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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

A Learn-to-Fly Approach for Adaptively Tuning Flight Control Systems

A method is presented for adaptively tuning feedback control gains in a flight control system to achieve desired closed-loop performance. The method combines efficient aircraft parameter estimation, for identifying closed-loop dynamics models, with online nonlinear optimization, for sequentially perturbing and updating control gains to improve performance. Access to flight measurements and the control gains is required, but no prior information about the aircraft dynamics or the flight control architecture is needed. After the procedure, the optimized control gains (with uncertainties), the open-loop dynamics model, and the closed-loop dynamics model are available. The method is demonstrated for tuning a longitudinal stability augmentation system using a realistic nonlinear flight dynamics simulation of a subscale airplane. Convergence was attained using five maneuvers that spanned approximately one minute of flight test time. Although shown for a relatively simple case, the method is general and can be applied to other aircraft, axes of motion, performance metrics, and control system designs.

Adaptive tuning↗
DOE OSTI2026

Diethylenetriamine-functionalized graphene oxide: Insights into ion adsorption and applications in rare earth element separation

The growing demand for critical minerals and materials requires atom- and energy-efficient, selective separations to overcome the challenges posed by the similar chemical and physical properties of the rare earth elements (REEs) and their low concentrations in unconventional domestic feedstocks. Here, in this study, we developed diethylenetriamine-functionalized graphene oxide (DETA-GO) as a membrane material for REE adsorption and separation. Synthesis conditions were optimized to maximize nitrogen incorporation while also preserving GO dispersibility for facile membrane fabrication. We investigated the mechanism of amine functionalization, the nitrogen-bonding configurations, the organization of the interlayer transport channels, and the resulting effects on ion and water transport for REE separations. Neat-GO and DETA-GO multilayer laminate membranes were fabricated by vacuum filtration onto polymer supports. To investigate the effects of amine functionalization, the membranes were characterized using scanning electron microscopy, Raman, Fourier transform infrared, and X-ray photoelectron spectroscopy, as well as grazing-incidence X-ray diffraction measurements. Ion permeation experiments with representative lanthanum (La 3+ ) and ytterbium (Yb 3+ ) solutions revealed enhanced ion adsorption and water transport through DETA-GO membranes compared to neat-GO. The strong affinity of the membranes for multivalent REEs was also validated with conductivity and inductively coupled plasma mass spectrometry measurements. Atomistic insight into the role of amine functionalization in modulating nanochannel architecture and long-term stability, optimizing adsorption sites, and regulating REE and water transport was obtained using classical molecular dynamics simulations. Collectively, our joint experimental and theoretical study demonstrates the potential of DETA-GO membranes for selective REE separations, offering insights into ion-binding mechanisms, water-transport properties, and nanochannel optimization for the recovery of critical materials from aqueous feedstocks.

Adsorbent↗
DOE OSTI2021

Efficient exascale discretizations: High-order finite element methods

Efficient exploitation of exascale architectures requires rethinking of the numerical algorithms used in many large-scale applications. These architectures favor algorithms that expose ultra fine-grain parallelism and maximize the ratio of floating point operations to energy intensive data movement. One of the few viable approaches to achieve high efficiency in the area of PDE discretizations on unstructured grids is to use matrix-free/partially assembled high-order finite element methods, since these methods can increase the accuracy and/or lower the computational time due to reduced data motion. In this paper we provide an overview of the research and development activities in the Center for Efficient Exascale Discretizations (CEED), a co-design center in the Exascale Computing Project that is focused on the development of next-generation discretization software and algorithms to enable a wide range of finite element applications to run efficiently on future hardware. CEED is a research partnership involving more than 30 computational scientists from two US national labs and five universities, including members of the Nek5000, MFEM, MAGMA and PETSc projects. We discuss the CEED co-design activities based on targeted benchmarks, miniapps and discretization libraries and our work on performance optimizations for large-scale GPU architectures. We also provide a broad overview of research and development activities in areas such as unstructured adaptive mesh refinement algorithms, matrix-free linear solvers, high-order data visualization, and list examples of collaborations with several ECP and external applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Development of lean, efficient, and fast physics-framed deep-learning-based proxy models for subsurface carbon storage

