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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Genetic Relationship Between Martian Chassignites and Nakhlites Revealed from Noble Gases

A group called as Martian meteorites is composed of shergottites, nakhlites, chassignites, and orthopyroxenite, and they are thought to be derived from Mars. Among the Martian meteorites nakhlites and chassignites show similar cosmic-ray exposure (CRE) ages of 11-12 million years, although petrologic characteristics are very different between them. Both nakhlites and chassignites indicate similar cooling rates, and would have cooled in identical scale of igneous bodies. However, the relationship between nakhlites and chassignites is still unclear, although they might have ejected at the same time, i.e., by accidentally coincidental impact events which occurred at different places on Mars or by a single impact which excavated both nakhlites and chassignites residing in a relatively small area. Here we propose that the chassignites show a genetically close relationship with nakhlites, i.e., both groups could be located within a relatively narrow area from where a single impact could have launched those meteorites, based on noble gas data obtained in our laboratory. If chassignites were really ejected with nakhlites by a single impact, both types of meteorites will provide us with geological/petrological profile in the area where both pyroxene-rich lava (nakhlites) and dunite-rich rocks (chassignites) are located close to the Martian surface. [i.e. discusses NWA 2737, etc. (Martian meteorites that fell in Northwest Africa)]

Nagao, K.↗
NASA NTRS2019

Performance Optimization of Plate Airfoils for Martian Rotor Applications Using a Genetic Algorithm

The Mars Helicopter Technology Demonstrator will be flying on the NASA Mars 2020 rover mission scheduled to launch in July of 2020. The goal is to demonstrate the viability and potential of heavier-than-air vehicles in the Martian atmosphere. Research is performed at the Jet Propulsion Laboratory and NASA Ames Research Center to extend these capabilities and develop the Mars Science Helicopter as the next possible step for Martian rotorcraft. The Mars Science Helicopter mass is scaled up to the 5 to 20 kg range, allowing a greater payload (approximately 0.5 to 2.0 kg), and greater range (approximately 3 km). Key to achieving these targets is careful aerodynamic rotor design. The Martian atmosphere’s low density and the small helicopter rotors result in very low chord-based Reynolds number flows, which reduces rotor performance. A continuous genetic algorithm is developed to optimize airfoil shapes at representative conditions for the Martian atmosphere. Previous research indicates that sharp leading edges and plate-like airfoils can out-perform conventional airfoil shapes. The present optimization allows for camber and thickness variation of curved and polygonal thin airfoils with sharp leading edges. The airfoil performance is evaluated at the highest attainable liftto- drag ratio near a moderate lift coefficient at compressible Mach numbers, as expected for Martian rotor application. Increases between 16% and 29% in airfoil lift-to-drag ratio at fixed lift coefficients are observed when compared with the Mars Helicopter Technology Demonstrator airfoils. Improvements in hover figure of merit are estimated to be between 4% and 10%, when applied to the Mars Helicopter Technology Demonstrator.

Koning, Witold J. F.↗
NASA NTRS2019

Thermal Property Estimation of Fibrous Insulation: Heat Transfer Modeling and the Continuous Genetic Algorithm

Thermal properties of high-temperature fibrous insulation materials were estimated from transient thermal tests using an inverse heat transfer technique. Transient temperature data from an experimental set up was collected and a simple, one-dimensional numerical model was constructed to replicate the temperatures within the test assembly. The Continuous Genetic Algorithm optimization technique in conjunction with a numerical thermal model and transient test data was used to estimate coefficients of a functional representation of the thermal property. The thermal properties, i.e., thermal conductivity and specific heat, of an alumina insulation felt were estimated over the temperature range of 300 K to 1700 K at various constant static pressures in nitrogen gas and compared with published data. The resulting thermal property estimates were within 10% of published values over the entire temperature range at various pressures. The methodology, application, and results are presented.

