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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 307 records · Page 17

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NASA NTRS1998

Markov Tracking for Agent Coordination

Partially observable Markov decision processes (POMDPs) axe an attractive representation for representing agent behavior, since they capture uncertainty in both the agent's state and its actions. However, finding an optimal policy for POMDPs in general is computationally difficult. In this paper we present Markov Tracking, a restricted problem of coordinating actions with an agent or process represented as a POMDP Because the actions coordinate with the agent rather than influence its behavior, the optimal solution to this problem can be computed locally and quickly. We also demonstrate the use of the technique on sequential POMDPs, which can be used to model a behavior that follows a linear, acyclic trajectory through a series of states. By imposing a "windowing" restriction that restricts the number of possible alternatives considered at any moment to a fixed size, a coordinating action can be calculated in constant time, making this amenable to coordination with complex agents.

Washington, Richard↗

NASA NTRS2002

The 1999 Hector Mine Earthquake, Southern California: Vector Near-Field Displacements from ERS InSAR

Two components of fault slip are uniquely determined from two line-of-sight (LOS) radar interferograms by assuming that the fault-normal component of displacement is zero. We use this approach with ascending and descending interferograms from the ERS satellites to estimate surface slip along the Hector Mine earthquake rupture. The LOS displacement is determined by visually counting fringes to within 1 km of the outboard ruptures. These LOS estimates and uncertainties are then transformed into strike- and dip-slip estimates and uncertainties; the transformation is singular for a N-S oriented fault and optimal for an E-W oriented fault. In contrast to our previous strike-slip estimates, which were based only on a descending interferogram, we now find good agreement with the geological measurements, except at the ends of the rupture. The ascending interferogram reveals significant west-sidedown dip-slip (approximately 1.0 m) which reduces the strike-slip estimates by 1 to 2 m, especially along the northern half of the rupture. A spike in the strike-slip displacement of 6 m is observed in central part of the rupture. This large offset is confirmed by subpixel cross correlation of features in the before and after amplitude images. In addition to strike slip and dip slip, we identify uplift and subsidence along the fault, related to the restraining and releasing bends in the fault trace, respectively. Our main conclusion is that at least two look directions are required for accurate estimates of surface slip even along a pure strike-slip fault. Models and results based only on a single look direction could have major errors. Our new estimates of strike slip and dip slip along the rupture provide a boundary condition for dislocation modeling. A simple model, which has uniform slip to a depth of 12 km, shows good agreement with the observed ascending and descending interferograms.

Sandwell, David T.↗

NASA NTRS2011

Incorporating JULES into NASA's Land Information System (LIS) and Investigations of Land-Atmosphere Coupling

NASA's Land Information System (LIS; lis.gsfc.nasa.gov) is a flexible land surface modeling and data assimilation framework developed over the past decade with the goal of integrating satellite- and ground-based observational data products and advanced land surface modeling techniques to produce optimal fields of land surface states and fluxes. LIS features a high performance and flexible design, and operates on an ensemble of land surface models for extension over user-specified regional or global domains. The extensible interfaces of LIS allow the incorporation of new domains, land surface models (LSMs), land surface parameters, meteorological inputs, data assimilation and optimization algorithms. In addition, LIS has also been demonstrated for parameter estimation and uncertainty estimation, and has been coupled to the Weather Research and Forecasting (WRF) mesoscale model. A visiting fellowship is currently underway to implement JULES into LIS and to undertake some fundamental science on the feedbacks between the land surface and the atmosphere. An overview of the LIS system, features, and sample results will be presented in an effort to engage the community in the potential advantages of LIS-JULES for a range of applications. Ongoing efforts to develop a framework for diagnosing land-atmosphere coupling will also be presented using the suite of LSM and PBL schemes available in LIS and WRF along with observations from the U. S .. Southern Great Plains. This methodology provides a potential pathway to study factors controlling local land-atmosphere coupling (LoCo) using the LIS-WRF system, which will serve as a testbed for future experiments to evaluate coupling diagnostics within the community.

