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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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At least 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Software-Defined Network for End-to-end Networked Science at the Exascale

Domain science applications and workflow processes are currently forced to view the network as an opaque infrastructure into which they inject data and hope that it emerges at the destination with an acceptable Quality of Experience. There is little ability for applications to interact with the network to exchange information, negotiate performance parameters, discover expected performance metrics, or receive status/troubleshooting information in real time. The work we presen here is motivated by a vision for a new smart network and smart application ecosystem that will provide a more deterministic and interactive environment for domain science workflows. The Software-Defined Network for End-to-end Networked Science at Exascale (SENSE) system includes a model-based architecture, implementation, and deployment which enables automated end-to-end network service instantiation across administrative domains. An intent based interface allows applications to express their high-level service requirements, an intelligent orchestrator and resource control systems allow for custom tailoring of scalability and real-time responsiveness based on individual application and infrastructure operator requirements. This allows the science applications to manage the network as a first-class schedulable resource as is the current practice for instruments, compute, and storage systems. Deployment and experiments on production networks and testbeds have validated SENSE functions and performance. Emulation based testing verified the scalability needed to support research and education infrastructures. Key contributions of this work include an architecture definition, reference implementation, and deployment. This provides the basis for further innovation of smart network services to accelerate scientific discovery in the era of big data, cloud computing, machine learning and artificial intelligence.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

MLPF: efficient machine-learned particle-flow reconstruction using graph neural networks

In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a comprehensive particle-level view of the event by combining information from the calorimeters and the trackers, significantly improving the detector resolution for jets and the missing transverse momentum. In view of the planned high-luminosity upgrade of the CERN Large Hadron Collider (LHC), it is necessary to revisit existing reconstruction algorithms and ensure that both the physics and computational performance are sufficient in an environment with many simultaneous proton–proton interactions (pileup). Machine learning may offer a prospect for computationally efficient event reconstruction that is well-suited to heterogeneous computing platforms, while significantly improving the reconstruction quality over rule-based algorithms for granular detectors. We introduce MLPF, a novel, end-to-end trainable, machine-learned particle-flow algorithm based on parallelizable, computationally efficient, and scalable graph neural network optimized using a multi-task objective on simulated events. We report the physics and computational performance of the MLPF algorithm on a Monte Carlo dataset of top quark–antiquark pairs produced in proton–proton collisions in conditions similar to those expected for the high-luminosity LHC. The MLPF algorithm improves the physics response with respect to a rule-based benchmark algorithm and demonstrates computationally scalable particle-flow reconstruction in a high-pileup environment.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

HPDR: High-Performance Portable Scientific Data Reduction Framework

The rapid growth in scientific data generation is outpacing advancements in computing systems necessary for efficient storage, transfer, and analysis, particularly in the context of exascale computing. With the deployment of first-generation exascale computing systems and next-generation experimental facilities, this gap is widening and necessitates effective data reduction techniques to manage enormous data volumes. Over the past decade, various data reduction methods, including lossless compression, error-controlled lossy compression, and data refactoring, have been developed to accelerate I/O in scientific workflows. Despite significant reductions in data volume, these methods introduce considerable computational overhead, which can become the new bottleneck in data processing. To mitigate this, GPU-accelerated data reduction algorithms have been introduced. However, challenges remain in their integration into exascale workflows, including limited portability across different GPU architectures, substantial memory transfer overhead, and reduced scalability on dense multi-GPU systems. To address these challenges, we propose HPDR, a high-performance and portable data reduction framework. HPDR is designed to enable the execution of state-of-the-art reduction algorithms across diverse processor architectures while reducing memory transfer overhead to 2.3 % of the original, resulting in up to 3.5× faster throughput compared to existing solutions. It also achieves up to 96% of the theoretical speedup in multi-GPU settings. In addition, evaluations on accelerating I/O operations at scale up to 1,024 nodes of the Frontier supercomputer demonstrate that HPDR can achieve up to 103 TB/s reduction throughput, providing up to 4× acceleration in parallel I/O performance compared to existing data reduction routines. This work highlights the potential of HPDR to significantly enhance data reduction efficiency in exascale computing environments.

