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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 NTRS2022

Model Evaluation of Short-Lived Climate Forcers for the Arctic Monitoring and Assessment Programme: A Multi-Species, Multi-Model Study

While carbon dioxide is the main cause for global warming, modeling short-lived climate forcers (SLCFs) such as methane, ozone, and particles in the Arctic allows us to simulate near-term climate and health impacts for a sensitive, pristine region that is warming at 3 times the global rate. Atmospheric modeling is critical for understanding the long-range transport of pollutants to the Arctic, as well as the abundance and distribution of SLCFs throughout the Arctic atmosphere. Modeling is also used as a tool to determine SLCF impacts on climate and health in the present and in future emissions scenarios. In this study, we evaluate 18 state-of-the-art atmospheric and Earth system models by assessing their representation of Arctic and Northern Hemisphere atmospheric SLCF distributions, considering a wide range of different chemical species (methane, tropospheric ozone and its precursors, black carbon, sulfate, organic aerosol, and particulate matter) and multiple observational datasets. Model simulations over 4 years (2008-2009 and 2014-2015) conducted for the 2022 Arctic Monitoring and Assessment Programme (AMAP) SLCF assessment report are thoroughly evaluated against satellite, ground, ship, and aircraft-based observations. The annual means, seasonal cycles, and 3-D distributions of SLCFs were evaluated using several metrics, such as absolute and percent model biases and correlation coefficients. The results show a large range in model performance, with no one particular model or model type performing well for all regions and all SLCF species. The multi-model mean (mmm) was able to represent the general features of SLCFs in the Arctic and had the best overall performance. For the SLCFs with the greatest radiative impact (CH4, 03, BC, and SO(sup 2-)(sub 4)), the mmm was within ±25 % of the measurements across the Northern Hemisphere. Therefore, we recommend a multi-model ensemble be used for simulating climate and health impacts of SLCFs. Of the SLCFs in our study, model biases were smallest for C"4 and greatest for OA. For most SLCFs, model biases skewed from positive to negative with increasing latitude. Our analysis suggests that vertical mixing, long-range transport, deposition, and wildfires remain highly uncertain processes. These processes need better representation within atmospheric models to improve their simulation of SLCFs in the Arctic environment. As model development proceeds in these areas, we highly recommend that the vertical and 3-D distribution of SLCFs be evaluated, as that information is critical to improving the uncertain processes in models.

Arctic Monitoring and Assessment Programme↗
NASA NTRSDate not supplied

Visioning a Global Carbon Monitoring System That Can Quantify and Attribute Ocean Carbon Dioxide Removal: Where Are We Now and How Might We Get There?

As atmospheric greenhouse gas concentrations and the resulting social costs rise, it is increasingly important to conduct research on technical approaches to remove carbon dioxide from the atmosphere at the gigaton scale. One highly uncertain but perhaps plausible solution is ocean carbon dioxide (CO2) removal (CDR), which encompasses a suite of proposed techniques for increasing the net flux of CO2 from the atmosphere to the ocean. As reflected in a 2022 National Academy of Sciences report, interest in ocean CDR is rapidly growing, and new stakeholders from a wide range of fields want access to air-sea CO2 flux and ocean carbon information to understand the potential risks and benefits of ocean CDR. Moreover, there is an urgent need for research to develop a scientific basis to support these societal needs. At the same time, there is a growing need to vision a global carbon monitoring system that can quantify natural and anthropogenic perturbations in the air-sea CO2 flux to quantify and attribute the impacts of ocean CDR efforts on the ocean carbon sink. Here, we propose to present nascent efforts at the Ames Research Center to leverage NASA's unique remote sensing and computational capabilities and expertise in basic and applied Earth science to meet the needs of the ocean CDR stakeholder community as represented by the OceanVisions network. Specifically we aim to develop a global ocean CDR scenario explorer with two objectives: 1) to make quantitative estimates of the air-sea CO2 flux and its uncertainty more accessible to a wide range of users, and 2) to enable these diverse users to interactively explore the uncertain impacts on air-sea CO2 fluxes in a wide range of ocean CDR scenarios. The information would be provided to users via a public graphical user interface, and the results would be based on a new synthesis of scientific observations and knowledge in a global ocean mixed layer inverse model run on supercomputers. The proposed tool aims to fill a unique and valuable niche for users and the scientific community by balancing tradeoffs between user interests, scientific knowledge, and technical capabilities. We will conclude by visioning the potential implications for a global carbon monitoring system in a world with gigaton scale ocean CDR.

