Search NASA⌕ Search

SEARCH · Search NASA

Results for “Data and model uncertainty”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Assessment of Response Surface Models using Independent Confirmation Point Analysis

This paper highlights various advantages that confirmation-point residuals have over conventional model design-point residuals in assessing the adequacy of a response surface model fitted by regression techniques to a sample of experimental data. Particular advantages are highlighted for the case of design matrices that may be ill-conditioned for a given sample of data. The impact of both aleatory and epistemological uncertainty in response model adequacy assessments is considered.

DeLoach, Richard↗
NASA NTRSDate not supplied

NASA Earth Systems Digital Twins (ESDT)

"Similarly to artificial intelligence, which is now revolutionizing many aspects of our daily lives, Earth system digital twin technologies have the potential to revolutionize the way Earth Science research will be conducted in the future, and how results and knowledge from this research will provide information to support decision making and yield impactful societal benefits. An Earth System Digital Twin or ESDT is a dynamic and interactive information system that first provides a digital replica of the past and current states of the Earth or Earth system as accurately and timely as possible; second, allows for computing forecasts of future states under nominal assumptions and based on the current replica; and third, offers the capability to investigate many hypothetical scenarios under varying impact assumptions. In other words, an ESDT provides the integrated What-Now, What-Next, and What-If pictures of the Earth or Earth system, by continuously ingesting newly observed data and by leveraging multiple interconnected models, machine learning as well advanced computing and visualization capabilities. Digital twins have been developed in engineering since 2002, but the interest in digital twins for the Earth domain is more recent and stems from the convergence of several developments: - The huge amount of diverse data that has now been collected continuously for more than 50 years, and that is becoming more and more difficult to access, understand, and utilize. - At the same time, because of climate change and its impacts the information produced by all of this data is becoming of interest to many new non-traditional users for analyzing and predicting various phenomena. - Because of advances in computational and visualization capabilities and the parallel unprecedented development of machine learning (ML), extracting relevant information from these large amounts of data and running complex models faster has become possible. As a result, it is becoming necessary and possible to build intuitive and interactive frameworks that will enable users with various skill levels and/or organizational hierarchy levels to easily access large amounts of targeted information along with the relevant tools and models (Earth system and human activity models), to support them in analyzing and visualizing this information, to help them understand interactions among models, to visualize the potential outcomes of various impacts, and to support decision or policy making. The full power of digital twins is that, through an integrated representation and standardized tools and software technologies, the same digital replica can address the needs of multiple users at various resolutions (spatial and temporal) and for various applications (science, economic, policy, etc.) – “from farmer to scientist”. With all these interests at stake, the challenges of building optimal digital twins are many and complex. The first challenge is to determine if a Digital Twin should be global or local, and multi-domain or thematic. For example, some domains such as Climate or Weather will require a global Digital Twin or Digital Twin capabilities while science areas such as Biodiversity might be more local. We can also envision that multiple thematic ESDTs, e.g., Air Quality, Wildfires, Hydrology could be federated or provide input to other ESDTs, either on a regional level or to a more global ESDT. Overall, we can imagine a future “web” of Digital Twins co-existing in a hierarchy or in a network, and capable of being connected or federated depending on the needs. This last point brings up the very important challenge of interoperability, including standards and protocols that will need to be built into these systems from the beginning. Each individual digital twin would have full flexibility in internal construction but would need standards-based interfaces (input and output) or hooks to make it compatible with others. Another challenge when building digital twins will be to decide how to organize each digital replica. Based on the applications targeted by the DT under implementation, various amounts and types of raw data, Analysis Ready Data (ARD) and information will need to be incorporated. Depending on the required latencies and needs of the users, various solutions can be considered, including Data Cubes, Data Lakes, pointers, or computing information on demand. We envision that each ESDT will choose a solution adapted to its specific objectives. Another important challenge is the type(s) of visualization that will be used, as well as the level of interactivity and refresh rate that will be required. Again, this will depend on the objectives of the ESDT, but also on the various users’ needs. In most cases, several types of visualizations and human interfaces will need to be offered depending on the projected users of that system. In parallel to the challenges highlighted above, there are also many tools and technologies that will need to be developed or improved for all types of digital twins. Among those are improved machine learning technologies, for example providing explainability, but also ML techniques for causality and providing a better integration of physics models. Additionally, reliable uncertainty quantification methods will be needed for all ESDT components, from validating data fusion and assimilation to assessing the accuracy of ML models and weighing the values of decisions supported by those systems. This presentation introduces the ESDT concept, presents several ESDT use cases, and a proposed ESDT architecture framework, as well as various technologies being developed by the Advanced Information Systems Technology (AIST) Program."

