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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Efficient Two-Way Coupled Analysis of Steady-State Particle-Laden Hypersonic Flows

A direct solution approach for surface erosion in particle-laden hypersonic flows is extended for use in low-cost two-way coupled solutions of dilute gas-particle flows. The Trajectory Control Volume method, which uses a sparse set of probe particles to predict surface erosion distributions on general vehicles, is reformulated for the solution of source terms by mean trajectory subdivision and computing a flux differencing. The approach is verified successfully against a boundary layer solution and shown to agree well with experimental measurements. A representative Mars entry case, with conditions and geometry based on the ExoMars Schiaparelli capsule, is solved with the approach to study the impact of two-way coupling on surface heating and erosion. Results indicate that for realistic loading conditions, heating is largely unmodified compared to one-way coupled results at peak heating trajectory conditions, and no measureable difference is observed in the surface erosion rate. At exaggerated loading conditions high enough to observe coupling effects, the worst case collisional heating can increase heating by up to 60%.

Particle Laden Flow↗
NASA NTRSDate not supplied

Supporting Global Air Quality Management Needs With A Flexible Data Fusion Tool for Estimation and Forecasting in Google Earth Engine

High spatial and temporal resolution air quality estimation and forecasting can be enhanced by combining global data sources, like chemical transport models and satellite remote sensing, with local information from regulatory and low-cost air quality monitors. Successful integration of data from these diverse sources is complicated by many factors, however, including differences in spatial and temporal resolution, data availability and latency issues, varying data quality, and large computational and data storage requirements. This presentation will provide an overview of a NASA-funded effort to develop the foundation for future operationalization of air quality forecasting for world-wide end-users and integration into their air quality management decision processes, which will be achieved in future phases of this multi-year project. We will summarize our progress in developing a data fusion system using the Google Earth Engine platform which can integrate model, satellite, and surface-level monitoring datasets to enhance estimation and forecasting of air-quality-relevant pollutants at sub-daily and sub-city scales. The tool is being developed in close cooperation with several city- and regional-level air quality managers in the USA and around the world. Our end-goal is to provide these air quality managers with the information they need to assess and anticipate the impacts of poor air quality, track changes in air quality due to ongoing mitigation efforts and land use changes, and identify ways to improve their air quality monitoring strategies. This presentation will focus on recent advances achieved through the project, including integration of multiple air quality datasets in a prototype data fusion system in Google Earth Engine, the quantification of uncertainties associated with our data fusion approach, and the development of user interfaces and visualization tools to convey air quality information in a way which best meets end-user needs.

Carl Malings↗
NASA NTRSDate not supplied

Empirical Noise Modeling of Internally Mixed Exhaust Systems

There appear to be no non-proprietary methods to predict the noise of internally mixed exhaust systems, and no guidance for how to adapt known jet noise models for these configurations. This paper surveys literature and historical databases acquired at NASA Glenn's Aero-Acoustic Propulsion Lab to give such guidance. The core premise is that an exhaust system with well-designed mixer produces noise that is to first approximation the same as a fully-mixed jet flow. Refinement of what is meant by a “fully mixed jet” can lead to more accurate prediction of the main jet noise. Additional noise is often generated within the nozzle, typically at high frequencies, whose source mechanism(s) are not obvious. However, a noise prediction method can be established that captures some aspects of the excess noise and provide an estimate of the total jet noise. Explorations of source mechanisms associated with the internal mixer have led to a new noise model which includes the effects of having an external plug nozzle, a feature desirable for near-term supersonic aircraft. Statistical analysis of historical data is provided to estimate the uncertainty in using this method given the variations found that cannot be directly computed without detailed mixer geometry.

Noise Prediction↗
NASA NTRSDate not supplied

Empirical Noise Modeling of Internally Mixed Exhaust Systems

There appears to be no non-proprietary methods to predict the noise of internally mixed exhaust systems, and no guidance for how to adapt known jet noise models for these configurations. This paper surveys literature and historical databases acquired at NASA Glenn's Aero-Acoustic Propulsion Lab to give such guidance. The core premise is that an exhaust system with well-designed mixer produces noise that is to first approximation the same as a fully-mixed jet flow. Refinement of what is meant by a “fully mixed jet” can lead to more accurate prediction of the main jet noise. Additional noise is often generated within the nozzle, typically at high frequencies, whose source mechanism(s) are not obvious. However, a noise prediction method can be established that captures some aspects of the excess noise and provide an estimate of the total jet noise. Explorations of source mechanisms associated with the internal mixer have led to a new noise model which includes the effects of having an external plug nozzle, a feature desirable for near-term supersonic aircraft. Statistical analysis of historical data is provided to estimate the uncertainty in using this method given the variations found that cannot be directly computed without detailed mixer geometry.

