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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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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

CFD Validation Study of a Hypersonic Cone-Slice-Flap Configuration

Model validation is the process of determining the degree of accuracy between the real world and the model. The result of model validation can either be used to improve the model through calibration or quantify the model-form uncertainty. This work focuses on providing the model-form uncertainty through an area metric for a hypersonic cone-slice-flap geometry configuration given uncertainty in both the simulation and experimental data. A procedure will be presented that can give an accurate representation of the model-form uncertainty using a small number of high-fidelity runs. The work will also assess the impact between using different high-fidelity solvers and different turbulence models.The goal of performing this comparison is to provide a quantifiable measurement for the accuracy of each solver and turbulence model for this type of design. A low-fidelity analysis will also be performed to get a model-form uncertainty for the lower fidelity analysis. The two high-fidelty CFD solvers used are VULCAN-CFD and FUN3D and the low-fidelity results will come from Cart3D. The experimental data came from the 20-inch Mach 6Tunnel located at NASA Langley Research Center.

Laura M. White↗
NASA NTRSDate not supplied

CFD Validation Study of a Hypersonic Cone-Slice-Flap Variable Geometry Configuration

Model validation is the process of determining the degree of accuracy between physical reality and the model. The result of model validation can either be used to improve the model through calibration or quantify the model-form uncertainty. This work focuses on providing the model-form uncertainty through an area metric for a hypersonic cone-slice-flap variable geometry configuration given uncertainty in both the simulation and experimental data. For a variable geometry, performing uncertainty quantification to capture the model-form uncertainty on every configuration is computationally challenging. This work lays out a procedure that can give an accurate representation of the model-form uncertainty using a small number of high-fidelity runs and many low-fidelity runs on multiple configurations. Running this comparison provides a quantifiable measurement for the accuracy of each turbulence model for this type of design. The high-fidelity CFD solver used was VULCAN-CFD and the low-fidelity results came from Cart3D. The experimental data came from the 20-Inch Mach 6 Tunnel located at NASA Langley Research Center. The present work showed that the turbulence simulation overpredicted the drag and lift coefficient, while underpredicting the pitching moment coefficient. The model-form uncertainty estimate resulted up to a 13.6% change in the total uncertainty for the drag coefficient, up to a 57.4% change in total uncertainty for the lift coefficient, and up to a 100% change in total uncertainty for the pitching moment coefficient.

Laura White↗
DOE OSTI2025

Toroid Signal Processing Container with Redis Integration

This report presents the research, design, and implementation of a Toroid Signal Processing Container with Redis Integration, conducted during my internship assignment. The project focused on developing a modular signal processing sys- tem capable of performing baseline correction, droop compensation, and real- time data communication for beam diagnostic signals. Using Redis as a message broker and configuration manager, the system ensures modularity, scalability, and efficient inter-process communication. Signal correction algorithms, such as Asymmetric Least Squares (ALS) baseline smoothing and a numerical droop correction approach, were implemented and tested using Linac beam data. The results confirm that this system improves signal fidelity and supports real-time analysis requirements, offering valuable contributions to the field of beam in- strumentation and data acquisition systems.

Bowers, Elliot [Cabrillo Coll.; Fermilab]↗
DOE OSTI2026

Development of physics-consistent conditional diffusion model to overcome data scarcity in critical heat flux

Deep generative modeling provides a powerful pathway to overcome data scarcity in energy-related applications where experimental data are often limited. By learning the underlying probability distribution of the training dataset, deep generative models, such as the diffusion model, can generate high-fidelity synthetic samples that statistically resemble the training data. Such synthetic data generation can significantly enrich the size and diversity of the available training data, and more importantly, improve the robustness of downstream machine learning models in predictive tasks. The objective of this paper is to investigate the effectiveness of diffusion models for overcoming data scarcity in nuclear energy applications. By leveraging a public dataset on critical heat flux which covers a wide range of commercial nuclear reactor operational conditions, we developed a diffusion model that can generate an arbitrary amount of synthetic samples. Since a vanilla diffusion model can only generate samples randomly, we also developed a conditional diffusion model capable of generating targeted critical heat flux data under user-specified thermal-hydraulic conditions. The performance of the diffusion model was evaluated based on its ability to capture empirical feature distributions and pair-wise correlations, as well as to maintain physical consistency. The results showed that both the diffusion model and conditional diffusion model can successfully generate realistic and physics-consistent critical heat flux data. Furthermore, uncertainty quantification results demonstrate that the conditional diffusion model is highly effective in augmenting critical heat flux data while maintaining acceptable levels of uncertainty.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2000