Chen, Jieyang [University of Oregon]↗
NASA NTRSDate not supplied

Extreme Environments Solar Power Project For Use Near Jupiter

The Extreme Environment Solar Power (EESP) Project goal was to develop solar cell and solar array design concept technologies for space power applications in high radiation and low solar intensity, low temperature (LILT) environments. NASA missions focused on outer planets, such as Jupiter, are subjected to intense radiation while experiencing less than ten percent of the solar flux relative to a mission in the general vicinity of Earth. Under these conditions, present solar array technology is not as efficient in converting the sun’s energy, and the solar array performance degrades quickly due to the additional radiation exposure. The EESP Project objectives were accomplished through four competitively selected contracts. These contracts were awarded with a Base and two option periods of performance. The Base requirements included System Design and Component Test & Analysis with a duration of nine and one half (9.5) months. The Option I requirements involved the fabrication of test hardware that NASA would evaluate following its delivery, and this period of performance had a duration of thirteen (13) months. The Option II requirements involved the fabrication of a scalable system prototype that can be integrated into a system level application and be demonstrated in a relevant environment. Option II had a duration of fifteen (15) months. The Transformational Array (TA) is an array concept developed by JHU-APL with Deployable Space Systems (DSS), SolAero Technologies and NewForge Technologies that proceeded through all three contract periods. The TA combines the DSS Roll Out Solar Array (ROSA) with Flexible Array Concentrator Technology (FACT) and SolAero’s Inverted Metamorphic Multijunction (IMM) solar cells for operation in the EESP Project environment. To achieve the EESP Project goals, the JHU-APL team conducted several investigations to improve performance of the solar cells and increase their efficiency, reduce outgassing contamination of the concentrators from the solar cell assembly adhesives, and to test the robustness of a sample blanket during deployment and retraction. Additionally, the team performed vibration, thermal cycling, and performance tests at a Standard Power Module (SPM) level. At the end of Option II, the JHU-APL team achieved the following performance metrics: • 39% beginning of life (BOL) cell efficiency at 5 AU and -125 C • 29% end of life (EOL) efficiency at the blanket level at 50 W/m2, -125 C and 4x1015 1 MeV e/cm2 • Increased specific power to 8 W/kg at EOL for the entire array including structure, deployment, and pointing mechanisms • Demonstrated the ability to survive numerous deploy-retract cycles without degradation • Reached a current System-Level Technology Readiness Level, TRL5 This presentation will describe the Transformational Array and component technologies and will discuss future potential uses for the system.

Anna Maria Pal↗
DOE OSTI2023

3D–Integrated, Multi–Functional Carbon Fibers for Stable, High–Areal–Capacity Batteries

Increasing lithium-ion batteries' (LIBs) electrode areal capacity can boost energy density and lower manufacturing costs, but faces challenges in manufacturing, rate performance, and cycling stability. A conductive framework made of commercial micro-sized carbon fibers (Cfs) is presented that serves as a host for both the LiNi 0.5 Mn 0.3 Co 0.2 O 2 (NMC 532) cathode and Cfs anode. The Cf framework has multiple functions that offer high electronic conductivity (270 mS cm –1 ), low tortuosity (1.7), low Li + diffusion resistance (22 Ω), and high thermal conductivity (200 W mK –1 ). Additionally, the Cf-integrated electrodes can have an extremely high mass loading of NMC 532 (70 mg cm –2 ) with a theoretical capacity of 14 mAh cm –2 . Thus, the practical full cells assembled with the Cfs-enabled electrodes exhibit an initial areal capacity of 4.1 mAh cm –2 and capacity retention of 90.4% at 500 cycles at a cycling rate of C/3, 1.5 mA cm –2 . Data collected from the operando isothermal microcalorimetry suggest that full cells utilizing the Cf anode experience less heat release from side reactions compared to cells utilizing a conventional graphite anode. Finally, this present approach is scalable and cost-effective and can fabricate practical LIBs that boast high areal capacity, rate performance, and a lengthy cycling lifetime.

