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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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NASA NTRS1982

Rain measurements from space using a modified Seasat-type radar altimeter

The incorporation in the 13.5 GHz Seasat-type radar altimeter of a mode to measure rain rate is investigated. Specifically, an algorithm is developed relating the echo power at the various range bins, to the rain rate taking into consideration Mie scattering and path attenuation. The dependence of the algorithm on rain drop size distribution and nonuniform rain structure are examined and associated uncertainties defined. A technique for obtaining drop size distribution through the measurements of power at the top of the raincell and power difference through the cell also is investigated together with an associated error analysis. A description of the minor hardware modifications to the basic Seasat design is given for implementing the rain measurements.

Goldhirsh, J.↗
NASA NTRS2006

Physics-based Tests to Identify the Accuracy of Solar Wind Ion Measurements: A Case Study with the Wind Faraday Cups

We present techniques for comparing measurements of velocity, temperature, and density with constraints imposed by the plasma physics of magnetized bi-Maxwellian ions. Deviations from these physics-based constraints are interpreted as arising from measurement errors. Two million ion spectra from the Solar Wind Experiment Faraday Cup instruments on the Wind spacecraft are used as a case study. The accuracy of velocity measurements is determined by the fact that differential flow between hydrogen and helium should be aligned with the ambient magnetic field. Modeling the breakdown of field alignment suggests velocity uncertainties are less than 0.16% in magnitude and 3deg in direction. Temperature uncertainty is found by examining the distribution of observed temperature anisotropies in high-beta solar wind intervals where the firehose, mirror, and cyclotron microinstabilities should drive the distribution to isotropy. The presence of a finite anisotropy at high beta suggests overall temperature uncertainties of 8%. Hydrogen and helium number densities are compared with the electron density inferred from observations of the local electron plasma frequency as a function of solar wind speed and year. We find that after accounting for the contribution of minor ions, the results are consistent with a systematic offset between the two instruments of 34%. The temperature and density methods are sensitive to non-Maxwellian features such as heat flux and proton beams and as a result are more suited to slow solar wind where these features are rare. These procedures are of general use in identifying the accuracy of observations from any solar wind ion instrument.

Kasper, J. C.↗
NASA NTRS2002

Approach for Input Uncertainty Propagation and Robust Design in CFD Using Sensitivity Derivatives

An implementation of the approximate statistical moment method for uncertainty propagation and robust optimization for quasi 3-D Euler CFD code is presented. Given uncertainties in statistically independent, random, normally distributed input variables, first- and second-order statistical moment procedures are performed to approximate the uncertainty in the CFD output. Efficient calculation of both first- and second-order sensitivity derivatives is required. In order to assess the validity of the approximations, these moments are compared with statistical moments generated through Monte Carlo simulations. The uncertainties in the CFD input variables are also incorporated into a robust optimization procedure. For this optimization, statistical moments involving first-order sensitivity derivatives appear in the objective function and system constraints. Second-order sensitivity derivatives are used in a gradient-based search to successfully execute a robust optimization. The approximate methods used throughout the analyses are found to be valid when considering robustness about input parameter mean values.

