures within the tube. Removal of this subsystem represents a potential reduction in weight, energy requirements and complexity of the pod. In light of these finding, the core concept still remains a compelling possibility, although additional engineering and economic analyses are markedly necessary before a more complete design can be developed.

design analysis↗
DOE OSTI2022

Consideration of alternative coolants in AGR-like FHR designs

Replacing carbon-dioxide coolant in AGR-like reactor with molten salt significantly affects the reactor performance characteristics. Hence, a new core design is needed. In this work, a Multi-Objective Particle Swarm Optimisation is used to identify the most favourable configurations for a new system layout. The performed analysis examines each configuration with respect to its thermal-hydraulic performance to assess the power uprate potential which is limited by multiple temperature constraints (e.g. fuel centreline and cladding temperatures as well as the coolant freezing/boiling). The estimated maximum power was then used in the fuel burnup calculations, from which a discharge burnup and cycle average Coolant Temperature Coefficients (CTC) were obtained. As a result of the optimisation process, several families of possible solutions were identified, which form an optimal Pareto Front. The most attractive configurations in terms of achievable power density, however, were not necessarily on the Pareto front. Several coolant options are examined and compared in this paper. The newly identified design options have the potential to achieve power density that is higher than that of a typical AGR by up to a factor of five while maintaining negative CTC through the burnup cycle. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS1977

Spacecraft flight control with the new phase space control law and optimal linear jet select

An autopilot designed for rotation and translation control of a rigid spacecraft is described. The autopilot uses reaction control jets as control effectors and incorporates a six-dimensional phase space control law as well as a linear programming algorithm for jet selection. The interaction of the control law and jet selection was investigated and a recommended configuration proposed. By means of a simulation procedure the new autopilot was compared with an existing system and was found to be superior in terms of core memory, central processing unit time, firings, and propellant consumption. But it is thought that the cycle time required to perform the jet selection computations might render the new autopilot unsuitable for existing flight computer applications, without modifications. The new autopilot is capable of maintaining attitude control in the presence of a large number of jet failures.

Bergmann, E. V.↗
DOE OSTI2021

Developing and testing capabilities for simulating cases with heterogeneous land/water surfaces in a novel atmospheric large eddy simulation code

Large eddy simulations (LES) are the primary computational tool used to simulate high Reynolds number three-dimensional turbulent flows. In the context of earth system sciences, particularly atmospheric science, LES are uniquely able to resolve the scales of atmospheric motion that are key for building process-level understanding of boundary layer turbulence, atmosphere-surface interaction, clouds, and cloud-aerosol-chemistry interaction, and are a core limited-area modeling capability. Increasing demands are being placed on LES code bases as growing high performance computing resources allow LES to address a wider range of scientific problems. In addition, LES are emerging as a source of high-quality machine learning training data. These demands necessitate an agile and extensible code base that allows the model to quickly adapt to emergent needs. However, LES have largely relied on legacy Fortran code bases that lack flexibility. A new, Python-based LES capability called Predicting INteractions of Aerosol and Clouds in Large Eddy Simulation (PINACLES) has been developed as part of the Department of Energy’s Earth System Model Development (ESMD) program area’s Enabling Aerosol-cloud interactions at Global convection-permitting scalES (EAGLES) project. PINACLES was developed from the ground up with a philosophy of maximizing scientific throughput, by attempting to optimize for both model throughput and software extensibility. The initial development of PINACLES delivered a state-of-the-art idealized LES capability solving the non-hydrostatic anelastic equations of motion with doubly periodic boundary conditions and idealized homogenous surface boundary conditions. Here we provide a final report on the outcomes of a fiscal year 2021 Seed Laboratory Directed Research Project that extended PINACLES in two key ways. First, PINACLES was coupled to a state-of-the-art land surface model enabling it to simulate spatially inhomogeneous land-atmosphere interactions that are known to control key atmospheric processes. Second, the dynamical core of PINACLES was modified to permit non-periodic boundary conditions. This model enhancement enables simulation of realistic cases with boundary conditions prescribed from atmospheric reanalysis and enables nested simulations conducted on a hierarchy of computational domains with increasing resolution. Together, these extensions to PINACLES make it a formidable modeling capability and expand its potential application to diverse components of DOE’s atmospheric science portfolio.