25 ENERGY STORAGE↗
DOE OSTI2024

Integrated photonic structures for photon-mediated entanglement of trapped ions

Trapped atomic ions are natural candidates for quantum information processing and have the potential to realize or improve quantum computing, sensing, and networking. These applications often require the collection of individual photons emitted from ions into guided optical modes, in some cases for the production of entanglement between separated ions. Proof-of-principle demonstrations of such photon collection from trapped ions have been performed using high-numerical-aperture lenses or cavities and single-mode fibers, but integrated photonic elements in ion-trap structures offer advantages in scalability and manufacturability over traditional optics. In this paper we analyze structures monolithically fabricated with an ion trap for collecting ion-emitted photons, coupling them into waveguides, and manipulating them via interference. We calculate geometric limitations on collection efficiency for this scheme, simulate a single-layer grating that shows performance comparable to demonstrated free-space optics, and discuss practical fabrication and fidelity considerations. Based on this analysis, we conclude that integrated photonics can support scalable systems of trapped ions that can distribute quantum information via photon-mediated entanglement.

Knollmann, Felix W.↗
NASA NTRS2006

Model-Based Self-Tuning Multiscale Method for Combustion Control

A multi-scale representation of the combustor dynamics was used to create a self-tuning, scalable controller to suppress multiple instability modes in a liquid-fueled aero engine-derived combustor operating at engine-like conditions. Its self-tuning features designed to handle the uncertainties in the combustor dynamics and time-delays are essential for control performance and robustness. The controller was implemented to modulate a high-frequency fuel valve with feedback from dynamic pressure sensors. This scalable algorithm suppressed pressure oscillations of different instability modes by as much as 90 percent without the peak-splitting effect. The self-tuning logic guided the adjustment of controller parameters and converged quickly toward phase-lock for optimal suppression of the instabilities. The forced-response characteristics of the control model compare well with those of the test rig on both the frequency-domain and the time-domain.

Le, Dzu, K.↗
DOE OSTI2020

Scalable Deep-Learning-Accelerated Topology Optimization for Additively Manufactured Materials

Topology optimization (TO) is a popular and powerful computational approach for designing novel structures, materials, and devices. Two computational challenges have limited the applicability of TO to a variety of industrial applications. First, a TO problem often involves a large number of design variables to guarantee sufficient expressive power. Second, many TO problems require a large number of expensive physical model simulations, and those simulations cannot be parallelized. To address these issues, we propose a general scalable deep-learning (DL) based TO framework, referred to as SDL-TO, which utilizes parallel schemes in high performance computing (HPC) to accelerate the TO process for designing additively manufactured (AM) materials. Unlike the existing studies of DL for TO, our framework accelerates TO by learning the iterative history data and simultaneously training on the mapping between the given design and its gradient. The surrogate gradient is learned by utilizing parallel computing on multiple CPUs incorporated with a distributed DL training on multiple GPUs. The learned TO gradient enables a fast online update scheme instead of an expensive update based on the physical simulator or solver. Using a local sampling strategy, we achieve to reduce the intrinsic high dimensionality of the design space and improve the training accuracy and the scalability of the SDL-TO framework. The method is demonstrated by benchmark examples and AM materials design for heat conduction. The proposed SDL-TO framework shows competitive performance compared to the baseline methods but significantly reduces the computational cost by a speed up of around 8.6x over the standard TO implementation.