Earth Science Remote Sensing; Information Systems↗
NASA NTRSDate not supplied

An Inverse Chance-constrained Approach to the Calibration of Robust Models

This paper proposes a strategy to calibrate computational models according to uncertain input-output data. To this end, uncertainty in the data is first quantified by creating adversarial data sets. Samples drawn from such sets are then mapped from the input-output space to the parameter space using an inverse mapping. This mapping minimizes the collective output spread of an ensemble of point predictions while satisfying a set of individual data-matching requirements. The distribution of the resulting parameter points, which often exhibits strong parameter dependencies, is then modeled using sliced-normals. The chance-constrained formulation used to learn this distribution enables the analyst to trade-off a greater likelihood for most of the data against a lower likelihood for some of the data thereby relaxing the conservatism of the calibrated model. This formulation not only neglects the worst-performing quantiles of each adversarial distribution but also eliminates the potentially serious effects that outliers might have on the resulting model. This calibration approach not only has a considerably lower computational cost than the standard forward approach but it also allows for the identification of suitable distribution classes, which in turn yield better calibrated models.

Calibration↗
NASA NTRS1985

A study of the ozone photochemistry in the upper stratosphere using LIMS data

LIMS data at vernal equinox conditions are used to study the photochemistry of the upper stratosphere. The results indicate, and it has been recently reported, that with the use of recommended reaction rates, current models underestimate ozone mixing ratio by 20-40 percent. For ozone, good agreement with data is realized with the modification of six key reaction rates within the published limits of uncertainty. These modifications also yield better agreement with data for daytime NO2. Model results for other parameters such as the ratio HNO3/NO2, OH mixing ratio, and the temperature sensitivity of O3 are compared with data.

Natarajan, M.↗
NASA NTRS1972

Apollo 14 visibility tests: Visibility of lunar surface features and lunar landing

An in-flight visibility test conducted on the Apollo 14 mission is discussed. The need for obtaining experimental data on lunar feature visibility arose from visibility problems associated with various aspects of the Apollo missions; and especially from anticipated difficulties of recognizing lunar surface features at the time of descent and landing under certain illumination conditions. Although visibility problems have influenced many other aspects of the Apollo mission, they have been particularly important for descent operations, due to the criticality of this mission phase and the crew's guidance and control role for landing site recognition and touchdown point selection. A series of analytical and photographic studies were conducted during the Apollo program (prior to as well as after the initial manned lunar operations) to delineate constraints imposed on landing operations by visibility limitations. The purpose of the visibility test conducted on Apollo 14 was to obtain data to reduce uncertainties and to extend the analytical models of visibility in the lunar environment.

Ziedman, K.↗
NASA NTRS1988

Uncertainties in large space systems

Uncertainties of a large space system (LSS) can be deterministic or stochastic in nature. The former may result in, for example, an energy spillover problem by which the interaction between unmodeled modes and controls may cause system instability. The stochastic uncertainties are responsible for mode localization and estimation errors, etc. We will address the effects of uncertainties on structural model formulation, use of available test data to verify and modify analytical models before orbiting, and how the system model can be further improved in the on-orbit environment.

