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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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Pyruvate-derived dialkoxyalkanoates (DAOAs) were synthesized in good yield using a mild protocol. Combustion performance and physical properties of DAOAs compare favorably to other low-carbon diesel fuels.
In the early 2000s, NASA made substantial progress in the development of solar sail propulsion systems for use in robotic science and exploration of the solar system. Two different 20-m solar sail systems were produced and they successfully completed functional vacuum testing in NASA Glenn Research Center's (GRC's) Space Power Facility at Plum Brook Station, Ohio. The sails were designed and developed by ATK Space Systems and L Garde, respectively. The sail systems consist of a central structure with four deployable booms that support the sails. These sail designs are robust enough for deployment in a one-atmosphere, one-gravity environment and were scalable to much larger solar sails perhaps as large as 150 m on a side. Computation modeling and analytical simulations were also performed to assess the scalability of the technology to the large sizes required to implement the first generation of missions using solar sails. Life and space environmental effects testing of sail and component materials were also conducted. NASA terminated funding for solar sails and other advanced space propulsion technologies shortly after these ground demonstrations were completed. In order to capitalize on the $30M investment made in solar sail technology to that point, NASA Marshall Space Flight Center (MSFC) funded the NanoSail-D, a subscale solar sail system designed for possible small spacecraft applications. The NanoSail-D mission flew on board the ill-fated Falcon-1 Rocket launched August 2, 2008, and due to the failure of that rocket, never achieved orbit. The NanoSail-D flight spare will be flown in the Fall of 2010. This paper will summarize NASA's investment in solar sail technology to-date and discuss future opportunities
Superconducting transmon qubits are a key technology in quantum computing, with niobium (Nb) widely used due to its favorable superconducting properties and compatibility with scalable fabrication. However, the performance of Nb-based qubits is hindered by two-level systems (TLS), which arise predominantly from native oxide layers at surfaces and interfaces. These oxides and suboxides form rapidly in air and contribute significantly to decoherence by introducing dielectric losses. While various mitigation strategies, including thermal-annealing, chemical-etching, and nitrogen plasma treatment, have been explored, many are challenging to integrate into large-scale manufacturing due to complexity and instability in ambient conditions. In this study, we investigate the structural, chemical, and superconducting properties of room-temperature sputtered Nb thin films grown on sapphire substrates. We focus on a surface protection strategy using noble metal encapsulation with platinum (Pt) to suppress oxides and reduce TLS-related losses. Structural and chemical characterization were performed using X-ray diffraction, X-ray photoelectron spectroscopy, and transmission electron microscopy. Superconducting properties were measured using a physical property measurement system. Results demonstrate that Pt encapsulation effectively suppresses surface oxidation and mitigates its detrimental impact on superconducting performance. In conclusion, this approach offers a promising path toward enhancing the coherence of Nb-based qubits while remaining compatible with scalable-fabrication processes.
All-inorganic perovskite cesium lead triiodide (CsPbI 3 ) with inorganic nature, low-temperature synthesis, and a suitable bandgap is desirable for high-performance photovoltaics. However, the scalable production of CsPbI 3 photovoltaics is still challenging due to a large nucleation energy barrier and slow phase transition during unassisted natural crystallization. Here, the crystallization dynamics of CsPbI 3 thin films is tailored via lead acetate (PbAc 2 ) substitution in the perovskite precursor ink, allowing the scalable fabrication of efficient all-inorganic perovskite solar cells and minimodules. Introducing PbAc 2 enlarges CsPbI 3 colloid size in the precursor and reduces the nucleation energy barrier. Additionally, reactions between acetate and dimethylammonium in the wet film accelerate the removal of dimethylammonium additives and generate solvent vapors for self-regulate internal solvent annealing, resulting in densely packed, uniform, and pinhole-free CsPbI 3 perovskite films over large areas. This strategy demonstrates inverted CsPbI 3 solar cells with 20.17% efficiency and good operational stability (retaining 95.5% of initial efficiency after continuous operation for 1800 h) and 15.1%-efficient CsPbI 3 minimodules with an active area of 26.8 cm 2 .
In the last decade, zeolitic imidazolate frameworks (ZIFs) have been studied extensively for their potential as selective separation membranes. In this review, we highlight unique structural properties of ZIFs that allow them to achieve certain important separations, like that of propylene from propane, and summarize the state of the art in ZIF thin-film deposition on porous substrates and their modification by postsynthesis treatments. We also review the reported membrane performance for representative membrane synthesis approaches and attempt to rank the synthesis methods with respect to potential for scalability. To compare the dependence of membrane performance on membrane synthesis methods and operating conditions, we map out fluxes and separation factors of selected ZIF-8 membranes for propylene/propane separation. Finally, we provide future directions considering the importance of further improvements in scalability, cost effectiveness, and stable performance under industrially relevant conditions.
