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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

NDARC NASA Design and Analysis of Rotorcraft - Input, Appendix 2

The NASA Design and Analysis of Rotorcraft (NDARC) software is an aircraft system analysis tool that supports both conceptual design efforts and technology impact assessments. The principal tasks are to design (or size) a rotorcraft to meet specified requirements, including vertical takeoff and landing (VTOL) operation, and then analyze the performance of the aircraft for a set of conditions. For broad and lasting utility, it is important that the code have the capability to model general rotorcraft configurations, and estimate the performance and weights of advanced rotor concepts. The architecture of the NDARC code accommodates configuration exibility, a hierarchy of models, and ultimately multidisciplinary design, analysis, and optimization. Initially the software is implemented with low-fidelity models, typically appropriate for the conceptual design environment. An NDARC job consists of one or more cases, each case optionally performing design and analysis tasks. The design task involves sizing the rotorcraft to satisfy specified design conditions and missions. The analysis tasks can include off-design mission performance calculation, flight performance calculation for point operating conditions, and generation of subsystem or component performance maps. For analysis tasks, the aircraft description can come from the sizing task, from a previous case or a previous NDARC job, or be independently generated (typically the description of an existing aircraft). The aircraft consists of a set of components, including fuselage, rotors, wings, tails, and propulsion. For each component, attributes such as performance, drag, and weight can be calculated; and the aircraft attributes are obtained from the sum of the component attributes. Description and analysis of conventional rotorcraft configurations is facilitated, while retaining the capability to model novel and advanced concepts. Specific rotorcraft configurations considered are single-main-rotor and tail-rotor helicopter, tandem helicopter, coaxial helicopter, and tilt-rotor. The architecture of the code accommodates addition of new or higher-fidelity attribute models for a component, as well as addition of new components.

Appendix↗
NASA NTRS1992

Ultrasonic nondestructive characterization of composites with 3-dimensional architectures

The development and implementation of advanced composite material systems and their associated technologies are critical for success in the highly competitive world aerospace market. Acceptance of advance production methods and field support of structures made with these new materials require the development of quantitative, cost-effective, inspection methods. The results of quantitative ultrasonic through-transmission imaging of composites with complex three-dimensional architecture using phase-insensitive and phase-sensitive techniques are presented.

Miller, James G.↗
DOE OSTI2020

High Temperature Additive Architectures for 65% Efficiency (Final Report)

GE Power (GEP) proposed a development program that targets advanced high temperature additive components that contribute towards the DOE’s goal for advanced gas turbines that are capable of 65%, or greater, efficiency in combined cycle application. GEP proposed to leverage state-of-the-art additive manufacturing to develop novel and innovative component airfoil and end wall architectures that provide cooling flow savings while maintaining the component durability expected in today’s gas turbines. The objective of this program was to develop advanced component designs and architectures enabled by additive manufacturing, and the materials systems and additive manufacturing technology required for these designs. This Phase I program leveraged existing design and analysis knowledge and techniques for additive materials and methods and utilized extensive analytical evaluations to develop and refine designs for advanced turbine hot gas path (HGP) components. The design will be the basis for development and testing in a potential Phase II program. The goal of this Phase I program was to establish a feasible conceptual design for advanced additive turbine hot section components along with a technology maturation plan involving testing to address risk areas and needed validation. It was approached through two primary elements: 1) conceptual design and analysis of wall architectures, advanced airfoil cooling concepts, and advanced system-level concepts, and 2) investigation of additive modalities and associated materials systems. The Phase I program has concluded with the down-select of a primary design concept for a potential Phase II program.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Summary Report on the SAE 2016 Range Extenders for Electric Vehicles Symposium

The SAE 2016 Range Extenders for Electric Vehicles Symposium was a 2-day technical meeting focused on the role of advanced internal combustion engines (ICEs) and other novel energy converter technologies for extending the range of electric vehicles (EVs). The first-of-its-kind symposium was notable for focusing solely on the range extender (REx) technologies and not the EVs. The technical program featured presentations from international leaders from industry, government, national laboratories, and academia. The opening keynote presentations covered a broad range of topics including consumer behavior, policy implications, regulatory considerations, and REx architectures as enablers for advanced technologies. The technical sessions focused on an array of REx technologies including conventional ICEs, as well as less conventional or emerging technologies such as microturbines, fuel cells, low-temperature combustion engines, and aluminum-air batteries. The symposium included two panel sessions. The trend toward increasing vehicle electrification and the changing role of ICEs and other auxiliary power unit technologies for use as REx’s is leading to new research and design development needs. The symposium captured the interest of the industry and research communities in exploring the opportunities and challenges associated with REx’s for EVs. This report includes key takeaways, summarized below, and draft notes for each presentation and panel discussion.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Hybrid learning techniques for scientific data reduction with performance guarantees

