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NASA NTRSDate not supplied

Evaluation of single-footprint AIRS CH4 Profile Retrieval Uncertainties Using Aircraft Profile Measurements

We evaluate the uncertainties of methane optimal estimation retrievals from single footprint thermal infrared observations from the Atmospheric Infrared Sounder (AIRS). These retrievals are primarily sensitive to atmospheric methane in the mid-troposphere through the lower stratosphere (~2 to ~17 km). We compare to in situ observations made from aircraft during the Hiaper Pole to Pole Observations (HIPPO), the NASA Atmospheric Tomography Mission (ATom) campaigns, and from the NOAA ESRL aircraft network, between the surface and 5-13 km, across a range of years, latitudes between 60 S to 80 N, and over land and ocean. After a global, pressure dependent bias correction, we find that the land and ocean have similar biases and that the reported observation error (combined measurement and interference errors) of ~27 ppb is consistent with the standard deviation between aircraft and individual AIRS observations. A single measurement has measurement (noise related) uncertainty of ~17 ppb, a ~20 ppb uncertainty from radiative interferences (e.g. from water, temperature, etc.), and ~ 30 ppb due to “smoothing error”, which is partially removed when making comparisons to in situ measurements or models in a way that account for this regularization. We estimate a 16 ppb validation error because the aircraft typically did not measure methane at altitudes where the AIRS measurements have some sensitivity, e.g. the stratosphere. Daily averaged AIRS measurements of at least 9 observations over spatio-temporal domains of < 1 degree and 1 hour have a standard deviation of ~17 ppb versus aircraft, likely because the observation errors from temperature and water vapor (for example) are only partly reduced through averaging. Seasonal averages can reduce this ~17 ppb uncertainty further to ~10 ppb, as determined through comparison with NOAA aircraft, likely because uncertainties related to radiative effects of temperature and water vapor can be reduced when averaged over a season.

Susan Kulawik↗

NASA NTRS1973

Two concepts for the reduction of payload attitude slewing times.

The concepts presented are particularly valuable in sounding rocket applications, where experiment data-gathering time above the atmosphere is measured in minutes per flight. One concept is a practical nonlinear control law for slewing a payload in one axis, with performance that approaches time-optimal as the uncertainty in angular acceleration approaches zero. The second concept is a practical transformation-of-coordinates system allowing slewing about an axis not coincident with any of the three control axes of the payload, so that the payload experiment axis may move along the great circle arc containing the initial and terminal target points. Implementation of both concepts is facilitated by the availability of variable-thrust cold-gas thrustors. Both concepts can result in significant reductions in slewing times compared with more conventional systems.

Riley, D. C.↗

NASA NTRS1992

The Middeck Active Control Experiment (MACE): Identification for robust control

Viewgraphs on identification for robust control for the Middeck Active Control Experiment (MACE) are presented. Topics covered include: identification for robust control; three levels of identification; basic elements of the approach; advantages of 'post-ID' model of uncertainty; advantages of optimization; and practical realization.

Karlov, Valery I.↗

NASA NTRS2001

Algorithms and Object-Oriented Software for Distributed Physics-Based Modeling

The project seeks to develop methods to more efficiently simulate aerospace vehicles. The goals are to reduce model development time, increase accuracy (e.g.,by allowing the integration of multidisciplinary models), facilitate collaboration by geographically- distributed groups of engineers, support uncertainty analysis and optimization, reduce hardware costs, and increase execution speeds. These problems are the subject of considerable contemporary research (e.g., Biedron et al. 1999; Heath and Dick, 2000).

Kenton, Marc A.↗

NASA NTRS2006

Difficult Decisions Made Easier

NASA missions are extremely complex and prone to sudden, catastrophic failure if equipment falters or if an unforeseen event occurs. For these reasons, NASA trains to expect the unexpected. It tests its equipment and systems in extreme conditions, and it develops risk-analysis tests to foresee any possible problems. The Space Agency recently worked with an industry partner to develop reliability analysis software capable of modeling complex, highly dynamic systems, taking into account variations in input parameters and the evolution of the system over the course of a mission. The goal of this research was multifold. It included performance and risk analyses of complex, multiphase missions, like the insertion of the Mars Reconnaissance Orbiter; reliability analyses of systems with redundant and/or repairable components; optimization analyses of system configurations with respect to cost and reliability; and sensitivity analyses to identify optimal areas for uncertainty reduction or performance enhancement.