Numerical Zooming Between a NPSS Engine System Simulation and a One-Dimensional High Compressor Analysis Code

Within NASA's High Performance Computing and Communication (HPCC) program, NASA Glenn Research Center is developing an environment for the analysis/design of aircraft engines called the Numerical Propulsion System Simulation (NPSS). NPSS focuses on the integration of multiple disciplines such as aerodynamics, structures, and heat transfer along with the concept of numerical zooming between zero-dimensional to one-, two-, and three-dimensional component engine codes. In addition, the NPSS is refining the computing and communication technologies necessary to capture complex physical processes in a timely and cost-effective manner. The vision for NPSS is to create a "numerical test cell" enabling full engine simulations overnight on cost-effective computing platforms. Of the different technology areas that contribute to the development of the NPSS Environment, the subject of this paper is a discussion on numerical zooming between a NPSS engine simulation and higher fidelity representations of the engine components (fan, compressor, burner, turbines, etc.). What follows is a description of successfully zooming one-dimensional (row-by-row) high-pressure compressor analysis results back to a zero-dimensional NPSS engine simulation and a discussion of the results illustrated using an advanced data visualization tool. This type of high fidelity system-level analysis, made possible by the zooming capability of the NPSS, will greatly improve the capability of the engine system simulation and increase the level of virtual test conducted prior to committing the design to hardware.

Follen, Gregory↗
DOE OSTI2021

Exploring Causal Physical Mechanisms via Non-Gaussian Linear Models and Deep Kernel Learning: Applications for Ferroelectric Domain Structures

Rapid emergence of multimodal imaging in scanning probe, electron, and optical microscopies has brought forth the challenge of understanding the information contained in these complex data sets, targeting the intrinsic correlations between different channels, and further exploring the underpinning causal physical mechanisms. Here, we develop such an analysis framework for Piezoresponse Force Microscopy. We argue that under certain conditions, we can bootstrap experimental observations with the prior knowledge of materials structure to get information on certain nonobserved properties, and demonstrate linear causal analysis for PFM observables. We further demonstrate that the strength of individual causal links between complex descriptors can be ascertained using the deep kernel learning (DKL) model. In this DKL analysis, we use the prior information on domain structure within the image to predict the physical properties. This analysis demonstrates the correlative relationships between morphology, piezoresponse, elastic property, etc., at nanoscale. The prediction of morphology and other physical parameters illustrates a mutual interaction between surface condition and physical properties in ferroelectric materials. Overall, this analysis is universal and can be extended to explore the correlative relationships of other multichannel data sets, and allow for high-fidelity reconstruction of underpinning functionalities and physical mechanisms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

CFD Validation Study of a Hypersonic Cone-Slice-Flap Variable Geometry Configuration

Model validation is the process of determining the degree of accuracy between physical reality and the model. The result of model validation can either be used to improve the model through calibration or quantify the model-form uncertainty. This work focuses on providing the model-form uncertainty through an area metric for a hypersonic cone-slice-flap variable geometry configuration given uncertainty in both the simulation and experimental data. The research here compares two different turbulence models for the simulations. For a variable geometry, performing uncertainty quantification to capture the model-form uncertainty on every configuration is computationally challenging. This work lays out a procedure that can give an accurate representation of the model-form uncertainty using a small number of high-fidelity runs and many low-fidelity runs on multiple configurations. Running this comparison provides a quantifiable measurement for the accuracy of each turbulence model for this type of design. The high-fidelity CFD solver used was VULCAN-CFD and the low-fidelity results came from Cart3D. The experimental data came from the 20-Inch Mach 6 Tunnel located at NASA Langley Research Center. The present work showed that the using both the Spalart and Allamaras and Menter Shear-Stress Transport turbulence models overpredicted the drag and lift coefficient, while underpredicting the pitching moment coefficient. The model-form uncertainty estimate resulted in up to a 13.6% change in the total uncertainty for the drag coefficient, up to a 57.4% change in total uncertainty for the lift coefficient, and up to a 100% change in total uncertainty for the pitching moment coefficient.

