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An algorithm has been devised for predicting the behaviors of sparsely spatially distributed particles impinging on a solid surface in a rarefied atmosphere. Under the stated conditions, prior particle-transport models in which (1) dense distributions of particles are treated as continuum fluids; or (2) sparse distributions of particles are considered to be suspended in and to diffuse through fluid streams are not valid.
This slide presentation demonstrates the data mining/machine learning capabilities of NASA Ames and Intelligent Data Understanding (IDU) group. This will encompass the work done recently in the group by various group members. The IDU group develops novel algorithms to detect, classify, and predict events in large data streams for scientific and engineering systems. This presentation for Knowledge Discovery and Data Mining 2009 is to demonstrate the data mining/machine learning capabilities of NASA Ames and IDU group. This will encompass the work done re cently in the group by various group members.
The Mars Science Laboratory (MSL) is a NASA rover mission that will be launched in late 2011 and will land on Mars in August of 2012. This paper describes the analyses performed to validate the navigation system for launch, interplanetary cruise, and approach. MSL will use guidance during its descent into Mars in order to minimize landing dispersions, and therefore will be able to use smaller landing zones that are closer to terrain of high scientific interest. This will require a more accurate delivery of the spacecraft to the atmospheric entry interface, and a late update of the state of the spacecraft at entry. During cruise and approach the spacecraft may perform up to six trajectory correction maneuvers (TCMs), to target to the desired landing site with the required flight path angle at entry. Approach orbit determination covariance analyses have been performed to evaluate the accuracy that can be achieved in delivering the spacecraft to the entry interface point, and to determine how accurately the state of the spacecraft can be predicted to initialize the guidance algorithm. In addition, a sensitivity analysis has been performed to evaluate which factors most contribute to the improvement or degradation of the navigation performance, for both entry flight path angle delivery and entry state knowledge.
Composite structures are gaining importance for use in the aerospace industry. Compared to metallic structures their behavior is less well understood. This lack of understanding may pose constraints on their use. One possible way to deal with some of the risks associated with potential failure is to perform in-situ monitoring to detect precursors of failures. Prognostic algorithms can be used to predict impending failures. They require large amounts of training data to build and tune damage model for making useful predictions. One of the key aspects is to get confirmatory feedback from data as damage progresses. These kinds of data are rarely available from actual systems. The next possible resource to collect such data is an accelerated aging platform. To that end this paper describes a fatigue cycling experiment with the goal to stress carbon-carbon composite coupons with various layups. Piezoelectric disc sensors were used to periodically interrogate the system. Analysis showed distinct differences in the signatures of growing failures between data collected at conditions. Periodic X-radiographs were taken to assess the damage ground truth. Results after signal processing showed clear trends of damage growth that were correlated to damage assessed from the X-ray images.
The Mars Science Laboratory (MSL) is a NASA rover mission that will be launched in late 2011 and will land on Mars in August of 2012. This paper describes the analyses performed to validate the navigation system for launch, interplanetary cruise, and approach. MSL will use guidance during its descent into Mars in order to minimize landing dispersions, and therefore will be able to use smaller landing zones that are closer to terrain of high scientific interest. This will require a more accurate delivery of the spacecraft to the atmospheric entry interface, and a late update of the state of the spacecraft at entry. During cruise and approach the spacecraft may perform up to six trajectory correction maneuvers (TCMs), to target to the desired landing site with the required flight path angle at entry. Approach orbit determination covariance analyses have been performed to evaluate the accuracy that can be achieved in delivering the spacecraft to the entry interface point, and to determine how accurately the state of the spacecraft can be predicted to initialize the guidance algorithm. In addition, a sensitivity analysis has been performed to evaluate which factors most contribute to the improvement or degradation of the navigation performance, for both entry flight path angle delivery and entry state knowledge.
For multiparametric mixed-integer convex programming problems such as those encountered in hybrid model predictive control, we propose an algorithm for generating a feasible partition of a subset of the parameter space. The result is a static map from the current parameter to a suboptimal integer solution such that the remaining convex program is feasible. Convergence is proved with a new insight that the overlap among the feasible parameter sets of each integer solution governs the partition complexity. The partition is stored as a tree which makes querying the feasible solution efficient. The algorithm can be used to warm start a mixed integer solver with a real-time guarantee or to provide a reference integer solution in several suboptimal MPC schemes. The algorithm is tested on randomly generated systems with up to six states, demonstrating the effectiveness of the approach.