Source record↗

NASA NTRS2007

Numerical Uncertainty Quantification for Radiation Analysis Tools

Recently a new emphasis has been placed on engineering applications of space radiation analyses and thus a systematic effort of Verification, Validation and Uncertainty Quantification (VV&UQ) of the tools commonly used for radiation analysis for vehicle design and mission planning has begun. There are two sources of uncertainty in geometric discretization addressed in this paper that need to be quantified in order to understand the total uncertainty in estimating space radiation exposures. One source of uncertainty is in ray tracing, as the number of rays increase the associated uncertainty decreases, but the computational expense increases. Thus, a cost benefit analysis optimizing computational time versus uncertainty is needed and is addressed in this paper. The second source of uncertainty results from the interpolation over the dose vs. depth curves that is needed to determine the radiation exposure. The question, then, is what is the number of thicknesses that is needed to get an accurate result. So convergence testing is performed to quantify the uncertainty associated with interpolating over different shield thickness spatial grids.

Anderson, Brooke↗

NASA NTRS2012

Creation of a Rapid High-Fidelity Aerodynamics Module for a Multidisciplinary Design Environment

In the traditional aerospace vehicle design process, each successive design phase is accompanied by an increment in the modeling fidelity of the disciplinary analyses being performed. This trend follows a corresponding shrinking of the design space as more and more design decisions are locked in. The correlated increase in knowledge about the design and decrease in design freedom occurs partly because increases in modeling fidelity are usually accompanied by significant increases in the computational expense of performing the analyses. When running high fidelity analyses, it is not usually feasible to explore a large number of variations, and so design space exploration is reserved for conceptual design, and higher fidelity analyses are run only once a specific point design has been selected to carry forward. The designs produced by this traditional process have been recognized as being limited by the uncertainty that is present early on due to the use of lower fidelity analyses. For example, uncertainty in aerodynamics predictions produces uncertainty in trajectory optimization, which can impact overall vehicle sizing. This effect can become more significant when trajectories are being shaped by active constraints. For example, if an optimal trajectory is running up against a normal load factor constraint, inaccuracies in the aerodynamic coefficient predictions can cause a feasible trajectory to be considered infeasible, or vice versa. For this reason, a trade must always be performed between the desired fidelity and the resources available. Apart from this trade between fidelity and computational expense, it is very desirable to use higher fidelity analyses earlier in the design process. A large body of work has been performed to this end, led by efforts in the area of surrogate modeling. In surrogate modeling, an up-front investment is made by running a high fidelity code over a Design of Experiments (DOE); once completed, the DOE data is used to create a surrogate model, which captures the relationships between input variables and responses into regression equations. Depending on the dimensionality of the problem and the fidelity of the code for which a surrogate model is being created, the initial DOE can itself be computationally prohibitive to run. Cokriging, a modeling approach from the field of geostatistics, provides a desirable compromise between computational expense and fidelity. To do this, cokriging leverages a large body of data generated by a low fidelity analysis, combines it with a smaller set of data from a higher fidelity analysis, and creates a kriging surrogate model with prediction fidelity approaching that of the higher fidelity analysis. When integrated into a multidisciplinary environment, a disciplinary analysis module employing cokriging can raise the analysis fidelity without drastically impacting the expense of design iterations. This is demonstrated through the creation of an aerodynamics analysis module in NASA’s OpenMDAO framework. Aerodynamic analyses including Missile DATCOM, APAS, and USM3D are leveraged to create high fidelity aerodynamics decks for parametric vehicle geometries, which are created in NASA’s Vehicle Sketch Pad (VSP). Several trade studies are performed to examine the achieved level of model fidelity, and the overall impact to vehicle design is quantified.

