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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.↗

NASA NTRS2016

Application of Compressive Sensing to Gravitational Microlensing Data and Implications for Miniaturized Space Observatories

Compressive Sensing is a technique for simultaneous acquisition and compression of data that is sparse or can be made sparse in some domain. It is currently under intense development and has been profitably employed for industrial and medical applications. We here describe the use of this technique for the processing of astronomical data. We outline the procedure as applied to exoplanet gravitational microlensing and analyze measurement results and uncertainty values. We describe implications for on-spacecraft data processing for space observatories. Our findings suggest that application of these techniques may yield significant, enabling benefits especially for power and volume-limited space applications such as miniaturized or micro-constellation satellites.

Gravitational Microlensing↗

NASA NTRS2016

Application of Compressive Sensing to Gravitational Microlensing Data and Implications for Miniaturized Space Observatories

Compressive Sensing↗

NASA NTRS2003

Structured Uncertainty Bound Determination From Data for Control and Performance Validation

This report attempts to document the broad scope of issues that must be satisfactorily resolved before one can expect to methodically obtain, with a reasonable confidence, a near-optimal robust closed loop performance in physical applications. These include elements of signal processing, noise identification, system identification, model validation, and uncertainty modeling. Based on a recently developed methodology involving a parameterization of all model validating uncertainty sets for a given linear fractional transformation (LFT) structure and noise allowance, a new software, Uncertainty Bound Identification (UBID) toolbox, which conveniently executes model validation tests and determine uncertainty bounds from data, has been designed and is currently available. This toolbox also serves to benchmark the current state-of-the-art in uncertainty bound determination and in turn facilitate benchmarking of robust control technology. To help clarify the methodology and use of the new software, two tutorial examples are provided. The first involves the uncertainty characterization of a flexible structure dynamics, and the second example involves a closed loop performance validation of a ducted fan based on an uncertainty bound from data. These examples, along with other simulation and experimental results, also help describe the many factors and assumptions that determine the degree of success in applying robust control theory to practical problems.

Lim, Kyong B.↗

NASA NTRS2019

Potential Improvements to the Nuclear Safety and Launch Approval Process for Nuclear Reactors Utilized for Space Power and Propulsion Applications

This study examines potential improvements that could be made to the nuclear safety and launch approval process for fission reactors to reduce the associated uncertainties in cost and schedule while continuing to ensure public safety and environmental protection. It concentrates on the launch approval and mission safety of fission power and propulsion applications of nuclear energy. Improvements to the launch approval process for radioisotope power systems (RPSs) are being considered elsewhere but are acknowledged throughout the report. The study considered technical, process, and organizational improvements to the launch approval processes. The study exclusively evaluated reactors that would not be started up prior to achieving a “sufficiently high orbit,” per United Nations (UN) Resolution 47/68.Potential criticality accidents were considered that could occur during a launch failure or abort or during reentry. Numerous scenarios were examined that might involve one or more Earth flybys as well as potential transportation missions that could intentionally return an active, or previously active fission reactor to Earth orbit. The Study Group was guided in its deliberations according to a number of fundamental principles. These included the paramount importance of adequate and appropriate levels of public safety and environmental protection as well as the importance of the inclusion of independent scientific, engineering, and safety reviews of the applications and proposals as a critical part of the process. Also considered was the need for the development of launch approval processes that might be different, depending upon the source of the application for launch approval, whether it be derived as a governmental launch, a commercial launch, or a hybrid/combination of the two. It is clear that all launches of nuclear reactors into space should have similar safety requirements; however, the safety review effort and the details of the analysis that are required should be commensurate with the potential hazards and the actual risk, which may differ based on the reactor design and its intended purpose. Finally, the study aimed at ensuring that whatever processes and procedures are developed should maximize the sufficiency, simplicity, and transparency of the processes. The Study Group reached five Conclusions and makes thirteen Recommendations. The Conclusions and Recommendations presented here are extensions of those presented previously in other studies. This report attempts to add specificity to the actions that need to be taken in order to move forward with successful space fission reactor programs. Without action to address the perceived and real problems in the launch approval process, designers and mission managers will be reluctant to commit the resources necessary to make space fission reactors a reality.

