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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 NTRS2005

Searching for 'Unknown Unknowns'

The NASA Engineering and Safety Center (NESC) was established to improve safety through engineering excellence within NASA programs and projects. As part of this goal, methods are being investigated to enable the NESC to become proactive in identifying areas that may be precursors to future problems. The goal is to find unknown indicators of future problems, not to duplicate the program-specific trending efforts. The data that is critical for detecting these indicators exist in a plethora of dissimilar non-conformance and other databases (without a common format or taxonomy). In fact, much of the data is unstructured text. However, one common database is not required if the right standards and electronic tools are employed. Electronic data mining is a particularly promising tool for this effort into unsupervised learning of common factors. This work in progress began with a systematic evaluation of available data mining software packages, based on documented decision techniques using weighted criteria. The four packages, which were perceived to have the most promise for NASA applications, are being benchmarked and evaluated by independent contractors. Preliminary recommendations for "best practices" in data mining and trending are provided. Final results and recommendations should be available in the Fall 2005. This critical first step in identifying "unknown unknowns" before they become problems is applicable to any set of engineering or programmatic data.

Parsons, Vickie S.↗
NASA NTRS2012

Plasma Outflows: Known Knowns, Known Unknowns, and The Unknown

A brief summary is given of i) what we know from observing ionospheric outflows and ii) how outflow parameterizations are being used in global simulations to evaluate their effects on magnetospheric dynamics. Then, a list of unanswered questions and issues to be resolved is given, followed by a description of the known future mission plans expressed in the Heliophysics Roadmap, such as Origin of Near-Earth Plasmas (ONEP), and Ion-Neutral Coupling in the Atmosphere (INCA). Finally, a set of requirements for definitive plasma outflow observations are identified, along with possible methods for fulfilling them in future missions. Since results of the current Heliophysics Decadal Survey are expected soon, it is hoped that future plans can be summarized and discussed without speculation at the GEM 2012 meeting.

Moore, T. E.↗
NASA NTRS2008

Identification of Unknown Contaminants in Water Samples from ISS Employing Liquid Chromatography/Mass Spectrometry/Mass Spectrometry

Mass Spectrometry/Mass Spectrometry (MS/MS) is a powerful technique for identifying unknown organic compounds. For non-volatile or thermally unstable unknowns dissolved in liquids, liquid chromatography/mass spectrometry/mass spectrometry (LC/MS/MS) is often the variety of MS/MS used for the identification. One type of LC/MS/MS that is rapidly becoming popular is time-of-flight (TOF) mass spectrometry. This technique is now in use at the Johnson Space Center for identification of unknown nonvolatile organics in water samples from the space program. An example of the successful identification of one unknown is reviewed in detail in this paper. The advantages of time-of-flight instrumentation are demonstrated through this example as well as the strategy employed in using time-of-flight data to identify unknowns.

Rutz, Jeffrey A.↗
NASA NTRS2007

Space Invariant Independent Component Analysis and ENose for Detection of Selective Chemicals in an Unknown Environment

In this paper, we present a space invariant architecture to enable the Independent Component Analysis (ICA) to solve chemical detection from two unknown mixing chemical sources. The two sets of unknown paired mixture sources are collected via JPL 16-ENose sensor array in the unknown environment with, at most, 12 samples data collected. Our space invariant architecture along with the maximum entropy information technique by Bell and Sejnowski and natural gradient descent by Amari has demonstrated that it is effective to separate the two mixing unknown chemical sources with unknown mixing levels to the array of two original sources under insufficient sampled data. From separated sources, they can be identified by projecting them on the 11 known chemical sources to find the best match for detection. We also present the results of our simulations. These simulations have shown that 100% correct detection could be achieved under the two cases: a) under-completed case where the number of input (mixtures) is larger than number of original chemical sources; and b) regular case where the number of input is as the same as the number of sources while the time invariant architecture approach may face the obstacles: overcomplete case, insufficient data and cumbersome architecture.