Aerodynamic Properties↗

NASA NTRSDate not supplied

Predictive Modeling of Carbon Ablators

Efforts to build a Predictive Material Modeling (PMM) framework from the micro-scale to the macro-scale are presented in this abstract. To reduce the need for extensive testing, accelerate the design cycle process, and reduce uncertainty margins applied to final designs, NASA is developing simulation and modeling tools that enable characterization of material properties and response to high-enthalpy environments. The Porous Microstructure Analysis (PuMA) code has been developed for computing macroscale (volume averaged) properties of porous materials using microscale images from micro-computed tomography (micro-CT). Microscale modeling requires a realistic representation of a material microstructure; these are obtained either synthetically during the design of the material or through X-ray micro-CT. Volume averaged properties are then used to inform macroscale material response models, such as those implemented in the Porous-material Analysis Toolbox based on OpenFOAM (PATO) software, also actively developed by NASA. The computational model in PATO is a generic heat and mass transfer model for porous reactive materials containing several solid phases and a single gas phase. The detailed chemical interactions occurring between the solid phases and the gas phase are modeled at the pore scale assuming local thermal equilibrium. These tools were developed to efficiently interface with other pre-existing codes such as SPARTA (direct simulation Monte Carlo), DPLR (hypersonic CFD), NEQAIR (radiative transport) and DAKOTA (uncertainty quantification and optimization). Detailed flight data (Mars Science Laboratory [MSL] Entry Descent and Landing Instrument [MEDLI]) is critical for validating these computational tools for NASA applications. Examples of modeling ablative material response using these codes will be presented including 3D simulations of the full-scale heatshield of the MSL capsule. The simulations demonstrate the ability of the modern material response code, PATO, to handle the material response of geometrically complex and large domains, through the use of massively parallel computations.

Thermal Protection Systems↗

NASA NTRSDate not supplied

Predictive Modeling of Carbon Ablators Using Micro and Macro-Scale Modeling

Thermal Protection Systems↗

NASA NTRSDate not supplied

Estimation of 3D Woven Design Sensitivities Using a Rapid Multiscale Analysis Technique

Highly-refined finite element models of three-dimension (3D) woven composite systems currently require excessive computational demands that limit their use in sensitivity analysis, uncertainty quantification, and optimization. An alternative analysis methodology was developed using the NASA Multiscale Analysis Tool (NASMAT) where multiscale models of a 3D woven composite (including inter-tow matrix voids and constituent failure) can be completed on a single central processing unit(CPU)on the order of ~30 s. To develop inputs and validation data for the NASMAT model, coupon and acid-digesting testing and x-ray computed tomography were performed. The NASMAT inputs were parameterized using a set of 25 input variables and distributions. These inputs were randomly sampled to generate a total of 100,000 NASMAT analyses that could be used to understand the influence of different material and geometric properties on the warp and weft-direction stiffness and strength. These analyses (including pre/post-processing) were performed in less than eight hours on a 120 CPU cluster. The computational efficiency of the NASMAT model enabled a sensitivity analysis to be performed, and dominant input variables were able to be identified. Key results were consistent with theoretical and experimental observations for the specific 3D woven system studied in this work.

NASMAT↗

NASA NTRSDate not supplied

Estimation of 3D Woven Design Sensitivities Using a Rapid Multiscale Analysis Technique

Highly-refined finite element models of three-dimension (3D) woven composite systems currently require excessive computational demands that limit their use in sensitivity analysis, uncertainty quantification, and optimization. An alternative analysis methodology was developed using the NASA Multiscale Analysis Tool (NASMAT) where multiscale models of a 3D woven composite (including inter-tow matrix voids and constituent failure) can be completed on a single central processing unit (CPU) on the order of ~30s. To develop inputs and validation data for the NASMAT model, coupon and acid-digesting testing and x-ray computed tomography were performed. The NASMAT inputs were parameterized using a set of 25 input variables and distributions. These inputs were randomly sampled to generate a total of 100,000 NASMAT analyses that could be used to understand the influence of different material and geometric properties on the warp and weft-direction stiffness and strength. These analyses (including pre/post-processing) were performed in less than eight hours on a 120 CPU cluster. The computational efficiency of the NASMAT model enabled a sensitivity analysis to be performed, and dominant input variables were able to be identified. Key results were consistent with theoretical and experimental observations for the specific 3D woven system studied in this work.