Bi, Sirui↗
DOE OSTI2023

Flexible Vanadium Dioxide Photodetectors for Visible to Longwave Infrared Detection at Room Temperature

Abstract Flexible optoelectronics is a rapidly growing field, with a wide range of potential applications. From wearable sensors to bendable solar cells, curved displays, and curved focal plane arrays, the possibilities are endless. The criticality of flexible photodetectors for many of these applications is acknowledged, however, devices that are demonstrated thus far are limited in their spectral range. In this study, flexible photodetectors are demonstrated using a VO x nanoparticle ink, with an extremely broad operating wavelength range of 0.4 to 20 µm. This ink is synthesized using a simple and scalable wet‐chemical process. These photodetectors operate at room temperature and exhibit minimal variance in performance even when bent at angles of up to 100 ° at a bend radius of 6.4 mm. In addition, rigorous strain testing of 100 bend and release cycles revealed a photoresponse with a standard deviation of only 0.55%. This combination of mechanical flexibility, wide spectral response, and ease of fabrication makes these devices highly desirable for a wide range of applications, including low‐cost wearable sensors and hyperspectral imaging systems.

36 MATERIALS SCIENCE↗
DOE OSTI2023

High–Yield Deterministic Focused Ion Beam Implantation of Quantum Defects Enabled by In Situ Photoluminescence Feedback

Focused ion beam implantation is ideally suited for placing defect centers in wide bandgap semiconductors with nanometer spatial resolution. However, the fact that only a few percent of implanted defects can be activated to become efficient single photon emitters prevents this powerful capability to reach its full potential in photonic/electronic integration of quantum defects. Here an industry adaptive scalable technique is demonstrated to deterministically create single defects in commercial grade silicon carbide by performing repeated low ion number implantation and in situ photoluminescence evaluation after each round of implantation. An array of 9 single defects in 13 targeted locations is successfully created—a ≈70% yield which is more than an order of magnitude higher than achieved in a typical single pass ion implantation. The remaining emitters exhibit non-classical photon emission statistics corresponding to the existence of at most two emitters. This approach can be further integrated with other advanced techniques such as in situ annealing and cryogenic operations to extend to other material platforms for various quantum information technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Identifying Suitable Front Contacts for High‐Efficiency Cd(Se,Te) Solar Cells on Space‐Qualified Cover Glass

Deployment of photovoltaics in space requires devices that combine high-efficiency, low areal mass, and resilience to harsh environments. Historically, high-efficiency multijunction III–V materials have dominated space power systems; however, their high cost and limited manufacturing throughput motivate the exploration of scalable alternatives. While CdTe-based thin-film photovoltaics offer an attractive option, their performance on non-conventional substrates can suffer from front contact instability under higher-temperature processing. Here, the role of front contact chemistry in limiting cell performance is investigated using CdTe-based devices fabricated on 150 μm thick Ceria-doped space-qualified 0214 Corning glass. A matrix of four transparent conducting oxides (TCOs: CTO, AZO, ITO, IZO) combined with two n-type emitters (MZO, IGO) reveals chemical stability at the front interface—rather than absorber composition alone—governs recombination losses, voltage deficits, and device reproducibility. Chemically stable front contact combinations suppress elemental diffusion and interfacial degradation, resulting in significantly improved carrier lifetimes and junction quality. These insights are validated through record-certified Cd(Se,Te) cell efficiencies of 18.4% under AM1.5G and 16.2% under AM0 illumination on ultra-thin glass. Beyond CdTe, this work provides a general framework for the rational selection of TCO/emitter interfaces in superstrate thin-film photovoltaics, including emerging technologies like metal halide perovskites, while enabling high-efficiency, lightweight photovoltaics for space applications.

14 SOLAR ENERGY↗
DOE OSTI2026

A scalable framework for efficient coupling of thermal and microstructural simulations in additive manufacturing

Predicting microstructure evolution in metal additive manufacturing (AM) is important for process optimization, but spatiotemporal scale disparities between thermal transport and microstructure evolution create significant challenges for efficient data transfer between simulation codes. To address this, we present Stork, a scalable framework for coupling thermal and microstructural simulations. Stork uses a sparse data representation to identify and store active solidification sub-volumes, enabling highly parallel quad-linear interpolation from coarse thermal grids to fine microstructure grids without large intermediate storage. We demonstrate the framework by coupling the semi-analytic heat transfer code 3DThesis with the time-parallel cellular automata code Toucan. This approach achieves over two orders of magnitude reduction in data generation time and file size compared to prior workflows. Numerical studies show that quad-linear interpolation preserves grain morphology and crystallographic texture in laser powder bed fusion (LPBF) simulations for coarsening ratios up to 16. Overall, Stork provides a scalable pathway for high-throughput, component-scale AM simulations on modern high-performance computing systems.