In this work, we present deep-learning-based surrogate models for CCUS developed with four different algorithms and a physics-framed two-phase flow problem involving displacement of water by CO 2 . The deep-learning models were trained using 3D datasets describing the pressure plume, CO 2 saturation plume, and water extraction rate generated by numerical simulation. The hyperparameters defining the architecture of the neural networks were optimized to determine the slimmest network size and training parameters that give the most efficient performance at the least training cost. To develop a robust model that closely mimics the governing physical laws, the discretized form of the two-phase fluid transport equation was used to formulate the supervised deep-learning task. The algorithms investigated in this study predicted the data to above 95% accuracy, with the multi-layer perceptron model demonstrating the best performance by balancing training speed, prediction time, and prediction accuracy with lean network capacity. Furthermore, the surrogate models simultaneously predict reservoir pressure and CO 2 saturation in every grid block, including the surface well extraction rate and bottomhole pressure, at all simulation times for a given static model realization in just a few seconds on a standard desktop computer. A key outcome of this study is that limits can be placed on network design parameters to avoid over designing neural networks, with associated efficiencies in training and prediction times. This is very useful because large volumes of data may be generated in CCUS projects and over-design of neural network architectures imposes penalties that are antithetical to the goal of near-real time forecasting.

58 GEOSCIENCES↗
DOE OSTI2018

A mechanical reduced order model for elastomeric 3D printed architectures

Direct ink writing of silicone elastomers enables printing with precise control of porosity and mechanical properties of ordered cellular solids, suitable for shock absorption and stress mitigation applications. With the ability to manipulate structure and feedstock stiffness, the design space becomes challenging to parse to obtain a solution producing a desired mechanical response. We derive an analytical design approach for a specific architecture. Results from finite element simulations and quasi-static mechanical tests of two different parallel strand architectures were analyzed to understand the structure-property relationships under uniaxial compression. Combining effective stiffness-density scaling with least squares optimization of the stress responses yielded general response curves parameterized by resin modulus and strand spacing. An analytical expression of these curves serves as a reduced order model, which, when optimized, provides a rapid design capability for filament-based 3D printed structures. Finally, as a demonstration, the optimal design of a face-centered tetragonal architecture is computed that satisfies prescribed minimum and maximum load constraints.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Universal Design Curves for Elastomeric Direct Ink-Writing Architectures

Direct ink writing of silicone elastomers enables printing with precise control of porosity and mechan- ical properties of ordered cellular solids, suitable for shock absorption and stress mitigation applications. With the ability to manipulate structure and feedstock stiffness, the design space becomes challenging to parse to obtain a solution producing a desired mechanical response. Here we derive an analytical design approach for a specific architecture. Results from finite element simulations and quasi-static mechanical tests of two different parallel strand architectures were analyzed to understand the structure-property relationships under compressive strain. Combining effective stiffness-density scaling with least squares optimization of the stress responses yielded general response curves parameterized by resin modulus and strand spacing. An analytical expression of these curves serves as a reduced order model, which, when optimized, provides a rapid design capability for filament-based 3D printed structures. As a demonstration, the optimal design of a face-centered tetragonal architecture is computed that satisfies prescribed minimum and maximum load constraints.

36 MATERIALS SCIENCE↗
NASA NTRS2010

A Concept and Implementation of Optimized Operations of Airport Surface Traffic

This paper presents a new concept of optimized surface operations at busy airports to improve the efficiency of taxi operations, as well as reduce environmental impacts. The suggested system architecture consists of the integration of two decoupled optimization algorithms. The Spot Release Planner provides sequence and timing advisories to tower controllers for releasing departure aircraft into the movement area to reduce taxi delay while achieving maximum throughput. The Runway Scheduler provides take-off sequence and arrival runway crossing sequence to the controllers to maximize the runway usage. The description of a prototype implementation of this integrated decision support tool for the airport control tower controllers is also provided. The prototype decision support tool was evaluated through a human-in-the-loop experiment, where both the Spot Release Planner and Runway Scheduler provided advisories to the Ground and Local Controllers. Initial results indicate the average number of stops made by each departure aircraft in the departure runway queue was reduced by more than half when the controllers were using the advisories, which resulted in reduced taxi times in the departure queue.