NASMAT↗

NASA NTRS2016

Conflict Resolution for Wind-Optimal Aircraft Trajectories in North Atlantic Oceanic Airspace with Wind Uncertainties

Air traffic in the North Atlantic oceanic airspace (NAT) experiences very strong winds caused by jet streams. Flying wind-optimal trajectories increases individual flight efficiency, which is advantageous when operating in the NAT. However, as the NAT is highly congested during peak hours, a large number of potential conflicts between flights are detected for the sets of wind-optimal trajectories. Conflict resolution performed at the strategic level of flight planning can significantly reduce the airspace congestion. However, being completed far in advance, strategic planning can only use predicted environmental conditions that may significantly differ from the real conditions experienced further by aircraft. The forecast uncertainties result in uncertainties in conflict prediction, and thus, conflict resolution becomes less efficient. This work considers wind uncertainties in order to improve the robustness of conflict resolution in the NAT. First, the influence of wind uncertainties on conflict prediction is investigated. Then, conflict resolution methods accounting for wind uncertainties are proposed.

wind uncertainties↗

NASA NTRS2017

Performance Optimizing Multi-Objective Adaptive Control with Time-Varying Model Reference Modification

This paper presents a new adaptive control approach that involves a performance optimization objective. The problem is cast as a multi-objective optimal control. The control synthesis involves the design of a performance optimizing controller from a subset of control inputs. The effect of the performance optimizing controller is to introduce an uncertainty into the system that can degrade tracking of the reference model. An adaptive controller from the remaining control inputs is designed to reduce the effect of the uncertainty while maintaining a notion of performance optimization in the adaptive control system.

Adaptive Control↗

NASA NTRSDate not supplied

Robust Cislunar Trajectory Optimization Via Midcourse Correction and Optical Navigation Scheduling

This paper presents a new approach to optimal trajectory design that considers uncertainties in the system, referred to herein as robust trajectory optimization. This approach assumes an existing reference trajectory and optimizes the locations of midcourse correction burns and utilization of onboard navigation sensors to minimize dispersions in ∆v or final position. Navigation errors, maneuver execution errors, orbit insertion errors, and environmental modeling errors are considered. The application in this paper is cislunar flight with the goal of injecting into a Near-Rectilinear Halo Orbit for rendezvous with a target vehicle. Two complementary optimization problems are proposed. One problem minimizes the total ∆v dispersion subject to a final position dispersion constraint. The other problem minimizes the final position dispersion subject to a total ∆v dispersion constraint. The results from each optimization problem are shown for a complete mission profile.

Linear Covariance Analysis↗

NASA NTRS2015

Impact of AMS-02 Measurements on Reducing GCR Model Uncertainties

For vehicle design, shield optimization, mission planning, and astronaut risk assessment, the exposure from galactic cosmic rays (GCR) poses a significant and complex problem both in low Earth orbit and in deep space. To address this problem, various computational tools have been developed to quantify the exposure and risk in a wide range of scenarios. Generally, the tool used to describe the ambient GCR environment provides the input into subsequent computational tools and is therefore a critical component of end-to-end procedures. Over the past few years, several researchers have independently and very carefully compared some of the widely used GCR models to more rigorously characterize model differences and quantify uncertainties. All of the GCR models studied rely heavily on calibrating to available near-Earth measurements of GCR particle energy spectra, typically over restricted energy regions and short time periods. In this work, we first review recent sensitivity studies quantifying the ions and energies in the ambient GCR environment of greatest importance to exposure quantities behind shielding. Currently available measurements used to calibrate and validate GCR models are also summarized within this context. It is shown that the AMS-II measurements will fill a critically important gap in the measurement database. The emergence of AMS-II measurements also provides a unique opportunity to validate existing models against measurements that were not used to calibrate free parameters in the empirical descriptions. Discussion is given regarding rigorous approaches to implement the independent validation efforts, followed by recalibration of empirical parameters.