Noise Prediction↗
NASA NTRSDate not supplied

Probabilistic Calibration of Expensive Models using Efficiently Trained Surrogates

Calibration of computational models in the presence of uncertainty is often cast as a Bayesian inference problem and solved via sampling methods, e.g., Markov chain Monte Carlo. When the computational model is expensive, this task becomes intractable due to the large number of samples required to accurately estimate the posterior distribution of the calibration parameters. A popular solution to this problem is to use machine learning to develop a faster-to-evaluate, lower-fidelity substitute for the original model to serve as a surrogate while solving the inference problem. Although considered an offline cost, generating training data to construct this surrogate model can still be an expensive task in practice. An active learning algorithm is presented that focuses training on improving surrogate accuracy specifically in and around the bulk of the posterior distribution, as this is where the model is exercised during calibration. Candidate samples are drawn from families of distributions related to an approximation of the posterior. The sample maximizing predictive variance is then selected for evaluation by the original computational model, yielding a label for the training point. Iterating this approach increases efficiency relative to space filling designs (e.g., Latin hypercube sampling) by avoiding low probability points. Practical considerations are discussed, including the benefits of using a sequential Monte Carlo sampling approach, convergence heuristics, and the importance of both exploration and exploitation given that the true posterior is unknown a priori.

uncertainty quantification↗
NASA NTRSDate not supplied

Results of the NASA Prediction Uncertainty Reduction Tech Challenge

In January 2021 the Advanced Air Vehicles Program approved a new Tech Challenge (TC) to be run out of the Commercial Supersonic Technology (CST) Project. The objective of the TC was to bring uncertainties in the empirical noise modeling for civilian supersonic aircraft into the same range as that of conventional aircraft. The TC goal statement was to “Produce data and demonstrate tools that reduce the uncertainty in predicting Landing & Takeoff Noise levels of supersonic-relevant designs by 5 EPNdB and are ready for use in studies to inform FAA rule-making.” To make the required improvements in noise prediction methods for supersonic aircraft, NASA decided to validate and use high-fidelity numerical simulations to acquire the needed noise data on supersonic propulsion configurations. High-fidelity component designs were developed, such as a two-stage propulsor behind a supersonic inlet designed by General Electric Aviation and variable area exhaust systems using input from recent contracts with GE and Rolls Royce. A noise database was generated as a function of geometric and flow parameters, providing corrections to the empirical noise models and added new input variables to describe the complexities created by the supersonic-specific design features. Statistically, the new models were able to predict the acoustic impact of supersonic-specific features, the reduction in uncertainty being reduced from the baseline 7.8 EPNdB at the beginning of the TC to 2.0 EPNdB at the end.

noise prediction↗
NASA NTRS2002

Probabilistic Methods for Uncertainty Propagation Applied to Aircraft Design

Three methods of probabilistic uncertainty propagation and quantification (the method of moments, Monte Carlo simulation, and a nongradient simulation search method) are applied to an aircraft analysis and conceptual design program to demonstrate design under uncertainty. The chosen example problems appear to have discontinuous design spaces and thus these examples pose difficulties for many popular methods of uncertainty propagation and quantification. However, specific implementation features of the first and third methods chosen for use in this study enable successful propagation of small uncertainties through the program. Input uncertainties in two configuration design variables are considered. Uncertainties in aircraft weight are computed. The effects of specifying required levels of constraint satisfaction with specified levels of input uncertainty are also demonstrated. The results show, as expected, that the designs under uncertainty are typically heavier and more conservative than those in which no input uncertainties exist.