ENose↗
NASA NTRSDate not supplied

PMSM Parameter Estimation using a Linear Unknown Input Interval Observer

In this paper, the problem of permanent magnet synchronous motor (PMSM) speed and unknown load torque estimation is addressed. For this purpose, a interval unknown input observer (UIO) for linear time-invariant (LTI) systems is used. First, the PMSM model is linearized in order to make it in a suitable form for the linear interval UIO. Then, the interval UIO is applied to allow the joint estimation of the motor speed and the unknown load torque disturbance. The main advantages of this approach is that it not only allows the joint state and unknown input estimation, but also to take the different uncertainty sources into account. Indeed, taking model and measurement uncertainty into account is crucial. Assuming that the measurement noise and disturbances are bounded, lower and upper bounds are first computed for the unmeasured state (motor speed) and then for the unknown input (load torque). The proposed approach and its limitations are demonstrated with the nonlinear PMSM model derived from its equivalent electrical circuit.

Elinirina I Robinson↗
NASA NTRSDate not supplied

Estimation of Permanent Magnet Synchronous Motor Parameters using a Linear Unknown Input Interval Observer

This paper develops and evaluate a technique to estimate the speed and unknown load torque of a permanent magnet synchronous motor (PMSM). An interval unknown input observer (UIO) for linear time-invariant (LTI) systems is designed and utilized to estimate the PMSM parameters. Interval UIOs are advantageous as they allow for the estimation of both states and unknown inputs, while accounting for varying uncertainty sources, such as model and measurement uncertainties inherent in dynamic systems and processes. First, the nonlinear PMSM model is linearized to transform it to a suitable form for application of the linear interval UIO. Then, the interval UIO is applied to jointly estimate the motor speed and the unknown load torque disturbance. Assuming that the measurement noise and disturbances are bounded, lower and upper bounds are first computed for the unmeasured state (motor speed) and then for the unknown input (load torque). The proposed approach and its limitations are demonstrated for the nonlinear PMSM model derived from its equivalent electrical circuit.

Elinirina I Robinson↗
NASA NTRS1990

Exploration of unknown mechanical assemblies through manipulation

If robots must function in unstructured environments, they must also possess the ability to acquire information and construct appropriate models of the unknown environment. This paper addresses the automatic generation of kinematic models of unknown objects with movable parts in the environment. If the relative motion between moving parts must be observed and characterized, vision alone cannot suffice. An approach in which manipulation is used with vision for sensing is better suited to the task of determining kinematic properties. In this paper, algorithms for constructing models of unknown mechanical assemblies and characterizing the relative motion are developed. Results of a simulation are described to demonstrate the role of manipulation in such an endeavor.

Kumar, Vijay R.↗
NASA NTRS2008

Direct Solve of Electrically Large Integral Equations for Problem Sizes to 1M Unknowns

Matrix methods for solving integral equations via direct solve LU factorization are presently limited to weeks to months of very expensive supercomputer time for problems sizes of several hundred thousand unknowns. This report presents matrix LU factor solutions for electromagnetic scattering problems for problem sizes to one million unknowns with thousands of right hand sides that run in mere days on PC level hardware. This EM solution is accomplished by utilizing the numerical low rank nature of spatially blocked unknowns using the Adaptive Cross Approximation for compressing the rank deficient blocks of the system Z matrix, the L and U factors, the right hand side forcing function and the final current solution. This compressed matrix solution is applied to a frequency domain EM solution of Maxwell's equations using standard Method of Moments approach. Compressed matrix storage and operations count leads to orders of magnitude reduction in memory and run time.

Shaeffer, John↗
NASA NTRS2022

Optimization Approach for Wind Tunnel Fan Blade Strain Gage Correlation with Test Fixture Unknowns