Santanello, Joseph↗

NASA NTRSDate not supplied

The Porous Microstructure Analysis (PuMA) software

The open-source Porous Microstructure Analysis (PuMA) software was implemented to offer an efficient framework for determining material characteristics from 3D microstructures. Its development was inspired by progress in X-ray microtomography, an imaging technology that captures the internal structure of materials in 3D, and even in a 4D temporal context. Over recent years, this method has transformed the domain of materials science due to its capability to non-destructively examine material microstructures while presenting digital data about their geometrical details. It has provided insights into materials relevant to several NASA missions, including heatshields, parachute fabrics, meteorites, and other advanced composites. PuMA, in its current version 3, delivers an array of features, spanning from basic geometric insights of a microstructure to intricate anisotropic thermo-elastic and chemical behavior. Specifically, the software evaluates morphological attributes (specific surface area, volume fractions, mean intercept lengths, orientation) and physical characteristics (conductivity, elasticity, permeability, and tortuosity). Additionally, it can model material degradation processes, such as oxidation and surface chemistry interactions. The software can generate synthetic microstructures, from straightforward geometrical designs to intricate woven and non-woven geometries. Coupling material generation and characterization enable parametric studies and sensitivity analysis to optimize the microstructural performance and inform design decisions and reliability assessment based on uncertainty quantification. A recent addition to PuMA includes the TomoSAM plugin, devised to incorporate the cutting-edge Segment Anything Model (SAM). SAM is a promptable deep learning model that can identify objects and create image masks in a zero-shot manner, based only on a few user clicks. The synergy between these tools aids in the segmentation of complex 3D datasets from tomography or other imaging techniques, which would otherwise require a laborious manual segmentation process.

Tomography↗

NASA NTRSDate not supplied

The Porous Microstructure Analysis (PuMA) software

The open-source Porous Microstructure Analysis (PuMA) software was created to offer an efficient framework for determining material properties from 3D microstructures. Its development was inspired by progress in X-ray microtomography, an imaging technology that captures the internal structure of materials in 3D, and even in a 4D temporal context. Over recent years, this method has transformed the domain of materials science due to its capability to non-destructively examine material microstructures while presenting digital data about their geometrical details. It has provided insights into materials relevant to several NASA missions, including heatshields, parachute fabrics, meteorites, and other advanced composites. PuMA, in its current version 3, delivers an array of features, spanning from basic geometric insights of a microstructure to intricate anisotropic thermo-elastic and chemical behavior. Specifically, the software evaluates morphological attributes (specific surface area, volume fractions, mean intercept lengths, orientation) and physical characteristics (conductivity, elasticity, permeability, and tortuosity). Additionally, it can model material degradation processes, such as oxidation and surface chemistry interactions. The software can generate synthetic microstructures, from straightforward geometrical designs to intricate woven and non-woven geometries. Coupling material generation and characterization enables parametric studies and sensitivity analysis to optimize the microstructural performance and inform design decisions and reliability assessment based on uncertainty quantification. A recent addition to PuMA includes the TomoSAM plugin, devised to incorporate the cutting-edge Segment Anything Model (SAM) into our image segmentation workflow. SAM is a promptable deep learning model that can identify objects and create image masks in a zero-shot manner, based only on a few user clicks. The synergy between these tools aids in the segmentation of complex 3D datasets from tomography and other imaging techniques, which would otherwise require a laborious manual segmentation process.