Green, Lawrence L.↗
NASA NTRS2010

Multiple Velocity Profile Measurements in Hypersonic Flows Using Sequentially-Imaged Fluorescence Tagging

Nitric-oxide planar laser-induced fluorescence (NO PLIF) was used to perform velocity measurements in hypersonic flows by generating multiple tagged lines which fluoresce as they convect downstream. For each laser pulse, a single interline, progressive scan intensified CCD (charge-coupled device) camera was used to obtain two sequential images of the NO molecules that had been tagged by the laser. The CCD configuration allowed for sub-microsecond acquisition of both images, resulting in sub-microsecond temporal resolution as well as sub-mm spatial resolution (0.5-mm horizontal, 0.7-mm vertical). Determination of axial velocity was made by application of a cross-correlation analysis of the horizontal shift of individual tagged lines. A numerical study of measured velocity error due to a uniform and linearly-varying collisional rate distribution was performed. Quantification of systematic errors, the contribution of gating/exposure duration errors, and the influence of collision rate on temporal uncertainty were made. Quantification of the spatial uncertainty depended upon the signal-to-noise ratio of the acquired profiles. This velocity measurement technique has been demonstrated for two hypersonic flow experiments: (1) a reaction control system (RCS) jet on an Orion Crew Exploration Vehicle (CEV) wind tunnel model and (2) a 10-degree half-angle wedge containing a 2-mm tall, 4-mm wide cylindrical boundary layer trip. The experiments were performed at the NASA Langley Research Center's 31-Inch Mach 10 Air Tunnel.

Bathel, Brett F.↗
NASA NTRS2010

Multiple Velocity Profile Measurements in Hypersonic Flows using Sequentially-Imaged Fluorescence Tagging

Nitric-oxide planar laser-induced fluorescence (NO PLIF) was used to perform velocity measurements in hypersonic flows by generating multiple tagged lines which fluoresce as they convect downstream. For each laser pulse, a single interline, progressive scan intensified CCD camera was used to obtain separate images of the initial undelayed and delayed NO molecules that had been tagged by the laser. The CCD configuration allowed for sub-microsecond acquisition of both images, resulting in sub-microsecond temporal resolution as well as sub-mm spatial resolution (0.5-mm x 0.7-mm). Determination of axial velocity was made by application of a cross-correlation analysis of the horizontal shift of individual tagged lines. Quantification of systematic errors, the contribution of gating/exposure duration errors, and influence of collision rate on fluorescence to temporal uncertainty were made. Quantification of the spatial uncertainty depended upon the analysis technique and signal-to-noise of the acquired profiles. This investigation focused on two hypersonic flow experiments: (1) a reaction control system (RCS) jet on an Orion Crew Exploration Vehicle (CEV) wind tunnel model and (2) a 10-degree half-angle wedge containing a 2-mm tall, 4-mm wide cylindrical boundary layer trip. The experiments were performed at the NASA Langley Research Center's 31-inch Mach 10 wind tunnel.

Bathel, Brett F.↗
NASA NTRS1991

Some Aspects of uncertainty in computational fluid dynamics results

Uncertainties are inherent in computational fluid dynamics (CFD). These uncertainties need to be systematically addressed and managed. Sources of these uncertainty analysis are discussed. Some recommendations are made for quantification of CFD uncertainties. A practical method of uncertainty analysis is based on sensitivity analysis. When CFD is used to design fluid dynamic systems, sensitivity-uncertainty analysis is essential.

Mehta, U. B.↗
NASA NTRS2019

Health Management and Prognostics for Electric Aircraft Powertrain

W and c Any air borne vehicle needs incorporating safety as key parameter of measure, and inclusion of autonomy raises the critical need for safety under autonomous operations. Management of faults and component degradation is key as complexity in autonomous operations grow over the period of time. Therefore, in addition to basic operational requirements, an autonomous electric vehicle should be able to make accurate estimates of its current system health and take the correct decisions to complete its mission successfully. Real-time safety and state-awareness tools are therefore essential for the vehicle to be able to reach its destination in a safe and successful manner. The need for safety assurance and health management capabilities is particularly relevant for aircraft electric propulsion systems, which are relatively new and with limited historical to learn. They are critical systems requiring high power density along with reliability, resilience, efficient management of weight, and operational costs. A model- based fault diagnosis and prognostics approach of complex critical systems can successfully accomplish the safety and state awareness goal for such electric propulsion systems, enabling autonomous decision making capability for safe and efficient operation. To identify critical components in the system a Qualitative Bayesian approach using FMECA is implemented. This requires the assessment of some quantities representing the state of the electric unmanned aerial systems (e-UAS), as well as look-ahead forecasts of such states during the entire flight, presented in form of safety metrics (SM). In-service data and performance data gathered from degraded components sup- ports diagnostic and prognostic methods for these systems, but this data can be difficult to obtain as weight and packaging restrictions reduce redundancy and instrumentation on-board the vehicle. Therefore, an model-based framework should be capable or operating with limited data. In addition to data scarcity, the variability of such complex critical systems re- quires the model-based framework to reason in the presence of uncertainty, such as sensor noise, and modeling imperfections. Quantification of errors and uncertainties in the measured states and quantities is therefore a fundamental step for a precise estimation of such SMs; un-modeled uncertainty may result in erroneous state assessment and un- reliable predictions of future states of e-UAVs. Typical, centralized model-based schemes suffer from inherent disadvantages such as computational complexity, single point of failure, and scalability issues, and therefore may fail in such a complex scenario. This paper presents a methodology for developing a system level diagnostics and prognostics approach using a Qualitative Bayesian FMECA approach along with a formal uncertainty management framework for an e-UAS. In this work we demonstrate the efficacy of the framework to predict effects of sub-system level degradation on vehicle operation incorporating uncertainty management to predict future behavior under different operating conditions.