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Final report- UFL - RAPIDS2: A SciDAC Institute for Computer Science, Data, and Artificial Intelligence

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1999

Demonstrating the Performance Benefits of the Strutjet RBCC for Space Launch Architectures

The Rocket Based Combined Cycle (RBCC) engine synergistically combines the best elements of airbreathing and rocket propulsion to benefit a wide range of future reusable launch vehicles (RLV). Aerojet's Strutjet RBCC offers high Isp during mid-phase acceleration, and high thrust for boost and final ascent phases. The result is a relatively low gross weight vehicle that reduces thrust requirements compared with all-rocket solutions. Relative to combination propulsion systems, the integrated propulsive elements of the Strutjet reduce engine weight and complexity. This paper will summarize the results of tests demonstrating our latest hydrogen-fueled Strutjet RBCC engine performance, including inlet operability and performance over a range of conditions, sea level static and Mach 2.4 rocket thrust augmentation, ramjet and scramjet performance, combined scramjet/rocket performance at Mach 8, and ascent mode rocket performance. These tests have significantly advanced the technology readiness of the Strutjet engine and substantiate the performance benefits of RBCC engines for reusable launch vehicle applications. A companion paper provides a focus on Strutjet as a basis for advanced air and space architecture, covering hydrogen and hydrocarbon fuels and cooled structures.

Siebenhaar, Adam↗
DOE OSTI2020

3D fault architecture controls the dynamism of earthquake swarms

The vibrant evolutionary patterns made by earthquake swarms are incompatible with standard, effectively two-dimensional (2D) models for general fault architecture. Here we leverage advances in earthquake monitoring with a deep-learning algorithm to image a fault zone hosting a 4-year-long swarm in southern California. We infer that fluids are naturally injected into the fault zone from below and diffuse through strike-parallel channels while triggering earthquakes. A permeability barrier initially limits up-dip swarm migration but ultimately is circumvented. This enables fluid migration within a shallower section of the fault with fundamentally different mechanical properties. Our observations provide high-resolution constraints on the processes by which swarms initiate, grow, and arrest. These findings illustrate how swarm evolution is strongly controlled by 3D variations in fault architecture.

58 GEOSCIENCES↗
NASA NTRS2010

Modular Approach to Launch Vehicle Design Based on a Common Core Element

With a heavy lift launch vehicle as the centerpiece of our nation's next exploration architecture's infrastructure, the Advanced Concepts Office at NASA's Marshall Space Flight Center initiated a study to examine the utilization of elements derived from a heavy lift launch vehicle for other potential launch vehicle applications. The premise of this study is to take a vehicle concept, which has been optimized for Lunar Exploration, and utilize the core stage with other existing or near existing stages and boosters to determine lift capabilities for alternative missions. This approach not only yields a vehicle matrix with a wide array of capabilities, but also produces an evolutionary pathway to a vehicle family based on a minimum development and production cost approach to a launch vehicle system architecture, instead of a purely performance driven approach. The upper stages and solid rocket booster selected for this study were chosen to reflect a cross-section of: modified existing assets in the form of a modified Delta IV upper stage and Castor-type boosters; potential near term launch vehicle component designs including an Ares I upper stage and 5-segment boosters; and longer lead vehicle components such as a Shuttle External Tank diameter upper stage. The results of this approach to a modular launch system are given in this paper.

Creech, Dennis M.↗
DOE OSTI2013

Efficient checkpointing schemes for depletion perturbation solutions on memory-limited architectures

We describe a methodology for decreasing the memory footprint and machine I/O load associated with the need to access a forward solution during an adjoint solve. Specifically, we are interested in the depletion perturbation equations, where terms in the adjoint Bateman and transport equations depend on the forward flux solution. Checkpointing is the procedure of storing snapshots of the forward solution to disk and using these snapshots to recompute the parts of the forward solution that are necessary for the adjoint solve. For large problems, however, the storage cost of just a few copies of an angular flux vector can exceed the available RAM on the host machine. We propose a methodology that does not checkpoint the angular flux vector; instead, we write and store converged source moments, which are typically of a much lower dimension than the angular flux solution. This reduces the memory footprint and I/O load of the problem, but requires that we perform single sweeps to reconstruct flux vectors on demand. We argue that this trade-off is exactly the kind of algorithm that will scale on advanced, memory-limited architectures. We analyze the cost, in terms of FLOPS and memory footprint, of five checkpointing schemes. We also provide computational results that support the analysis and show that the memory-for-work trade off does improve time to solution. (authors)

