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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Mineralogical and Geochemical Analyses of Antarctic Lake Sediments: A Reflectance and Moessbauer Spectroscopy Study with Applications for Remote Sensing on Mars

Lakebottom sediments from the Dry Valleys region of Antarctica have been analyzed here in order to study the influence of water chemistry on the mineralogy and geochemistry of these sediments, as well as to evaluate techniques for remote spectral identification of potential biomarker minerals on Mars. Lakes from the Dry Valleys region of Antarctica have been investigated as possible analogs for extinct lake environments on early Mars. Sediment cores were collected in the present study from perennially ice-covered Lake Hoare in the Taylor Valley. These sediments were taken from a core in an oxic region of the lake and another core in an anoxic zone. Differences between the two cores were observed in the sediment color, Fe(II)/Fe(III) ratio, the presence of pyrite, the abundance of Fe, S and some trace elements, and the C, N and S isotope fractionation patterns. The results of visible-infrared reflectance spectroscopy (0.3-25 microns) Mossbauer spectroscopy (77 and 4 K) and X-ray diffraction are combined to determine the mineralogy and composition of these samples. The sediments are dominated by plagioclase, K-feldspar, quartz and pyroxene. Algal mats grow on the bottom of the lake and organic material has been found throughout the cores. Calcite is abundant in some layers of the aerobic core (shallow region) and pyrite is abundant in some layers of the anaerobic core (deep region). Analysis of the spectroscopic features due to organics and carbonates with respect to the abundance of organic C and carbonate contents was performed in order to select optimal spectral bands for remote identification of these components in planetary regoliths. Carbonate bands near 4 and 6.8 microns (approx.2500 and 1500/cm) were detected for carbonate abundances as low as 0.1 wt.% CO2. Organic features at 3.38, 3.42 and 3.51 microns (2960, 2925 and 2850/cm) were detected for organic C abundances as low as 0.06 wt.% C. The d13C trends show a more complex organic history for the anaerobic sediments than for the aerobic sediments. The biogenic pyrite found in the anaerobic core is associated with lighter d34S values and high organic C levels and could be used as a biomarker mineral for paleolakes on Mars.

Froeschl, Heinz↗
NASA NTRSDate not supplied

Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection

We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft. We highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). Our main contribution is the application of a structured method that can be used for machine learning in many aerospace applications. This method optimizes the structure and training parameters of a neural network for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE analysis was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from the NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications.

Loss of Control↗
NASA NTRSDate not supplied

Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection

We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft. We highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). Our main contribution is the application of a structured method that can be used for machine learning in many aerospace applications. This method optimizes the structure and training parameters of a neural network for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE analysis was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from the NASA T 2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications.

Loss of Control↗
NASA NTRSDate not supplied

Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection

Abstract—We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft. We highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). Our main contribution is the application of a structured method that can be used for machine learning in many aerospace applications. This method optimizes the structure and training parameters of a neural network for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE analysis was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from the NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications.

neural networks↗
NASA NTRS1990

Recursive dynamics for flexible multibody systems using spatial operators

Due to their structural flexibility, spacecraft and space manipulators are multibody systems with complex dynamics and possess a large number of degrees of freedom. Here the spatial operator algebra methodology is used to develop a new dynamics formulation and spatially recursive algorithms for such flexible multibody systems. A key feature of the formulation is that the operator description of the flexible system dynamics is identical in form to the corresponding operator description of the dynamics of rigid multibody systems. A significant advantage of this unifying approach is that it allows ideas and techniques for rigid multibody systems to be easily applied to flexible multibody systems. The algorithms use standard finite-element and assumed modes models for the individual body deformation. A Newton-Euler Operator Factorization of the mass matrix of the multibody system is first developed. It forms the basis for recursive algorithms such as for the inverse dynamics, the computation of the mass matrix, and the composite body forward dynamics for the system. Subsequently, an alternative Innovations Operator Factorization of the mass matrix, each of whose factors is invertible, is developed. It leads to an operator expression for the inverse of the mass matrix, and forms the basis for the recursive articulated body forward dynamics algorithm for the flexible multibody system. For simplicity, most of the development here focuses on serial chain multibody systems. However, extensions of the algorithms to general topology flexible multibody systems are described. While the computational cost of the algorithms depends on factors such as the topology and the amount of flexibility in the multibody system, in general, it appears that in contrast to the rigid multibody case, the articulated body forward dynamics algorithm is the more efficient algorithm for flexible multibody systems containing even a small number of flexible bodies. The variety of algorithms described here permits a user to choose the algorithm which is optimal for the multibody system at hand. The availability of a number of algorithms is even more important for real-time applications, where implementation on parallel processors or custom computing hardware is often necessary to maximize speed.

Jain, A.↗
NASA NTRS1991

Manual discrimination of force

Optimal design of human-machine interfaces for teleoperators and virtual-environment systems which involve the tactual and kinesthetic modalities requires knowledge of the human's resolving power in these modalities. The resolution of the interface should be appropriately matched to that of the human operator. We report some preliminary results on the ability of the human hand to distinguish small differences in force under a variety of conditions. Experiments were conducted on force discrimination with the thumb pushing an interface that exerts a constant force over the pushing distance and the index finger pressing against a fixed support. The dependence of the sensitivity index d' on force increment can be fit by a straight line through the origin and the just-noticeable difference (JND) in force can thus be described by the inverse of the slope of this line. The receiver operating characteristic (ROC) was measured by varying the a priori probabilities of the two alternatives, reference force and reference force plus an increment, in one-interval, two-alternative, forced-choice experiments. When plotted on normal deviate coordinates, the ROC's were roughly straight lines of unit slope, thus supporting the assumption of equal-variance normal distributions and the use of the conventional d' measure. The JND was roughly 6-8 percent for reference force ranging from 2.5 to 10 newtons, pushing distance from 5 to 30 mm, and initial finger-span from 45 to 125 mm. Also, the JND remained the same when the subjects were instructed to change the average speed of pushing from 23 to 153 mm/sec. The pushing was terminated by reaching either a wall or a well, and the JND's were essentially the same in both cases.

Pang, Xiao-Dong↗
NASA NTRSDate not supplied

Use of Design of Experiments and Rule-Based Inference in Determining Neural Network Architectures for Loss of Control Detection

In this work, we describe methods for selecting the neural network architectures and input spaces to implement belief state inference on generic commercial transport aircraft. First, we highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). We present a structured method that can be used in a number of aerospace applications, to optimize the structure and training parameters of the CVAE for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from a NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications. While theoretically, we could create a single CVAE with an input space the size of all measurable flight variables and environmental dynamics, it becomes intractable to use such a neural network in an in-situ intelligent multi-agent system. Using the recommendations from our case study, we introduce a technical approach for feasibly describing the belief space by (1) identifying significant statistical relationships among flight variables using rule induction, (2) using a set of rules that cover all features to define the input space of multiple CVAEs, and (3) forming a belief space based on the joint probability density of their collective latent spaces. This results in a series of relatively small matrix multiplications that can be performed in real time, as opposed to large matrix computations in a single CVAE. We demonstrate the application of this approach on the T-2 flight loss-of control experiments, using the architecture and hyperparameter recommendations from the case study. We compare the utilities of an individual CVAE trained on all flight variables and multiple CVAEs defined on subsets of flight variables for detecting qualitative changes in flight. We demonstrate that the use of multiple CVAEs with smaller input spaces permits the CVAE to capture more granular relationships in the latent space, permitting better state space characterization and loss-of-control detection.

Design of experiments↗