42 ENGINEERING↗
DOE OSTI2021

Computational design of microarchitected porous electrodes for redox flow batteries

Porous electrodes are used as the core reactive component across electrochemical technologies. In flowing systems, controlling the fluid distribution, species transport, and reactive environment is critical to attaining high performance. However, conventional electrode materials like felts and papers provide few opportunities for precise engineering of the electrode and its microstructure. To address these limitations, architected electrodes composed of unit cells with spatially varying geometry determined via computational optimization are proposed. Resolved simulation is employed to develop a homogenized description of the constituent unit cells. These effective properties serve as inputs to a continuum model for the electrode when used in the negative half-cell of a vanadium redox flow battery. Porosity distributions minimizing power loss are then determined via computational design optimization to generate architected porosity electrodes. The architected electrodes are compared to bulk, uniform porosity electrodes and found to lead to increased power efficiency across operating flow rates and currents. The design methodology is further used to generate a scaled-up electrode with comparable power efficiency to the bench-scale systems. Finally, the variable porosity architecture and computational design methodology presented here thus offers a novel pathway for automatically generating spatially engineered electrode structures with improved power performance.

25 ENERGY STORAGE↗
DOE OSTI2017

Linker Length-Dependent Electron-Injection Dynamics of Trimesitylporphyrins on SnO 2 Films

Electron-injection dynamics of dye-sensitized photoelectrochemical cells depend on the length of the linker connecting the molecular photosensitizer to the metal oxide electron acceptor. However, systematic studies of the effect of chromophore–oxide distance are scarce. In this work, we present the synthesis, characterization, spectroscopy, and computational modeling of electron-injection dynamics from free-base trimesitylporphyrins with varying linker lengths into tin(IV) oxide (SnO 2 ). In each system, the porphyrin core and metal oxide film remain the same while only the linker binding the porphyrin to the carboxylate anchor group is varied. A length range spanning 8.5–17.2 Å is studied by employing phenylene, biphenylene, terphenylene, and benzanilide groups as the linker. We find a clear correlation between linker length and injection rates, providing insights that will be exploited in the optimization of dye-sensitized photoelectrochemical cells.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Controlling 3-D Morphology of Ni-Fe-Based Nanocatalysts for Oxygen Evolution Reaction

Controlling the 3-D morphology of nanocatalysts is one of the underexplored but important approaches for improving the sluggish kinetics of the oxygen evolution reaction (OER) in water electrolysis. This work reports a scalable, oil-based method based on thermal decomposition of organometallic complexes to yield highly uniform Ni–Fe-based nanocatalysts with a well-defined morphology (i.e. Ni–Fe core–shell, Ni/Fe alloy, and Fe–Ni core–shell). Transmission electron microscopy reveals their morphology and composition to be NiOx–FeOx/NiOx core-mixed shell, NiOx/FeOx alloy, and FeOx–NiOx core–shell. X-ray techniques resolve the electronic structures of the bulk and are supported by electron energy loss spectroscopy analysis of individual nanoparticles. These results suggest that the crystal structure of Ni is most likely to contain α-Ni(OH)2 and that the chemical environment of Fe is variable, depending on the morphology of the nanoparticle. The Ni diffusion from the amorphous Ni-based core to the iron oxide shell makes the NiOx–NiOx/FeOx core-mixed shell structure the most active and the most stable nanocatalyst, which outperforms the comparison NiOx/FeOx alloy nanoparticles expected to be active for the OER. This study suggests that the chemical environment of the mixed NiOx/FeOx alloy composition is important to achieve high electrocatalytic activity for the OER and that the 3-D morphology plays a key role in the optimization of the electrocatalytic activity and stability of the nanocatalyst for the OER.