Jung, Yoon C.↗
NASA NTRS2015

Asteroid Redirect Crewed Mission Space Suit and EVA System Maturation

The Asteroid Redirect Crewed Mission (ARCM) requires a Launch/Entry/Abort (LEA) suit capability and short duration Extra Vehicular Activity (EVA) capability from the Orion spacecraft. For this mission, the pressure garment that was selected, for both functions, is the Modified Advanced Crew Escape Suit (MACES) with EVA enhancements and the life support option that was selected is the Exploration Portable Life Support System (PLSS). The proposed architecture was found to meet the mission constraints, but much more work is required to determine the details of the required suit upgrades, the integration with the PLSS, and the rest of the tools and equipment required to accomplish the mission. This work has continued over the last year to better define the operations and hardware maturation of these systems. EVA simulations have been completed in the NBL and interfacing options have been prototyped and analyzed with testing planned for late 2014. For NBL EVA simulations, in 2013, components were procured to allow in-house build up for four new suits with mobility enhancements built into the arms. Boots outfitted with clips that fit into foot restraints have also been added to the suit and analyzed for possible loads. Major suit objectives accomplished this year in testing include: evaluation of mobility enhancements, ingress/egress of foot restraint, use of foot restraint for worksite stability, ingress/egress of Orion hatch with PLSS mockup, and testing with two crew members in the water at one time to evaluate the crew's ability to help one another. Major tool objectives accomplished this year include using various other methods for worksite stability, testing new methods for asteroid geologic sampling and improving the fidelity of the mockups and crew equipment. These tests were completed on a medium fidelity capsule mockup, asteroid vehicle mockup, and asteroid mockups that were more accurate for an asteroid type EVA than previous tests. Another focus was the design and fabrication of the interface between the MACES and the PLSS. The MACES was not designed to interface with a PLSS, hence an interface kit must accommodate the unique design qualities of the MACES and provide the necessary life support function connections to the PLSS. A prototype interface kit for MACES to PLSS has been designed and fabricated. Unmanned and manned testing of the interface will show the usability of the kit while wearing a MACES. The testing shows viability of the kit approach as well as the operations concept. The design will be vetted through suit and PLSS experts and, with the findings from the testing, the best path forward will be determined. As the Asteroid Redirect Mission matures, the suit/life support portion of the mission will mature along with it and EVA Tools & Equipment can be iterated to accommodate the overall mission objectives and compromises inherent in EVA Suit optimization. The goal of the EVA architecture for ARCM is to continue to build on the previously developed technologies and lessons learned, and accomplish the ARCM EVAs while providing a stepping stone to future missions and destinations.

Bowie, Jonathan T.↗
NASA NTRS1998

High-Performance Monitoring Architecture for Large-Scale Distributed Systems Using Event Filtering