Fuh, Jon-Shen↗
NASA NTRS1997

Atmospheric Effects of Subsonic Aircraft: Interim Assessment Report of the Advanced Subsonic Technology Program

This first interim assessment of the subsonic assessment (SASS) project attempts to summarize concisely the status of our knowledge concerning the impacts of present and future subsonic aircraft fleets. It also highlights the major areas of scientific uncertainty, through review of existing data bases and model-based sensitivity studies. In view of the need for substantial improvements in both model formulations and experimental databases, this interim assessment cannot provide confident numerical predictions of aviation impacts. However, a number of quantitative estimates are presented, which provide some guidance to policy makers.

Friedl, Randall R.↗
NASA NTRS2000

Quantifying Vegetation Change in Semiarid Environments: Precision and Accuracy of Spectral Mixture Analysis and the Normalized Difference Vegetation Index

Because in situ techniques for determining vegetation abundance in semiarid regions are labor intensive, they usually are not feasible for regional analyses. Remotely sensed data provide the large spatial scale necessary, but their precision and accuracy in determining vegetation abundance and its change through time have not been quantitatively determined. In this paper, the precision and accuracy of two techniques, Spectral Mixture Analysis (SMA) and Normalized Difference Vegetation Index (NDVI) applied to Landsat TM data, are assessed quantitatively using high-precision in situ data. In Owens Valley, California we have 6 years of continuous field data (1991-1996) for 33 sites acquired concurrently with six cloudless Landsat TM images. The multitemporal remotely sensed data were coregistered to within 1 pixel, radiometrically intercalibrated using temporally invariante surface features and geolocated to within 30 m. These procedures facilitated the accurate location of field-monitoring sites within the remotely sensed data. Formal uncertainties in the registration, radiometric alignment, and modeling were determined. Results show that SMA absolute percent live cover (%LC) estimates are accurate to within ?4.0%LC and estimates of change in live cover have a precision of +/-3.8%LC. Furthermore, even when applied to areas of low vegetation cover, the SMA approach correctly determined the sense of clump, (i.e., positive or negative) in 87% of the samples. SMA results are superior to NDVI, which, although correlated with live cover, is not a quantitative measure and showed the correct sense of change in only 67%, of the samples.

Elmore, Andrew J.↗
NASA NTRS2009

Aerothermodynamic Design of the Mars Science Laboratory Heatshield

Aerothermodynamic design environments are presented for the Mars Science Laboratory entry capsule heatshield. The design conditions are based on Navier-Stokes flowfield simulations on shallow (maximum total heat load) and steep (maximum heat flux, shear stress, and pressure) entry trajectories from a 2009 launch. Boundary layer transition is expected prior to peak heat flux, a first for Mars entry, and the heatshield environments were defined for a fully-turbulent heat pulse. The effects of distributed surface roughness on turbulent heat flux and shear stress peaks are included using empirical correlations. Additional biases and uncertainties are based on computational model comparisons with experimental data and sensitivity studies. The peak design conditions are 197 W/sq cm for heat flux, 471 Pa for shear stress, 0.371 Earth atm for pressure, and 5477 J/sq cm for total heat load. Time-varying conditions at fixed heatshield locations were generated for thermal protection system analysis and flight instrumentation development. Finally, the aerothermodynamic effects of delaying launch until 2011 are previewed.