Nanocatalysts↗
DOE OSTI2023

Explicit solvent machine-learned coarse-grained model of sodium polystyrene sulfonate to capture polymer structure and dynamics

In this article, strongly charged polyelectrolytes (PEs) demonstrate complex solution behavior as a function of chain length, concentrations, and ionic strength. The viscosity behavior is important to understand and is a core quantity for many applications, but aspects remain a challenge. Molecular dynamics simulations using implicit solvent coarse-grained (CG) models successfully reproduce structure, but are often inappropriate for calculating viscosities. To address the need for CG models which reproduce viscoelastic properties of one of the most studied PEs, sodium polystyrene sulfonate (NaPSS), we report our recent efforts in using Bayesian optimization to develop CG models of NaPSS which capture both polymer structure and dynamics in aqueous solutions with explicit solvent. We demonstrate that our explicit solvent CG NaPSS model with the ML-BOP water model [Chan et al. Nat Commun 10, 379 (2019)] quantitatively reproduces NaPSS chain statistics and solution structure. The new explicit solvent CG model is benchmarked against diffusivities from atomistic simulations and experimental specific viscosities for short chains. We also show that our Bayesian-optimized CG model is transferable to larger chain lengths across a range of concentrations. Overall, this work provides a machine-learned model to probe the structural, dynamic, and rheological properties of polyelectrolytes such as NaPSS and aids in the design of novel, strongly charged polymers with tunable structural and viscoelastic properties

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Ultra compact Bragg grating devices with broadband selectivity

Current silicon waveguide Bragg gratings typically introduce perturbation to the optical mode in the form of modulation of the waveguide width or cladding. However, since such a perturbation approach is limited to weak perturbations to avoid intolerable scattering loss and higher-order modal coupling, it is difficult to produce ultra-wide stopbands. In this Letter, we report an ultra-compact Bragg grating device with strong perturbations by etching nanoholes in the waveguide core to enable an ultra-large stopband with apodization achieved by proper location of the nanoholes. With this approach, a 15 µm long device can generate a stopband as wide as 110 nm that covers the entire C + L band with a 40 dB extinction ratio and over a 10 dB sidelobe suppression ratio (SSR). Similar structures can be further optimized to achieve higher SSR of > <#comment/> 17 d B for a stopband of about 80 nm.

Li, Ang (ORCID:0000000232251280)↗
NASA NTRS2003

A Tabletop Tool for Modeling Life Support Systems

This paper describes the development plan for a comprehensive research and diagnostic tool for aspects of advanced life support systems in space-based laboratories. Specifically it aims to build a high fidelity tabletop model that can be used for the purpose of risk mitigation, failure mode analysis, contamination tracking, and testing reliability. We envision a comprehensive approach involving experimental work coupled with numerical simulation to develop this diagnostic tool. It envisions a 10% scale transparent model of a space platform such as the International Space Station that operates with water or a specific matched index of refraction liquid as the working fluid. This allows the scaling of a 10 ft x 10 ft x 10 ft room with air flow to 1 ft x 1 ft x 1 ft tabletop model with water/liquid flow. Dynamic similitude for this length scale dictates model velocities to be 67% of full-scale and thereby the time scale of the model to represent 15% of the full- scale system; meaning identical processes in the model are completed in 15% of the full- scale time. The use of an index matching fluid (fluid that matches the refractive index of cast acrylic, the model material) allows making the entire model (with complex internal geometry) transparent and hence conducive to non-intrusive optical diagnostics. So using such a system one can test environment control parameters such as core flows (axial flows), cross flows (from registers and diffusers), potential problem areas such as flow short circuits, inadequate oxygen content, build up of other gases beyond desirable levels, test mixing processes within the system at local nodes or compartments and assess the overall system performance. The system allows quantitative measurements of contaminants introduced in the system and allows testing and optimizing the tracking process and removal of contaminants. The envisaged system will be modular and hence flexible for quick configuration change and subsequent testing. The data and inferences from the tests will allow for improvements in the development and design of next generation life support systems and configurations.

