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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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Systems are changing and engineering practices must mind the balance between evolutionary and revolutionary change as we move towards increasingly agile processes, enabled by interconnected tools, to best provide for partnered collaboration. This is the first example of Goddard's alignment between Digital Engineering strategy and Model-Based Systems Engineering strategy.
Oxidative stress adaptation strategies are important to cell function and are linked to cardiac, neurodegenerative disease, and cancer. Representatives of the Archaea domain are used as model organisms based on their extreme tolerance to oxidants and close evolutionary relationship with eukaryotes. A study of the halophilic archaeon Haloferax volcanii reveals lysine acetylation to be associated with oxidative stress responses. The strong oxidant hypochlorite: (i) stimulates an increase in lysine acetyltransferase HvPat2 to HvPat1 abundance ratios and (ii) selects for lysine deacetylase sir2 mutants. Here we report the dynamic occupancy of the lysine acetylome of glycerol-grown H. volcanii as it shifts in profile in response to hypochlorite. These findings are revealed by the: (1) quantitative multiplex proteomics of the SILAC-compatible parent and Δsir2 mutant strains and (2) label-free proteomics of H26 ‘wild type’ cells. The results show that lysine acetylation is associated with key biological processes including DNA topology, central metabolism, cobalamin biosynthesis, and translation. Lysine acetylation targets are found conserved across species. Moreover, lysine residues modified by acetylation and ubiquitin-like sampylation are identified suggesting post-translational modification (PTM) crosstalk. Overall, the results of this study expand the current knowledge of lysine acetylation in Archaea, with the long-term goal to provide a balanced evolutionary perspective of PTM systems in living organisms.
The topics covered are presented in view graph form and include a space research and technology (R&T) mission statement and a NASA action plan. With respect to an integrated technology plan for the civil space program, the following topics are also covered: (1) an R&T strategy; (2) a NASA technology maturation strategy; (3) an Office of Space Flight (OSF) technology requirements evaluation; (4) an external technology perspectives summary; (5) a work breakdown structure; (6) critical user requirements/ strategic plan element categorization; (7) technology flight experiments; (8) proposed station paylods by thrust; and (9) an integrated traffic model
This paper proposes the development of intelligent sensors as an integrated systems approach, i.e. one treats the sensors as a complete system with its own sensing hardware (the traditional sensor), A/D converters, processing and storage capabilities, software drivers, self-assessment algorithms, communication protocols and evolutionary methodologies that allow them to get better with time. Under a project being undertaken at the Stennis Space Center, an integrated framework is being developed for the intelligent monitoring of smart elements. These smart elements can be sensors, actuators or other devices. The immediate application is the monitoring of the rocket test stands, but the technology should be generally applicable to the Intelligent Systems Health Monitoring (ISHM) vision. This paper outlines progress made in the development of intelligent sensors by describing the work done till date on Physical Intelligent Sensors (PIS) and Virtual Intelligent Sensors (VIS).
This paper describes the strategy of the NASA astronomical program that would follow if observations were performed from a Lunar Outpost. Contemporaneous observations across the entire electromagnetic spectrum are the underlying strategies of this astronomical program. To conduct these observations, Spacelab, Explorers, suborbital programs, Great Observatories, and Space Station Freedom are utilized, along with unique moon-based astronomical facilities. It is concluded that addition of the evolutionary facilities studied for the Lunar Outpost element would increase the sensitivity and angular resolution of the astronomical program by orders of magnitude.
Study Objective is to characterize the performance capabilities of an inline, shuttle-derived launch vehicle using two design strategies: the first as an early program demonstrator utilizing high structural margins, maximum shuttle assets, and minimal pad impact, the later having undergone structural optimization, flying operational mission GR&A and serving as a baseline for evolutionary upgrades.
