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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 793 records · Page 44

All sources NASA NTRS DOE OSTI

NASA NTRS2023

Fan Noise Predictions of the NASA Source Diagnostic Test Using Unsteady Simulations with LAVA Part I: Near-Field Aerodynamics and Turbulence

A sliding mesh technique within the Launch, Ascent, and Vehicle Aerodynamics (LAVA) computational framework is validated using the experimental dataset collected as part of the NASA Source Diagnostic Test (SDT) campaign. Two modeling approaches are explored: the unsteady Reynolds-Averaged Navier Stokes (URANS) with Spalart-Allmaras (SA) turbulence model closure, and a hybrid Reynolds-Averaged Navier Stokes/Large Eddy Simulation (RANS/LES) paradigm employing a Zonal Detached Eddy Simulation (ZDES) closure with enhanced shielding protection. Fan stage performance metrics, aerodynamic quantities and turbulent flow structures are analyzed in this work. Initial studies focusing on grid and time-step sensitivity are presented. Sensitivity to different variants of the SA turbulence model is analyzed, supporting the use of the baseline SA model in the production runs. Two conditions are analyzed in detail using URANS and hybrid RANS/LES (HRLES). Mean flow quantities are well-captured by both methods in the low-speed (approach) regime. While URANS misses all the upstream-propagating noise in the inlet due to the rotor-locked tones being evanescent in nature at subsonic fan tip speeds, HRLES captures this broadband component in its pressure field. At the high-speed (sideline) condition, URANS shows better agreement with the SDT data than HRLES in the interstage flow-field. In this regime, URANS captures the tonal content propagating through the inlet, since the tones are now cut-on. Both methods are suitable to capture fan stage performance metrics and mean flow quantities, but only HRLES is able to resolve the fine turbulent structures responsible for broadband noise. The results support the use of the sliding mesh technique implemented in this work for future turbomachinery applications within the LAVA solver framework.

AATT↗

NASA NTRS2023

Fan Noise Predictions of the NASA Source Diagnostic Test Using Unsteady Simulations with LAVA Part I: Near-Field Aerodynamics and Turbulence

AATT↗

DOE OSTI2024

Controlling structure and interfacial interaction of monolayer TaSe2 on bilayer graphene

Abstract Tunability of interfacial effects between two-dimensional (2D) crystals is crucial not only for understanding the intrinsic properties of each system, but also for designing electronic devices based on ultra-thin heterostructures. A prerequisite of such heterostructure engineering is the availability of 2D crystals with different degrees of interfacial interactions. In this work, we report a controlled epitaxial growth of monolayer TaSe 2 with different structural phases, 1 H and 1 T , on a bilayer graphene (BLG) substrate using molecular beam epitaxy, and its impact on the electronic properties of the heterostructures using angle-resolved photoemission spectroscopy. 1 H -TaSe 2 exhibits significant charge transfer and band hybridization at the interface, whereas 1 T -TaSe 2 shows weak interactions with the substrate. The distinct interfacial interactions are attributed to the dual effects from the differences of the work functions as well as the relative interlayer distance between TaSe 2 films and BLG substrate. The method demonstrated here provides a viable route towards interface engineering in a variety of transition-metal dichalcogenides that can be applied to future nano-devices with designed electronic properties.

36 MATERIALS SCIENCE↗

NASA NTRS1980

Propulsion system research and development for electric and hybrid vehicles

An approach to propulsion subsystem technology is presented. Various tests of component reliability are described to aid in the production of better quality vehicles. component characterization work is described to provide engineering data to manufacturers on component performance and on important component propulsion system interactions.

Schwartz, H. J.↗

DOE OSTI2023

Biogenic Straw Aerogel Thermal Insulation Materials

Biogenic wheat straw is a carbon-storing building insulation material. However, the enzymatic hydrolysis ratio of its cellulose is relatively low due to the presence of hemicellulose and lignin hindering its thermal insulation performance. In this work, we report aerogel and straw composites with thermal conductivity of 35 mW m –1 K –1 , while high cellulose fiber conversion in straw is obtained by using a hybrid mechanical and chemical process. Furthermore, we show that the in-situ cellulose-reinforced silica aerogel nanocomposites exhibit an optimal thermal conductivity of 32 mW m –1 K –1 by using 50 wt% of aerogel. Moreover, the hydrophobic aerogel-cellulose composites show a low density, high porosity (90 %), high compression modulus (1.9 MPa), superhydrophobicity, and superior reusability. This work provides a cost-effective and facile method to manufacture biogenic composites from agriculture waste materials, promising for carbon-sequestration building insulation applications.