Frye, Elora↗
NASA NTRS2021

Thermal Conductivity Estimation from Transient Test Data with Embedded Thermocouples using Genetic Algorithm Optimization

Inverse heat transfer methodology previously developed to estimate thermal properties of high temperature fibrous insulation from embedded thermocouples was applied to the thermoplastic polymer, polyether-ether-ketone (PEEK). A small experimental setup was utilized to test the PEEK sample between temperatures of 300 K and 525 K at atmospheric pressure. Cylindrical plugs of PEEK with three thermocouples embedded at various depths were incorporated in the test sample. The experimental PEEK thermocouple data were used as the boundary and initial conditions of a one-dimensional numerical thermal model to predict the internal temperatures of the material. The thermal conductivity of PEEK was estimated with the Continuous Genetic Algorithm optimization technique by searching for the coefficients of a functional form of thermal conductivity that minimized the difference between the experimentally measured and model predicted internal temperature values. The thermal testing, one-dimensional numerical thermal model, optimization algorithm, analysis, and results are presented.

Thermal Properties↗
NASA NTRSDate not supplied

Bayesian Model Selection for Reducing Bloat and Overfitting in Genetic Programming for Symbolic Regression

When performing symbolic regression using genetic programming, overfitting and bloat can negatively impact generalizability and interpretability of the resulting equations as well as increase computation times. A Bayesian fitness metric is introduced and its impact on bloat and overfitting during population evolution is studied and compared to common alternatives in the literature. The proposed approach was found to be more robust to noise and data sparsity in numerical experiments, guiding evolution to a level of complexity appropriate to the dataset. Further evolution of the population resulted not in overfitting or bloat, but rather in slight simplifications in model form. The ability to identify an equation of complexity appropriate to the scale of noise in the training data was also demonstrated. In general, the Bayesian model selection algorithm was shown to be an effective means of regularization which resulted in less bloat and overfitting when any amount of noise was present in the training data.

G F Bomarito↗
NASA NTRS2021

Surface Cancellation in Wideband Ground Penetrating Radar Employing Genetic Algorithm AI for Waveform Synthesis

This paper presents a wideband 600-1200 MHz ground penetrating radar (GPR) system for sub-surface exploration of the moon and other planetary bodies. The presented radar system uses an arbitrary waveform generator (AWG) to directly produce the frequency-modulated continuous wave (FMCW) chirp waveform. To address the key challenges of Tx-to-Rx leakage and surface reflections, the system uses a second AWG channel coupled directly to the receiver that provides a cancellation waveform to mitigate the unwanted signals. The system also uses a genetic algorithm AI engine which assesses the radar IF and iteratively improves the parameters of the cancellation waveform injected at the receiver.

Chang, Frank↗
NASA NTRS2022

Genetic Optimization of Planetary Gearboxes Based on Analytical Gearing Equations

Electric and hybrid electric vertical takeoff and landing vehicles will require high performance electric motor driven propulsion systems to enable fuel burn and emissions benefits over traditional liquid fuel powered rotorcraft. One key to the design of a high performance electric motor driven propulsion systems is to be able to estimate the mass and efficiency of a gearbox at various motor and propellor operating speeds. Knowing how gearbox mass and efficiency trade with motor and propellor rotational speed, allows motor and propellor RPM to be traded in an overall optimization of the propulsion system. In this paper, a genetic optimization tool for estimating the mass and efficiency of gearboxes is presented. Example results from the tool are presented and compared to existing correlations in use by NASA aircraft design codes.

Thomas Tallerico↗
NASA NTRSDate not supplied

Bayesian Model Selection for Reducing Bloat and Overfitting in Genetic Programming for Symbolic Regression

When performing symbolic regression using genetic programming, overfitting and bloat can negatively impact generalizability and interpretability of the resulting equations as well as increase computation times. A Bayesian fitness metric is introduced and its impact on bloat and overfitting during population evolution is studied and compared to common alternatives in the literature. The proposed approach was found to be more robust to noise and data sparsity in numerical experiments, guiding evolution to a level of complexity appropriate to the dataset. Further evolution of the population resulted not in overfitting or bloat, but rather in slight simplifications in model form. The ability to identify an equation of complexity appropriate to the scale of noise in the training data was also demonstrated. In general, the Bayesian model selection algorithm was shown to be an effective means of regularization which resulted in less bloat and overfitting when any amount of noise was present in the training data.