Camp, Allen↗

DOE OSTI2021

Machine learning based algorithms for uncertainty quantification in numerical weather prediction models

Complex numerical weather prediction models incorporate a variety of physical processes, each described by multiple alternative physical schemes with specific parameters. The selection of the physical schemes and the choice of the corresponding physical parameters during model configuration can significantly impact the accuracy of model forecasts. There is no combination of physical schemes that works best for all times, at all locations, and under all conditions. It is therefore of considerable interest to understand the interplay between the choice of physics and the accuracy of the resulting forecasts under different conditions. This paper demonstrates the use of machine learning techniques to study the uncertainty in numerical weather prediction models due to the interaction of multiple physical processes. The first problem addressed herein is the estimation of systematic model errors in output quantities of interest at future times, and the use of this information to improve the model forecasts. The second problem considered is the identification of those specific physical processes that contribute most to the forecast uncertainty in the quantity of interest under specified meteorological conditions. In order to address these questions we employ two machine learning approaches, random forests and artificial neural networks. The discrepancies between model results and observations at past times are used to learn the relationships between the choice of physical processes and the resulting forecast errors. Numerical experiments are carried out with the Weather Research and Forecasting (WRF) model. The output quantity of interest is the model precipitation, a variable that is both extremely important and very challenging to forecast. The physical processes under consideration include various micro-physics schemes, cumulus parameterizations, short wave, and long wave radiation schemes. The experiments demonstrate the strong potential of machine learning approaches to aid the study of model errors.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

The MCNP6 Analytic Criticality Benchmark Suite

Analytical benchmarks provide an invaluable tool for verifying computer codes used to simulate neutron transport. Several collections of analytical benchmark problems [1-4] are used routinely in the verification of production Monte Carlo codes such as MCNP® [5,6]. Verification of a computer code is a necessary prerequisite to the more complex validation process. The verification process confirms that a code performs its intended functions correctly. The validation process involves determining the absolute accuracy of code results vs. nature. In typical validations, results are computed for a set of benchmark experiments using a particular methodology (code, cross-section data with uncertainties, and modeling) and compared to the measured results from the set of benchmark experiments. The validation process determines bias, bias uncertainty, and possibly additional margins. Verification is generally performed by the code developers, while validation is generally performed by code users for a particular application space. The VERIFICATION_KEFF suite of criticality problems [1,2] was originally a set of 75 criticality problems found in the literature for which exact analytical solutions are available. Even though the spatial and energy detail is necessarily limited in analytical benchmarks, typically to a few regions or energy groups, the exact solutions obtained can be used to verify that the basic algorithms, mathematics, and methods used in complex production codes perform correctly. The present work has focused on revisiting this benchmark suite. A thorough review of the problems resulted in discarding some of them as not suitable for MCNP benchmarking. For the remaining problems, many of them were reformulated to permit execution in either multigroup mode or in the normal continuous-energy mode for MCNP. Execution of the benchmarks in continuous-energy mode provides a significant advance to MCNP verification methods.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

New Version of the MCNP Analytic Criticality Benchmark Suite

Analytical benchmarks provide an invaluable tool for verifying computer codes used to simulate neutron transport. Several collections of analytical benchmark problems are used routinely in the verification of production Monte Carlo codes such as MCNP{sup R}. Verification of a computer code is a necessary prerequisite to the more complex validation process. The verification process confirms that a code performs its intended functions correctly. The validation process involves determining the absolute accuracy of code results vs. nature. In typical validations, results are computed for a set of benchmark experiments using a particular methodology (code, cross-section data with uncertainties, and modeling) and compared to the measured results from the set of benchmark experiments. The validation process determines bias, bias uncertainty, and possibly additional margins. Verification is generally performed by the code developers, while validation is generally performed by code users for a particular application space. The VERIFICATION-KEFF suite of criticality problems was originally a set of 75 criticality problems found in the literature for which exact analytical solutions are available. Even though the spatial and energy detail is necessarily limited in analytical benchmarks, typically to a few regions or energy groups, the exact solutions obtained can be used to verify that the basic algorithms, mathematics, and methods used in complex production codes perform correctly. The present work has focused on revisiting this benchmark suite. A thorough review of the problems resulted in discarding some of them as not suitable for MCNP benchmarking. For the remaining problems, many of them were reformulated to permit execution in either multigroup mode or in the normal continuous-energy mode for MCNP. Execution of the benchmarks in continuous-energy mode provides a significant advance to MCNP verification methods. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2017

To address surface reaction network complexity using scaling relations machine learning and DFT calculations