As particle accelerator control systems evolve in complexity and scale, the need for responsive, scalable, and cost-effective computational infrastructure becomes increasingly critical. Function-as-a-Service (FaaS) offers an alternative to traditional monolithic architecture by enabling event-driven execution, automatic scaling, and fine-grained resource utilization. This paper explores the applicability and performance of FaaS frameworks in the context of a modern particle accelerator control system, with the objective of evaluating their suitability for short lived and triggered workloads. In this paper, we evaluate prominent open-source FaaS platforms in executing functional logic, triggers, and diagnostics routines. Evaluation metrics consist of cold-start latency, scalability, performance, integration with other open-source tools like Kafka. Experimental workloads were designed to simulate real-world control tasks when implemented as stateless FaaS functions. These workloads were benchmarked under various invocation loads and network conditions. Self-hosted FaaS platforms, when deployed within accelerator networks, offer greater control over execution environment, better integration with legacy systems, and support for real-time guarantees when paired with message queues. Based on lessons learned and evaluation metrics, this paper describes reliability of the FaaS framework for the Accelerator Control Systems (ACS).
Employing quantum mechanical resources in computing and networking opens the door to new computation and communication models and potential disruptive advantages over classical counterparts. However, quantifying and realizing such advantages face extensive scientific and engineering challenges. Investments by the Department of Energy (DOE) have driven progress toward addressing such challenges. Quantum algorithms have been recently developed, in some cases offering asymptotic exponential advantages in speed or accuracy, for fundamental scientific problems such as simulating physical systems, solving systems of linear equations, or solving differential equations. Empirical demonstrations on nascent quantum hardware suggest better performance than classical analogs on specialized computational tasks favorable to the quantum computing systems. However, demonstration of an end-to-end, substantial and rigorously quantifiable quantum performance advantage over classical analogs remains a grand challenge, especially for problems of practical value. The definition of requirements for quantum technologies to exhibit scalable, rigorous, and transformative performance advantages for practical applications also remains an outstanding open question, namely, what will be required to ultimately demonstrate practical quantum advantage?
Employing quantum mechanical resources in computing and networking opens the door to new computation and communication models and potential disruptive advantages over classical counterparts. However, quantifying and realizing such advantages face extensive scientific and engineering challenges. Investments by the Department of Energy (DOE) have driven progress toward addressing such challenges. Quantum algorithms have been recently developed, in some cases offering asymptotic exponential advantages in speed or accuracy, for fundamental scientific problems such as simulating physical systems, solving systems of linear equations, or solving differential equations. Empirical demonstrations on nascent quantum hardware suggest better performance than classical analogs on specialized computational tasks favorable to the quantum computing systems. However, demonstration of an end-to-end, substantial and rigorously quantifiable quantum performance advantage over classical analogs remains a grand challenge, especially for problems of practical value. The definition of requirements for quantum technologies to exhibit scalable, rigorous, and transformative performance advantages for practical applications also remains an outstanding open question, namely, what will be required to ultimately demonstrate practical quantum advantage?
Supporting NASA's Science Mission Directorate, the In-Space Propulsion Technology Program is developing solar sail propulsion for use in robotic science and exploration of the solar system. Solar sail propulsion has the potential to provide longer on-station operation, increased scientific payload mass fraction, and access to previously inaccessible orbits for multiple potential science missions. Two different 20-meter solar sail systems were produced and successfully completed functional vacuum testing last year in NASA Glenn s Space Power Facility at Plum Brook Station Ohio. The sails were designed and developed by ATK Space Systems and L'Garde, respectively. The sail systems consist of a central structure with four deployable booms that support the sails. The sail designs are robust enough for deployments in a one atmosphere, one gravity environment and are scalable to much larger solar sails - perhaps as large as 150 meters on a side. In addition, computational modeling and analytical simulations have been performed to assess the scalability of the technology to the large sizes (150 meters) required to implement the first generation of missions using solar sails. Life and space environmental effects testing of sail and component materials are also nearly complete. This paper will summarize recent technology advancements in solar sails and their successful ambient and vacuum environment testing.