Based on a detailed analysis of recent loss-of-control events, the Aircraft State Awareness Joint Safety Analysis Team has identified the need to develop algorithms and display strategies to provide control guidance for recovery from approach-to-stall or stall. In order to be effective, such guidance should enhance the pilots ability to execute the Federal Aviation Administrations recommended stall recovery procedure. This paper explores the use of a fast model predictive control algorithm that determines near optimal recovery guidance, which quantifies the aircraft configuration and situation dependent recovery information required to maximize the effectiveness of the recovery. This information includes the magnitude of the initial pitch down maneuver, the specific amount of airspeed and thrust needed before pulling out of the recovery dive, as well as the maximum pitch-up rate that can be sustained without causing a secondary stall. The algorithm was integrated and tested with an in-house desktop simulator that implements the General Transport Aircraft model and the associated stall aircraft dynamics. Preliminary results are presented to demonstrate the use of the proposed approach as a recovery aid for pilots.
The National Aeronautics and Space Administration (NASA) is developing and testing fast-time wake transport and decay models to safely enhance the capacity of the National Airspace System (NAS). The fast-time wake models are empirical algorithms used for real-time predictions of wake transport and decay based on aircraft parameters and ambient weather conditions. The aircraft dependent parameters include the initial vortex descent velocity and the vortex pair separation distance. The atmospheric initial conditions include vertical profiles of temperature or potential temperature, eddy dissipation rate, and crosswind. The current distribution includes the latest versions of the APA (3.4) and the TDP (2.1) models. This User's Guide provides detailed information on the model inputs, file formats, and the model output. An example of a model run and a brief description of the Memphis 1995 Wake Vortex Dataset is also provided.
Integral property calculation is an important application for solid modeling systems. Algorithms for computing integral properties for various solid representation schemes are fairly well known. It is important to deigners and users of solid modeling systems to understand the behavior of such algorithms. Specifically the trade-off between execution time and accuracy is critical to effective use of integral property calculation. The average behavior of two algorithms for Constructive Solid Geometry (CSG) representations is investigated. Experimental results from the PADL-2 solid modeling system show that coarse decompositions can be used to predict execution time and error estimates for finer decompositions. Exploiting this predictability allow effective use of the algorithms in a solid modeling system.
In this study, the crack-tip opening angle (CTOA) approach was incorporated into a damage growth finite element program, MADGIC (Micromechanics Analysis and Damage Growth in Composites), and was used to predict stable tearing in a middle-crack tension 2024-T3 aluminum alloy specimen. The MADGIC code is a displacement based finite element program implemented with an incremental elastic-plastic algorithm used to model elastic-plastic behavior and a nodal splitting and nodal force relaxation algorithm used to generate crack surfaces. Predictions of the applied stress as a function of crack extension and applied stress as a function of load-line displacement were in good agreement with experiments and with similar predictions made using an existing finite element program, ZIP2D. In addition, path integrals, namely, the J-integral and T*-integral, were also evaluated and compared with the CTOA approach. There appears to be a weak relationship between the CTOA and the T*-integral evaluated on a specific integration path during crack extension beyond maximum applied stress. This study further verifies that the CTOA can be used as an effective elastic-plastic fracture mechanics parameter to predict crack growth.
The effective applications of land surface models (LSMs) and hydrologic models pose a varied set of data input and processing needs, ranging from ensuring consistency checks to more derived data processing and analytics. This article describes the development of the Land surface Data Toolkit (LDT), which is an integrated framework designed specifically for processing input data to execute LSMs and hydrological models. LDT not only serves as a preprocessor to the NASA Land Information System (LIS), which is an integrated framework designed for multi-model LSM simulations and data assimilation (DA) integrations, but also as a land-surface-based observation and DA input processor. It offers a variety of user options and inputs to processing datasets for use within LIS and stand-alone models. The LDT design facilitates the use of common data formats and conventions. LDT is also capable of processing LSM initial conditions and meteorological boundary conditions and ensuring data quality for inputs to LSMs and DA routines. The machine learning layer in LDT facilitates the use of modern data science algorithms for developing data-driven predictive models. Through the use of an object-oriented framework design, LDT provides extensible features for the continued development of support for different types of observational datasets and data analytics algorithms to aid land surface modeling and data assimilation.
