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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

POPSnet-SGP: A Pilot Aerosol Microphysics Network for Targeting Climate Model Uncertainty Interim Field Campaign Report

Aerosols mediate the radiative fluxes in clear and cloudy skies and dominate the uncertainty in the radiative forcing of climate. Relatively dense networks of aerosol optical depth measurements have been used to effectively constrain simulated aerosol optical properties, but model diversity of aerosol microphysical properties is much larger (Mann et al. 2014, Myhre et al. 2009). A better understanding of aerosol microphysics is essential as they are the fundamental pieces of information required to convert aerosol emissions information to radiative and cloud-nucleating properties that drive radiative effects and forcing of climate. Model representation of aerosol microphysical properties has lagged in part due to their inherently greater spatial variability, but also due to a lack of available observations. This project – the Printed Optical Particle Spectrometer Network (POPSnet)-Southern Great Plains (SGP) Pilot – launched the first spatially dense network of aerosol size distribution measurements over an area the size of a global model grid cell and demonstrated its use in providing valuable information regarding the spatial variability of aerosol microphysical properties and its drivers for improving model constraints.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Predicting Cognitive States Using Machine Learning Fusion Paradigms to Reduce Model Uncertainty

The development of a synergetic system between humans and technology is a challenge that the scientific community has been facing for many years. Our aeronautic research aims to enhance this synergy between humans and machines through predictive human performance modeling for systems to mitigate high-risk situations. By being able to predict and anticipate human states, the crew monitoring system should be able to adjust and support the pilot for aviation safety. Our work focusing on attention-related human performance-limiting states (AHPLS) that impact a pilot’s performance and introduce high-risk catastrophic situations [1]. For example, AHPLS has been cited as a causal factor in more than 50% of all loss control in flight and thus contributes significantly toward commercial aviation fatalities [1, 2]. Cognitive state and its physiological fingerprint can be valuable information for this detecting AHPLS, but human cognitive state detection is still a major limitation for these crew monitoring systems.

machine learning↗
NASA NTRS1991

An experimental evaluation of uncertainty models for a flexible structure control problem

The authors describe results in applying robust control techniques of an active precision truss structure at the Jet Propulsion Laboratory. The active structure incorporates piezoelectric members which serve as both structural and actuator/sensor elements. Several characterizations of uncertainty are studied and the controllers are compared experimentally.

Smith, Roy S.↗
NASA NTRS2015

Ocean Heat and Carbon Uptake in Transient Climate Change: Identifying Model Uncertainty

Global warming on decadal and centennial timescales is mediated and ameliorated by the oceansequestering heat and carbon into its interior. Transient climate change is a function of the efficiency by whichanthropogenic heat and carbon are transported away from the surface into the ocean interior (Hansen et al. 1985).Gregory and Mitchell (1997) and Raper et al. (2002) were the first to identify the importance of the ocean heat uptakeefficiency in transient climate change. Observational estimates (Schwartz 2012) and inferences from coupledatmosphere-ocean general circulation models (AOGCMs; Gregory and Forster 2008; Marotzke et al. 2015), suggest thatocean heat uptake efficiency on decadal timescales lies in the range 0.5-1.5 W/sq m/K and is thus comparable to theclimate feedback parameter (Murphy et al. 2009). Moreover, the ocean not only plays a key role in setting the timing ofwarming but also its regional patterns (Marshall et al. 2014), which is crucial to our understanding of regional climate,carbon and heat uptake, and sea-level change. This short communication is based on a presentation given by A.Romanou at a recent workshop, Oceans Carbon and Heat Uptake: Uncertainties and Metrics, co-hosted by US CLIVARand OCB. As briefly reviewed below, we have incomplete but growing knowledge of how ocean models used in climatechange projections sequester heat and carbon into the interior. To understand and thence reduce errors and biases inthe ocean component of coupled models, as well as elucidate the key mechanisms at work, in the final section we outlinea proposed model intercomparison project named FAFMIP. In FAFMIP, coupled integrations would be carried out withprescribed overrides of wind stress and freshwater and heat fluxes acting at the sea surface.

periodic variations↗
DOE OSTI2024

Model uncertainty obscures major driver of soil carbon

Understanding the formation and stabilization mechanisms of soil organic carbon (SOC) is important for managing land carbon (C) and mitigating climate change. Tao et al. reported that microbial C use efficiency (CUE) is the primary determinant of global SOC storage and that the relative impact of plant C inputs on SOC is minor. Although soil microbes undoubtedly play an important role in SOC cycling, we are concerned about the robustness of the approach taken by Tao et al. In conclusion, the potential biases in their analyses may lead to misleading, model-dependent results.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2011

The Crucial Role of Error Correlation for Uncertainty Modeling of CFD-Based Aerodynamics Increments

The Ares I ascent aerodynamics database for Design Cycle 3 (DAC-3) was built from wind-tunnel test results and CFD solutions. The wind tunnel results were used to build the baseline response surfaces for wind-tunnel Reynolds numbers at power-off conditions. The CFD solutions were used to build increments to account for Reynolds number effects. We calculate the validation errors for the primary CFD code results at wind tunnel Reynolds number power-off conditions and would like to be able to use those errors to predict the validation errors for the CFD increments. However, the validation errors are large compared to the increments. We suggest a way forward that is consistent with common practice in wind tunnel testing which is to assume that systematic errors in the measurement process and/or the environment will subtract out when increments are calculated, thus making increments more reliable with smaller uncertainty than absolute values of the aerodynamic coefficients. A similar practice has arisen for the use of CFD to generate aerodynamic database increments. The basis of this practice is the assumption of strong correlation of the systematic errors inherent in each of the results used to generate an increment. The assumption of strong correlation is the inferential link between the observed validation uncertainties at wind-tunnel Reynolds numbers and the uncertainties to be predicted for flight. In this paper, we suggest a way to estimate the correlation coefficient and demonstrate the approach using code-to-code differences that were obtained for quality control purposes during the Ares I CFD campaign. Finally, since we can expect the increments to be relatively small compared to the baseline response surface and to be typically of the order of the baseline uncertainty, we find that it is necessary to be able to show that the correlation coefficients are close to unity to avoid overinflating the overall database uncertainty with the addition of the increments.

Hemsch, Michael J.↗
NASA NTRS2005

Modeling uncertainty in requirements engineering decision support

One inherent characteristic of requrements engineering is a lack of certainty during this early phase of a project. Nevertheless, decisions about requirements must be made in spite of this uncertainty. Here we describe the context in which we are exploring this, and some initial work to support elicitation of uncertain requirements, and to deal with the combination of such information from multiple stakeholders.

risk analysis↗
NASA NTRS2019

Modeling Uncertainty in Time and Fuel Benefit Estimation for TASAR Operational Evaluation

The Traffic Aware Strategic Aircrew Requests concept aims to reduce weather-induced delays, improve route efficiency, and efficiently share route modification options by combining onboard avionics data, Automatic Dependent Surveillance-Broadcast data, and broadband internet data to generate optimal, traffic-compatible trajectory changes based on real-time traffic and weather data. Time and fuel benefits due to use of the Traffic Aware Planner (TAP) software can be estimated by taking the difference in predicted flight time and fuel usage before and after a TAP-inspired trajectory change is completed. Although TAP’s optimization algorithm predicts flight and fuel usage based on the current flight route and weather data, it does not account for possible air traffic controller-initiated trajectory changes, reroutes due to sudden weather changes, or other pilot/controller actions that may occur during flight. This paper introduces an approach for quantifying the uncertainty in estimated time and fuel benefits.

Burris, Joseph↗