Energy recovery linacs (ERLs), focus on recycling the kinetic energy of electron beam for the purpose of accelerating a newly injected beam within the same accelerating structure. The rising developments in the super conducting radio frequency technology, ERL technology has achieved several noteworthy milestones over the past few decades. In year 2003, Jefferson Lab has successfully demonstrated a single pass energy recovery at the CEBAF accelerator. Furthermore, they conducted successful experiments with IR-FEL demo and upgrades, as well as the UV FEL driver. This multi-pass, multi-GeV range energy recovery demonstration proposed to be carried out at CEBAF accelerator at Jefferson Lab focuses on demonstrating highest energy recovery in super conducting linac in the low-current range. Continuous electron beam accelerate up to 7.5 GeV within 5-passes and decelerate in the next 5-passes recovering RF energy and dumps at a low energy dump. The beamline optics design for recirculating linacs require special attention to avoid beam instabilities due to RF wakefields. Usually, multi-pass linac beam lines require stronger focusing at lower energies as that is necessary to avoid beam breakup (BBU) instabilities, even with this small beam current. The CEBAF linac optics optimization is focused on balancing over-focusing at higher energies and beta excursions at lower energies. The race-track-shaped geometry of CEBAF accelerator allows its linacs to accommodate multiple energy beams simultaneously, while individual recirculating arcs transporting one beam energy, are shared between accelerating/decelerating beams. For the linac optics optimization process, an extended strategy is used that is originally used in 6-pass Recirculating Linac design of the LHeC, to represent the ten passes through a single linac. Using proper mathematical expressions, linac optics optimization can be achieved with evolutionary genetic algorithms, with Multi-Objective optimization. This thesis introduces a CEBAF optics redesign tailored to accommodates the ER@CEBAF multi-pass ER scheme. The isochronous arcs were retuned to match into optics solutions for optimized 10-pass linacs. Within this work, a single bunch particle tracking analysis presented here focuses on the further improvements of the beamline and beam transportation.
Researchers in the Astrobiology Technology Branch at Ames Research Center have begun investigating alternate concepts for the detection of extraterrestrial life. We suggest searching for extraterrestrial ecology, exoecology, as well as for extraterrestrial biology, exobiology. Ecology describes the interactions of living things with their environment. All ecosystems are highly constrained by their environment and are constrained by well-known system design principles. Ecology could exist wherever there is an energy source and living I things have discovered some means to capture, store, and use the available energy. Terrestrial ecosystems use as energy sources, light, organic molecules, and in thermal vents and elsewhere, simple inorganic molecules. Ecosystem behavior is controlled by matter and energy conservation laws and can be described by linear and nonlinear dynamic systems theory. Typically in an ecosystem different molecules are not in chemical equilibrium and scarce material is conserved, stored, or recycled. Temporal cycles and spatial variations are often observed. These and other -eneral principles of exoecology can help guide the search for extraterrestrial life. The chemical structure observed in terrestrial biology may be highly contingent on evolutionary accidents. Oxygen was not always abundant on Earth. Primitive sulfur bacteria use hydrogen sulfide and sulfur to perform photosynthesis instead of water and oxygen. Astrobiologists have assumed, for the sake of narrowing and focusing our life detection strategies, that extraterrestrial life will have detailed chemical similarities with terrestrial life. Such assumptions appear very reasonable and they allow us to design specific and highly sensitive life detection experiments. But the fewer assumptions we make, the less chance we have of being entirely wrong The best strategy for the detection of extraterrestrial life could be a mixed strategy. We should use detailed assumptions based on terrestrial biology to guide some but not all future searches for alien life. The systems principles of exoecology seem much more fundamental and inescapable than the terrestrial biology analogies of exobiology. We should search for exoecology as well as exobiology.
This paper addresses the evolution of control strategies for a collective: a set of entities that collectively strives to maximize a global evaluation function that rates the performance of the full system. Directly addressing such problems by having a population of collectives and applying the evolutionary algorithm to that population is appealing, but the search space is prohibitively large in most cases. Instead, we focus on evolving control policies for each member of the collective. The fundamental issue in this approach is how to create an evaluation function for each member of the collective that is both aligned with the global evaluation function and is sensitive to the fitness changes of the member, while relatively insensitive to the fitness changes of other members. We show how to construct evaluation functions in dynamic, noisy and communication-limited collective environments. On a rover coordination problem, a control policy evolved using aligned and member-sensitive evaluations outperfoms global evaluation methods by up to 400%. More notably, in the presence of a larger number of rovers or rovers with noisy and communication limited sensors, the proposed method outperforms global evaluation by a higher percentage than in noise-free conditions with a small number of rovers.