Monitoring is an essential process to observe and improve the reliability and the performance of large-scale distributed (LSD) systems. In an LSD environment, a large number of events is generated by the system components during its execution or interaction with external objects (e.g. users or processes). Monitoring such events is necessary for observing the run-time behavior of LSD systems and providing status information required for debugging, tuning and managing such applications. However, correlated events are generated concurrently and could be distributed in various locations in the applications environment which complicates the management decisions process and thereby makes monitoring LSD systems an intricate task. We propose a scalable high-performance monitoring architecture for LSD systems to detect and classify interesting local and global events and disseminate the monitoring information to the corresponding end- points management applications such as debugging and reactive control tools to improve the application performance and reliability. A large volume of events may be generated due to the extensive demands of the monitoring applications and the high interaction of LSD systems. The monitoring architecture employs a high-performance event filtering mechanism to efficiently process the large volume of event traffic generated by LSD systems and minimize the intrusiveness of the monitoring process by reducing the event traffic flow in the system and distributing the monitoring computation. Our architecture also supports dynamic and flexible reconfiguration of the monitoring mechanism via its Instrumentation and subscription components. As a case study, we show how our monitoring architecture can be utilized to improve the reliability and the performance of the Interactive Remote Instruction (IRI) system which is a large-scale distributed system for collaborative distance learning. The filtering mechanism represents an Intrinsic component integrated with the monitoring architecture to reduce the volume of event traffic flow in the system, and thereby reduce the intrusiveness of the monitoring process. We are developing an event filtering architecture to efficiently process the large volume of event traffic generated by LSD systems (such as distributed interactive applications). This filtering architecture is used to monitor collaborative distance learning application for obtaining debugging and feedback information. Our architecture supports the dynamic (re)configuration and optimization of event filters in large-scale distributed systems. Our work represents a major contribution by (1) survey and evaluating existing event filtering mechanisms In supporting monitoring LSD systems and (2) devising an integrated scalable high- performance architecture of event filtering that spans several kev application domains, presenting techniques to improve the functionality, performance and scalability. This paper describes the primary characteristics and challenges of developing high-performance event filtering for monitoring LSD systems. We survey existing event filtering mechanisms and explain key characteristics for each technique. In addition, we discuss limitations with existing event filtering mechanisms and outline how our architecture will improve key aspects of event filtering.

Maly, K.↗
DOE OSTI2018

Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources

Apache Calcite is a foundational software framework that provides query processing, optimization, and query language support to many popular open-source data processing systems such as Apache Hive, Apache Storm, Apache Flink, Druid, and MapD. The goal of this paper is to formally introduce Calcite to the broader research community, brie y present its history, and describe its architecture, features, functionality, and patterns for adoption. Calcite's architecture consists of a modular and extensible query optimizer with hundreds of built-in optimization rules, a query processor capable of processing a variety of query languages, an adapter architecture designed for extensibility, and support for heterogeneous data models and stores (relational, semi-structured, streaming, and geospatial). This exible, embeddable, and extensible architecture is what makes Calcite an attractive choice for adoption in big-data frameworks. It is an active project that continues to introduce support for the new types of data sources, query languages, and approaches to query processing and optimization.

Begoli, Edmon↗
DOE OSTI2025

The spherical tokamak advanced reactor (STAR) fusion power plant design

Scientific and technical advancements have been made that improve fusion’s prospects to provide a new energy source, showing enhanced plasma confinement conditions with plasma temperatures reaching or exceeding 100 million degrees. Overshadowing this progress is the challenge involved in developing an economically viable fusion power plant design. Many proposed next-step DEMO and pilot plant designs are extensions of existing physics-focused experimental devices defined to understand and control plasma operations to achieve and sustain a fusion reaction. Transitioning scientific and technical advancements into a functional power plant requires a dedicated focus on architectural designs that integrate diverse technologies, while optimizing physics conditions, with a focus on economic viability. This holistic approach is essential in turning the promise of fusion energy into a reality. The Spherical Tokamak Advanced Reactor (STAR) is a fusion power plant conceptual design with the architectural focus that strives to balance physics, engineering, and cost considerations. In conclusion, it has been set up to introduce relevant physics, engineering and concept features that an intermediate pilot plant might follow, with the goal of meeting system performances and economic requirements that lead to a commercially competitive fusion power plant.

Blanket segmentation↗
DOE OSTI2025

Surrogate Neural Architecture Codesign Package (SNAC-Pack)

Neural Architecture Search is a powerful approach for automating model design, but existing methods struggle to accurately optimize for real hardware performance, often relying on proxy metrics such as bit operations. We present Surrogate Neural Architecture Codesign Package (SNAC-Pack), an integrated framework that automates the discovery and optimization of neural networks focusing on FPGA deployment. SNAC-Pack combines Neural Architecture Codesign's multi-stage search capabilities with the Resource Utilization and Latency Estimator, enabling multi-objective optimization across accuracy, FPGA resource utilization, and latency without requiring time-intensive synthesis for each candidate model. We demonstrate SNAC-Pack on a high energy physics jet classification task, achieving 63.84% accuracy with resource estimation. When synthesized on a Xilinx Virtex UltraScale+ VU13P FPGA, the SNAC-Pack model matches baseline accuracy while maintaining comparable resource utilization to models optimized using traditional BOPs metrics. This work demonstrates the potential of hardware-aware neural architecture search for resource-constrained deployments and provides an open-source framework for automating the design of efficient FPGA-accelerated models.