High-performance quantum circuit simulation in a classic HPC is still imperative in the NISQ era. Observing that the major obstacle of scalable state-vector quantum simulation arises from the massively fine-grained irregular data-exchange with remote nodes, in this paper we present SV-Sim to apply the emerging PGAS-based communication models (i.e., direct peer access for intra-node CPUs/GPUs and SHMEM for inter-node CPU/GPU clusters) for efficient scalable quantum circuit simulation. Through an orchestrated device functional pointer design, SV-Sim is able to abstract the quantum gate sets across various heterogeneous backends, including IBM/Intel/AMD CPUs, NVIDIA /AMD GPUs, and Intel MIC, in a unified framework, but still asserting outstanding performance and tractable interface to higher-level quantum programming environments, such as IBM Qiskit, Microsoft Q\# and Google Cirq. Circumventing the disability of polymorphism in GPUs and leveraging the device-initiated one-sided communication, SV-Sim can process dynamically synthesized quantum circuit in a single GPU/CPU kernel without the need of expensive JIT or runtime branching, significantly improving the performance and simplifying the programming complexity for the emerging variational quantum algorithms. Evaluations on NVIDIA A100-DGX-1, V100-DGX-2, AMD MI100, ALCF Theta, and OLCF Summit HPCs show that SV-Sim can delivery scalable performance on various state-of-the-art HPC platforms, offering a useful tool for quantum algorithm validation and verification.
Supporting NASA's Science Mission Directorate, the In-Space Propulsion Technology Program is developing solar sail propulsion for use in robotic science and exploration of the solar system. Solar sail propulsion will provide longer on-station operation, increased scientific payload mass fraction, and access to previously inaccessible orbits for multiple potential science missions. Two different 20-meter solar sail systems were produced and successfully completed functional vacuum testing last year in NASA Glenn's Space Power Facility at Plum Brook Station, Ohio. The sails were designed and developed by ATK Space Systems and L'Garde, respectively. These sail systems consist of a central structure with four deployable booms that support the sails. This sail designs are robust enough for deployments in a one atmosphere, one gravity environment, and are scalable to much larger solar sails-perhaps as much as 150 meters on a side. In addition, computation modeling and analytical simulations have been performed to assess the scalability of the technology to the large sizes (>150 meters) required for first generation solar sails missions. Life and space environmental effects testing of sail and component materials are also nearly complete. This paper will summarize recent technology advancements in solar sails and their successful ambient and vacuum testing.
Our research group of 4 faculty and about 10-15 graduate students was actively involved (as a group) in the development of computer communication networks for the last five years. Many of its individuals have been involved in related research for a much longer period. The overall research goal is to extend network performance to higher data rates, to improve protocol performance at most ISO layers and to improve network operational performance. We briefly state our research goals, then discuss the research accomplishments and direct your attention to attached and/or published papers which cover the following topics: scalable parallel communications; high performance interconnection between high data rate networks; and a simple, effective media access protocol system for integrated, high data rate networks.
Employing quantum mechanical resources in computing, information processing, and networking opens the door to potential exponential advantages over classical counterparts. However, quantifying and realizing such advantages poses extensive scientific and engineering challenges. Department of Energy (DOE) investments have driven steady progress in addressing such challenges. Recently developed quantum algorithms offer asymptotic exponential advantages in speed or accuracy for fundamental scientific problems. These problems include simulating physical systems, solving systems of linear equations, differential equations, and optimization problems. Empirical demonstrations on nascent quantum hardware suggest better performance on contrived computational tasks than classical analogs. However, the requirements for a quantum computer or network to demonstrate an end-to-end rigorously quantifiable performance improvement over classical analogs remains a grand challenge, especially for problems of practical value. In particular, what will be required for quantum technology to ultimately exhibit scalable, rigorous, and transformative performance advantages for practical applications? In July 2023, DOE’s Advanced Scientific Computing Research program in the Office of Science convened the Workshop on Basic Research Needs in Quantum Computing and Networking, where major opportunities and grand challenges were identified. The following five priority research directions (PRDs) were identified as a result of the workshop.
An order of magnitude rise in the thermoelectric (TE) performance of the PbSe, a scalable and easy-to-manufacture TE material, has been achieved by incorporating reduced graphene oxide (Gr) nanoplatelets in a PbSe/PbSeO 3 heterostructure formed by acoustic cavitation-assisted oxidation. The fabricated Gr/PbSe/PbSeO 3 nanocomposites exhibit high TE performance with an exceptionally high Seebeck coefficient coupled with low thermal conductivity. The variation in the Seebeck coefficient has been attributed to a reduction in charge carrier mobility due to the ferroelectric polarization effect. Furthermore, the increase in electrical resistivity is minimized by adding graphene. At an optimal weight fraction (0.2 wt%), graphene nano-inclusions lead to superior Seebeck coefficient values as high as ~2000 μV/K at ~500 K, providing high overall TE performance. This study shows substantial changes in the TE properties of PbSe through the incorporation of graphene and PbSeO 3 . The understanding and methodology developed in this study can be exploited for the scalable manufacturing of high-performance TE materials.