A space-marching method has been developed to compute 3-D viscous flows in internal geometries. The Navier-Stokes equations have been posed as an initial-value problem by neglecting the effects of streamwise diffusion and treating the streamwise pressure gradient as a known source term. The fully coupled system of equations has been solved by a noniterative algorithm at each streamwise step of the computation. A low Mach number formulation of the equations has been used to compute incompressible flow fields. A computer program has been written to implement all aspects of the space-marching algorithm. The program is modular and is easily adapted to the widely varying geometries of internal flows. The space-marching algorithm has been tested by computing simple flows with known analytical solutions. The method has been used to predict complex 3-D turbulent flows. The algorithm is stable and very economical. A single sweep of the flow field by the space-marching method is approximately equivalent to one time-step of the time-marching method.
Feature extraction and classification algorithms for high dimensional data are investigated. Developments with regard to sensors for Earth observation are moving in the direction of providing much higher dimensional multispectral imagery than is now possible. In analyzing such high dimensional data, processing time becomes an important factor. With large increases in dimensionality and the number of classes, processing time will increase significantly. To address this problem, a multistage classification scheme is proposed which reduces the processing time substantially by eliminating unlikely classes from further consideration at each stage. Several truncation criteria are developed and the relationship between thresholds and the error caused by the truncation is investigated. Next an approach to feature extraction for classification is proposed based directly on the decision boundaries. It is shown that all the features needed for classification can be extracted from decision boundaries. A characteristic of the proposed method arises by noting that only a portion of the decision boundary is effective in discriminating between classes, and the concept of the effective decision boundary is introduced. The proposed feature extraction algorithm has several desirable properties: it predicts the minimum number of features necessary to achieve the same classification accuracy as in the original space for a given pattern recognition problem; and it finds the necessary feature vectors. The proposed algorithm does not deteriorate under the circumstances of equal means or equal covariances as some previous algorithms do. In addition, the decision boundary feature extraction algorithm can be used both for parametric and non-parametric classifiers. Finally, some problems encountered in analyzing high dimensional data are studied and possible solutions are proposed. First, the increased importance of the second order statistics in analyzing high dimensional data is recognized. By investigating the characteristics of high dimensional data, the reason why the second order statistics must be taken into account in high dimensional data is suggested. Recognizing the importance of the second order statistics, there is a need to represent the second order statistics. A method to visualize statistics using a color code is proposed. By representing statistics using color coding, one can easily extract and compare the first and the second statistics.
Collision Detection for Spacecraft Proximity Operations This thesis describes the development of a new collision detection algorithm to be used when two spacecraft are operating in the same vicinity. The two spacecraft are modelled as unions of convex polyhedra, where the polyhedron resulting from the union may be either convex or nonconvex. The relative motion of the two spacecraft is assumed to be such that one vehicle is moving with constant linear and angular velocity with respect to the other. The algorithm determines if a collision is possible and, if so, predicts the time when the collision will take place. The theoretical basis for the new collision detection algorithm is the C-function formulation of the configuration space approach recently introduced by researchers in robotics. Three different types of C-functions are defined that model the contacts between the vertices, edges, and faces of the polyhedra representing the two spacecraft. These C-functions are used to formulate three "collision" conditions. The first of these conditions limits the points representing potential collisions to the zeros of the C-functions. The new algorithm is fundamentally a search for the smallest zero of any C-function that satisfies the second and third collision conditions. The C-functions are shown to be transcendental functions of time for the assumed trajectory of the moving spacecraft. The zeros of these functions cannot be expressed in dosed form. Therefore, numerical search procedures are developed to find aLl of the zeros of a C-function in specified bounded intervals of time. These bounded intervals of time are found by examining the second and third collision conditions. The capabilities of the new algorithm are demonstrated for several example cases. These include examples of collisions determined by zeros of each of the three different types of C-functions. In addition to predicting the time of first contact of the polyhedra, the algorithm identifies the features of the two polyhedra that are touching at this time. The new collision detection algorithm is the first such algorithm that is capable of solving the collision detection problem exactly for the case where the moving object has constant linear and angular velocities. This is a significant improvement on previous collision detection algorithms described in the literature. In particular, the ability to handle constant angular velocity represents a more realistic type of rotational motion than those which have been used in other algorithms.