Weitz, Jason [UC, San Diego] (ORCID:00090004631535↗
NASA NTRSDate not supplied

An Integrated Software Architecture for Solar Cruiser Mission Design and Navigation

Solar Cruiser is a solar sailing mission, riding as a secondary payload to the Interstellar Mapping and Acceleration Probe (IMAP) mission, slated to launch in February of 2025. The Solar Cruiser vehicle will generate thrust via a complex, low-thrust solar sail. The extreme low-thrust nature of the solar sail will leave Solar Cruiser highly sensitive to external environmental effects (such as solar radiation pressure and high-order gravitational perturbations) throughout the entirety of flight. Because of this, preliminary operational optimization routines must be intricately tied to high-order predictive propagation models to ensure the greatest possible confidence in mission success. The Solar Cruiser Mission Design and Navigation (MDNav) team has designed a software tool suite, employing the latest in software containerization technology, to accomplish this task; allowing for seamless development across several users and operating systems. Combining JPL’s Monte toolkit with high-performance optimizers written by the University of Alabama, the proposed architecture allows for instant verification of optimized trajectories within the same development environment that the optimization takes place, removing the need for mission designers and navigators to switch between tools. The MDNav suite image deployment process is separated from the development and operational scripts to be used in flight, which allows for maintaining a low-footprint version control profile without unnecessary file bloating. This paper discusses the historical differences between previous iterations of the Solar Cruiser MDNav tool suite and the current iteration, planned operational interfaces of the tool with other software and subsystems, and the planned path forward in maintaining containerization services for the software throughout the lifetime of Solar Cruiser.

optimization↗
NASA NTRS2012

Optimization of the Multi-Spectral Euclidean Distance Calculation for FPGA-based Spaceborne Systems

Due to the high quantity of operations that spaceborne processing systems must carry out in space, new methodologies and techniques are being presented as good alternatives in order to free the main processor from work and improve the overall performance. These include the development of ancillary dedicated hardware circuits that carry out the more redundant and computationally expensive operations in a faster way, leaving the main processor free to carry out other tasks while waiting for the result. One of these devices is SpaceCube, a FPGA-based system designed by NASA. The opportunity to use FPGA reconfigurable architectures in space allows not only the optimization of the mission operations with hardware-level solutions, but also the ability to create new and improved versions of the circuits, including error corrections, once the satellite is already in orbit. In this work, we propose the optimization of a common operation in remote sensing: the Multi-Spectral Euclidean Distance calculation. For that, two different hardware architectures have been designed and implemented in a Xilinx Virtex-5 FPGA, the same model of FPGAs used by SpaceCube. Previous results have shown that the communications between the embedded processor and the circuit create a bottleneck that affects the overall performance in a negative way. In order to avoid this, advanced methods including memory sharing, Native Port Interface (NPI) connections and Data Burst Transfers have been used.

Cristo, Alejandro↗
NASA NTRS1995

The Evolution of Mission Architectures for Human Lunar Exploration

Defining transportation architectures for the human exploration of the Moon is a complex task due to the multitude of mission scenarios available. The mission transportation architecture recently proposed for the First Lunar Outpost (FLO) was not designed from carefully predetermined mission requirements and goals, but evolved from an initial set of requirements, which were continually modified as studies revealed that some early assumptions were not optimal. This paper focuses on the mission architectures proposed for FLO and investigates how these transportation architectures evolved. A comparison of the strengths and weaknesses of the three distinct mission architectures are discussed, namely (1) Lunar Orbit Rendezvous, (2) staging from the Cislunar Libration Point, and (3) direct to the lunar surface. In addition, several new and revolutionary architectures are discussed.