36 MATERIALS SCIENCE↗

DOE OSTI2024

A deep learning-based battery sizing optimization tool for hybridizing generation plants

Hybrid generation and energy storage systems offer the ability to increase flexibility of the combined asset. This flexibility can be used to increase provision of services already provided by the generation asset, such as timing sale of electricity to the energy market during high price periods, and also enable provision of additional services, such as ancillary services or contribute to resource adequacy. From a generation asset owner perspective, the decision to hybridize includes selecting an energy storage system that, among other factors, maximizes financial performance of the energy storage investment. Yet, existing tools to optimize energy storage sizing are either too rudimentary (i.e., based on “rules of thumb”) or too complex to implement (i.e., require specialized engineering and software knowledge and a high-performance computer to run). This work presents a novel deep learning-based battery sizing optimization tool that is designed to help generation asset owners easily assess preliminary sizing considerations for potential battery investments to hybridize their generation facility. The tool uses deep learning to predict revenue over a broad search space of potential battery sizes, estimates capital and operating costs (including accounting for battery degradation), and computes financial performance of each potential battery system investment, recommending a system with maximum financial performance. The tool is tested and validated for hydropower assets. Finally, this tool will help a greater cross-section of industry consider investments in battery systems, increasing their revenue and helping them compete in rapidly evolving electrify markets.

13 HYDRO ENERGY↗

DOE OSTI2023

Electronic and Thermal Properties of the Cation Substitution-Derived Quaternary Chalcogenide CuInSnSe 4

Quaternary chalcogenides continue to be of interest for a variety of technological applications, with physical properties stemming from their structural complexity and stoichiometric variation. In certain structure types, partial vacancies on specific lattice positions present an opportunity to investigate electrical and thermal properties in light of these lattice defects. In this work, we investigated the structural, thermal, and electronic properties of CuInSnSe 4 , a material that belongs to a relatively unexplored class of quaternary chalcogenides with a defect adamantine crystal structure. First-principles calculations together with experimental measurements revealed a chalcopyrite-like structure with inherent vacancies and characteristic s–p and p–d orbital hybridizations in the electronic structure of the material. Cation disorder and lattice anharmonicity result in very low thermal conductivity with values significantly lower than those for related compositions. In conclusion, this work reveals the fundamental physical properties of a previously uninvestigated quaternary chalcogenide and may aid investigations of similar as well as other quaternary chalcogenide compositions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

A hybrid-kinetic simulation tool for non-thermal warm x-ray z-pinch sources, with gas-puff and wire array exemplars

Increasing the fluence of z-pinch x-ray radiation sources above ∼ 10 keV has been a long-standing goal for scientists at Sandia National Laboratories’ Z Machine. Optimizing sources for non-thermal “cold Kα” emission in higher atomic-number materials appears to be a promising path to increase warm x-ray yield. However, this emission is generated by supra-thermal electrons, which are not treated in the magnetohydrodynamic (MHD) codes that are typically used in z-pinch source development. MHD codes do not allow for charge separation or space-charge-generated electric fields, and constrain particle kinematics to Maxwellian distributions. The kinetic codes which do accommodate discrete, non-thermal energy distributions are computationally prohibitive when modeling plasmas near solid density and when modeling/tracking higher ionization states. Thus, modeling non-thermal z-pinch sources requires a new simulation tool. In this report, we present a new hybrid modeling capability that uses the fast features of MHD-type particles to the greatest extent possible, then transitions to the slower but more complete kinetic particle treatment to correctly capture the particle energy spectra that generate non-thermal emission. This capability is founded on the fully-relativistic particle-in-cell code Chicago, which already includes fluid particle treatments. The governing equations and hybrid methodology presented here are applied in simulations of an argon gas-puff and a molybdenum wire-array to provide preliminary code validation. The argon simulation is compared to measured implosion times and yields from Jones et al., Phys. Plasmas 22, 020706 (2015). The simulated x-ray yield is within 25% of measurements and the implosion times agree within a few percent. The molybdenum wire array simulation captures the implosion timing reported in Hansen et al., Phys. Plasmas 21, 031202 (2014), but work is needed to verify the available EOS table. These exemplar simulations represents the type of non-thermal sources that will be developed using the hybrid code capability going forward.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Shifting institutional culture to develop climate solutions with Open Science

To address our climate emergency, “we must rapidly, radically reshape society”—Johnson & Wilkinson, All We Can Save. In science, reshaping requires formidable technical (cloud, coding, reproducibility) and cultural shifts (mindsets, hybrid collaboration, inclusion). We are a group of cross-government and academic scientists that are exploring better ways of working and not being too entrenched in our bureaucracies to do better science, support colleagues, and change the culture at our organizations. We share much-needed success stories and action for what we can all do to reshape science as part of the Open Science movement and 2023 Year of Open Science.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Mode connectivity in the loss landscape of parameterized quantum circuits