Kulkarni, Chetan↗
NASA NTRS2004

Uncertainties in estimates of the risks of late effects from space radiation

Methods used to project risks in low-Earth orbit are of questionable merit for exploration missions because of the limited radiobiology data and knowledge of galactic cosmic ray (GCR) heavy ions, which causes estimates of the risk of late effects to be highly uncertain. Risk projections involve a product of many biological and physical factors, each of which has a differential range of uncertainty due to lack of data and knowledge. Using the linear-additivity model for radiation risks, we use Monte-Carlo sampling from subjective uncertainty distributions in each factor to obtain an estimate of the overall uncertainty in risk projections. The resulting methodology is applied to several human space exploration mission scenarios including a deep space outpost and Mars missions of duration of 360, 660, and 1000 days. The major results are the quantification of the uncertainties in current risk estimates, the identification of factors that dominate risk projection uncertainties, and the development of a method to quantify candidate approaches to reduce uncertainties or mitigate risks. The large uncertainties in GCR risk projections lead to probability distributions of risk that mask any potential risk reduction using the "optimization" of shielding materials or configurations. In contrast, the design of shielding optimization approaches for solar particle events and trapped protons can be made at this time and promising technologies can be shown to have merit using our approach. The methods used also make it possible to express risk management objectives in terms of quantitative metrics, e.g., the number of days in space without exceeding a given risk level within well-defined confidence limits. Published by Elsevier Ltd on behalf of COSPAR.

Non-NASA Center↗
NASA NTRS2002

Uncertainties in Estimates of the Risks of Late Effects from Space Radiation

The health risks faced by astronauts from space radiation include cancer, cataracts, hereditary effects, and non-cancer morbidity and mortality risks related to the diseases of the old age. Methods used to project risks in low-Earth orbit are of questionable merit for exploration missions because of the limited radiobiology data and knowledge of galactic cosmic ray (GCR) heavy ions, which causes estimates of the risk of late effects to be highly uncertain. Risk projections involve a product of many biological and physical factors, each of which has a differential range of uncertainty due to lack of data and knowledge. Within the linear-additivity model, we use Monte-Carlo sampling from subjective uncertainty distributions in each factor to obtain a Maximum Likelihood estimate of the overall uncertainty in risk projections. The resulting methodology is applied to several human space exploration mission scenarios including ISS, lunar station, deep space outpost, and Mar's missions of duration of 360, 660, and 1000 days. The major results are the quantification of the uncertainties in current risk estimates, the identification of factors that dominate risk projection uncertainties, and the development of a method to quantify candidate approaches to reduce uncertainties or mitigate risks. The large uncertainties in GCR risk projections lead to probability distributions of risk that mask any potential risk reduction using the "optimization" of shielding materials or configurations. In contrast, the design of shielding optimization approaches for solar particle events and trapped protons can be made at this time, and promising technologies can be shown to have merit using our approach. The methods used also make it possible to express risk management objectives in terms of quantitative objective's, i.e., the number of days in space without exceeding a given risk level within well defined confidence limits.