NASA has a significant maintenance challenge with the aging wind tunnel infrastructure across the Agency. Some wind tunnel fan blades, such as those in the National Transonic Facility at NASA Langley Research Center, have been in use for far longer than their planned 10-year lifetime. No detailed analytical models of the fan blades from their initial fabrication were created. To support the fabrication of either new, nominally identical fan blades or newly designed fan blades, accurate analytical models of the existing fan blades are necessary to demonstrate an understanding of the capabilities of the existing fan blades. Additionally, the existing documentation of the testing of the existing fan blades has gaps. In this report, a process for improving test-analysis correlation for these wind tunnel fan blades is discussed. This process involved a detailed study of unknown parameters in test fixture parameters from the old and incomplete test documentation. To improve correlation, these unknown parameters were used as design variables in an optimization study to minimize the error between test data and computed structural responses. Legacy strain gage data from tests conducted in 1981 were compared with results from a finite element model created in 2019. The use of test data from 1981 was necessary because none of the existing fan blades could be spared for use in destructive structural testing. While this process was demonstrated for wind tunnel fan blades, this process could be utilized for evaluating unknowns in test fixtures for other structural test configurations.

wind tunnel↗
NASA NTRS1970

Analysis of a Decision-Directed Receiver with Unknown Priors

A decision-directed receiver (DDR) uses previous outputs (decisions) to estimate unknown parameters and, on the basis of these estimates, modifies the detector structure for subsequent decisions. Although the DDR is less complex to instrument than other adaptive schemes, inherent in the decision-directed approach is the possibility of a runaway. This occurs when the detector commits a sequence of decision errors resulting in a degradation of parameter estimates, which, in turn, results in a further deterioration of detector performance. Because of the dependencies introduced by the learning process, runaway is difficult to analyze. In this paper, a DDR with unknown a priori probabilities is considered. The priors are estimated by the relative frequency of decisions of that event. For binary detection, it is shown that there is a positive probability of a runaway (the estimates converge to 1 or 0), which equals 1 if the signal-to-noise ratio is below a critical value. A tight bound on the probability of a runaway is obtained by approximating the learning process by a random walk with independent increments. The analysis demonstrates that a runaway is quite improbable even for moderate signal-to-noise ratios. The analysis is extended to multiple signals and to the situation where the estimates of the priors are updated continuously through exponential weighting rather than allowed to converge.

Davisson, Lee D.↗
NASA NTRS1989

Learned navigation in unknown terrains: A retraction method

The problem of learned navigation of a circular robot R, of radius delta (is greater than or equal to 0), through a terrain whose model is not a-priori known is considered. Two-dimensional finite-sized terrains populated by an unknown (but, finite) number of simple polygonal obstacles are also considered. The number and locations of the vertices of each obstacle are unknown to R. R is equipped with a sensor system that detects all vertices and edges that are visible from its present location. In this context two problems are covered. In the visit problem, the robot is required to visit a sequence of destination points, and in the terrain model acquisition problem, the robot is required to acquire the complete model of the terrain. An algorithmic framework is presented for solving these two problems using a retraction of the freespace onto the Voronoi diagram of the terrain. Algorithms are then presented to solve the visit problem and the terrain model acquisition problem.

Rao, Nageswara S. V.↗
NASA NTRS1990

Venus atmospheric circulation - Known and unknown

Temporally and longitudinally averaged circulation determined from images acquired since 1979 from the Pioneer Venus orbiter has shown significant changes in th meridional flow. The solar-locked structure in the zonal and meridional components of the cloud motions is also observed to change with time. Mechanisms for such changes remain unknown, although planetary waves and a slight modulation of the cloud level have been suggested as explanations. Decomposition of the solar-locked structure into diurnal and semidiurnal components suggests that nightside zonal flow may be 10-20 m/s faster than the average dayside flow. Many aspects of the circulation remain unknown, including the horizontal and vertical structure on the nightside and at levels higher and lower than the UV cloud features, sources, and sinks of UV absorbers, and meridional transports of heat, momentum, and water vapor at different levels.

Limaye, Sanjay Shridhar↗
NASA NTRS1993

A direct approach to finding unknown boundary conditions in steady heat conduction

The capability of the boundary element method (BEM) in determining thermal boundary conditions on surfaces of a conducting solid where such quantities are unknown was demonstrated. The method uses a non-iterative direct approach in solving what is usually called the inverse heat conduction problem (IHCP). Given any over-specified thermal boundary conditions such as a combination of temperature and heat flux on a surface where such data is readily available, the algorithm computes the temperature field within the object and any unknown thermal boundary conditions on surfaces where thermal boundary values are unavailable. A two-dimensional, steady-state BEM program was developed and was tested on several simple geometries where the analytic solution was known. Results obtained with the BEM were in excellent agreement with the analytic values. The algorithm is highly flexible in treating complex geometries, mixed thermal boundary conditions, and temperature-dependent material properties and is presently being extended to three-dimensional and unsteady heat conduction problems. The accuracy and reliability of this technique was very good but tended to deteriorate when the known surface conditions were only slightly over-specified and far from the inaccessible surface.