Global↗
NASA NTRSDate not supplied

Implementation of the Lifetime Method in Unsteady Pressure Sensitive Paint Measurements

At NASA Ames Research Center, unsteady pressure-sensitive paint (uPSP) measurements are obtained using the ‘intensity method’ which measures paint luminescence in response to a continuous, constant excitation. These measurements are obtained using high-speed cameras and are processed into fluctuating components of pressure. However, the nature of the intensity method also requires a separate steady state (time mean) pressure measurement to be obtained. This steady state measurement has typically been obtained using a separate set of PSP equipment that uses the ‘lifetime method’, which uses pulsed excitation to measure paint decay lifetime. If the lifetime method were implemented in the high-speed uPSP system, both the fluctuating and mean components of pressure could be obtained with a single system. This would greatly streamline setup, operations, and processing. In this paper, we describe work performed at the Fluid Mechanics Laboratory at NASA Ames to implement the lifetime method in our uPSP system. The uPSP acquisition system uses Phantom v2512 high-speed cameras, and it was initially uncertain if results of adequate quality could be obtained - their high framerate comes at the cost of several undesirable characteristics, which are explored in this paper. It was also uncertain if illumination using LED lamps, rather than a stronger source such as lasers, would be adequate. The data acquisition and data processing are discussed and the results analyzed. It was found that satisfactory lifetime method results can indeed be obtained using these high-speed cameras and LED lamps. This will allow the uPSP system to be greatly simplified and will have a large operational impact on how uPSP data is acquired in future wind tunnel tests.

Pressure Sensitive Paint↗
NASA NTRSDate not supplied

Implementation of the Lifetime Method in uPSP Measurements

At NASA Ames Research Center, unsteady pressure-sensitive paint (uPSP) measurements are obtained using the ‘intensity method’ which measures paint luminescence in response to a continuous, constant excitation. These measurements are obtained using high-speed cameras and are processed into fluctuating components of pressure. However, the nature of the intensity method also requires a separate steady state (time mean) pressure measurement to be obtained. This steady state measurement has typically been obtained using a separate set of PSP equipment that uses the ‘lifetime method’, which uses pulsed excitation to measure paint decay lifetime. If the lifetime method were implemented in the high-speed uPSP system, both the fluctuating and mean components of pressure could be obtained with a single system. This would greatly streamline setup, operations, and processing. In this paper, we describe work performed at the Fluid Mechanics Laboratory at NASA Ames to implement the lifetime method in our uPSP system. The uPSP acquisition system uses Phantom v2512 high-speed cameras, and it was initially uncertain if results of adequate quality could be obtained - their high framerate comes at the cost of several undesirable characteristics, which are explored in this paper. It was also uncertain if illumination using LED lamps, rather than a stronger source such as lasers, would be adequate. The data acquisition and data processing are discussed and the results analyzed. It was found that satisfactory lifetime method results can indeed be obtained using these high-speed cameras and LED lamps. This will allow the uPSP system to be greatly simplified and will have a large operational impact on how uPSP data is acquired in future wind tunnel tests.

Pressure Sensitive Paint↗
NASA NTRS2021

Stochastic Guidance of Buoyancy Controlled Vehicles under Ice Shelves using Ocean Currents