Identification of thrombus-related mechanical prosthetic valve dysfunction (MPVD) has important therapeutic implications. We sought to develop an algorithm, combining clinical and echocardiographic parameters, for prediction of thrombus-related MPVD in a series of 53 patients (24 men, age 52 +/- 16 years) who had intraoperative diagnosis of thrombus or pannus from 1992 to 1997. Clinical and echocardiographic parameters were analyzed to identify predictors of thrombus and pannus. Prevalence of thrombus and diagnostic yields relative to the number of predictors were determined. There were 22 patients with thrombus, 19 patients with pannus, and 12 patients with both. Forty-two of 53 masses were visualized using transesophageal echocardiography (TEE), including 29 of 34 thrombi or both thrombi and panni and 13 of 19 isolated panni. Predictors of thrombus or mixed presentation include mobile mass (p = 0.009), attachment to occluder (p = 0.02), elevated gradients (p = 0.04), and an international normalized ratio of < or = 2.5 (p = 0.03). All 34 patients with thrombus or mixed presentation had > or = 1 predictor. The prevalence of thrombus in the presence of < or = 1, 2, and > or = 3 predictors is 14%, 69%, and 91%, respectively. Thus, TEE is sensitive in the identification of abnormal mass in the setting of MPVD. An algorithm based on clinical and transesophageal echocardiographic predictors may be useful to estimate the likelihood of thrombus in the setting of MPVD. In the presence of > or = 3 predictors, the probability of thrombus is high.
We present a new algorithm in the Hall2De code to simulate the ion hydrodynamics in the acceleration channel and near plume regions of Hall-effect thrusters. This implementation constitutes an upgrade of the capabilities built in the Hall2De code. The equations of mass conservation and momentum for unmagnetized ions are solved using a conservative, finite-volume, cell-centered scheme on a magnetic-field-aligned grid. Major computational savings are achieved by making use of an implicit predictor/multi-corrector algorithm for time evolution. Inaccuracies in the prediction of the motion of low-energy ions in the near plume in hydrodynamics approaches are addressed by implementing a multi-fluid algorithm that tracks ions of different energies separately. A wide range of comparisons with measurements are performed to validate the new ion algorithms. Several numerical experiments with the location and value of the anomalous collision frequency are also presented. Differences in the plasma properties in the near-plume between the single fluid and multi-fluid approaches are discussed. We complete our validation by comparing predicted erosion rates at the channel walls of the thruster with measurements. Erosion rates predicted by the plasma properties obtained from simulations replicate accurately measured rates of erosion within the uncertainty range of the sputtering models employed.
The US National Airspace System (NAS) is making its transition to the NextGen system and assuring safety is one of the top priorities in NextGen. At present, safety is managed reactively (correct after occurrence of an unsafe event). While this strategy works for current operations, it may soon become ineffective for future airspace designs and high density operations. There is a need for proactive management of safety risks by identifying hidden and "unknown" risks and evaluating the impacts on future operations. To this end, NASA Ames has developed data mining algorithms that finds anomalies and precursors (high-risk states) to safety issues in the NAS. In this paper, we describe a recently developed algorithm called ADOPT that analyzes large volumes of data and automatically identifies precursors from real world data. Precursors help in detecting safety risks early so that the operator can mitigate the risk in time. In addition, precursors also help identify causal factors and help predict the safety incident. The ADOPT algorithm scales well to large data sets and to multidimensional time series, reduce analyst time significantly, quantify multiple safety risks giving a holistic view of safety among other benefits. This paper details the algorithm and includes several case studies to demonstrate its application to discover the "known" and "unknown" safety precursors in aviation operation.
A graph managing strategy referred to as the Algorithm to Architecture Mapping Model (ATAMM) appears useful for the time-optimized execution of application algorithm graphs in embedded multiprocessors and for the performance prediction of graph designs. This paper reports the modeling of ATAMM in the Architecture Design and Assessment System (ADAS) to make an independent verification of ATAMM's performance prediction capability and to provide a user framework for the evaluation of arbitrary algorithm graphs. Following an overview of ATAMM and its major functional rules are descriptions of the ADAS model of ATAMM, methods to enter an arbitrary graph into the model, and techniques to analyze the simulation results. The performance of a 7-node graph example is evaluated using the ADAS model and verifies the ATAMM concept by substantiating previously published performance results.