Martin, Thomas J.↗
NASA NTRS2012

Pattern Recognition Algorithm for High-Sensitivity Odorant Detection in Unknown Environments

In a realistic odorant detection application environment, the collected sensory data is a mix of unknown chemicals with unknown concentrations and noise. The identification of the odorants among these mixtures is a challenge in data recognition. In addition, deriving their individual concentrations in the mix is also a challenge. A deterministic analytical model was developed to accurately identify odorants and calculate their concentrations in a mixture with noisy data.

Duong, Tuan A.↗
NASA NTRS2015

M-MRAC Backstepping for Systems with Unknown Virtual Control Coefficients

The paper presents an over-parametrization free certainty equivalence state feedback backstepping adaptive control design method for systems of any relative degree with unmatched uncertainties and unknown virtual control coefficients. It uses a fast prediction model to estimate the unknown parameters, which is independent of the control design. It is shown that the system's input and output tracking errors can be systematically decreased by the proper choice of the design parameters. The benefits of the approach are demonstrated in numerical simulations.

Certainty equivalence control↗
NASA NTRS2016

Dynamic Modeling from Flight Data with Unknown Time Skews

A method for estimating dynamic model parameters from flight data with unknown time skews is described and demonstrated. The method combines data reconstruction, nonlinear optimization, and equation-error parameter estimation in the frequency domain to accurately estimate both dynamic model parameters and the relative time skews in the data. Data from a nonlinear F-16 aircraft simulation with realistic noise, instrumentation errors, and arbitrary time skews were used to demonstrate the approach. The approach was further evaluated using flight data from a subscale jet transport aircraft, where the measured data were known to have relative time skews. Comparison of modeling results obtained from time-skewed and time-synchronized data showed that the method accurately estimates both dynamic model parameters and relative time skew parameters from flight data with unknown time skews.

Morelli, Eugene A.↗
NASA NTRS2017

Beyond Nanopore Sequencing in Space: Identifying the Unknown

Astronaut Kate Rubins sequenced DNA on the International Space Station (ISS) for the first time in August 2016 (Figure 1A). A 2D sequencing library containing an equal mixture of lambda bacteriophage, Escherichia coli, and Mus musculus was prepared on the ground with a SQK_MAP006 kit and sent to the ISS frozen and loaded into R7.3 flow cells. After a total of 9 on-orbit sequencing runs over 6 months, it was determined that there was no decrease in sequencing performance on-orbit compared to ground controls (1). A total of ~280,000 and ~130,000 reads generated on-orbit and on the ground, respectively, identified 90% of reads that were attributed to 30% lambda bacteriophage, 30% Escherichia coli, and 30% M. musculus (Figure 1B). Extensive bioinformatics analysis determined comparable 2D and 1D read accuracies between flight and ground runs (Figure 1C), and data collected from the ISS were able to construct directed assemblies of E.coli and lambda genomes at 100% and M. musculus mitochondrial genome at 96.7%. These findings validate sequencing as a viable option for potential on-orbit applications such as environmental microbial monitoring and disease diagnosis. Current microbial monitoring of the ISS applies culture-based techniques that provide colony forming unit (CFU) data for air, water, and surface samples. The identity of the cultured microorganisms in unknown until sample return and ground-based analysis, a process that can take up to 60 days. For sequencing to benefit ISS applications, spaceflight-compatible sample preparation techniques are required. Subsequent to the testing of the MinION on-orbit, a sample-to-sequence method was developed using miniPCR™ and basic pipetting, which was only recently proven to be effective in microgravity. The work presented here details the in- flight sample preparation process and the first application of DNA sequencing on the ISS to identify unknown ISS-derived microorganisms.

Stahl, Sarah E.↗