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1991

A mission executor for an autonomous underwater vehicle

The Naval Postgraduate School has been conducting research into the design and testing of an Autonomous Underwater Vehicle (AUV). One facet of this research is to incrementally design a software architecture and implement it in an advanced testbed, the AUV II. As part of the high level architecture, a Mission Executor is being constructed using CLIPS (C Language Integrated Production System) version 5.0. The Mission Executor is an expert system designed to oversee progress from the AUV launch point to a goal area and back to the origin. It is expected that the executor will make informed decisions about the mission, taking into account the navigational path, the vehicle subsystem health, and the sea environment, as well as the specific mission profile which is downloaded from an offboard mission planner. Heuristics for maneuvering, avoidance of uncharted obstacles, waypoint navigation, and reaction to emergencies (essentially the expert knowledge of a submarine captain) are required. Many of the vehicle subsystems are modeled as objects using the CLIPS Object Oriented Language (COOL) embedded in CLIPS 5.0. Also, truth maintenance is applied to the knowledge base to keep configurations updated.

Lee, Yuh-Jeng↗
DOE OSTI2019

Compressed Optimization of Device Architectures for Semiconductor Quantum Devices

Recent advances in nanotechnology have enabled researchers to manipulate small collections of quantum-mechanical objects with unprecedented accuracy. In semiconductor quantum-dot qubits, this manipulation requires controlling the dot orbital energies, the tunnel couplings, and the electron occupations. These properties all depend on the voltages placed on the metallic electrodes that define the device, the positions of which are fixed once the device is fabricated. While there has been much success with small numbers of dots, as the number of dots grows, it will be increasingly useful to control these systems with as few electrode voltage changes as possible. Here, we introduce a protocol, which we call the "compressed optimization of device architectures" (CODA), in order both to efficiently identify sparse sets of voltage changes that control quantum systems and to introduce a metric that can be used to compare device designs. As an example of the former, we apply this method to simulated devices with up to 100 quantum dots and show that CODA automatically tunes devices more efficiently than other common nonlinear optimizers. To demonstrate the latter, we determine the optimal lateral scale for a triple quantum dot, yielding a simulated device that can be tuned with small voltage changes on a limited number of electrodes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2005

A Ka-Band Wide-Bandgap Solid-State Power Amplifier: Architecture Performance Estimates

Motivated by recent advances in wide-bandgap (WBG) gallium nitride (GaN) semiconductor technology, there is considerable interest in developing efficient solidstate power amplifiers (SSPAs) as an alternative to the traveling-wave tube amplifier (TWTA) for space applications. This article documents the results of a study to investigate power-combining technology and SSPA architectures that can enable a 120-W, 40 percent power-added efficiency (PAE) SSPA. Results of the study indicate that architectures based on at least three power combiner designs are likely to enable the target SSPA. The proposed architectures can power combine 16 to 32 individual monolithic microwave integrated circuits (MMICs) with >80 percent combining efficiency. This corresponds to MMIC requirements of 5- to 10-W output power and >48 percent PAE. For the three proposed architectures [1], detailed analysis and design of the power combiner are presented. The first architecture studied is based on a 16-way septum combiner that offers low loss and high isolation over the design band of 31 to 36 GHz. Analysis of a 2-way prototype septum combiner had an input match >25 dB, output match >30 dB, insertion loss <0.02 dB, and isolation >30 dB over the design band. A 16-way design, based on cascading this combiner in a binary fashion, is documented. The second architecture is based on a 24-way waveguide radial combiner. A prototype 24-way radial base was analyzed to have an input match >30 dB (under equal excitation of all input ports). The match of the mode transducer that forms the output of a radial combiner was found to be >27 dB. The functional bandwidth of the radial base and mode transducer, which together will form a radial combiner/divider, exceeded the design band. The third architecture employs a 32-way, parallel-plate radial combiner. Simulation results indicated an input match >24 dB, output match >22 dB, insertion loss <0.23 dB, and adjacent port isolation >20 dB over the design band. All three architectures utilize a low-loss MMIC amplifier module based on commercial MMIC packaging and a custom microstrip-to-rectangular-waveguide transition. The insertion loss of the module is expected to be 0.45 dB over the design band.