Slaba, T. C.↗

NASA NTRS2001

Optimal Control Design Using an H2 Method for the Glovebox Integrated Microgravity Isolation Technology (g-LIMIT)

The acceleration environment on the International Space Station (ISS) will likely exceed the requirements of many micro-gravity experiments. The Glovebox Integrated Microgravity Isolation Technology (g-LIMIT) is being built by the NASA Marshall Space Flight Center to attenuate the nominal acceleration environment and provide some isolation for micro-gravity science experiments. G-LIMIT uses Lorentz (voice-coil) magnetic actuators to isolate a platform for mounting science payloads from the nominal acceleration environment. The system utilizes payload acceleration, relative position, and relative orientation measurements in a feedback controller to accomplish the vibration isolation task. The controller provides current command to six magnetic actuators, producing the required experiment isolation from the ISS acceleration environment. This paper presents the development of a candidate control law to meet the acceleration attenuation requirements for the g-LIMIT experiment platform. The controller design is developed using linear optimal control techniques for both frequency-weighted H(sub 2) and H(sub infinity) norms. Comparison of the performance and robustness to plant uncertainty for these two optimal control design approaches are included in the discussion.

Calhoun, Phillip C.↗

NASA NTRSDate not supplied

Probing Sensitivity of Discharge Characteristics to Model Selection using Uncertainty Quantification in an aprotic Li-Oxygen Battery

Currently, there are several models in the literature, such as kinetic models, microstructural models, and mass transport models that describe a Li-air battery's discharge behavior. Many of these models are calibrated and tested at low current densities and cannot be easily transferred to high current densities. Even at low current densities, there is no quantitative method for a researcher to choose a reaction kinetic model such as classical Butler-Volmer and its derivatives, and modified Marcus-Hush-Chidsey, a resistance model for lithium peroxide such as electron transport via tunneling or linear resistivity, a surface coverage model (lithium peroxide growth) such as partial coverage or full coverage, and mass transport model (discussed in Ref. [1]). Also, it is time-consuming to test different models at high current density (1C) due to a lack of well-tested models and well-calibrated model parameters. For this presentation, we will develop an analytical model, which acts as a surrogate model for a sophisticated finite element model to predict discharge time and discharge voltage. Next, we use an uncertainty quantifying technique called reduced-order stochastic optimization [2, 3] to determine the uncertainty in model parameters for rate kinetics, lithium peroxide resistivity, and parasitic resistance. Finally, a finite element simulation is performed to determine the error introduced by the surrogate model and its influence on the uncertainty in the model parameters.

M Mehta↗

NASA NTRS2017

Impact of Archeomagnetic Field Model Data on Modern Era Geomagnetic Forecasts

A series of geomagnetic data assimilation experiments have been carried out to demonstrate the impact of assimilating archeomagnetic data via the CALS3k.4 geomagnetic field model from the period between 10 and 1590 CE. The assimilation continues with the gufm1 model from 1590 to 1990 and CM4 model from 1990 to 2000 as observations, and comparisons between these models and the geomagnetic forecasts are used to determine an optimal maximum degree for the archeomagnetic observations, and to independently estimate errors for these observations. These are compared with an assimilation experiment that uses the uncertainties provided with CALS3k.4. Optimal 20 year forecasts in 1990 are found when the Gauss coefficients up to degree 3 are assimilated. In addition we demonstrate how a forecast and observation bias correction scheme could be used to reduce bias in modern era forecasts. Initial experiments show that this approach can reduce modern era forecast biases by as much as 50 percent.

Tangborn, Andrew↗