Ramachandran, N.↗
NASA NTRS2005

Space Laboratory on a Table Top: A Next Generative ECLSS design and diagnostic tool

This paper describes the development plan for a comprehensive research and diagnostic tool for aspects of advanced life support systems in space-based laboratories. Specifically it aims to build a high fidelity tabletop model that can be used for the purpose of risk mitigation, failure mode analysis, contamination tracking, and testing reliability. We envision a comprehensive approach involving experimental work coupled with numerical simulation to develop this diagnostic tool. It envisions a 10% scale transparent model of a space platform such as the International Space Station that operates with water or a specific matched index of refraction liquid as the working fluid. This allows the scaling of a 10 ft x 10 ft x 10 ft room with air flow to 1 ft x 1 ft x 1 ft tabletop model with water/liquid flow. Dynamic similitude for this length scale dictates model velocities to be 67% of full-scale and thereby the time scale of the model to represent 15% of the full- scale system; meaning identical processes in the model are completed in 15% of the full- scale-time. The use of an index matching fluid (fluid that matches the refractive index of cast acrylic, the model material) allows making the entire model (with complex internal geometry) transparent and hence conducive to non-intrusive optical diagnostics. So using such a system one can test environment control parameters such as core flows (axial flows), cross flows (from registers and diffusers), potential problem areas such as flow short circuits, inadequate oxygen content, build up of other gases beyond desirable levels, test mixing processes within the system at local nodes or compartments and assess the overall system performance. The system allows quantitative measurements of contaminants introduced in the system and allows testing and optimizing the tracking process and removal of contaminants. The envisaged system will be modular and hence flexible for quick configuration change and subsequent testing. The data and inferences from the tests will allow for improvements in the development and design of next generation life support systems and configurations. Preliminary experimental and modeling work in this area will be presented. This involves testing of a single inlet-exit model with detailed 3-D flow visualization and quantitative diagnostics and computational modeling of the system.

Ramachandran, N.↗
DOE OSTI2025

Extreme Longitudinal Compression of Optimized Beams for MEV Ultrafast Electron Diffraction (Final Technical Report)

We worked out the design of a high repetition rate MeV energy ultrafast electron diffraction instrument based on the existing Cornell photoinjector, which can readily be applied to the presented findings. This example is a blueprint of other similarly arranged UED setups. Using particle tracking simulations in conjunction with multiobjective genetic algorithm optimization, we explored the smallest bunch lengths, emittance, and probe spot sizes achievable. As two limits, we defined stroboscopic conditions (with single electrons per pulse) and operation with 10 5 electrons per bunch which may be suitable for single-shot diffraction images. In the stroboscopic case, the flexibility provided by the many cavity bunching and acceleration allows for longitudinal phase space linearization without a higher harmonic field, providing sub-fs bunch lengths at the sample. Given low emittance photoemission conditions, these small bunch lengths can be maintained with probe transverse sizes at the single micron (1 μm) scale and below. In the case of 10 5 electrons per pulse, we simulated state-of-the-art 5D brightness conditions: rms bunch lengths of 10 fs with 3-nm normalized emittances, while permitting repetition rates as high as 1.3 GHz. We showed that in conjunction with collimating apertures, a novel focusing scheme achieves very high-quality emittance compensation for the central core of the beam composing 40% of particles, for a resulting beam size of 5 μm (rms). Finally, to aid in the design of new SRF-based ultrafast electron diffraction machines, we simulated the trade-off between the number of cavities used and achievable bunch length and emittance. In the longitudinal dimension, we made use of the fact that MeV UED requires much lower energy than the 15-MeV maxi mum energy of Cornell’s CBETA injector, and we may therefore use several of the SRF cavities for bunch length compression. In practice, we used a genetic optimization algorithm to choose the phases and amplitudes of the cavities appropriately for optimal bunching. In the zero space charge case, we found that bunching and acceleration are distributed across the six cavities in a way that produces a linearizing effect. And we showed that the ultimate bunch length can be limited by time-of-flight differences arising from transverse size and transverse momentum spread. The space charge code developed and used for this development is now permanent part of the Bmad accelerator simulation code and has already contributed to other developments, e.g., for the EIC electron cooler design.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Rapid Bayesian High Entropy Alloy Designs Fabricated via Wire Arc Additive Manufacturing