Cucinotta, F. A.↗
NASA NTRS1990

A comparison of ISCCP and FIRE satellite cloud parameters

One of the goals of the First ISCCP Regional Experiment (FIRE) is the quantification of the uncertainties in the cloud parameter products derived by the International Satellite Cloud Climatology Project (ISCCP). This validation effort has many facets including sensitivity analyses and comparisons to similar data or theoretical results with known accuracies. The FIRE provides cloud-truth data at particular points or along particular lines from surface and aircraft measurement systems. Relating these data to the larger, area-averaged ISCCP results requires intermediate steps using higher resolution satellite data analyses. Errors in the cloud products derived with a particular method can be determined by performing analyses of high resolution satellite data over the area surrounding the point or line measurement. This same analysis technique may then be used to derive cloud parameters over a larger area containing similar cloud fields. It is assumed that the uncertainties found for the small scale analyses are the same for the large scale so that the method has been calibrated for the particular cloud type; i.e., its accuracy is known. Differences between the large scale results using the ISCCP technique and the calibrated method can be computed and used to determine if any significant biases or rms errors occur in the ISCCP results. Selected ISCCP results are compared to cloud parameters derived using the hybrid bispectral threshold method over the FIRE IFO and extended observation areas.

Gibson, Gary G.↗
NASA NTRS1990

The aerospace plane design challenge: Credible computational fluid dynamics results

Computational fluid dynamics (CFD) is necessary in the design processes of all current aerospace plane programs. Single-stage-to-orbit (STTO) aerospace planes with air-breathing supersonic combustion are going to be largely designed by means of CFD. The challenge of the aerospace plane design is to provide credible CFD results to work from, to assess the risk associated with the use of those results, and to certify CFD codes that produce credible results. To establish the credibility of CFD results used in design, the following topics are discussed: CFD validation vis-a-vis measurable fluid dynamics (MFD) validation; responsibility for credibility; credibility requirement; and a guide for establishing credibility. Quantification of CFD uncertainties helps to assess success risk and safety risks, and the development of CFD as a design tool requires code certification. This challenge is managed by designing the designers to use CFD effectively, by ensuring quality control, and by balancing the design process. For designing the designers, the following topics are discussed: how CFD design technology is developed; the reasons Japanese companies, by and large, produce goods of higher quality than the U.S. counterparts; teamwork as a new way of doing business; and how ideas, quality, and teaming can be brought together. Quality control for reducing the loss imparted to the society begins with the quality of the CFD results used in the design process, and balancing the design process means using a judicious balance of CFD and MFD.

Mehta, Unmeel B.↗
NASA NTRS1993

Quantification of model error via an interval model with nonparametric error bound

The quantification of model uncertainty is becoming increasingly important as robust control is an important tool for control system design and analysis. This paper presents an algorithm that effectively characterizes the model uncertainty in terms of parametric and nonparametric uncertainties. The algorithm utilizes the frequency domain model error which is estimated from the spectra of output error and input data. The parametric uncertainty is represented as an interval transfer function while the nonparametric uncertainty is bounded by a designed error bound transfer function. Both discrete and continuous systems are discussed in this paper. The algorithm is applied to the Mini-Mast example, and the detail analysis is given.

Lew, Jiann-Shiun↗
NASA NTRS2006

An Overview of the Role of Systems Analysis in NASA's Hypersonics Project

NASA's Aeronautics Research Mission Directorate recently restructured its Vehicle Systems Program, refocusing it towards understanding the fundamental physics that govern flight in all speed regimes. Now called the Fundamental Aeronautics Program, it is comprised of four new projects, Subsonic Fixed Wing, Subsonic Rotary Wing, Supersonics, and Hypersonics. The Aeronautics Research Mission Directorate has charged the Hypersonics Project with having a basic understanding of all systems that travel at hypersonic speeds within the Earth's and other planets atmospheres. This includes both powered and unpowered systems, such as re-entry vehicles and vehicles powered by rocket or airbreathing propulsion that cruise in and accelerate through the atmosphere. The primary objective of the Hypersonics Project is to develop physics-based predictive tools that enable the design, analysis and optimization of such systems. The Hypersonics Project charges the systems analysis discipline team with providing it the decision-making information it needs to properly guide research and technology development. Credible, rapid, and robust multi-disciplinary system analysis processes and design tools are required in order to generate this information. To this end, the principal challenges for the systems analysis team are the introduction of high fidelity physics into the analysis process and integration into a design environment, quantification of design uncertainty through the use of probabilistic methods, reduction in design cycle time, and the development and implementation of robust processes and tools enabling a wide design space and associated technology assessment capability. This paper will discuss the roles and responsibilities of the systems analysis discipline team within the Hypersonics Project as well as the tools, methods, processes, and approach that the team will undertake in order to perform its project designated functions.

Robinson, Jeffrey S.↗