On-demand mobility (ODM) through aviation refers to the ability to quickly and easily move people or equivalent cargo without delays introduced by lack of, or infrequently, scheduled service. A necessary attribute of ODM is that it be easy to use, requiring a minimum of special training, skills, or workload. Fully-autonomous vehicles would provide the ultimate in ease-of-use (EU) but are currently unproven for safety-critical applications outside of a few, situationally constrained applications (e.g. automated trains operating in segregated systems). Applied to aviation, the current and near-future state of the art of full-autonomy, may entail undesirable trade-offs such as very conservative operational margins resulting in reduced trip reliability and transportation utility. Furthermore, acceptance by potential users and regulatory authorities will be challenging without confidence in autonomous systems in developed in less critical, but still challenging applications. A question for the aviation community is how we can best develop practical ease-of-use for aircraft that are sized to carry a small number of passengers (e.g. 1-9) or equivalent cargo. Such development is unlikely to be a single event, but rather a managed, evolutionary process where responsibility and authority transitions from human to automation agents as operational experience is gained with increasingly intelligent systems. This talk presents a technology road map being developed at NASA Langley, as part of an overall strategy to foster ODM, for the development of ease-of-use for ODM aviation.
Abstract Motivation Accurate prediction of residue–residue distances is important for protein structure prediction. We developed several protein distance predictors based on a deep learning distance prediction method and blindly tested them in the 14th Critical Assessment of Protein Structure Prediction (CASP14). The prediction method uses deep residual neural networks with the channel-wise attention mechanism to classify the distance between every two residues into multiple distance intervals. The input features for the deep learning method include co-evolutionary features as well as other sequence-based features derived from multiple sequence alignments (MSAs). Three alignment methods are used with multiple protein sequence/profile databases to generate MSAs for input feature generation. Based on different configurations and training strategies of the deep learning method, five MULTICOM distance predictors were created to participate in the CASP14 experiment. Results Benchmarked on 37 hard CASP14 domains, the best performing MULTICOM predictor is ranked 5th out of 30 automated CASP14 distance prediction servers in terms of precision of top L/5 long-range contact predictions [i.e. classifying distances between two residues into two categories: in contact (<8 Angstrom) and not in contact otherwise] and performs better than the best CASP13 distance prediction method. The best performing MULTICOM predictor is also ranked 6th among automated server predictors in classifying inter-residue distances into 10 distance intervals defined by CASP14 according to the precision of distance classification. The results show that the quality and depth of MSAs depend on alignment methods and sequence databases and have a significant impact on the accuracy of distance prediction. Using larger training datasets and multiple complementary features improves prediction accuracy. However, the number of effective sequences in MSAs is only a weak indicator of the quality of MSAs and the accuracy of predicted distance maps. In contrast, there is a strong correlation between the accuracy of contact/distance predictions and the average probability of the predicted contacts, which can therefore be more effectively used to estimate the confidence of distance predictions and select predicted distance maps. Availability and implementation The software package, source code and data of DeepDist2 are freely available at https://github.com/multicom-toolbox/deepdist and https://zenodo.org/record/4712084#.YIIM13VKhQM. Supplementary information Supplementary data are available at Bioinformatics online.
An important phase of any Stereo Imaging Velocimetry experiment is particle tracking. Particle tracking seeks to identify and characterize the motion of individual particles entrained in a fluid or air experiment. We analyze a cylindrical chamber filled with water and seeded with density-matched particles. In every four-frame sequence, we identify a particle track by assigning a unique track label for each camera image. The conventional approach to particle tracking is to use an exhaustive tree-search method utilizing greedy algorithms to reduce search times. However, these types of algorithms are not optimal due to a cascade effect of incorrect decisions upon adjacent tracks. We examine the use of a guided evolutionary neural net with simulated annealing to arrive at a globally optimal assignment of tracks. The net is guided both by the minimization of the search space through the use of prior limiting assumptions about valid tracks and by a strategy which seeks to avoid high-energy intermediate states which can trap the net in a local minimum. A stochastic search algorithm is used in place of back-propagation of error to further reduce the chance of being trapped in an energy well. Global optimization is achieved by minimizing an objective function, which includes both track smoothness and particle-image utilization parameters. In this paper we describe our model and present our experimental results. We compare our results with a nonoptimizing, predictive tracker and obtain an average increase in valid track yield of 27 percent
The double-fed induction generator (DFIG) usually experiences high rotor current and DC capacitor link voltage during system fault events. In this paper, a novel data-driven approach is proposed to enhance the DFIG performance under fault scenarios. An advanced reinforcement learning (RL) algorithm called guided surrogate-gradient-based evolution strategy (GSES) is used to control the DFIG power and capacitor DClink voltage by adjusting the optional reference signals. This controller is able to prevent the rotor of DFIG from overcurrent risk and maintain the grid-connected operation. The proposed GSES-based control algorithm was evaluated through simulations on a 3.6-MW DFIG in PSCAD/EMTDC software. Results have validated the effectiveness of the proposed GSESbased control algorithm in improving the DFIG performance under fault scenarios.