Surface reaction networks involving hydrocarbons exhibit enormous complexity with thousands of species and reactions for all but the very simplest of chemistries. We present a framework for optimization under uncertainty for heterogeneous catalysis reaction networks using surrogate models that are trained on the fly. The surrogate model is constructed by teaching a Gaussian process adsorption energies based on group additivity fingerprints, combined with transition-state scaling relations and a simple classifier for determining the rate-limiting step. The surrogate model is iteratively used to predict the most important reaction step to be calculated explicitly with computationally demanding electronic structure theory. Applying these methods to the reaction of syngas on rhodium(111), we identify the most likely reaction mechanism. Lastly, propagating uncertainty throughout this process yields the likelihood that the final mechanism is complete given measurements on only a subset of the entire network and uncertainty in the underlying density functional theory calculations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Optimizing observations of drizzle onset with millimeter-wavelength radars

Cloud Doppler radars are increasingly used to study cloud and precipitation microphysical processes. Typical bulk cloud properties such as liquid or ice content are usually derived using the first three standard moments of the radar Doppler spectrum. Recent studies demonstrated the value of higher moments for the reduction of retrieval uncertainties and for providing additional insights into microphysical processes. Large effort has been undertaken, e.g., within the Atmospheric Radiation Measurement (ARM) program to ensure high quality of radar Doppler spectra. However, a systematic approach concerning the accuracy of higher moment estimates and sensitivity to basic radar system settings, such as spectral resolution, integration time and beam width, are still missing. Here In this study, we present an approach on how to optimize radar settings for radar Doppler spectra moments in the specific context of drizzle detection. The process of drizzle development has shown to be particularly sensitive to higher radar moments such as skewness. We collected radar raw data (I/Q time series) from consecutive zenith-pointing observations for two liquid cloud cases observed at the cloud observatory JOYCE in Germany. The I/Q data allowed us to process Doppler spectra and derive their moments using different spectral resolutions and integration times during identical time intervals. This enabled us to study the sensitivity of the spatiotemporal structure of the derived moments to the different radar settings. The observed signatures were further investigated using a radar Doppler forward model which allowed us to compare observed and simulated sensitivities and also to study the impact of additional hardware-dependent parameters such as antenna beam width. For the observed cloud with drizzle onset we found that longer integration times mainly modify spectral width (S w ) and skewness (S k ), leaving other moments mostly unaffected. An integration time of 2 s seems to be an optimal compromise: both observations and simulations revealed that a 10 s integration time – as it is widely used for European cloud radars – leads to a significant turbulence-induced increase of S w and reduction of S k compared to 2 s integration time. This can lead to significantly different microphysical interpretations with respect to drizzle water content and effective radius. A change from 2 s to even shorter integration times (0. 4 s) has much smaller effects on S w and S k . We also find that spectral resolution has a small impact on the moment estimations, and thus on the microphysical interpretation of the drizzle signal. Even the coarsest spectral resolution studied, 0. 08 ms -1 , seems to be appropriate for calculation moments of drizzling clouds. Moreover, simulations provided additional insight into the microphysical interpretation of the skewness signatures observed: in low (high)-turbulence conditions, only drizzle larger than 20 µm (40 µm) can generate S k values above the S k noise level (in our case 0.4). Higher S k values are also obtained in simulations when smaller beam widths are adopted.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2024

Uncertainty Budget for Detector-Based Absolute Radiometric Calibration With GLAMR

The accuracy of the absolute radiometric calibration (RadCal) for remote sensing instruments is essential to their wide range of applications. The uncertainty associated to the traditional source-based RadCal method is assessed at a 2% (k=1) or higher level for radiance measurement. To further improve the accuracy to meet the demands of climate studies, a detector-based approach using tunable lasers as a light source has been devised. The Goddard Laser for Absolute Measurement of Radiance, known as the GLAMR system, is a notable example of the incorporation of such technology. Using transfer radiometers calibrated at NIST as calibration standards, the absolute spectral response (ASR) function of a remote sensing instrument is measured with its uncertainty traceable to the International System of Units. This paper presents a comprehensive uncertainty analysis of the detector-based absolute RadCal using the GLAMR system. It identifies and examines uncertainty sources during the GLAMR RadCal test, including those from the GLAMR system, the testing configuration, and data processing methodologies. Analysis is carried out to quantify the contribution of each source and emphasize the most influential factors. It is shown that the calibration uncertainty of GLAMR RadCal can be better than 0.3% (k=1) in the wavelength range of 350-950 nm and 0.6% (k=1) between 950-2300 nm, with the exception of regions with strong water absorption. In addition, recommendations are made to refine the calibration process to further reduce the uncertainty.