Tomography↗

NASA NTRS1992

Optimal Estimation of Rain Rate Profiles from Single-Frequency Radar Echoes

The significant ambiguities inherent in the determination of a particular vertical rain intensity profile from a given time profile of radar echo powers measured by a downward-looking (spaceborne or airborne) radar at a single attenuating frequency are well-documented. Indeed, one already knows that by appropriately varying the parameters of the reflectivity-rain-rate (Z - R) and/or attenuation-rain-rate (k - R) relationships, one can produce several substantially different hypothetical rain rate profiles which would have the same radar power profile. Imposing the additional constraint that the path-averaged rain-rate be a given fixed number does reduce the ambiguities but falls far short of eliminating them. While we now know how to generate as many mutually ambiguous rain-rate profiles from a given profile of received radar reflectivities as we like, there remains to produce a quantitative measure to assess how likely each of these profiles is, what the appropriate 'average' profile should be, and what the 'variance' of these multiple solutions is. Of course, in order to do this, one needs to spell out the stochastic constraints that can allow us to make sense of the words 'average' and 'variance' in a mathematically rigorous way. Such a quantitative approach would be particularly well-suited for such systems as the proposed Precipitation Radar of the Tropical Rainfall Measuring Mission (TRMM). Indeed, one would then be able to use the radar reflectivities measured by the TRMM radar from one particular look in order to estimate the most likely rain-rate profile that would have produced the measurements, as well as the uncertainty in the estimated rain-rates as a function of range. Such an optimal approach is described in this paper.

deep space flight computer real-time advanced spac↗

NASA NTRS2006

Postoptimality Analysis in the Selection of Technology Portfolios

This slide presentation reviews a process of postoptimally analysing the selection of technology portfolios. The rationale for the analysis stems from the need for consistent, transparent and auditable decision making processes and tools. The methodology is used to assure that project investments are selected through an optimization of net mission value. The main intent of the analysis is to gauge the degree of confidence in the optimal solution and to provide the decision maker with an array of viable selection alternatives which take into account input uncertainties and possibly satisfy non-technical constraints. A few examples of the analysis are reviewed. The goal of the postoptimality study is to enhance and improve the decision-making process by providing additional qualifications and substitutes to the optimal solution.

prioritization↗

NASA NTRS1987

Meteor velocity distribution and an optimum monitoring mathematical model

At present, there are a great number of radio meteor, ionosphere and rocket observation data for the altitude range of 80 to 100 km which indicate the existence of large scale circulation systems in the mesopause to low thermosphere range which change regularly with season and latitude. But the existing observation network and observation programs are not optimal for revealing the main factors forming the circulation mode at these altitudes. A generalized optimum monitoring mathematical model is offered for consideration. The model input data are distribution density, response function, individual measurement root mean square uncertainty and detection effectiveness function. The model makes it possible to obtain the observation distribution density, the minimal possible dispersion and optimized system effectiveness.

Volkov, N. G.↗

NASA NTRS1982

Optimizing and comparing laser anemometers

Comparisons can be made only if some single valued measure of the quality of each system can be defined. This concept is illustrated here by specifying the quality variable to be the measurement uncertainty in the mean velocity of a turbulent flow in the presence of a large background noise. The Fisher number is used as a figure of merit for some parts of the system. It is noted that in many cases of practical interest, the reciprocal Fisher number gives the actual variance of an estimator. Its principal advantage here is that it can be computed without detailed assumptions about the statistics of the estimated quantities. The concept of a Fisher number is related to the concept of information gain in a measurement. The variance of the measured mean velocity is expressed in terms of the uncertainty for the individual measurements, the measurement rate, and the velocity correlation time. Conditions are given under which the fringe and the two-spot anemometer are optimized.