Edquist, Karl T.↗
NASA NTRS2009

A Mixed Integer Linear Program for Airport Departure Scheduling

Aircraft departing from an airport are subject to numerous constraints while scheduling departure times. These constraints include wake-separation constraints for successive departures, miles-in-trail separation for aircraft bound for the same departure fixes, and time-window or prioritization constraints for individual flights. Besides these, emissions as well as increased fuel consumption due to inefficient scheduling need to be included. Addressing all the above constraints in a single framework while allowing for resequencing of the aircraft using runway queues is critical to the implementation of the Next Generation Air Transport System (NextGen) concepts. Prior work on airport departure scheduling has addressed some of the above. However, existing methods use pre-determined runway queues, and schedule aircraft from these departure queues. The source of such pre-determined queues is not explicit, and could potentially be a subjective controller input. Determining runway queues and scheduling within the same framework would potentially result in better scheduling. This paper presents a mixed integer linear program (MILP) for the departure-scheduling problem. The program takes as input the incoming sequence of aircraft for departure from a runway, along with their earliest departure times and an optional prioritization scheme based on time-window of departure for each aircraft. The program then assigns these aircraft to the available departure queues and schedules departure times, explicitly considering wake separation and departure fix restrictions to minimize total delay for all aircraft. The approach is generalized and can be used in a variety of situations, and allows for aircraft prioritization based on operational as well as environmental considerations. We present the MILP in the paper, along with benefits over the first-come-first-serve (FCFS) scheme for numerous randomized problems based on real-world settings. The MILP results in substantially reduced delays as compared to FCFS, and the magnitude of the savings depends on the queue and departure fix structure. The MILP assumes deterministic aircraft arrival times at the runway queues. However, due to taxi time uncertainty, aircraft might arrive either earlier or later than these deterministic times. Thus, to incorporate this uncertainty, we present a method for using the MILP with "overlap discounted rolling planning horizon". The approach is based on valuing near-term decision results more than future ones. We develop a model of taxitime uncertainty based on real-world data, and then compare the baseline FCFS delays with delays using the above MILP in a simple rolling-horizon method and in the overlap discounted scheme.

Gupta, Gautam↗
NASA NTRS2022

Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary Landing

Hazard detection is critical for enabling autonomous landing on planetary surfaces. Current state-of-the-art methods leverage traditional computer vision approaches to automate the identification of safe terrain from input digital elevation models (DEMs). However, performance for these methods can degrade for input DEMs with increased sensor noise. In the last decade, deep learning techniques have been developed for various applications. Nevertheless, their applicability to safety-critical space missions has often been limited due to concerns regarding their outputs’ reliability. In response to these limitations, this paper proposes an application of the Bayesian deep learning segmentation method for hazard detection. The developed approach enables reliable, safe landing site detection by i) generating simultaneously a safety prediction map and its uncertainty map via Bayesian deep learning and semantic segmentation, and ii) using the uncertainty map to filter out the uncertain pixels in the prediction map so that the safe site identification is performed only based on the certain pixels (i.e., pixels for which the model is certain about its safety prediction). Experiments are presented with simulated data based on a Mars HiRISE digital terrain model by varying uncertainty threshold and noise levels to demonstrate the performance of the proposed approach.

Kento Tomita↗
NASA NTRS1989

Evaluating atmospheric correction models for retrieving surface temperatures from the AVHRR over a tallgrass prairie

The effects of atmospheric attenuation on surface radiative temperatures obtained by the AVHRR over a tallgrass prairie area in the Flint Hills of Kansas are examined. Six atmospheric correction models developed primarily for sea-surface temperature studies are used to test their utility for retrieval of radiative temperatures over the land surface. An uncertainty of + or - 3.0 C was found for the AVHRR data, and used to evaluate the performance of a given model. When the difference between in situ and AVHRR surface temperatures was smaller than the uncertainty, the model was judged to be adequate. Among the six models evaluated, only the NOAA split-window model consistently adjusted the AVHRR surface temperatures within + or - 3.0 C of the in situ measurements.

Cooper, D. I.↗
NASA NTRS2015

The Agricultural Model Intercomparison and Improvement Project: Phase I Activities by a Global Community of Science