Variational training of parameterized quantum circuits (PQCs) underpins many workflows employed on near-term noisy intermediate scale quantum (NISQ) devices. It is a hybrid quantum-classical approach that minimizes an associated cost function in order to train a parameterized ansatz. In this work we adapt the qualitative loss landscape characterization for neural networks introduced in Goodfellow et al. (2014); Li et al. (2017) and tests for connectivity used in Draxler et al. (2018) to study the loss landscape features in PQC training. We present results for PQCs trained on a simple regression task, using the bilayer circuit ansatz, which consists of alternating layers of parameterized rotation gates and entangling gates. Multiple circuits are trained with 3 different batch gradient optimizers: stochastic gradient descent, the quantum natural gradient, and Adam. We identify large features in the landscape that can lead to faster convergence in training workflows.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Role of Transition Metals in Metal–Organic Frameworks as Nanoporous Ion Emitters for Thermal Ionization Mass Spectrometry

Thermal ionization mass spectroscopy (TIMS) is a powerful analytical technique that allows for precise determination of isotopic ratios. Analysis on low abundance samples, however, can be limited by the ionization efficiency. Following an investigation into a new type of metal-organic hybrid material devised to promote the emission of analyte ions (nano-PIES) and reduce traditional sample loading problems, this work probes the impact that changing the metal in the material has on the ionization of Uranium-238. Being derived from metal-organic frameworks (MOFs), nano-PIEs inherit the tunability of the parent MOFs; the MOF-74 series has been well studied for probing the impact various framework metals (i.e., Mg, Mn, Co, Ni, Cu, Zn, and Cd) have on material properties, and thus, a series of nano-PIEs with different metals were derived from an isoreticular MOF-74 series. In conclusion, trends in ionization efficiency were studied as a function of ionization potential, volatility, and work function of the framework metals as well as the mechanism of ionization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Elucidating electrochemical nitrate and nitrite reduction over atomically-dispersed transition metal sites

Abstract Electrocatalytic reduction of waste nitrates (NO 3 − ) enables the synthesis of ammonia (NH 3 ) in a carbon neutral and decentralized manner. Atomically dispersed metal-nitrogen-carbon (M-N-C) catalysts demonstrate a high catalytic activity and uniquely favor mono-nitrogen products. However, the reaction fundamentals remain largely underexplored. Herein, we report a set of 14; 3 d -, 4 d -, 5 d - and f -block M-N-C catalysts. The selectivity and activity of NO 3 − reduction to NH 3 in neutral media, with a specific focus on deciphering the role of the NO 2 − intermediate in the reaction cascade, reveals strong correlations (R=0.9) between the NO 2 − reduction activity and NO 3 − reduction selectivity for NH 3 . Moreover, theoretical computations reveal the associative/dissociative adsorption pathways for NO 2 − evolution, over the normal M-N 4 sites and their oxo-form (O-M-N 4 ) for oxyphilic metals. This work provides a platform for designing multi-element NO 3 RR cascades with single-atom sites or their hybridization with extended catalytic surfaces.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Measurement of the light yield response of the Gd-compatible water-based liquid scintillator with the Brookhaven one-ton testbed

The water-based liquid scintillator (WbLS) enables hybrid detection by combining scintillation and Cherenkov signals, providing superior event reconstruction capabilities compared to conventional neutrino detectors. For this work, we measured the light yield of Gd-compatible WbLS at varying concentrations from 0.35% to 1% by mass, using cosmic-ray muons in a 1-ton scale detector at Brookhaven National Laboratory. The light yield is measured as (69.2 ± 6.9) ph/MeV at 0.35% concentration, which increased to (87.3 ± 8.7) ph/MeV at 1%. These results establish a quantitative basis for optimizing future WbLS-based detectors in neutrino physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

A Unifying Framework to Enable Artificial Intelligence in High-Performance Computing Workflows

Current trends point to a future where large-scale scientific applications are tightly coupled high-performance computing/artificial intelligence (HPC/AI) hybrids. Hence, we urgently need to invest in creating a seamless, scalable framework where HPC and AI/machine learning can efficiently work together and adapt to novel hardware and vendor libraries without starting from scratch every few years. Finally, the current ecosystem and sparsely connected community are not sufficient to tackle these challenges, and we require a breakthrough catalyst for science similar to what PyTorch enabled for AI.

high-performance computing↗

DOE OSTI2025

Learning-Based Quantum Compilation: Translating QASM to QIR with CodeBERT

We propose a learning-based approach to quantum compilation by translating OpenQASM to Quantum Intermediate Representation (QIR) using a fine-tuned CodeBERT model. Trained on 10,000 synthetic QASM-QIR pairs, the model captures code semantics while addressing QIR verbosity and the 512-token limit via a custom token compression scheme. Finetuning was performed on the Frontier supercomputer, with results showing syntactic correctness and stable validation loss reduction. Our method moves toward enabling flexible, language-modeldriven quantum software tools. It also introduces syntax error handling and the possibility of incorporating classical control constructs, addressing limitations in existing rule-based compilers like qBraid-QIR. While the current model has been validated on quantum-only circuits, we propose future evaluations on hybrid quantum-classical examples. This poster will provide architecture insights, compression examples, training loss plots, and QIR outputs. Our work highlights the potential for scalable, adaptable compilation in future quantum toolchains.