Zhipeng Wang↗

NASA NTRS2000

Section on Observed Impacts on El Nino

Agricultural applications of El Nino forecasts are already underway in some countries and need to be evaluated or re-evaluated. For example, in Peru, El Nino forecasts have been incorporated into national planning for the agricultural sector, and areas planted with rice and cotton (cotton being the more drought-tolerant crop) are adjusted accordingly. How well are this and other such programs working? Such evaluations will contribute to the governmental and intergovernmental institutions, including the Inter-American Institute for Global Change Research and the US National Ocean and Atmospheric Agency that are fostering programs to aid the effective use of forecasts. As El Nino climate forecasting grows out of the research mode into operational mode, the research focus shifts to include the design of appropriate modes of utilization. Awareness of and sensitivity to the costs of prediction errors also grow. For example, one major forecasting model failed to predict the very large El Nino event of 1997, when Pacific sea-surface temperatures were the highest on record. Although simple correlations between El Nino events and crop yields may be suggestive, more sophisticated work is needed to understand the subtleties of the interplay among the global climate system, regional climate patterns, and local agricultural systems. Honesty about the limitations of an forecast is essential, especially when human livelihoods are at stake. An end-to-end analysis links tools and expertise from the full sequence of ENSO cause-and-effect processes. Representatives from many disciplines are needed to achieve insights, e.g, oceanographers and atmospheric scientists who predict El Nino events, climatologists who drive global climate models with sea-surface temperature predictions, agronomists who translate regional climate connections in to crop yield forecasts, and economists who analyze market adjustments to the vagaries of climate and determine the value of climate forecasts. Methods include historical studies to understand past patterns and to test hindcasts of the prediction tools, crop modeling, spatial analysis and remote sensing. This research involves expanding, deepening, and applying the understanding of physical climate to the fields of agronomy and social science; and the reciprocal understanding of crop growth and farm economics to climatology. Delivery of a regional climate forecast with no information about how the climate forecast was derived limits its effectiveness. Explanation of a region's major climate driving forces helps to place a seasonal forecast in context. Then, a useful approach is to show historical responses to previous El Nino events, and projections, with uncertainty intervals, of crop response from dynamic process crop growth models. Regional ID forecasts should be updated with real-time weather conditions. Since every El Nino event is different, it is important to track, report and advise on each new event as it unfolds. The stability of human enterprises depends on understanding both the potentialities and the limits of predictability. Farmers rely on past experience to anticipate and respond to fluctuations in the biophysical systems on which their livelihoods depend. Now scientists are improving their ability to predict some major elements of climate variability. The improvements in the reliability of El Nino forecasts are encouraging, but seasonal forecasts for agriculture are not, and will probably never be completely infallible, due to the chaotic nature of the climate system. Uncertainties proliferate as we extend beyond Pacific sea-surface temperatures to climate teleconnections and agricultural outcomes. The goal of this research is to shed as a clear light as possible on these inherent uncertainties and thus to contribute to the development of appropriate responses to El Nino and other seasonal forecasts for a range of stakeholders, which, ultimately, includes food consumers everywhere.

Rosenzweig, Cynthia↗

DOE OSTI2014

A Sensitivity Study on Modeling Black Carbon in Snow and its Radiative Forcing over the Arctic and Northern China

Black carbon in snow (BCS) simulated in the Community Atmosphere Model (CAM5) is evaluated against measurements over Northern China and the Arctic, and its sensitivity to atmospheric deposition and two parameters that affect post-depositional enrichment is explored. The BCS concentration is overestimated (underestimated) by a factor of two in Northern China (Arctic) in the default model, but agreement with observations is good over both regions in the simulation with improvements in BC transport and deposition. Sensitivity studies indicate that uncertainty in the melt-water scavenging efficiency (MSE) parameter substantially affects BCS and its radiative forcing (by a factor of 2-7) in the Arctic through post-depositional enrichment. The MSE parameter has a relatively small effect on the magnitude of BCS seasonal cycle but can alter its phase in Northern China. The impact of the snow aging scaling factor (SAF) on BCS, partly through the post-depositional enrichment effect, shows more complex latitudinal and seasonal dependence. Similar to MSE, SAF affects more significantly the magnitude (phase) of BCS season cycle over the Arctic (Northern China). While uncertainty associated with the representation of BC transport and deposition processes in CAM5 is more important than that associated with the two snow model parameters in Northern China, the two uncertainties have comparable effect in the Arctic.