The Agricultural Model Intercomparison and Improvement Project (AgMIP) was founded in 2010. Its mission is to improve substantially the characterization of world food security as affected by climate variability and change, and to enhance adaptation capacity in both developing and developed countries. The objectives of AgMIP are to: Incorporate state-of-the-art climate, crop/livestock, and agricultural economic model improvements into coordinated multi-model regional and global assessments of future climate impacts and adaptation and other key aspects of the food system. Utilize multiple models, scenarios, locations, crops/livestock, and participants to explore uncertainty and the impact of data and methodological choices. Collaborate with regional experts in agronomy, animal sciences, economics, and climate to build a strong basis for model applications, addressing key climate related questions and sustainable intensification farming systems. Improve scientific and adaptive capacity in modeling for major agricultural regions in the developing and developed world, with a focus on vulnerable regions. Improve agricultural data and enhance data-sharing based on their intercomparison and evaluation using best scientific practices. Develop modeling frameworks to identify and evaluate promising adaptation technologies and policies and to prioritize strategies.

farm crops↗
NASA NTRS1988

Two-temperature interpretation of dissociation rate data for N2 and O2

The existing experimental data on dissociation of nitrogen and oxygen obtained using shock-tubes during the 1960's and 1970's are reinterpreted using the two-temperature thermo-chemical model developed recently in order to determine the rate coefficients consistent with the model. In this model, the vibrational-electronic temperature is calculated by integrating a separate conservation equation accounting for the suppression of vibrational energy during dissociation due to preferential removal of high vibrational states. The rate coefficient is assumed to be a function of the geometrically-averaged temperature between the translational-rotational temperature and the vibrational-electronic temperature. By comparing the computed overall and species densities with the experimental data, the rate coefficient values most consistent with the model, and their ranges of uncertainty, are deduced for dissociation of N2 through collisions with N2 or N, and for O2 through collisions with O2, O or N2. It is seen that a single set of such rate coefficients fit all existing experimental data closely. According to the two-temperature model, density and species density are insensitive to the rate coefficients, and so the rate coefficients so determined are uncertain to within a factor of at least 1.5.

Park, Chul↗
NASA NTRS1973

Preliminary lift and drag characteristics of the F-8 supercritical wing airplane

A flight research program is being conducted using a supercritical wing attached to a modified F-8 fuselage to demonstrate the potential benefits of the supercritical wing concept in a realistic cruise flight environment. One of the primary goals of the flight program is to determine the drag-divergence Mach number, that is, the Mach number at which the total vehicle drag increases significantly. Another objective of the program is to show how well wind-tunnel data can be used to predict the performance of a supercritical wing configuration in the transonic Mach number region from 0. 9 to 1. 0. Comparisons of this nature are important in view of the uncertainties in the present methods for extrapolating wind-tunnel-model data to flight Reynolds numbers, determining the effect of wall interference on the model results, and simulating the flow separation characteristics on the model wing to correspond to the large-scale wing. However, in comparing these wind-tunnel and flight results, it must be taken into account that this supercritical wing test configuration is of only intermediate scale und that a full-scale transport would have Reynolds numbers three to five times larger.

Jon S. Pyle↗
NASA NTRSDate not supplied

Regional and Model-Specific Response Types in A Global Gridded Crop Model Ensemble

Crop models are often employed to project crop yields under changing conditions such as global warming and associated management change for adaptation. Multi-model ensembles are promoted to enhance the robustness of projections, but questions remain on what causes often large differences between projections of individual models. Global Gridded Crop Models (GGCMs) are especially exposed to this question when applied for assessing climate change impacts, adaptation, environmental impacts of agricultural production, because their results are used in downstream analyses, such as in integrated assessment or economic modeling for projecting future land-use change. Even though global gridded crop models are often based on detailed field-scale models or have implemented similar modeling principles in other ecosystem models, global-scale models are subject to substantial uncertainties from both model structure and parametrization as well as from calibration and input data quality. AgMIP’s Global Gridded Crop Model Intercomparison (GGCMI) has thus set out to intercompare GGCMs in order to evaluate model performance, describe model uncertainties, identify inconsistencies within the ensemble and underlying reasons, and to ultimately improve models and modeling capacities. In phase 2 of the GGCMI activities, 12 modeling groups followed a modeling protocol that asked for up to 1404 31-year global simulations at 0.5 arc-degree spatial resolution to assess models’ sensitivities to changes in carbon dioxide (C; 4 different levels) temperature (T; 7 different offset levels), water supply (W; 9 levels), and nitrogen (N; 3 levels), the so-called CTWN experiment (Franke et al. 2020; http://dx.doi.org/10.5194/gmd-13-2315-2020). We here present analyses of model response types using impact response surfaces along the C, T, W, and N dimensions, respectively and collectively. Doing so, we can understand differences in simulated responses per driver rather than aggregated changes in yields. We find that models’ sensitivities to the individual driver dimensions are substantially different and often more different across models than across regions. A cluster analysis finds regional and model-specific patterns. There is some agreement across models with respect to the spatial patterns of response types but strong differences in the distribution of response type clusters across models suggests that models need to undergo further scrutiny. We suggest establishing standards in model process evaluation not only against historical dynamics but also against dedicated experiments across the CTWN dimensions.