Everett, S. F.↗
DOE OSTI2015

A microfluidic platform for precision small-volume sample processing and its use to size separate biological particles with an acoustic microdevice [Precision size separation of biological particles in small-volume samples by an acoustic microfluidic system]

Here, a major advantage of microfluidic devices is the ability to manipulate small sample volumes, thus reducing reagent waste and preserving precious sample. However, to achieve robust sample manipulation it is necessary to address device integration with the macroscale environment. To realize repeatable, sensitive particle separation with microfluidic devices, this protocol presents a complete automated and integrated microfluidic platform that enables precise processing of 0.15–1.5 ml samples using microfluidic devices. Important aspects of this system include modular device layout and robust fixtures resulting in reliable and flexible world to chip connections, and fully-automated fluid handling which accomplishes closed-loop sample collection, system cleaning and priming steps to ensure repeatable operation. Different microfluidic devices can be used interchangeably with this architecture. Here we incorporate an acoustofluidic device, detail its characterization, performance optimization, and demonstrate its use for size-separation of biological samples. By using real-time feedback during separation experiments, sample collection is optimized to conserve and concentrate sample. Although requiring the integration of multiple pieces of equipment, advantages of this architecture include the ability to process unknown samples with no additional system optimization, ease of device replacement, and precise, robust sample processing.

42 ENGINEERING↗
DOE OSTI2016

Synergistic enhancement of cellulase pairs linked by consensus ankyrin repeats: Determination of the roles of spacing, orientation, and enzyme identity: Inserting Multiple Cellulases into Ankyrin Scaffolds

Biomass deconstruction to small simple sugars is a potential approach to biofuels production, however the highly recalcitrant nature of biomass limits the economic viability of this approach. Thus, research on efficient biomass degradation is necessary to achieve large-scale production of biofuels. Enhancement of cellulolytic activity by increasing synergism between cellulase enzymes holds promise in achieving high-yield biofuels production. Here we have inserted cellulase pairs from extremophiles into hyper-stable α-helical consensus ankyrin repeat domain scaffolds. Such chimeric constructs allowed us to optimize arrays of enzyme pairs against a variety of cellulolytic substrates. We found that endocellulolytic domains CelA (CA) and Cel12A (C12A) act synergistically in the context of ankyrin repeats, with both three and four repeat spacing. The extent of synergy differs for different substrates. Also, having C12A N-terminal to CA provides greater synergy than the reverse construct, especially against filter paper. In contrast, we do not see synergy for these enzymes in tandem with CelK (CK) catalytic domain, a larger exocellulase, demonstrating the importance of enzyme identity in synergistic enhancement. Furthermore, we found endocellulases CelD and CA with three repeat spacing to act synergistically against filter paper. Importantly, connecting CA and C12A with a disordered linker of similar contour length, shows no synergistic enhancement, indicating that synergism results from connecting these domains with folded ankyrin repeats. These results show that ankyrin arrays can be used to vary spacing and orientation between enzymes, helping to design and optimize artificial cellulosomes, providing a novel architecture for synergistic enhancement of enzymatic cellulose degradation.

09 BIOMASS FUELS↗
DOE OSTI2020

Engineering the atomic structure of sequence-defined peptoid polymers and their assemblies

Peptoids are a family of sequence-defined, non-natural biomimetic polymers which show excellent properties including good chemical and enzymatic stability and high structural tunability. The solid-phase submonomer synthesis method allows precise control over the identity and sequence of chemically diverse side chains, enabling the atomic engineering of their chemical structures for a variety of applications. This unprecedented level of structural control enables access to atomically defined three-dimensional chain conformations and assemblies, facilitating the design and optimization of a variety of nanoscale architectures that can function in biology and materials science. In order to approach the rational design of peptoid materials in a more predictive and precise manner, it is crucial to fully understand how chemical information, in the form of the monomer sequence, encodes their folding and assembly into structurally defined, functional 3D shapes. Furthermore, this perspective focuses on recent studies into the atomic engineering of peptoid nanostructures by examining the impact of sequence variations on their secondary and three-dimensional structures, as well as their functional properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