We propose a novel technique for guidance ofbuoyancy-controlled vehicles in uncertain under-ice ocean flows.In-situ melt rate measurements collected at the grounding zoneof Antarctic ice shelves, where the ice shelf meets the underlyingbedrock, are essential to constrain models of future sea levelrise. Buoyancy-controlled vehicles, which control their verticalposition in the water column but have no means of horizontalpropulsion, offer an affordable and reliable platform for suchin-situ data collection. However, reaching the grounding zonerequires vehicles to traverse tens of kilometers under the iceshelf, with approximate position knowledge and no meansof communication, in highly variable and uncertain oceancurrents. To address this challenge, we propose a partiallyobservable MDP approach that exploits model-based knowledgeof the under-ice currents and, critically, of their uncertainty,to synthesize effective guidance policies. The approach usesapproximate dynamic programming to model uncertainty inthe currents, and QMDP to address localization uncertainty.Numerical experiments show that the policy can deliver upto 88.8% of underwater vehicles to the grounding zone – a33% improvement compared to state-of-the-art guidance techniques, and a 262% improvement over uncontrolled drifters.Collectively, these results show that model-based under-iceguidance is a highly promising technique for exploration ofunder-ice cavities, and has the potential to enable cost-effectiveand scalable access to these challenging and rarely observed environments.

Clark, Evan B.↗
NASA NTRS2023

Risks of Synchronized Low Yields Are Underestimated in Climate and Crop Model Projections

Simultaneous harvest failures across major crop-producing regions are a threat to global food security. Concurrent weather extremes driven by a strongly meandering jet stream could trigger such events, but so far this has not been quantified. Specifically, the ability of state-of-the art crop and climate models to adequately reproduce such high impact events is a crucial component for estimating risks to global food security. Here we find an increased likelihood of concurrent low yields during summers featuring meandering jets in observations and models. While climate models accurately simulate atmospheric patterns, associated surface weather anomalies and negative effects on crop responses are mostly underestimated in bias-adjusted simulations. Given the identified model biases, future assessments of regional and concurrent crop losses from meandering jet states remain highly uncertain. Our results suggest that model-blind spots for such high-impact but deeply-uncertain hazards have to be anticipated and accounted for in meaningful climate risk assessments.

Atmospheric dynamics↗
NASA NTRSDate not supplied

Coupling of Dust Mineralogy to Radiation in NASA GISS ModelE: Impact Upon Dust Optical Properties and Direct Radiative Effect

Estimates of dust direct radiative effect (DRE) are still largely uncertain mainly due to the poor constraining of regional variations of dust absorption, whose known connection with mineral composition is still largely neglected by most Earth System Models. The inaccurate knowledge of soil mineralogy and insufficient measurements of dust physical and optical properties still present the main challenges for a proper representation of the dust-radiation interaction in climate models. We recently upgraded the coupling of dust minerals to shortwave radiation in the NASA GISS ModelE. In particular, we extended the interaction to online-calculated minerals and implemented two different mixing configurations: i) external mixing of three main mineral components and ii) internal mixing of all minerals, allowing a dynamic fraction of iron oxides. We used a semi-empirical approach for the optical calculations, mostly based on aerosol chamber measurements from natural soils, aiming to reduce the influence of uncertain assumptions on the optical properties and mixing state of individual minerals. In this contribution, we present the results of our first calculations of dust optical properties and DREs based on different mixing configurations of interactive minerals. We show the changes to dust optical depth and single scattering albedo, with respect to the control case using homogeneous dust composition, and the consequent global and regional variations in dust DREs at the top of atmosphere and at surface. We also compare the model optical properties with AERONET data, filtered to identify dust scenes. We find that accounting for the diverse properties of dust minerals modifies the pattern of regional dust absorption and therefore its impact upon climate. We also show that the mineralogy effect is sensitive to the assumed mixing state of minerals.

dust direct radiative effect↗
NASA NTRSDate not supplied

The Response of the Quasi-Biennial Oscillation to Increased CO and its Modulation by Composition Feedbacks