Edwards, R. V.↗

NASA NTRS2004

Launch Vehicle Propulsion Parameter Design Multiple Selection Criteria

The optimization tool described herein addresses and emphasizes the use of computer tools to model a system and focuses on a concept development approach for a liquid hydrogen/liquid oxygen single-stage-to-orbit system, but more particularly the development of the optimized system using new techniques. This methodology uses new and innovative tools to run Monte Carlo simulations, genetic algorithm solvers, and statistical models in order to optimize a design concept. The concept launch vehicle and propulsion system were modeled and optimized to determine the best design for weight and cost by varying design and technology parameters. Uncertainty levels were applied using Monte Carlo Simulations and the model output was compared to the National Aeronautics and Space Administration Space Shuttle Main Engine. Several key conclusions are summarized here for the model results. First, the Gross Liftoff Weight and Dry Weight were 67% higher for the design case for minimization of Design, Development, Test and Evaluation cost when compared to the weights determined by the minimization of Gross Liftoff Weight case. In turn, the Design, Development, Test and Evaluation cost was 53% higher for optimized Gross Liftoff Weight case when compared to the cost determined by case for minimization of Design, Development, Test and Evaluation cost. Therefore, a 53% increase in Design, Development, Test and Evaluation cost results in a 67% reduction in Gross Liftoff Weight. Secondly, the tool outputs define the sensitivity of propulsion parameters, technology and cost factors and how these parameters differ when cost and weight are optimized separately. A key finding was that for a Space Shuttle Main Engine thrust level the oxidizer/fuel ratio of 6.6 resulted in the lowest Gross Liftoff Weight rather than at 5.2 for the maximum specific impulse, demonstrating the relationships between specific impulse, engine weight, tank volume and tank weight. Lastly, the optimum chamber pressure for Gross Liftoff Weight minimization was 2713 pounds per square inch as compared to 3162 for the Design, Development, Test and Evaluation cost optimization case. This chamber pressure range is close to 3000 pounds per square inch for the Space Shuttle Main Engine.

Shelton, Joey Dewayne↗

NASA NTRS2014

Learning the Task Management Space of an Aircraft Approach Model

Validating models of airspace operations is a particular challenge. These models are often aimed at finding and exploring safety violations, and aim to be accurate representations of real-world behavior. However, the rules governing the behavior are quite complex: nonlinear physics, operational modes, human behavior, and stochastic environmental concerns all determine the responses of the system. In this paper, we present a study on aircraft runway approaches as modeled in Georgia Tech's Work Models that Compute (WMC) simulation. We use a new learner, Genetic-Active Learning for Search-Based Software Engineering (GALE) to discover the Pareto frontiers defined by cognitive structures. These cognitive structures organize the prioritization and assignment of tasks of each pilot during approaches. We discuss the benefits of our approach, and also discuss future work necessary to enable uncertainty quantification.

Validation↗

NASA NTRS2008

Ares-I Bending Filter Design using a Constrained Optimization Approach

The Ares-I launch vehicle represents a challenging flex-body structural environment for control system design. Software filtering of the inertial sensor output is required to ensure adequate stable response to guidance commands while minimizing trajectory deviations. This paper presents a design methodology employing numerical optimization to develop the Ares-I bending filters. The design objectives include attitude tracking accuracy and robust stability with respect to rigid body dynamics, propellant slosh, and flex. Under the assumption that the Ares-I time-varying dynamics and control system can be frozen over a short period of time, the bending filters are designed to stabilize all the selected frozen-time launch control systems in the presence of parameter uncertainty. To ensure adequate response to guidance command, step response specifications are introduced as constraints in the optimization problem. Imposing these constrains minimizes performance degradation caused by the addition of the bending filters. The first stage bending filter design achieves stability by adding lag to the first structural frequency to phase stabilize the first flex mode while gain stabilizing the higher modes. The upper stage bending filter design gain stabilizes all the flex bending modes. The bending filter designs provided here have been demonstrated to provide stable first and second stage control systems in both Draper Ares Stability Analysis Tool (ASAT) and the MSFC MAVERIC 6DOF nonlinear time domain simulation.