The Control of Flexible Structures (COFS) Flight Experiment Program consists of a series of four Shuttle-attached experiments specifically designed to systematically evaluate algorithms and techniques for on-orbit systems identification and flexible body control of large flexible structures, and as such, develops the enabling technologies for future NASA missions such as the evolutionary Space Station and large orbital antennas. These flight experiments are conducted on 1-year centers starting in 1989. This paper presents a description of the test article for the first two flights (COFS-I) and a discussion of the strategy for conducting the various experiments. A guest investigator program provides the opportunity for the research community to participate in the structures/controls research associated with the multi-flight COFS Program.
The arbitrariness of the definition of life is discussed in relation to both the archaic biological entities that preceded cells during the Molecular Evolution era, and the hypothetical, primitive, 'living' entities that presumably can be synthesized in the laboratory. Several experimental approaches to the synthesis, detection, and characterization of 'living' entities are discussed. The experimental approaches considered for the synthesis are the constructionist strategy, the whole-environment strategy, and the modular strategy, which is a combination of the first two. The whole-environment strategy is discussed in more detail and the establishment of an Evolution Synthesizer, based on this strategy, is proposed and rationalized. The guidelines for the detection and characterization of populations and processes of 'living' entities include chemical and physical analyses, but are based mainly on the reproductive characterization of these entities. It is expected that the higher the evolutionary level of the 'living' entities, the longer and more difficult it will be to synthesize them, but the easier it will be to detect them.
Systems are changing and engineering practices must mind the balance between evolutionary and revolutionary change as we move towards increasingly agile processes, enabled by interconnected tools, to best provide for partnered collaboration. The Digital Thread is a foundational capability for a functional Digital Engineering Ecosystem, which is Phase 1 of Goddard's Digital Engineering strategy in an effort to achieve Goddard 2040 vision.
Adaptive laboratory evolution (ALE) is a powerful strategy for improving microbial phenotypes by harnessing natural selection under defined environmental conditions. Through applying selection regimes, beneficial mutations accumulate, enabling the generation of strains with enhanced properties. However, conventional ALE is labor-intensive and difficult to scale, limiting reproducibility and broader discovery of evolutionary principles. Recent advances in robotics, automation, and computational infrastructure are transforming ALE into a scalable, data-rich experimental paradigm. Automated platforms enable standardized and complex protocols, real-time monitoring, and highly parallel evolution campaigns, improving consistency while generating longitudinal datasets that reveal convergent adaptive mechanisms. Here, we discuss the role of specialized biofoundries in advancing automated ALE and enabling large-scale evolutionary engineering. We review major automated ALE formats and outline key design principles for effective ALE biofoundries, highlighting how automated ALE can support autonomous experimentation and AI-guided strain engineering.
The strategy for a major exploration initiative leading to permanent human presence beyond earth orbit is still being developed; however enough is known to begin defining the role of nuclear technologies. Three broad areas are discussed: low power (less than 10 kWe) rover/vehicle power systems; integrated, evolutionary base power systems (25 to 100 kW) and nuclear energy for electric propulsion (2 to 100 MWe); and direct thermal propulsion (1000s MW). A phased, evolutionary approach is described for both the moon and Mars, and the benefits of nuclear technologies relative to solar and their integration are described.