36 MATERIALS SCIENCE↗
DOE OSTI2026

scPlantAnnotate: an accurate and robust transformer-based model for plant cell type annotation

Accurate cell type annotation remains a major bottleneck in plant single-cell RNA sequencing (scRNA-seq), where existing tools are often adapted from animal studies and perform sub-optimally on plant data. The lack of plant-specific computational frameworks limits the construction of plant cell atlases and downstream biological discovery. We develop and evaluate scPlantAnnotate, a Transformer-based reference annotation framework tailored for plant scRNA-seq data, and benchmark it against state-of-the-art deep learning and conventional methods across multiple plant species. Species-specific scPlantAnnotate models were trained using curated datasets from Arabidopsis thaliana, Zea mays, Oryza sativa, and Glycine max. We compared scPlantAnnotate with leading baselines under both standard random-split evaluation and a more stringent leave-one-dataset-out setting, which tests robustness to completely unseen datasets and tissue types. scPlantAnnotate consistently outperforms existing approaches across all four species under random-split evaluation. In the leave-one-dataset-out setting for A. thaliana, where performance drops markedly for all methods due to strong batch effects and dataset heterogeneity, scPlantAnnotate nonetheless achieves the highest Accuracy, Macro-F1, Balanced Accuracy, and Macro-AUROC on average and ranks first on most held-out datasets. These results demonstrate improved robustness to dataset shifts, a critical yet underexplored challenge in plant scRNA-seq analysis. A freely accessible web server enables users to annotate their own datasets using pretrained models. scPlantAnnotate provides a plant-specific, Transformer-based framework for single-cell annotation that delivers state-of-the-art performance and enhanced robustness to unseen datasets. By addressing limitations of existing tools and enabling scalable reference-based annotation, scPlantAnnotate supports the development of comprehensive plant cell atlases and facilitates broader use of single-cell genomics in plant biology.

Bioinformatics↗
DOE OSTI2020

Adaptive Activation Functions Accelerate Convergence in Deep and Physics-informed Neural Networks

We employ adaptive activation functions for regression in deep and physics-informed neural networks (PINNs) to approximate smooth and discontinuous functions as well as solutions of linear and nonlinear partial differential equations. In particular, we solve the nonlinear Klein-Gordon equation, which has smooth solutions, the nonlinear Burgers equation, which can admit high gradient solutions, and the Helmholtz equation. We introduce a scalable hyper-parameter in the activation function, which can be optimized to achieve best performance of the network as it changes dynamically the topology of the loss function involved in the optimization process. The adaptive activation function has better learning capabilities than the traditional one (fixed activation) as it improves greatly the convergence rate, especially at early training, as well as the solution accuracy. To better understand the learning process, we plot the neural network solution in the frequency domain to examine how the network captures successively different frequency bands present in the solution. We consider both forward problems, where the approximate solutions are obtained, as well as inverse problems, where parameters involved in the governing equation are identified. Our simulation results show that the proposed method is a very simple and effective approach to increase the efficiency, robustness and accuracy of the neural network approximation of nonlinear functions as well as solutions of partial differential equations, especially for forward problems. We theoretically prove that in the proposed method, gradient descent algorithms are not attracted to suboptimal critical points or local minima.

machine leaning, Bad minima, Inverse problems, Phy↗
DOE OSTI2023

A perspective on the pathway to a scalable quantum internet using rare-earth ions

