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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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The feasibility of novel all-dielectric waveguide grating filters is demonstrated, using a genetic algorithm to solve for material dielectric constants and geometric boundaries separating homogeneous regions of the periodic cell.
This paper documents the application of genetic algorithms (GAs) to the problem of robust flight path determination for Mars precision landing.
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.
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.
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.
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.
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.
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.
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)
Multi-objective optimization techniques are widely used in an extremely broad range of fields. Genetic optimization for multi-objective optimization was introduced in the accelerator community in relatively recent times and quickly spread becoming a fundamental tool in multi-dimensional optimization problems. This discussion introduces the basics of the technique and reviews applications in accelerator problems.
The invention relates to identification of mutations and genetic targets for enhanced L-tyrosine production, and bacterial strains capable of L-tyrosine production.
This invention provides compositions and methods for producing translational components that expand the number of genetically encoded amino acids in eukaryotic cells. The components include orthogonal tRNAs, orthogonal aminoacyl-tRNA synthetases, orthogonal pairs of tRNAs/synthetases and unnatural amino acids. Proteins and methods of producing proteins with unnatural amino acids in eukaryotic cells are also provided.
This invention provides compositions and methods for producing translational components that expand the number of genetically encoded amino acids in eukaryotic cells. The components include orthogonal tRNAs, orthogonal aminoacyl-tRNA synthetases, orthogonal pairs of tRNAs/synthetases and unnatural amino acids. Proteins and methods of producing proteins with unnatural amino acids in eukaryotic cells are also provided.
This paper presents a feature selection methodology that can be applied to datasets containing a mixture of continuous and categorical variables. Using a Genetic Algorithm (GA), this method explores a dataset and selects a small set of features relevant for the prediction of a binary (1/0) response. Binary classification trees and an objective function based on conditional probabilities are used to measure the fitness of a given subset of features. The method is applied to health data in order to find factors useful for the prediction of diabetes. Results show that our algorithm is capable of narrowing down the set of predictors to around 8 factors that can be validated using reputable medical and public health resources.
Previously, researchers have attempted to apply machine learning techniques to network anomaly detection problems. Due to the staggering amount of variety that can occur in normal networks, the results have often been underwhelming. These challenges are far less pronounced when considering industrial control system (ICS) networks. The recurrent nature of these networks results in less noise and more consistent patterns for a machine learning algorithm to recognize. Here, we propose a method of evolving decision trees through genetic programming (GP) in order to detect network anomalies, such as device outages. Our approach extracts over a dozen features from network packet captures and netflows, normalizes them, and relates them in decision trees using fuzzy logic operators. Furthermore, the trees were used to detect three specific network events from three different points on the network across a statistically significant number of runs and achieved 100% accuracy on five of the nine experiments. When the trees attempted to detect more challenging events at points of presence further from the occurrence, the accuracy averaged to above 98%. Finally, using our method, all of the evolutionary cycles of the GP algorithm are computed a-priori, allowing the best resultant trees to be deployed as semi-real-time sensors with little overhead.
This invention provides compositions and methods for producing translational components that expand the number of genetically encoded amino acids in eukaryotic cells. The components include orthogonal tRNAs, orthogonal aminoacyl-tRNA synthetases, orthogonal pairs of tRNAs/synthetases and unnatural amino acids. Proteins and methods of producing proteins with unnatural amino acids in eukaryotic cells are also provided.
The invention relates to genetically modified agricultural plants with increased oil content in vegetative tissues, as well as to expression systems, plant cells, seeds and vegetative tissues related thereto.
This invention provides compositions and methods for producing translational components that expand the number of genetically encoded amino acids in eukaryotic cells. The components include orthogonal tRNAs, orthogonal aminoacyl-tRNA synthetases, orthogonal pairs of tRNAs/synthetases and unnatural amino acids. Proteins and methods of producing proteins with unnatural amino acids in eukaryotic cells are also provided.