Purpose: This project seeks to demonstrate a new high-throughput (rapid) alloy design technique applied to creating new high entropy alloys (HEAs) for extreme environments. High entropy alloys shift the design paradigm from being focused on a single principal element (e.g. nickel-based alloys) to target alloys that include high atomic fractions (X >10%) of multiple elements. These HEA materials can exhibit sluggish diffusion and enhanced corrosion resistance, ideal for potential applications in advanced ultra supercritical (A-USC) steam cycles for power generation. Scope: The addition of multiple elements in high atomic fractions creates an enormous design space that cannot easily be investigated by traditional material design strategies such as designed of experiments (DOE). This project utilizes a Bayesian machine learning algorithm that has been modified to work with calculation of phase diagrams (CALPHAD) software. This Bayesian algorithm reduces manual inputs and increase the likelihood of achieving an optimal solution. Compositional inputs to this algorithm will be assessed using existing material property models for high temperature strength and corrosion resistance. The target for alloy performance will be a 15% (~100 ⁰C) increase in allowable service temperature beyond heat-resistant stainless steels while maintaining or improving alloy cost and corrosion resistance. Haynes 230 was selected as a baseline, which is 57 wt% Ni with 22 wt% Cr 14 wt% W, and 2 wt% Mo as solid solution strengtheners. In addition to rapid design via Bayesian machine learning, the alloys were rapidly fabricated using a multi-wire arc additive manufacturing (mWAAM) technique which allows for precise control of alloy composition and assessing of alloy design “windows” to study composition effects. Build speeds for wire-arc additive processes are among the highest for additive technologies enabling rapid and reliable sample fabrication when compared to conventional methods such as arc button melting. The mWAAM samples will be rapidly characterized via instrumented indentation for room temperature modulus and strength and for elevated temperature strength via hot hardness tests. After being screened with hardness testing, potential alloys will be further evaluated with conventional microscopy techniques including scanning electron microscopy (SEM) and transmission electron microscopy (TEM) to assess agreement with modeling results. The most promising compositions will also be evaluated by printing full sized tensile specimens for mechanical behavior tests at elevated temperatures. Results: Bayesian machine learning of a single performance function was initially used to optimize five performance metrics: 1) single phase stability, 2) yield strength, 3) creep resistance (low diffusion coefficient), 4) freezing range (weldability), and 5) material cost. The single performance function was suboptimal as assumptions had to be made about the results while formulating the optimization. A goal-oriented Bayesian optimization strategy (Hanaoka, 2021) was implemented with CALPHAD for use with the five metrics above. This multi-objective Bayesian optimization (MOBO) enabled the design of NiCrCoFe alloys with V and W additions. A base composition of NiCoCr was selected as Ni provides a stable FCC matrix, Cr aids corrosion/oxidation resistance, and Co is a solid-solutions strengthener that also improves creep by increasing the activation energy. Fe helps reduce diffusion coefficients and cost. Finally, V and W were selected for their reasonable solubility and high atomic misfit to aid in solid solution strengthening. Cracking of the mWAAM specimens was an early issue, and the Easton solidification cracking model (Easton et al., 2014a) was selected for addition to the MOBO function. High performing alloys fabricated by mWAAM included Ni 28 Cr 25 Co 26 Fe 15 V 8 and Ni 62 Cr 18 Co 1 Fe 3 W 15 . It was observed that even after adapting the mWAAM process for W, the W did not fully dissolve. To fully evaluate the Ni 62 Cr 18 Co 1 Fe 3 W 15 composition, a cored wire (80-20 NiCr sheath/powder core) was manufactured and printed via WAAM, and HIP’ing was utilized to homogenize and densify the printed alloy. The V and W alloys produced met metrics 1 (solid solution), 4 (solidification cracking), and 5 (cost). However, an unmodeled mechanism of thermal stress cracking was identified in the WAAM produced materials, perhaps exacerbated by the lack of grain boundary strengthening elements (B, C). Conclusions & Recommendations: A high-throughput (rapid) alloy design technique was applied to designing and manufacturing new high entropy alloys (HEAs) for extreme environments utilizing MOBO and mWAAM. The developed process was rapid and effective in addressing the mechanisms included in the model. The lack of grain boundary strengthening element additions (e.g., B, C) was a simplification that likely produced thermal stress cracking that turned into a large part of the investigation. Additions on the order of 0.005 wt% B and 0.05 wt% C likely would have minimized thermal stress grain boundary cracking. Overall, the high throughput design strategy is promising for rapid design of metrics-driven alloys for advanced ultra supercritical (A-USC) steam cycles for power generation. The MOBO and mWAAM process could be commercialized to accelerate metrics-driven alloy design. In addition, the cored-wire process utilized for scale-up is a promising high-volume process for WAAM alloy development and scale-up.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Experimental Analysis of File Transfer Rates over Wide-Area Dedicated Connections