Conjugated polymer films are promising in wearable X-ray detection. However, achieving optimal film microstructure possessing good electrical and detection performance under large deformation via scalable printing remains challenging. Herein, we report bar-coated high-performance stretchable films based on a conjugated polymer P(TDPP-Se) and elastomer SEBS blend by optimizing the solution-processing conditions. The moderate preaggregation in solution and prolonged growth dynamics from a solvent mixture with limited dissolving capacity is critical to forming aligned P(TDPP-Se) chains/crystalline nanofibers in the SEBS phase with enhanced π–π stacking for charge transport and stress dissipation. The film shows a large elongation at break of >400% and high mobilities of 5.29 cm 2 V –1 s –1 at 0% strain and 1.66 cm 2 V –1 s –1 over 500 stretch–release cycles at 50% strain, enabling good X-ray imaging with a high sensitivity of 1501.52 μC Gy air –1 cm –2 . Finally, our work provides a morphology control strategy toward high-performance conjugated polymer film-based stretchable electronics.
The Crossroads supercomputer was designed to simulate some of the most complex physical devices in the world. These simulations routinely require 1/2 petabyte or more of system memory running on thousands of compute nodes for months at a time on the most powerful supercomputers. Improvements in time to solutions for these workloads can have major impact on our mission capabilities. In this paper we present early results of representative application workloads on 4th Gen Intel Xeon and Intel Xeon Processors codenamed Sapphire Rapids with HBM. These results demonstrate an extremely promising 8.57x improvement (node to node) over our prior generation Intel Broadwell (BDW) based HPC systems. No code modifications were required to achieve this speedup, providing a compelling path forward toward major reductions in time to solution and the complexity of physical systems that can be simulated in the future.
This research is conducted in the context of the Joint NSF/NASA Initiative on Evaluation (JNNIE). JNNIE is an inter-agency research program that goes beyond typical.bencbking to provide and in-depth evaluations and understanding of the factors that limit the scalability of high-performance computing systems. Many NSF and NASA centers have participated in the effort. Our research effort was an integral part of implementing JNNIE in the NASA ESS grand challenge applications context. Our research work under this program was composed of three distinct, but related activities. They include the evaluation of NASA ESS high- performance computing testbeds using the wavelet decomposition application; evaluation of NASA ESS testbeds using astrophysical simulation applications; and developing an experimental model for workload characterization for understanding workload requirements. In this report, we provide a summary of findings that covers all three parts, a list of the publications that resulted from this effort, and three appendices with the details of each of the studies using a key publication developed under the respective work.
Watershed models such as the Soil and Water Assessment Tool (SWAT) consist of high-dimensional physical and empirical parameters. These parameters often need to be estimated/calibrated through inverse modeling to produce reliable predictions on hydrological fluxes and states. Existing parameter estimation methods can be time consuming, inefficient, and computationally expensive for high-dimensional problems. In this paper, we present an accurate and robust method to calibrate the SWAT model (i.e., 20 parameters) using scalable deep learning (DL). We developed inverse models based on convolutional neural networks (CNN) to assimilate observed streamflow data and estimate the SWAT model parameters. Scalable hyperparameter tuning is performed using high-performance computing resources to identify the top 50 optimal neural network architectures. We used ensemble SWAT simulations to train, validate, and test the CNN models. We estimated the parameters of the SWAT model using observed streamflow data and assessed the impact of measurement errors on SWAT model calibration. We tested and validated the proposed scalable DL methodology on the American River Watershed, located in the Pacific Northwest-based Yakima River basin. Our results show that the CNN-based calibration is better than two popular parameter estimation methods (i.e., the generalized likelihood uncertainty estimation [GLUE] and the dynamically dimensioned search [DDS], which is a global optimization algorithm). For the set of parameters that are sensitive to the observations, our proposed method yields narrower ranges than the GLUE method but broader ranges than values produced using the DDS method within the sampling range even under high relative observational errors. The SWAT model calibration performance using the CNNs, GLUE, and DDS methods are compared using R 2 and a set of efficiency metrics, including Nash-Sutcliffe, logarithmic Nash-Sutcliffe, Kling-Gupta, modified Kling-Gupta, and non-parametric Kling-Gupta scores, computed on the observed and simulated watershed responses. The best CNN-based calibrated set has scores of 0.71, 0.75, 0.85, 0.85, 0.86, and 0.91. The best DDS-based calibrated set has scores of 0.62, 0.69, 0.8, 0.77, 0.79, and 0.82. The best GLUE-based calibrated set has scores of 0.56, 0.58, 0.71, 0.7, 0.71, and 0.8. The scores above show that the CNN-based calibration leads to more accurate low and high streamflow predictions than the GLUE and DDS sets. Our research demonstrates that the proposed method has high potential to improve our current practice in calibrating large-scale integrated hydrologic models.