Putko, Michele M.↗
NASA NTRS2004

Picturing Data With Uncertainty

NASA is in the business of creating maps for scientific purposes to represent important biophysical or geophysical quantities over space and time. For example, maps of surface temperature over the globe tell scientists where and when the Earth is heating up; regional maps of the greenness of vegetation tell scientists where and when plants are photosynthesizing. There is always uncertainty associated with each value in any such map due to various factors. When uncertainty is fully modeled, instead of a single value at each map location, there is a distribution expressing a set of possible outcomes at each location. We consider such distribution data as multi-valued data since it consists of a collection of values about a single variable. Thus, a multi-valued data represents both the map and its uncertainty. We have been working on ways to visualize spatial multi-valued data sets effectively for fields with regularly spaced units or grid cells such as those in NASA's Earth science applications. A new way to display distributions at multiple grid locations is to project the distributions from an individual row, column or other user-selectable straight transect from the 2D domain. First at each grid cell in a given slice (row, column or transect), we compute a smooth density estimate from the underlying data. Such a density estimate for the probability density function (PDF) is generally more useful than a histogram, which is a classic density estimate. Then, the collection of PDFs along a given slice are presented vertically above the slice and form a wall. To minimize occlusion of intersecting slices, the corresponding walls are positioned at the far edges of the boundary. The PDF wall depicts the shapes of the distributions very dearly since peaks represent the modes (or bumps) in the PDFs. We've defined roughness as the number of peaks in the distribution. Roughness is another useful summary information for multimodal distributions. The uncertainty of the multi-valued data can also be interpreted by the number of peaks and the widths of the peaks as shown by the PDF walls.

Kao, David↗
NASA NTRS2002

Risk-Based Probabilistic Approach to Aeropropulsion System Assessment

In an era of shrinking development budgets and resources, where there is also an emphasis on reducing the product development cycle, the role of system assessment, performed in the early stages of an engine development program, becomes very critical to the successful development of new aeropropulsion systems. A reliable system assessment not only helps to identify the best propulsion system concept among several candidates, it can also identify which technologies are worth pursuing. This is particularly important for advanced aeropropulsion technology development programs, which require an enormous amount of resources. In the current practice of deterministic, or point-design, approaches, the uncertainties of design variables are either unaccounted for or accounted for by safety factors. This could often result in an assessment with unknown and unquantifiable reliability. Consequently, it would fail to provide additional insight into the risks associated with the new technologies, which are often needed by decision makers to determine the feasibility and return-on-investment of a new aircraft engine. In this work, an alternative approach based on the probabilistic method was described for a comprehensive assessment of an aeropropulsion system. The statistical approach quantifies the design uncertainties inherent in a new aeropropulsion system and their influences on engine performance. Because of this, it enhances the reliability of a system assessment. A technical assessment of a wave-rotor-enhanced gas turbine engine was performed to demonstrate the methodology. The assessment used probability distributions to account for the uncertainties that occur in component efficiencies and flows and in mechanical design variables. The approach taken in this effort was to integrate the thermodynamic cycle analysis embedded in the computer code NEPP (NASA Engine Performance Program) and the engine weight analysis embedded in the computer code WATE (Weight Analysis of Turbine Engines) with the fast probability integration technique (FPI). FPI was developed by Southwest Research Institute under contract with the NASA Glenn Research Center. The results were plotted in the form of cumulative distribution functions and sensitivity analyses and were compared with results from the traditional deterministic approach. The comparison showed that the probabilistic approach provides a more realistic and systematic way to assess an aeropropulsion system. The current work addressed the application of the probabilistic approach to assess specific fuel consumption, engine thrust, and weight. Similarly, the approach can be used to assess other aspects of aeropropulsion system performance, such as cost, acoustic noise, and emissions. Additional information is included in the original extended abstract.

Tong, Michael T.↗
NASA NTRS1979

H/+/ and He/2+/ in the dawnside magnetosheath

Mass and energy analyses of energetic ions in the dawnside magnetosheath have been made by the energetic ion composition experiment on the ISEE-1 spacecraft over the energy range 10 eV/e to 17 keV/e. The significant observations are: the bulk flows of the H(+) and He(2+) components determined from first moments of the distributions averaged over several minutes are equal within the uncertainties of the measurement. The H(+) and He(2+) distributions are similar in that they both show temperature anisotropies with perpendicular temperature not less than parallel temperature; however, the distributions are not well characterized by temperatures derived from second moments. In at least one case high energy tails of both distributions can be characterized by an additional higher temperature component. The major difference between the H(+) and He(2+) distributions is the depletion of the low velocity (in the center of mass frame) part of the He(2+) distributions, relative to the H(+) distributions.