Afrose, Sharmin [ORNL]↗

DOE OSTI2021

Development of an Assessment Methodology That Enables the Nuclear Industry to Evaluate Adoption of Advanced Automation

Nuclear power has a crucial role in providing safe, reliable, and economical carbon-free electricity for today and the future. For continued operation, many of the existing United States nuclear power plants will begin the subsequent license renewal process for extending their operating license periods. As plants extend their expected operating lifetimes, there is a significant opportunity to modernize. These plants have a much stronger business case with these extended mission periods to modernize and significantly enhance their economic viability in current and future energy markets by implementing digital technologies that support innovation, efficiency gains, and business-model transformation. Ensuring continued safety and reliability is crucial. Transformative digital technologies—including automation—that fundamentally change the concept of operation for the nuclear power plant operating model requires a critical focus on the human and technology integration element. Further, the nuclear industry has historically been reluctant to modernize due to having a risk adverse culture and lack of clarity for a transformative new state vision (Joe & Remer, 2019; Thomas et al., 2020). Common barriers include (1) the perceived value and return on investment (ROI) of digital technology, (2) the perceived risk associated with licensing, regulatory, and cybersecurity, and (3) insufficient guidance for performing digital modifications to power generation systems. This work presents a methodology to address these barriers and support the industry in adopting advanced automation and digital technology through developing a transformative vision and implementation strategy that will address the human and technology integration element. This research leverages previous LWRS Program and industry results. It draws specifically on previous LWRS Program research in the areas of advanced alarm systems, computer-based procedures, model informed decision support, and advanced human-system interface displays (e.g., overviews and task-based). The modernization methodology can be used to guide transformative thinking when integrating a set of vendor-specific capabilities to support a new concept of operations and a utility’s end-state vision. The results of this research are organized into six major sections: - Section 1 introduces the need for supporting large-scale digital modifications that will renew the technology base for extended operating life beyond 60 years - Section 2 describes the challenges that the nuclear industry is enduring with modernizing. - Section 3 summarizes the primary standards and guidance. - Section 4 presents earlier work from the LWRS Program regarding the development of a transformative conceptual design for an advanced control room of a hybrid plants. - Section 5 presents a methodology that is designed at addressing the challenges in the industry today in achieving a transformative new state vision and concept of operations. - Conclusions and next steps of this research are provided in Section 6.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

OEDI—Solar Grid Integration Data and Analytics Library

As a part of the Open Energy Data Initiative, this effort aims to develop and demonstrate novel distribution state estimation, control optimization, and transient analysis as well as provide access to data, data integration, and mapping information. More specifically, the focus of the effort will be on physics-based distribution system state estimation, hybrid (physics-based and machine learning) distribution optimal power flow, and event detection/analysis for solar integration and analytics. This work will enable reproducible, robust, replicable, and generalizable R&D in simulation and emulation of solar system integration. These test models and datasets will provide an integrated library for developing and testing power system operation technologies. To make the library user-friendly, this project will provide data curation tools such as data translators, mapping scripts and APIs, database schemas and metadata, interfaces and user dashboard, source code for the reference algorithms, description of the use-cases/scenarios, and comprehensive information on all the assumptions.

14 SOLAR ENERGY↗

NASA NTRS1992

Man-equivalent telepresence through four fingered human-like hand system

The author describes a newly developed mechanical hand system. The robot hand is in human-like configuration with a thumb and three fingers, a palm, a wrist, and the forearm in which the hand and wrist actuators are located. Each finger and the wrist has its own active electromechanical compliance system, allowing the joint drive trains to be stiffened or loosened. This mechanism imitates the human muscle dual function of positioner and stiffness controller. This is essential for soft grappling operations. The hand-wrist assembly has 16 finger joints, three wrist joints, and five compliance mechanisms for a total of 24 degrees of freedom. The strength of the hand is roughly half that of the human hand and its size is comparable to a male hand. The hand is controlled through an exoskeleton glove controller that the operator wears. The glove provides the man-machine interface in telemanipulation control mode: it senses the operator's inputs to guide the mechanical hand in hybrid position and force control. The hand system is intended for dexterous manipulations in structured environments. Typical applications will include work in hostile environment such as space operations and nuclear power plants.

Jau, Bruno M.↗