Artic↗

NASA NTRS2007

Development of Probabilistic Structural Analysis Integrated with Manufacturing Processes

An effort has been initiated to integrate manufacturing process simulations with probabilistic structural analyses in order to capture the important impacts of manufacturing uncertainties on component stress levels and life. Two physics-based manufacturing process models (one for powdered metal forging and the other for annular deformation resistance welding) have been linked to the NESSUS structural analysis code. This paper describes the methodology developed to perform this integration including several examples. Although this effort is still underway, particularly for full integration of a probabilistic analysis, the progress to date has been encouraging and a software interface that implements the methodology has been developed. The purpose of this paper is to report this preliminary development.

Pai, Shantaram S.↗

DOE OSTI2025

Assessment of MiniFuel Subcapsule Design Recommendations on Previous Experiments

MiniFuel describes the class of separate effects nuclear fuels irradiation experiments that have been conducted in the High Flux Isotope Reactor (HFIR) since 2018. These experiments comprise a stack of six fuel-bearing subcapsules contained in a stainless steel target housing that is in contact with HFIR coolant on its exterior. All MiniFuel targets have a near-standardized architecture, and the primary design variables that change between experiments are the radial gap size between the subcapsule and housing and the target fill gas composition. Finite element heat transfer models are used to determine the optimum gas composition and gap sizes, and recent studies were performed to identify model parameters that contribute the most uncertainty to fuel specimen temperature predictions. That work, which is referenced herein, also recommended a set of design modifications to the subcapsule internal architecture and assembly process. These modifications are intended to reduce fuel temperature uncertainty in future experiments. In this report, the subcapsule design modifications were retroactively applied to a previously conducted MiniFuel experiment to determine how these changes affect the established safety and performance envelope of the experimental capability. These effects were determined in two steps. First, the modifications were applied to the subcapsule design without any other changes to determine their isolated effect on the predicted fuel specimen temperatures. This portion of the analysis showed that fuel temperatures were modestly reduced because the implemented changes improved heat transfer efficacy. Next, traditional MiniFuel design activities (i.e., sizing the gas gaps and determining the fill gas composition) were reperformed, and they confirmed that the original desired fuel temperatures could be achieved while remaining within established safety limits. Therefore, this report demonstrates improved performance resulting from the subcapsule modifications, which mitigate uncertainty while meeting the objectives of past experiments. An additional benefit of the design changes is reduced sensitivity of the fuel temperature to the evolving flux spectrum in HFIR, leading to more stable temperatures and enhanced utility of MiniFuel as a separate effects irradiation platform.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2016

An experimental seasonal hydrological forecasting system over the Yellow River basin – Part 2: The added value from climate forecast models

This is the second paper of a two-part series on introducing an experimental seasonal hydrological forecasting system over the Yellow River basin in northern China. While the natural hydrological predictability in terms of initial hydrological conditions (ICs) is investigated in a companion paper, the added value from eight North American Multimodel Ensemble (NMME) climate forecast models with a grand ensemble of 99 members is assessed in this paper, with an implicit consideration of human-induced uncertainty in the hydrological models through a post-processing procedure. The forecast skill in terms of anomaly correlation (AC) for 2 m air temperature and precipitation does not necessarily decrease over leads but is dependent on the target month due to a strong seasonality for the climate over the Yellow River basin. As there is more diversity in the model performance for the temperature forecasts than the precipitation forecasts, the grand NMME ensemble mean forecast has consistently higher skill than the best single model up to 6 months for the temperature but up to 2 months for the precipitation. The NMME climate predictions are downscaled to drive the variable infiltration capacity (VIC) land surface hydrological model and a global routing model regionalized over the Yellow River basin to produce forecasts of soil moisture, runoff and streamflow. And the NMME/VIC forecasts are compared with the Ensemble Streamflow Prediction method (ESP/VIC) through 6-month hindcast experiments for each calendar month during 1982–2010. As verified by the VIC offline simulations, the NMME/VIC is comparable to the ESP/VIC for the soil moisture forecasts, and the former has higher skill than the latter only for the forecasts at long leads and for those initialized in the rainy season. The forecast skill for runoff is lower for both forecast approaches, but the added value from NMME/VIC is more obvious, with an increase of the average AC by 0.08–0.2. To compare with the observed streamflow, both the hindcasts from NMME/VIC and ESP/VIC are post-processed through a linear regression model fitted by using VIC offline-simulated streamflow. The post-processed NMME/VIC reduces the root mean squared error (RMSE) from the post-processed ESP/VIC by 5–15 %. And the reduction occurs mostly during the transition from wet to dry seasons. As a result, with the consideration of the uncertainty in the hydrological models, the added value from climate forecast models is decreased especially at short leads, suggesting the necessity of improving the large-scale hydrological models in human-intervened river basins.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2012