The Quasi-Biennial Oscillation (QBO) impacts the large-scale circulation by altering lower stratospheric meridional temperature gradients which alter the propagation of upward propagating planetary waves. Proposed teleconnections of the QBO include impacts on the stratospheric polar vortices, extratropical surface winter climate, and the Madden-Julian Oscillation. However, long-term projections of the QBO remain highly uncertain. While recent multi-model investigations (Richter et al. 2019, Butchart et al. 2020) show that the amplitude of the QBO weakens robustly among models, changes in QBO period and stability remain highly uncertain. Here we examine the QBO response to increased greenhouse gases using the NASA Goddard Institute for Space Studies Middle Atmosphere Model E2.2 (Rind et al., 2020; Orbe et al. 2020). Compared to lower vertical resolution versions of ModelE, E2.2 also has a higher model top (0.002 hPa) and employs additional interactive non-orographic gravity wave drag sources from convection and shear, which produce a sufficiently realistic QBO, thus rendering it suitable for use in climate change studies. Overall, we find that both the QBO period and amplitude decrease in response to increased CO , the former related to increased lower stratospheric momentum fluxes (associated with convection) and the latter associated partly with a stronger residual mean circulation. Experiments integrated using fully interactive chemistry also reveal that ozone feedbacks significantly impact the magnitude of the QBO amplitude response. Finally, integrations using fixed (pre-industrial) sea surface temperatures (SST) show that the QBO amplitude responds differently to rapid adjustments versus SST feedbacks, as compared to the QBO period.

Quasi-Biennial Oscillation↗
NASA NTRSDate not supplied

End-To-End Uncertainty Quantification with Analytical Derivatives for Design Under Uncertainty

Uncertainty quantification (UQ) is a rapidly growing and evolving discipline, especially within the aerospace community. Performing analysis with UQ can provide decision makers with a wealth of information about a candidate design. However, the value of UQ is fully realized when the information gained during UQ analysis is leveraged in a feedback loop of a design optimization process, often referred to as design under uncertainty. Although design under uncertainty can be a powerful risk mitigation technique, there are a number of roadblocks that prevent its implementation. Two primary factors are computational costs and added complexity of the analysis. High fidelity simulations on the order tens of uncertain variables quickly become computationally infeasible. Also, implementing UQ into an existing multidisciplinary design and optimization (MDO) process often requires extensive knowledge of the UQ methods and careful treatment of the problem formulation. The objective of this work is to address these two primary roadblocks and enable practitioners to efficiently perform design under uncertainty with limited knowledge of the UQ discipline. Methods outlined in this paper demonstrate MDO incorporating UQ into the design process, leveraging an analytic derivative tool chain through the entire optimization. The proposed approach leverages machine learning techniques to generate a differentiable confidence interval output from polynomial chaos models. This technique, coupled with the incorporation of analytical derivatives through the Polynomial Chaos Expansion (PCE) process, eliminates the need to estimate derivatives which are usually obtained from finite difference, complex step, or similar methods. Developing a differentiable confidence interval allows mixed uncertainty problems (both epistemic and aleatory) to be modeled. Without such modeling, these problems cannot accurately predict objective functions containing statistical quantities such as mean and variance. The addition of analytic derivatives to a polynomial chaos-based UQ method decreases the computational costs of performing design under uncertainty by orders of magnitude in comparison with methods such as complex step. The method and codes developed are modular in nature and are a drop-in solution for design under uncertainty within existing MDO problems. A low-fidelity analytical multidisciplinary optimization under uncertainty for a wing design in OpenMDAO is detailed in this paper. This demonstration case will include both objective functions and constraints which are influenced by uncertain parameters.

Ben D Phillips↗
NASA NTRSDate not supplied

End-To-End Uncertainty Quantification with Analytical Derivatives for Design Under Uncertainty

Uncertainty quantification (UQ) is a rapidly growing and evolving discipline, especially within the aerospace community. Performing analysis with UQ can provide decision makers with a wealth of information about a candidate design. However, the value of UQ is fully realized when the information gained during UQ analysis is leveraged in a feedback loop of a design optimization process, often referred to as design under uncertainty. Although design under uncertainty can be a powerful risk mitigation technique, there are a number of roadblocks that prevent its implementation. Two primary factors are computational costs and added complexity of the analysis. High fidelity simulations on the order tens of uncertain variables quickly become computationally infeasible. Also, implementing UQ into an existing multidisciplinary design and optimization (MDO) process often requires extensive knowledge of the UQ methods and careful treatment of the problem formulation. The objective of this work is to address these two primary roadblocks and enable practitioners to efficiently perform design under uncertainty with limited knowledge of the UQ discipline. Methods outlined in this paper demonstrate MDO incorporating UQ into the design process, leveraging an analytic derivative tool chain through the entire optimization. The proposed approach leverages machine learning techniques to generate a differentiable confidence interval output from polynomial chaos models. This technique, coupled with the incorporation of analytical derivatives through the Polynomial Chaos Expansion (PCE) process, eliminates the need to estimate derivatives which are usually obtained from finite difference, complex step, or similar methods. Developing a differentiable confidence interval allows mixed uncertainty problems (both epistemic and aleatory) to be modeled. Without such modeling, these problems cannot accurately predict objective functions containing statistical quantities such as mean and variance. The addition of analytic derivatives to a polynomial chaos-based UQ method decreases the computational costs of performing design under uncertainty by orders of magnitude in comparison with methods such as complex step. The method and codes developed are modular in nature and are a drop-in solution for design under uncertainty within existing MDO problems. A low-fidelity analytical multidisciplinary optimization under uncertainty for a wing design in OpenMDAO is detailed in this paper. This demonstration case will include both objective functions and constraints which are influenced by uncertain parameters.