Peterson, W. K.↗
NASA NTRS2001

Approach for Uncertainty Propagation and Robust Design in CFD Using Sensitivity Derivatives

This paper presents an implementation of the approximate statistical moment method for uncertainty propagation and robust optimization for a quasi 1-D Euler CFD (computational fluid dynamics) code. Given uncertainties in statistically independent, random, normally distributed input variables, a first- and second-order statistical moment matching procedure is performed to approximate the uncertainty in the CFD output. Efficient calculation of both first- and second-order sensitivity derivatives is required. In order to assess the validity of the approximations, the moments are compared with statistical moments generated through Monte Carlo simulations. The uncertainties in the CFD input variables are also incorporated into a robust optimization procedure. For this optimization, statistical moments involving first-order sensitivity derivatives appear in the objective function and system constraints. Second-order sensitivity derivatives are used in a gradient-based search to successfully execute a robust optimization. The approximate methods used throughout the analyses are found to be valid when considering robustness about input parameter mean values.

Putko, Michele M.↗
NASA NTRS2017

The Velocity and Density Distribution of Earth-Intersecting Meteoroids: Implications for Environment Models

Meteoroids are known to damage spacecraft: they can crater or puncture components, disturb a spacecraft's attitude, and potentially create secondary electrical effects. Because the damage done depends on the speed, size, density, and direction of the impactor, accurate environment models are critical for mitigating meteoroid-related risks. Yet because meteoroid properties are derived from indirect observations such as meteors and impact craters, many characteristics of the meteoroid environment are uncertain. In this work, we present recent improvements to the meteoroid speed and density distributions. Our speed distribution is derived from observations made by the Canadian Meteor Orbit Radar. These observations are de-biased using modern descriptions of the ionization efficiency. Our approach yields a slower meteoroid population than previous analyses (see Fig. 1 for an example) and we compute the uncertainties associated with our derived distribution. We adopt a higher fidelity density distribution than that used by many older models. In our distribution, meteoroids with TJ less than 2 are assigned to a low-density population, while those with TJ greater than 2 have higher densities (see Fig. 2). This division and the distributions themselves are derived from the densities reported by Kikwaya et al. These changes have implications for the environment: for instance, the helion/antihelion sporadic sources have lower speeds than the apex and toroidal sources and originate from high-T(sub J) parent bodies. Our on-average slower and denser distributions thus imply that the helion and antihelion sources dominate the meteoroid environment even more completely than previously thought. Finally, for a given near-Earth meteoroid cratering rate, a slower meteoroid population produces a comparatively higher rate of satellite attitude disturbances.