Laura M. White↗
NASA NTRS2005

Shuttle Orbiter Contingency Abort Aerodynamics: Real-Gas Effects and High Angles of Attack

An important element of the Space Shuttle Orbiter safety improvement plan is the improved understanding of its aerodynamic performance so as to minimize the "black zones" in the contingency abort trajectories [1]. These zones are regions in the launch trajectory where it is predicted that, due to vehicle limitations, the Orbiter will be unable to return to the launch site in a two or three engine-out scenario. Reduction of these zones requires accurate knowledge of the aerodynamic forces and moments to better assess the structural capability of the vehicle. An interesting aspect of the contingency abort trajectories is that the Orbiter would need to achieve angles of attack as high as 60deg. Such steep attitudes are much higher than those for a nominal flight trajectory. The Orbiter is currently flight certified only up to an angle of attack of 44deg at high Mach numbers and has never flown at angles of attack larger than this limit. Contingency abort trajectories are generated using the data in the Space Shuttle Operational Aerodynamic Data Book (OADB) [2]. The OADB, a detailed document of the aerodynamic environment of the current Orbiter, is primarily based on wind-tunnel measurements (over a wide Mach number and angle-of-attack range) extrapolated to flight conditions using available theories and correlations, and updated with flight data where available. For nominal flight conditions, i.e., angles of attack of less than 45deg, the fidelity of the OADB is excellent due to the availability of flight data. However, at the off-nominal conditions, such as would be encountered on contingency abort trajectories, the fidelity of the OADB is less certain. The primary aims of a recent collaborative effort (completed in the year 2001) between NASA and Boeing were to determine: 1) accurate distributions of pressure and shear loads on the Orbiter at select points in the contingency abort trajectory space; and 2) integrated aerodynamic forces and moments for the entire vehicle and the control surfaces (body flap, speed brake, and elevons). The latter served the useful purpose of verification of the aerodynamic characteristics that went into the generation of the abort trajectories.

Prabhu, Dinesh K.↗
NASA NTRS2019

Bounding Methods for Heterogeneous Lidar-derived Navigational Geofences

Safe Unmanned Aerial Vehicle (UAV) operations near the ground require navigation methods that avoid fixed obstacles such as buildings, power lines and trees. Aerial lidar surveys of ground structures are available with the precision and accuracy to geolocate obstacles, but the high volume of raw survey data can exceed the compute power of onboard processors and the rendering ability of ground-based flight planning maps. Representing ground structures with bounding polyhedra instead of point clouds greatly reduces the data size and can enable effective obstacle avoidance, as long as the bounding geometry envelopes the structures with high spatial fidelity. This report describes in detail four methods to compute bounding geometries of ground obstacles from lidar point clouds. The four methods are: 1) 2.5D Maximum Elevation Box, 2) 2.5D Ground Map Extrusion, 3) 3D Bounding Cylinder, and 4) 3D Bounding Box. The methods are applied to five point cloud datasets from lidar surveys of UAV flight research sites in Georgia and Virginia with an average point spacing that ranges from 0.1m to 0.6m. The methods are assessed using survey areas with geometrically heterogeneous ground structures: buildings, vegetation, power lines, and sub-meter structures such as road signs and guy wires. The 2.5D Maximum Elevation Box method is useful for simple structures. The 2.5D Ground Map Extrusion method efficiently encloses vegetation, but requires handdrawn ground footprints. The 3D Bounding Cylinder method excels at enclosing linear structures such as power lines and fences. The 3D Bounding Box method excels at enclosing planar structures such as buildings. The methods are compared on the basis of data compression and boundary fidelity on selected areas. The 2.5D methods yield the highest data compression but the polyhedra produced by them enclose significant amounts of empty space. Boundary fidelity is superior for the 3D methods, though this fidelity comes at the cost of a roughly thirtyfold lower data compression ratio than the 2.5D Maximum Elevation Box method. A mix of these output geometries is proposed for autonomous UAV navigation with limited on-board computing. Both the accuracy and spatial detail of emerging satellite-based survey technology lower than that of aerial lidar scanning survey technology. Sub-meter structures and thin linear structures are not reliably mapped at present by satellite-based surveys.

Moore, Andrew J.↗
DOE OSTI2025

A New Vehicle-to-Vehicle Communication System: Visual-Enhanced Cooperative Traffic Operations