Ben Phillips↗
NASA NTRSDate not supplied

Role of PHM in Autonomous Decision-Making: Aerospace applications

There is an increased need for onboard decision-making capabilities in cyber-physical systems be it in energy, automotive, aviation, space, or other industries as they aim for increased efficiency, resiliency, and mission assurance capabilities. Emerging next-gen technologies such as multi-rover planetary missions, distributed satellites, unmanned ground and aerial vehicle operations and smart grid systems rely on in-time risk assessment and autonomous decision-making. One critical piece of the autonomy puzzle is reliable prediction of system behavior under time-varying and potentially uncertain environmental conditions. Further, if agent states change during operation such as initiation of faults or degradation, reliable diagnostic tools need to be investigated. In this tutorial, we will revise approaches that integrates existing physics-based and data-driven models of agents interacting with probability models of the environment and component operation state. Role of existing PHM methodologies as they feed into decision-making under uncertainty will be studied. Balancing critical trade-offs between high-fidelity prognostic models, prediction time-horizons and the computational requirements for in-time cost-effective decision-making will be discussed through the implementation of surrogate models. Finally, the audience will be introduced to a real-time application of in-time trajectory planning of an unmanned aerial system (UAS) based on its PHM assessments under uncertain and varying wind conditions.

decision-making↗
NASA NTRS1962

Linearized Theory of Wind-tunnel Jet-boundary Corrections and Ground Effect for VTOL-STOL Aircraft

A linearized theory is developed to obtain interference factors for wind tunnels and ground effect. The calculated results, presented in tabular form in NASA Technical Notes D-933, D-934, D-935, and D-936, indicate that the degree to which the wake is deflected downward has a primary effect on the magnitude of the interference. When the wake is undeflected the corrections are essentially the same as those of classical theory. When the wake is deflected severely downward, the corrections are primarily determined by the floor of the wind tunnel. Under these conditions, the corrections are, in general, much larger than those given by previously available results. Because of deformations at the lower boundary of the jet, the corrections for an open wind tunnel are uncertain. Testing at low speeds and high lift coefficients in open wind tunnels, therefore, is not recommended. Although the theory is developed expressly for single-element, vanishingly small models, methods, with sample calculations, are given for extending the results to multielement and finite span models. The theory is at least partially verified by available test data; however, complete verification would require a substantially more detailed experimental study.

V/STOL aircraft↗
NASA NTRS1967

Reusable Aerospace Passenger Transport - Study of Incremental Development Approaches Summary Report

This report summarizes the results of studies of economical orbital transportation systems, exploring, in particular , possible options for time - phases and incremental development of such systems. The major conclusions are : 1. Incremental development is a practical way for development of an economic logistics system, minimizing development risk and annual funding, and offering planning flexibility at only a nominal penalty in total systems cost. 2. The most promising first development appears to be that of a re- usable payload carrier with a capacity of 9 to 12 passengers. 3. Systems comparisons show that, for the foreseeable variations of the mission market, a partially reusable concept could bring about most of the program savings that can be expected from reusability at moderate development risk and funding rate. 4. A further development leading to a fully reusable system has high risk and uncertain payoff. Conceivably, at that time, a more advanced concept might be introduced.

Dietrich W Fellenz↗