The ultimate realization of a global quantum internet will require advances in scalable technologies capable of generating, storing, and manipulating quantum information. The essential devices that will perform these tasks in a quantum network are quantum repeaters, which will enable the long-range distribution of entanglement between distant network nodes. Here, in this review, we provide an overview of the primary functions of a quantum repeater and discuss progress that has been made toward the development of repeaters with rare-earth ion doped materials while noting challenges that are being faced as the technologies mature. We give particular attention to erbium, which is well suited for networking applications. Finally, we provide a discussion of near-term benchmarks that can further guide rare-earth ion platforms for impact in near-term quantum networks.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Substrate effects on phase formation and interfacial stability in superconducting vanadium silicide thin films

Thin films of vanadium silicide (V-silicide) in the A15 cubic phase (V 3 Si) are promising for superconducting quantum devices due to their high transition temperature and potential compatibility with scalable semiconductor fabrication. However, solid-phase synthesis often yields secondary silicide phases that degrade performance. Here, in this study, we investigate the influence of substrate properties using two silicon-on-insulator architectures: one with a polycrystalline HfO 2 buried layer (group A) and the other with amorphous SiO 2 (group B). Both systems exhibit superconductivity consistent with V 3 Si formation, yet structural analysis reveals mixed-phase films in both cases. Crucially, only group A maintains an atomically sharp and chemically stable interface, a prerequisite for phase purity. Prolonged annealing in group A reduces the unwanted V 5 Si 3 phase but also leads to the emergence of Si-rich VSi 2 , likely due to localized substrate degradation. Preliminary atomic-resolution imaging suggests that HfO 2 crystallinity may promote local phase-selective nucleation. These findings highlight the importance of substrate design in promoting phase control and maintaining interfacial integrity in superconducting silicides.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Quantification of Cas9 binding and cleavage across diverse guide sequences maps landscapes of target engagement

The RNA-guided nuclease Cas9 has unlocked powerful methods for perturbing both the genome through targeted DNA cleavage and the regulome through targeted DNA binding, but limited biochemical data have hampered efforts to quantitatively model sequence perturbation of target binding and cleavage across diverse guide sequences. We present scalable, sequencing-based platforms for high-throughput filter binding and cleavage and then perform 62,444 quantitative binding and cleavage assays on 35,047 on- and off-target DNA sequences across 90 Cas9 ribonucleoproteins (RNPs) loaded with distinct guide RNAs. We observe that binding and cleavage efficacy, as well as specificity, vary substantially across RNPs; canonically studied guides often have atypically high specificity; sequence context surrounding the target modulates Cas9 on-rate; and Cas9 RNPs may sequester targets in nonproductive states that contribute to “proofreading” capability. Lastly, we distill our findings into an interpretable biophysical model that predicts changes in binding and cleavage for diverse target sequence perturbations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Tape Casting of Thin Electrolyte and Thick Cathode for Halide-Based All-Solid-State Batteries

Most previous studies about halide solid-state electrolytes have used pellets prepared by uniaxial pressing, which is a good approach for materials development but is not suitable for commercialization. Thinner electrolyte layers that can be scaled up to large cell areas are required, and tape casting is a promising approach. It is challenging, however, as halide materials are reactive with most of the conventional solvents used in the process. In this study, solvents with low polarity, such as toluene, are found to be compatible with the Li 3 YBr 6 halide material. A wide variety of candidate binders that are soluble in toluene are studied. MSB1–13 binder is preferred, based on the ionic conductivity and mechanical properties of the tape. Electrolyte tapes (<70 μ m) are successfully cast on Al substrates, using 2 wt% binder. The resulting room temperature ionic conductivity is 2 × 10 −4 S cm −1 . Two composite cathodes including active material (LiFePO 4 or LiNi 0.82 Mn 0.07 Co 0.11 O 2 ) and 1 to 1.5 wt% MSB1–13 are tape cast as proof-of-concept for a scalable cell fabrication approach. A LiFePO 4 cell shows good retention at 25 °C. The performance of NMC cells with tape electrolyte or pellet electrolyte is similar. This study demonstrates the feasibility of tape casting halide-based electrolytes and cathodes.

25 ENERGY STORAGE↗