Hall, Charles↗

NASA NTRSDate not supplied

Optimal Sensor Mobility Design for Target Tracking with Distributed Sensing, Communication and Computing Infrastructure

The paper presents an airborne target tracking approach with data fusion from distributed stationary and mobile sensor network transmitted through a wireless/wired communication network and using a distributed computing infrastructure. To obtain high quality measurements, sensor flight platforms' positions and orientations are optimized with respect to the available target's position and velocity estimates, and a formation control strategy is applied to derive desired trajectories for the flight platforms. As sensors move, the communication network topology switching is determined using the worst-case approach to handle uncertainties in the target's and sensors' positions, which alters communication delays for sensors' data transmission to computing centers. An optimal data migration algorithm is periodically applied to determine these communication delays for each sensor and the optimal location with minimum end-to-end latency for the target tracking algorithm execution, which is based on adaptive information fusion from multi-modal mobile and stationary sensors inside an Extended Kalman Filter framework. The approach is validated in a desktop simulation environment using synthetic sensor data generated for a simulated target's flight.

Distributed sensing↗

NASA NTRS1987

A combined stochastic feedforward and feedback control design methodology with application to autoland design

A combined stochastic feedforward and feedback control design methodology was developed. The objective of the feedforward control law is to track the commanded trajectory, whereas the feedback control law tries to maintain the plant state near the desired trajectory in the presence of disturbances and uncertainties about the plant. The feedforward control law design is formulated as a stochastic optimization problem and is embedded into the stochastic output feedback problem where the plant contains unstable and uncontrollable modes. An algorithm to compute the optimal feedforward is developed. In this approach, the use of error integral feedback, dynamic compensation, control rate command structures are an integral part of the methodology. An incremental implementation is recommended. Results on the eigenvalues of the implemented versus designed control laws are presented. The stochastic feedforward/feedback control methodology is used to design a digital automatic landing system for the ATOPS Research Vehicle, a Boeing 737-100 aircraft. The system control modes include localizer and glideslope capture and track, and flare to touchdown. Results of a detailed nonlinear simulation of the digital control laws, actuator systems, and aircraft aerodynamics are presented.

Halyo, Nesim↗

NASA NTRS1993

Robust time-optimal control of uncertain structural dynamic systems

A time-optimal open-loop control problem of flexible spacecraft in the presence of modeling uncertainty has been investigated. The results indicate that the proposed approach significantly reduces the residual structural vibrations caused by modeling uncertainty. The results also indicate the importance of proper jet placement for practical tradeoffs among the maneuvering time, fuel consumption, and performance robustness.

Wie, Bong↗

NASA NTRS1993

Confidence of the three-point estimator of frequency drift

It was shown that a three-point second difference estimator is nearly optimal for estimating frequency drift in many common atomic oscillators. A formula for the uncertainty of this estimate as a function of the integration time and of the Allan variance associated with this integration time is derived.

Weiss, Marc A.↗

NASA NTRS2006

On the Biases in AIRS Retrieval of Ozone (Work in Progress)

A viewgraph presentation on various biases in AIRS retrieval of ozone is given. The topics include: 1) AIRS captures UTLS ozone events; 2) Simplified AIRS retrieval of ozone; 3) How much is AIRS getting its skill in ozone from regression? biases are similar to ECMWF; 4) How does channel selection and damping affect the retrieval?; 5) Decreasing damping worsens results in upper"trop/lower"strat with current channel selection; 6) If results worse with decreased damping, let s give the retrieval more information; 7) Adding channels at current damping doesn't change anything.; 8) Adding channels and decreasing damping gives mixed results; 9) Let's look at the radiances and their uncertainties ; 10) Systematic biases in radiance uncertainties?; 11) Surface Temperature; 12) Temperature Profile; and 13) x vs Brightness Temperature Error Optimal Estimation Retrieval.

Atmospheric Infrared Sounder (AIRS)↗

NASA NTRS2008

Probabilistic Usage of the Multi-Factor Interaction Model

A Multi-Factor Interaction Model (MFIM) is used to predict the insulating foam mass expulsion during the ascending of a space vehicle. The exponents in the MFIM are evaluated by an available approach which consists of least squares and an optimization algorithm. These results were subsequently used to probabilistically evaluate the effects of the uncertainties in each participating factor in the mass expulsion. The probabilistic results show that the surface temperature dominates at high probabilities and the pressure which causes the mass expulsion at low probabil

Chamis, Christos C.↗