Moorhead, A. V.↗
NASA NTRSDate not supplied

Probabilistic Modeling of a Three-Stage Human Landing System Architecture

Space Policy Directive-1 has led to NASA partnerships with commercial entities on procurement which includes the development of the Human Landing System (HLS) [1]. With the goal of delivering human crew to the lunar surface by 2024, system uncertainties become an important obstacle to the maturation of multiple new, driving technologies and mission concepts of the HLS program. As unmitigated uncertainties have previously led to failed development programs, these risks and their impacts must be understood and handled to ensure program success [2]. Sources of uncertainty include novel engine designs and configurations, increased reliance on cryogenic fluid management(CFM), and refueling technologies—which propagate as high-level performance metrics such as overall propellant mass and engine performance. Also, the occurrence of operational uncertainties—e.g. launch conditions or need to abort during the mission—can cause cascading effects on the rest of the mission that are difficult to definitively quantify, and are outside the scope of control. These concrete examples and other occurrences can be categorized as either epistemic or aleatory uncertainties.Epistemic uncertainty arises due to a lack of knowledge and can be alleviated with design and program maturation. Aleatory uncertainty is due to the inherent randomness of the system and cannot be directly reduced, unlike epistemic uncertainty. Robust design and probabilistic methods can compensate for aleatory effects. A taxonomy of uncertainty is referred to for this work [3]. In this paper, a probabilistic methodology to handle uncertainties has been demonstrated on a three-element HLS concept [1, 4], which allows tracking of current best estimates of the concept and assessment of concept design robustness against uncertainties. A sample case has been completed for this abstract, and an expansion on the methodology will be included in the final paper. This methodology has two key parts: first, the creation of a dynamic architecture model of a three-element HLS concept; and second, its use with surrogate modeling and range estimating techniques to capture and propagate uncertainties. This abstract will cover the basics of the approach used, and further details and justifications will be in the final paper.The mission profile associated with this three-element concept (Fig 1) was modeled as a set of mission events that facilitated mass changes, idles, or spacecraft maneuvers. The mission profile scope starts with each element’s NRHO orbit insertion and aggregation and ends at post-sortie rendezvous with Orion. More detail on the mission profile will be in the final paper. The DYnamic Rocket EQuation Tool (DYREQT), a space systems synthesis and sizing framework used by NASA, was used as the physics framework to model the HLS architecture for applying the probabilistic methodology [5, 6]. Specifically, a parametric representation of the lander, ascent, and transfer elements and the mission profile of each element was established, with vehicle and mission parameters available as inputs to allow for a dynamic model. Each vehicle stage was modeled with high-level performance metrics, using Isp and propellant mass fraction (PMF) to remain parametric. For the probabilistic analysis, uncertainties of interest within the HLS concept were enumerated and represented as parameters within the DYREQT model as inputs for vehicle stages or mission profile events. These parameters were frozen at their nominal values for the purposes of baselining architecture performance and sizing the vehicle appropriately based on reference documentation [1]. Range estimating—a probabilistic method that combines Monte Carlo sampling, focus on critical parameters, and heuristics to assess risk and opportunities—is traditionally used with Mass Equipment Lists (MELs), but has been adapted with operational parameters as well as vehicle parameters in theDYREQT model to capture mission uncertainty alongside vehicle uncertainty [7, 3]. This method was selected due to its application and insight on a system from a bottom-up perspective, independence from historical rules of thumb, and ability to generate sensitivities based on design decisions and uncertainties. As a sample case for the abstract, the boiloff rates of the vehicle elements and the loiter times during the mission (simulating launch time variations and changing window of opportunities) were used with range estimating to provide preliminary results. To perform the range estimation portion of this methodology (depicted in Fig. 3, further details in final paper), the DYREQT model was sampled using a Design of Experiments (DoE) to efficiently explore the architecture design space with respect to the sample set of uncertainty parameters; 5,000 cases via Latin Hypercube Sampling were computed on the DYREQT architecture model. Then, the results were used to create surrogate models, multivariate regressions that can visualize hypercube trends in the design space, of the architecture with respect to the uncertainty parameters. Range estimating was applied to the surrogates instead of the actual models, which saves computational expense due to the bulk of cases needed for the Monte Carlo simulation as part of range estimating. Uncertainty parameters were sampled independently from triangular distributions using the DoE ranges as ‘min’ and ‘max’, and the nominal value as ‘most likely’. Based engineering intuition, some uncertainty parameters are correlated—e.g. if the main propellant has a high boil-off rate, the oxidizer should follow suit as both are related to CFM technology.While a Monte Carlo simulation samples all inputs as independent, the results would show model correlations; thus, it is efficient to sample the inputs as correlated. Using a correlation matrix constructed for the uncertainty parameters, previously independent samples were transformed to perform a Correlated Monte Carlo. A table for the DoE ranges and probability distribution parameters is shown in Table 1, and more details on Correlated Monte Carlo Simulations will be discussed in the final paper. The model’s resulting DoE showed that multivariate polynomial equations fit via least squares method captured its behavior accurately for the sample case. For the Correlated Monte Carlo Simulation, a positive correlation between fuel and oxidizer boiloff rates was used as a demonstration. 10,000 cases were computed with the surrogates and the launched masses for each vehicle element was collated. The results can be displayed in a probability density function (PDF), showing the impact of the uncertainty parameters chosen. Integrating the PDFs will yield a cumulative distribution function (CDF) that shows the cumulative probability of a given value on the x-axis. For the sample case, the elements’ launch mass margin was calculated and represented in as CDFs, as a demonstrated representation of figures of merit for the HLS concept. For the lander and ascent elements, the NRHO mass insertion limit is 16t; the transfer element has a limit of 30t [1]. It can be seen with Figure 2 that this probabilistic methodology can provide insight into mass margin with respect to the uncertainties being modeled. Currently, the results show that the lander (descent) vehicle element has the most restrictive design space; it is the only element to show a 10% probability of negative margin. Further analysis on the Monte Carlo results will show sensitivities for driving constraints and parameters for architecture feasibility, which can lead to establishing potential mission rules.The combination of range estimating with a parametric architecture model for HLS demonstrated the capability of this probabilistic methodology in a sample case. As the HLS development progresses, this methodology has the potential for keeping current best estimates of architecture performance for awarded concepts due to the flexibility in DYREQT’s modeling framework and its parametric nature. Concept maturation and increased epistemic knowledge can be injected into the model probabilistic modeling, and thus continue to track probability of mission success.