Uncertainty quantification↗
NASA NTRS2024

Genetic Relationships and the Nucleosynthetic Heritage of the Asteroid Bennu

The OSIRIS-REx sample return mission visited the B-type asteroid Bennu and delivered 121.6 gof pristine material to Earth in September 2023. Genetic links between this pristine material and different meteorite groups can be determined using nucleosynthetic isotope variations. These variations reflect the heterogeneous distribution of isotopically distinct dust from different stellar environments within the protoplanetary disk. They can therefore be used to constrain different formation locations of parent asteroids [e.g., 1]. In addition, isotope variations are powerful tracers to establish the nucleosynthetic heritage of returned samples [e.g., 2] and to evaluate mixing and reservoir formation within the disk. Here we present nucleosynthetic Ti and Fe isotope data for samples from Bennu and several carbonaceous chondrites. We also report the Ti isotope composition of hand-picked matrix fractions from CR and CV meteorites.

M Rüfenacht↗
NASA NTRSDate not supplied

Development of Genetic Countermeasures for Enhancing Cellular Stress Tolerance on a Lunar Surface Mission

The Lunar Explorer Instrument for space biology Applications (LEIA) LEIA investigates the response to partial gravity and ionizing radiation of: Different DNA damage and stress response pathways and Bioproduction of antioxidants LEIA utilizes: Various strains of the yeast Saccharomyces cerevisiae, which will be desiccated in fluidic cards and rehydrated on the lunar surface LEIA develops: Genetic countermeasures to improve tolerance to the desiccation process and the constraints associated with long duration missions beyond low Earth orbit (LEO)

Neha Lingam↗
DOE OSTI2025

Methods of improving drought and salt resistance in a plant and genetically engineered plants with improved drought and salt resistance

The present disclosure provides methods for increasing drought resistance, salt resistance, and biomass production of a plant. The methods encompass expression of DiGeorge-Syndrome Critical Region 14 (DGCR14) gene in the plant. In comparison to a plant not manipulated in this manner, the disclosed, genetically-modified, plants display improved drought resistance and salt resistance. Also provided are plants that can be obtained by the method according to the invention, and nucleic acid vectors to be used in the described methods.

Xie, Meng↗
DOE OSTI2025

Knowledge graph-aided Bayesian active learning for top- K genetic interaction discovery

In silico methods for predicting the effects of multi-gene perturbations hold great promise for advancing functional genomics, computational drug discovery, and disease modeling. However, the development of these predictive algorithms for mammalian systems has been hampered by limited datasets and high experimental costs. In this study, we present a Bayesian active learning framework designed to discover pairwise host gene knockdowns that effectively inhibit viral proliferation in an in vitro HIV-1 infection model. Our method leverages a biological knowledge graph as side information and employs a computationally efficient batch diversification approach. We evaluated this framework using a dataset of viral load measurements obtained from multi-day dual-gene depletion experiments, encompassing all possible pairwise knockdowns of over 350 host genes associated with HIV infection. We demonstrate that our framework rapidly identifies the most effective gene knockdown pairs for reducing viral load. Furthermore, we show that incorporating side information enhances performance during the early stages of active learning (low data regime), while our batch diversification strategy significantly boosts performance in later stages (high data regime). This framework is general and can be adapted to explore gene interactions in other contexts, such as synthetic lethality prediction and mapping epistatic effects across quantitative trait loci.

Computational biology and bioinformatics↗
DOE OSTI2025

A split ribozyme system for in vivo plant RNA imaging and genetic engineering

RNA plays a central role in plants, governing various cellular and physiological processes. Monitoring its dynamic abundance provides a discerning understanding of molecular mechanisms underlying plant responses to internal (developmental) and external (environmental) stimuli, paving the way for advances in plant biotechnology to engineer crops with improved resilience, quality and productivity. In general, traditional methods for analysis of RNA abundance in plants require destructive, labour-intensive and time-consuming assays. To overcome these limitations, we developed a transformative innovation for in vivo RNA imaging in plants. Specifically, we established a synthetic split ribozyme system that converts various RNA signals to orthogonal protein outputs, enabling in vivo visualisation of various RNA signals in plants. We demonstrated the utility of this system in transient expression experiments (i.e., leaf infiltration in Nicotiana benthamiana ) to detect RNAs derived from transgenes and tobacco rattle virus, respectively. Also, we successfully engineered a split ribozyme-based biosensor in Arabidopsis thaliana for in vivo visualisation of endogenous gene expression at the cellular level, demonstrating the feasibility of multi-scale (e.g., cellular and tissue level) RNA imaging in plants. Furthermore, we developed a platform for easy incorporation of different protein outputs, allowing for flexible choice of reporters to optimise the detection of target RNAs.

59 BASIC BIOLOGICAL SCIENCES↗