Upper-Ocean Heat Balance Processes and the Walker Circulation in CMIP5 Model Projections

Considerable uncertainty remains as to the importance of mechanisms governing decadal and longer variability of the Walker Circulation, its connection to the tropical climate system, and prospects for tropical climate change in the face of anthropogenic forcing. Most contemporary climate models suggest that in response to elevated CO2 and a warmer but more stratified atmosphere, the required upward mass flux in tropical convection will diminish along with the Walker component of the tropical mean circulation as well. Alternatively, there is also evidence to suggest that the shoaling and increased vertical stratification of the thermocline in the eastern Pacific will enable a muted SST increase there-- preserving or even enhancing some of the dynamical forcing for the Walker cell flow. Over the past decade there have been observational indications of an acceleration in near-surface easterlies, a strengthened Pacific zonal SST gradient, and globally-teleconnected dislocations in precipitation. But is this evidence in support of an ocean dynamical thermostat process posited to accompany anthropogenic forcing, or just residual decadal fluctuations associated with variations in warm and cold ENSO events and other stochastic forcing? From a modeling perspective we try to make headway on this question by examining zonal variations in surface energy fluxes and dynamics governing tropical upper ocean heat content evolution in the WCRP CMIP5 model projections. There is some diversity among model simulations; for example, the CCSM4 indicates net ocean warming over the IndoPacific region while the CSIRO model concentrates separate warming responses over the central Pacific and Indian Ocean regions. The models, as with observations, demonstrate strong local coupling between variations in column water vapor, downward surface longwave radiation and SST; but the spatial patterns of changes in the sign of this relationship differ among models and, for models as a whole, with observations. Our analysis focuses initially on probing the inter-model differences in energy fluxes / transports and Walker Circulation response to forcing. We then attempt to identify statistically the El Nino- / La Nina-related ocean heat content variability unique to each model and regress out the associated energy flux, ocean heat transport and Walker response on these shorter time scales for comparison to that of the anthropogenic signals.

Robertson, F. R.↗

NASA NTRS2017

Evaluation of the Uncertainty in JP-7 Kinetics Models Applied to Scramjets

One of the challenges of designing and flying a scramjet-powered vehicle is the difficulty of preflight testing. Ground tests at realistic flight conditions introduce several sources of uncertainty to the flow that must be addressed. For example, the scales of the available facilities limit the size of vehicles that can be tested and so performance metrics for larger flight vehicles must be extrapolated from ground tests at smaller scales. To create the correct flow enthalpy for higher Mach number flows, most tunnels use a heater that introduces vitiates into the flow. At these conditions, the effects of the vitiates on the combustion process is of particular interest to the engine designer, where the ground test results must be extrapolated to flight conditions. In this paper, the uncertainty of the cracked JP-7 chemical kinetics used in the modeling of a hydrocarbon-fueled scramjet was investigated. The factors that were identified as contributing to uncertainty in the combustion process were the level of flow vitiation, the uncertainty of the kinetic model coefficients and the variation of flow properties between ground testing and flight. The method employed was to run simulations of small, unit problems and identify which variables were the principal sources of uncertainty for the mixture temperature. Then using this resulting subset of all the variables, the effects of the uncertainty caused by the chemical kinetics on a representative scramjet flow-path for both vitiated (ground) and nonvitiated (flight) flows were investigated. The simulations showed that only a few of the kinetic rate equations contribute to the uncertainty in the unit problem results, and when applied to the representative scramjet flowpath, the resulting temperature variability was on the order of 100 K. Both the vitiated and clean air results showed very similar levels of uncertainty, and the difference between the mean properties were generally within the range of uncertainty predicted.

Norris, A. T.↗