Stephanie Y Zhu↗
NASA NTRS2012

Mapping of CO2 at High Spatiotemporal Resolution using Satellite Observations: Global distributions from OCO-2

Satellite observations of CO2 offer new opportunities to improve our understanding of the global carbon cycle. Using such observations to infer global maps of atmospheric CO2 and their associated uncertainties can provide key information about the distribution and dynamic behavior of CO2, through comparison to atmospheric CO2 distributions predicted from biospheric, oceanic, or fossil fuel flux emissions estimates coupled with atmospheric transport models. Ideally, these maps should be at temporal resolutions that are short enough to represent and capture the synoptic dynamics of atmospheric CO2. This study presents a geostatistical method that accomplishes this goal. The method can extract information about the spatial covariance structure of the CO2 field from the available CO2 retrievals, yields full coverage (Level 3) maps at high spatial resolutions, and provides estimates of the uncertainties associated with these maps. The method does not require information about CO2 fluxes or atmospheric transport, such that the Level 3 maps are informed entirely by available retrievals. The approach is assessed by investigating its performance using synthetic OCO-2 data generated from the PCTM/ GEOS-4/CASA-GFED model, for time periods ranging from 1 to 16 days and a target spatial resolution of 1deg latitude x 1.25deg longitude. Results show that global CO2 fields from OCO-2 observations can be predicted well at surprisingly high temporal resolutions. Even one-day Level 3 maps reproduce the large-scale features of the atmospheric CO2 distribution, and yield realistic uncertainty bounds. Temporal resolutions of two to four days result in the best performance for a wide range of investigated scenarios, providing maps at an order of magnitude higher temporal resolution relative to the monthly or seasonal Level 3 maps typically reported in the literature.

Hammerling, Dorit M.↗
NASA NTRS1996

Experimental and Computational Aerothermodynamics of a Mars Entry Vehicle

An aerothermodynamic database has been generated through both experimental testing and computational fluid dynamics simulations for a 70 deg sphere-cone configuration based on the NASA Mars Pathfinder entry vehicle. The aerothermodynamics of several related parametric configurations were also investigated. Experimental heat-transfer data were obtained at hypersonic test conditions in both a perfect gas air wind tunnel and in a hypervelocity, high-enthalpy expansion tube in which both air and carbon dioxide were employed as test gases. In these facilities, measurements were made with thin-film temperature-resistance gages on both the entry vehicle models and on the support stings of the models. Computational results for freestream conditions equivalent to those of the test facilities were generated using an axisymmetric/2D laminar Navier-Stokes solver with both perfect-gas and nonequilibrium thermochemical models. Forebody computational and experimental heating distributions agreed to within the experimental uncertainty for both the perfect-gas and high-enthalpy test conditions. In the wake, quantitative differences between experimental and computational heating distributions for the perfect-gas conditions indicated transition of the free shear layer near the reattachment point on the sting. For the high enthalpy cases, agreement to within, or slightly greater than, the experimental uncertainty was achieved in the wake except within the recirculation region, where further grid resolution appeared to be required. Comparisons between the perfect-gas and high-enthalpy results indicated that the wake remained laminar at the high-enthalpy test conditions, for which the Reynolds number was significantly lower than that of the perfect-gas conditions.