File transfers over dedicated connections, supported by large parallel file systems, have become increasingly important in high-performance computing and big data workflows. It remains a challenge to achieve peak rates for such transfers due to the complexities of file I/O, host, and network transport subsystems, and equally importantly, their interactions. We present extensive measurements of disk-to-disk file transfers using Lustre and XFS file systems mounted on multi-core servers over a suite of 10 Gbps emulated connections with 0-366 ms round trip times. Our results indicate that large buffer sizes and many parallel flows do not always guarantee high transfer rates. Furthermore, large variations in the measured rates necessitate repeated measurements to ensure confidence in inferences based on them. We propose a new method to efficiently identify the optimal joint file I/O and network transport parameters using a small number of measurements. We show that for XFS and Lustre with direct I/O, this method identifies configurations achieving 97% of the peak transfer rate while probing only 12% of the parameter space.

Rao, Nageswara S.↗
DOE OSTI2026

Memory-Aware External Facelist Calculation: A Data-Parallel Atomic Hash Counting Approach

Unstructured volumetric meshes serve as fundamental data representations in various scientific simulations and analyses. They play a crucial role in representing complex computational domains and are essential for important numerical techniques, such as finite element analysis. Whenever such a mesh is read from a file, streamed in-situ, or generated by algorithms, scientific visualization libraries rely on calculating the external surface of a geometry, named “external facelist”, to produce a polygonal mesh for rendering. Consequently, external facelist calculation has become one of the most widely used algorithms in the scientific visualization domain, necessitating optimal performance. In this paper, we explore relevant work on external facelist calculation algorithms in two common visualization libraries, VTK and Viskores, assess their performance and memory constraints, and introduce a novel memory-aware external facelist calculation algorithm employing an atomic hash counting approach. This algorithm fully leverages Viskores' data-parallel primitive operations, facilitating its execution across diverse many-core architectures. Our algorithm features the lowest memory footprint on the GPU and the second-lowest on the CPU among all evaluated methods, and it also delivers the fastest performance on both CPU and GPU. It has been made available under an open-source license in the VTK and Viskores visualization systems.

Tsalikis, Spiros [Kitware] (ORCID:0000000151137195↗