crop models↗
NASA NTRS1992

Comparison of Echo 7 field line length measurements to magnetospheric model predictions

The Echo 7 sounding rocket experiment injected electron beams on central tail field lines near L = 6.5. Numerous injections returned to the payload as 'conjugate echoes' after mirroring in the southern hemisphere. Field line lengths calculated from measured conjugate echo bounce times and energies are compared to predictions made by integrating electron trajectories through various magnetospheric models. Although Kp at launch was 3-, quiet time magnetic models best fit the echo measurements. Geosynchronous satellite magnetometer measurements near the Echo 7 field lines during the flight were best modeled by the Olson-Pfitzer Dynamic Model and the Tsyganenko-Usmanov model for Kp = 3. The discrepancy between the models that best fit the Echo 7 data and those that fit the satellite data was most likely due to uncertainties in the small-scale configuration of the magnetospheric models. The field line length measured by the conjugate echoes showed some temporal variation in the magnetic field, also indicated by the satellite magnetometers. This demonstrates the utility an Echo-style experiment could have in substorm studies.

Nemzek, R. J.↗
NASA NTRS2020

Ensemble Methodologies for Astronaut Cancer Risk Assessment in the face of Large Uncertainties

A new approach to NASA space radiation risk modeling has successfully extended the current NASA probabilistic cancer risk model to an ensemble framework able to consider sub-model parameter uncertainty (e.g. uncertainty in a radiation quality parameter) as well as model-form uncertainty associated with differing theoretical or empirical formalisms (e.g. combined dose-rate and radiation quality effects). Ensemble methodologies are already widely used in weather prediction, modeling of infectious disease outbreaks, and certain terrestrial radiation protection applications to better understand how uncertainty may influence risk decision-making. Applying ensemble methodologies to space radiation risk projections offers the potential to efficiently incorporate emerging research results, allow for the incorporation of future (including international) models, improve uncertainty quantification for underlying sub-models developed against sparse experimental data, and reduce the impact of subjective bias on risk projections. Moreover, risk forecasting across an ensemble of multiple predictive models can provide stakeholders additional information on risk acceptance if current health/medical standards cannot be met or the level of knowledge doesn’t permit a specific risk or exposure limit to be developed for future space exploration missions. In this work, ensemble risk projections implementing multiple sub-models of radiation quality, dose and dose-rate effectiveness factors, excess risk, and latency as ensemble members are presented. Initial consensus methods for ensemble model weights and correlations to account for individual model bias are discussed. In these analyses, the ensemble forecast compares well to results from NASA's current operational cancer risk projection model used to assess permissible exposure limits and permissible mission durations for astronauts. However, a large range of projected risk values are obtained at the upper 95th confidence level where models must extrapolate beyond available biological data sets; closer agreement is seen at the median + one sigma due to the inherent similarities in available models. Future work, including the addition of new models and methods for statistical correlation between predictive members are discussed to define alternate ways of thinking about risk and ‘acceptable’ uncertainty with respect to NASA’s current permissible exposure limits.

space radiation↗