Hollis, Brian R.↗
NASA NTRS1981

Potential of dual-measurement techniques for accurate determination of instantaneous rainfall rate from space

The incorporation in the 13.5 GHz SEASAT type radar altimeter of a mode to measure rain rate is investigated. Specifically, an algorithm is developed relating the echo power at the various range bins to the rain rate, taking into consideration Mie scattering and path attenuation. The dependence of the algorithm on rain drop size distribution, and non-uniform rain structure are examined and associated uncertainties defined. A technique for obtaining drop size distribution through the measurements of power at the top of the raincell and power difference through the cell is also investigated together with an associated error analysis. A description of the minor hardware modifications to the basic SEASAT design is given for implementing the rain measurements.

Ulbrich, C. W.↗
NASA NTRS2010

Five-Year Wilkinson Microwave Anisotropy Probe Observations: Data Processing, Sky Maps, and Basic Results

We present new full-sky temperature and polarization maps in five frequency bands from 23 to 94 GHz, based on data from the first five years of the Wilkinson Microwave Anisotropy Probe (WMAP) sky survey. The new maps are consistent with previous maps and are more sensitive. The five-year maps incorporate several improvements in data processing made possible by the additional years of data and by a more complete analysis of the instrument calibration and in-flight beam response. We present several new tests for systematic errors in the polarization data and conclude that W-band polarization data is not yet suitable for cosmological studies, but we suggest directions for further study. We do find that Ka-band data is suitable for use; in conjunction with the additional years of data, the addition of Ka band to the previously used Q- and V-band channels significantly reduces the uncertainty in the optical depth parameter, tau. Further scientific results from the five-year data analysis are presented in six companion papers and are summarized in Section 7 of this paper. With the five-year WMAP data, we detect no convincing deviations from the minimal six-parameter ACDM model: a flat universe dominated by a cosmological constant, with adiabatic and nearly scale-invariant Gaussian fluctuations. Using WMAP data combined with measurements of Type Ia supernovae and Baryon Acoustic Oscillations in the galaxy distribution, we find (68% CL uncertainties): OMEGA(sub b)h(sup 2) = 0.02267(sup +0.00058)(sub -0.00059), OMEGA(sub c)h(sup 2) = 0.1131 plus or minus 0.0034, OMEGA(sub logical and) = 0.726 plus or minus 0.015, ns = .960 plus or minus 0.013, tau = 0.84 plus or minus 0.016, and DELTA(sup 2)(sub R) = (22.445 plus or minus 0.096) x 10(exp -9) at k = 0.002 Mpc(exp -1). From these we derive sigma(sub 8) = 0.812 plus or minus 0.026, H(sub 0) = 70.5 plus or minus 1.3 kilometers per second Mpc(exp -1), OMEGA(sub b) = 0.0456 plus or minus 0.0015, OMEGA(sub c) = .228 plus or minus 0.013, OMEGA(sub m)h(sup 2) = 0.1358(sup +0.0037)(sub -0.0036), z reion = 10.9 plus or minus 1.4, and t(sub 0) = 13.72 plus or minus 0.12 Gyr. The new limit on the tensor-to-scalar ration is r less than 0.22 (95% CL), while the evidence for a running spectral index is insignificant, dn(sub s)/d ln k = -0.028 plus or minus 0.020 (68% CL). We obtain tight, simultaneous limits on the (constant) dark energy equation of state and the spatial curvature of the universe: -0.14 less than 1 + w less than 0.12 (95% CL) and -0.0179 less than OMEGA(sub k) less than 0.0081 (95% CL). The number of relativistic degrees of freedom, expressed in units of the effective number of neutrino species, is found to be N(sub eff) = 4.4 plus or minus 1.5 (69% CL), consistent with the standard value of 3.04. Models with N(sub eff) = 0 are disfavored at greater than 99% confidence. Finally, new limits on physically motivated primordial non-Gaussianity parameters are -9 less than f(sup local)(sub NL) less than 111 (95% CL) and -151 less than f(sup equal)(sub NL) less than 253 (95% CL) for the local and equilateral models, respectively.