Epp, L.↗
NASA NTRS2016

Science and Technology (S and T) Roadmap Collaboration between SMC, NASA, and Government Partners

National Security Space (NSS) presents multi-faceted S and T challenges. We must continually innovate enterprise and information management; provide decision support; develop advanced materials; enhance sensor technology; transform communication technology; develop advanced propulsion and resilient space architectures and capabilities; and enhance multiple additional S and T domains. These challenges are best met by leveraging advanced S and T research and technology development from a number of DoD agencies and civil agencies such as NASA. The authors of this paper have engaged in these activities since 2006 and over the past decade developed multiple strategic S and T relationships. This paper highlights the Office of the Space Missile Systems Center (SMC) Chief Scientist (SMC/ST) collaboration with the NASA Office of Chief Technologist (NASA OCT), which has multiple S and T activities that are relevant to NSS. In particular we discuss the development of the Technology Roadmaps that benefit both Civil Space and NSS. Our collaboration with NASA OCT has been of mutual benefit to multiple participants. Some of the other DoD components include the Defense Advanced Research Projects agency (DARPA), Air Force Research Laboratory (AFRL), Naval Research Laboratory (NRL), The USAF Office of Chief Scientist, the USAF Science Advisory Board (SAB), Space and Naval Warfare Systems Command (SPAWAR), and a number of other services and agencies. In addition, the human talent is a key enabler of advanced S and T activities; it is absolutely critical to have a strong supply of talent in the fields of Science Technology, Engineering, and Mathematics (STEM). Consequently, we continually collaborate with the USAF Institute of Technology (AFIT), other service academies and graduate schools, and other universities and colleges. This paper highlights the benefits that result from such strategic S and T partnerships and recommends a way forward that will continually build upon these achievements into the future.

Science Technology Collaboration↗
DOE OSTI2024

Time Series Foundation Models and Deep Learning Architectures for Earthquake Temporal and Spatial Nowcasting

Advancing the capabilities of earthquake nowcasting, the real-time forecasting of seismic activities, remains crucial for reducing casualties. This multifaceted challenge has recently gained attention within the deep learning domain, facilitated by the availability of extensive earthquake datasets. Despite significant advancements, the existing literature on earthquake nowcasting lacks comprehensive evaluations of pre-trained foundation models and modern deep learning architectures; each focuses on a different aspect of data, such as spatial relationships, temporal patterns, and multi-scale dependencies. This paper addresses the mentioned gap by analyzing different architectures and introducing two innovative approaches called Multi Foundation Quake and GNNCoder. We formulate earthquake nowcasting as a time series forecasting problem for the next 14 days within 0.1-degree spatial bins in Southern California. Earthquake time series are generated using the logarithm energy released by quakes, spanning 1986 to 2024. Our comprehensive evaluations demonstrate that our introduced models outperform other custom architectures by effectively capturing temporal-spatial relationships inherent in seismic data. The performance of existing foundation models varies significantly based on the pre-training datasets, emphasizing the need for careful dataset selection. However, we introduce a novel method, Multi Foundation Quake, that achieves the best overall performance by combining a bespoke pattern with Foundation model results handled as auxiliary streams.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2002

How to Quickly Import CAD Geometry into Thermal Desktop

There are several groups at JPL (Jet Propulsion Laboratory) that are committed to concurrent design efforts, two are featured here. Center for Space Mission Architecture and Design (CSMAD) enables the practical application of advanced process technologies in JPL's mission architecture process. Team I functions as an incubator for projects that are in the Discovery, and even pre-Discovery proposal stages. JPL's concurrent design environment is to a large extent centered on the CAD (Computer Aided Design) file. During concurrent design sessions CAD geometry is ported to other more specialized engineering design packages.

Wright, Shonte↗
DOE OSTI2016

IDEAL: Images Across Domains, Experiments, Algorithms and Learning

Research across science domains is increasingly reliant on image-centric data. Software tools are in high demand to uncover relevant, but hidden, information in digital images, such as those coming from faster next generation high-throughput imaging platforms. The challenge is to analyze the data torrent generated by the advanced instruments efficiently, and provide insights such as measurements for decision-making. In this article, we overview work performed by an interdisciplinary team of computational and materials scientists, aimed at designing software applications and coordinating research efforts connecting (1) emerging algorithms for dealing with large and complex datasets; (2) data analysis methods with emphasis in pattern recognition and machine learning; and (3) advances in evolving computer architectures. Engineering tools around these efforts accelerate the analyses of image-based recordings, improve reusability and reproducibility, scale scientific procedures by reducing time between experiments, increase efficiency, and open opportunities for more users of the imaging facilities. This paper describes our algorithms and software tools, showing results across image scales, demonstrating how our framework plays a role in improving image understanding for quality control of existent materials and discovery of new compounds.

97 MATHEMATICS AND COMPUTING↗