Hinshaw, G.↗
NASA NTRS2021

Projected land ice contributions to twenty-first-century sea level rise

The land ice contribution to global mean sea level rise has not yet been predicted1 using ice sheet and glacier models for the latest set of socio-economic scenarios, nor using coordinated exploration of uncertainties arising from the various computer models involved. Two recent international projects generated a large suite of projections using multiple models2,3,4,5,6,7,8, but primarily used previous-generation scenarios9 and climate models10, and could not fully explore known uncertainties. Here we estimate probability distributions for these projections under the new scenarios11,12 using statistical emulation of the ice sheet and glacier models. We find that limiting global warming to 1.5 degrees Celsius would halve the land ice contribution to twenty-first-century sea level rise, relative to current emissions pledges. The median decreases from 25 to 13 centimetres sea level equivalent (SLE) by 2100, with glaciers responsible for half the sea level contribution. The projected Antarctic contribution does not show a clear response to the emissions scenario, owing to uncertainties in the competing processes of increasing ice loss and snowfall accumulation in a warming climate. However, under risk-averse (pessimistic) assumptions, Antarctic ice loss could be five times higher, increasing the median land ice contribution to 42 centimetres SLE under current policies and pledges, with the 95th percentile projection exceeding half a metre even under 1.5 degrees Celsius warming. This would severely limit the possibility of mitigating future coastal flooding. Given this large range (between 13 centimetres SLE using the main projections under 1.5 degrees Celsius warming and 42 centimetres SLE using risk-averse projections under current pledges), adaptation planning for twenty-first-century sea level rise must account for a factor-of-three uncertainty in the land ice contribution until climate policies and the Antarctic response are further constrained.

Tamsin L. Edwards↗
NASA NTRS1993

Physical model-set identification for robust control of flexible structures

An approach to dynamic system identification is presented taking into account the goal of enhancing robust control performance of flexible structures. Identification techniques are derived which take advantage of the physics of structural dynamics and can provide realistic bounds for all potential parameter uncertainties. The developed approach includes input optimization which distributes excitation energy in such a way that the influence of residual uncertainties on robust control performance is reduced.

Karlov, Valeri I.↗
NASA NTRS2024

Quantification of Uncertainty and Risk Sensitivity for Safety of Emerging Operations

The growing need to develop and deploy small unmanned aerial vehicles (sUAVs) for various applications in the airspace necessitates reliable tools to accurately predict the flight trajectories of the sUAVs. The knowledge of the predicted trajectories help decision makers anticipate potential conflict, assess the risk, and take appropriate risk mitigation actions. In addition, uncertainties in vehicle models, weather, and controller action further highlights the need for reliable prediction tools. In this project, the application of mixed sparse grid-based quadrature and generalized polynomial chaos(gPC) expansion method for uncertainty quantification and collision assessment in air traffic consisting of fixed-wing small unmanned aerial vehicles (sUAV) was studied. From the results obtained, it can be concluded that this provides a reliable framework to carry out quantitative conflict assessment in an unmanned air traffic, which when employed, can improve the functionalities of the unmanned traffic management system. It was observed that the results from the gPC expansion framework developed in the project can be utilized to conduct rapid probabilistic collision assessment for near real-time unmanned traffic management in the airspace. From the vehicle models, position updates, and wind-field data, a priori gPC based 3-σcon-fidence ellipses can provide estimates of potential conflict at some future instants. The computational costs scaled linearly when the uncertain inputs were fewer. Further, the largest allowable distribution of para-metric uncertainties that leads to the smallest risk of collision in traffic of small unmanned aerial vehicles could be calculated. The time of closest approach between two sUAVs can be established paving way for development of proactive mitigation strategies. The separation between the sUAVs was found to be most significantly affected by uncertainties in the maximum available thrusts, zero-lift drag coefficients, and wing planform areas of the sUAVs. The study of uncertain wind-fields indicated that a heterogeneous traffic mix resulted in an increased probability of conflict. Increased measurement update rate reduced the uncertain-ties in the trajectories of the vehicles, further reducing the probability of conflict but rapid updates of all vehicles in the airspace poses a stringent communication limitation. The gPC framework also provided the means to analyze vehicle impact (crash region) due to loss of control resulting from actuator failure in sUAS traffic, essentially to predict impact and crash zones for representative vehicles. The predicted regions when compared with non-participant density, provides a means to develop an early mitigation strategy, should the sUAV detect an imminent actuator failure.

Rajnish Bhusal↗
NASA NTRS2010

Sources of Uncertainty in Predicting Land Surface Fluxes Using Diverse Data and Models

In the domain of predicting land surface fluxes, models are used to bring data from large observation networks and satellite remote sensing together to make predictions about present and future states of the Earth. Characterizing the uncertainty about such predictions is a complex process and one that is not yet fully understood. Uncertainty exists about initialization, measurement and interpolation of input variables; model parameters; model structure; and mixed spatial and temporal supports. Multiple models or structures often exist to describe the same processes. Uncertainty about structure is currently addressed by running an ensemble of different models and examining the distribution of model outputs. To illustrate structural uncertainty, a multi-model ensemble experiment we have been conducting using the Terrestrial Observation and Prediction System (TOPS) will be discussed. TOPS uses public versions of process-based ecosystem models that use satellite-derived inputs along with surface climate data and land surface characterization to produce predictions of ecosystem fluxes including gross and net primary production and net ecosystem exchange. Using the TOPS framework, we have explored the uncertainty arising from the application of models with different assumptions, structures, parameters, and variable definitions. With a small number of models, this only begins to capture the range of possible spatial fields of ecosystem fluxes. Few attempts have been made to systematically address the components of uncertainty in such a framework. We discuss the characterization of uncertainty for this approach including both quantifiable and poorly known aspects.

Dungan, Jennifer L.↗
NASA NTRS2018

Attribution of Flux Partitioning Variations Between Land Surface Models over the Continental U.S.

Accurate quantification of the terrestrial evapotranspiration (ET) components of plant transpiration (T), soil evaporation (E) and evaporation of the intercepted water (I) is necessary for improving our understanding of the links between the carbon and water cycles. Recent studies have noted that, among the modeled estimates, large disagreements exist in the relative contributions of T, E and I to the total ET. As these models are often used in data assimilation environments for incorporating and extending ET relevant remote sensing measurements, understanding the sources of inter-model differences in ET components is also necessary for improving the utilization of such remote sensing measurements. This study quantifies the contributions of two key factors explaining inter-model disagreements to the uncertainty in total ET: (1) contribution of the local partitioning and (2) regional distribution of ET. The analysis is conducted by using outputs from a suite of land surface models in the North American Land Data Assimilation System (NLDAS) configuration. For most of these models, transpiration is the dominant component of the ET partition. The results indicate that the uncertainty in local partitioning dominates the inter-model spread in modeled soil evaporation E. The inter-model differences in T are dominated by the uncertainty in the distribution of ET over the Eastern U.S. and the local partitioning uncertainty in the Western U.S. The results also indicate that uncertainty in the T estimates is the primary driver of total ET errors. Over the majority of the U.S., the contribution of the two factors of uncertainty to the overall uncertainty is non-trivial.

uncertainty↗