The advent of Connected and Autonomous Vehicles (CAVs) has highlighted the necessity for robust communication systems between vehicles and their environment. This study introduces a novel vehicle-to-vehicle (V2V) communication system, termed the Visual-Enhanced Cooperative Traffic Operations (VECTOR) system. The VECTOR system addresses the need for robust communication by converting dynamic data (including velocity and yaw angle data) into binary code, which is displayed on an LED panel mounted on the top of the vehicle. Following vehicles detect this panel and decode the information using a camera, implementing a visual-based communication method. VECTOR system employs a comprehensive five-module process. Initially, polynomial fitting techniques are applied to velocity data over fixed time intervals using third-degree polynomials, with validation via R² and MSE metrics. The second module converts velocity and yaw angle data into binary form, thereby enhancing detection and processing efficiency. The third module focuses on improving detection stability across various environmental conditions to enhance traffic safety. The fourth module decodes the binary data back into trajectory information, ensuring the fidelity of velocity and yaw angles. The final module integrates eco-control through the VECTOR system, employing advanced control algorithms to minimize energy consumption in CAVs. Experimental evaluations conducted using a modified CAV test platform based on the Lincoln MKZ demonstrate the feasibility and efficiency of the VECTOR system, achieving a 75% R-squared accuracy rate in replicating original velocity data. This methodology not only highlights potential applications but also underscores significant implications for advancing CAV technology.

Ma, Ke↗
NASA NTRS2014

Experimental and Computational Sonic Boom Assessment of Boeing N+2 Low Boom Models

Near-field pressure signatures were measured and computational predictions made for several sonic boom models representing Boeing’s Quiet Experimental Validation Concept (QEVC) supersonic transport, as well as for three axisymmetric calibration models. The concept was designed under a NASA Research Announcement (NRA) contract to address environmental and performance goals, specifically for low sonic boom loudness levels and high cruise efficiency, for an aircraft anticipated to enter service in the 2020-timeframe. Wind tunnel tests were conducted on the aircraft and calibration models during Phases I and II of the NRA contract from 2011 to 2013 in the NASA Ames 9- by 7-Foot and NASA Glenn 8- by 6-Foot Supersonic Wind Tunnels. Sonic boom pressure signatures were acquired primarily at Mach 1.6 and 1.8, and force and moment data were acquired from Mach 0.8 to 1.8. The sonic boom test data were obtained using a 2-in. flat-top pressure rail and a 14-in. tapered “reflection factor 1” (RF1) pressure rail. Both rails capture an entire pressure signature in one data point, and successive signatures at varying positions along or above the rail were used to improve data quality through spatial averaging. The sonic boom data obtained by the rails were validated with high-fidelity numerical simulations of offbody pressures. The test results showed good agreement between the computational and experimental data when a variety of testing techniques including spatial averaging of a series of pressure signatures were employed. The two wind tunnels generally produced comparable data.

Durston, Donald A.↗
DOE OSTI2021

ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather

Abstract. Identifying, detecting, and localizing extreme weather events is a crucial first step in understanding how they may vary under different climate change scenarios. Pattern recognition tasks such as classification, object detection, and segmentation (i.e., pixel-level classification) have remained challenging problems in the weather and climate sciences. While there exist many empirical heuristics for detecting extreme events, the disparities between the output of these different methods even for a single event are large and often difficult to reconcile. Given the success of deep learning (DL) in tackling similar problems in computer vision, we advocate a DL-based approach. DL, however, works best in the context of supervised learning – when labeled datasets are readily available. Reliable labeled training data for extreme weather and climate events is scarce. We create “ClimateNet” – an open, community-sourced human-expert-labeled curated dataset that captures tropical cyclones (TCs) and atmospheric rivers (ARs) in high-resolution climate model output from a simulation of a recent historical period. We use the curated ClimateNet dataset to train a state-of-the-art DL model for pixel-level identification – i.e., segmentation – of TCs and ARs. We then apply the trained DL model to historical and climate change scenarios simulated by the Community Atmospheric Model (CAM5.1) and show that the DL model accurately segments the data into TCs, ARs, or “the background” at a pixel level. Further, we show how the segmentation results can be used to conduct spatially and temporally precise analytics by quantifying distributions of extreme precipitation conditioned on event types (TC or AR) at regional scales. The key contribution of this work is that it paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data using a curated expert-labeled dataset – ClimateNet. ClimateNet and the DL-based segmentation method provide several unique capabilities: (i) they can be used to calculate a variety of TC and AR statistics at a fine-grained level; (ii) they can be applied to different climate scenarios and different datasets without tuning as they do not rely on threshold conditions; and (iii) the proposed DL method is suitable for rapidly analyzing large amounts of climate model output. While our study has been conducted for two important extreme weather patterns (TCs and ARs) in simulation datasets, we believe that this methodology can be applied to a much broader class of patterns and applied to observational and reanalysis data products via transfer learning.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Effect of Geometric Fidelity on the Aerodynamics of a Swept Wing with Glaze Ice Accretion

Aerodynamic assessment of icing effects on swept wings is an important component of a larger effort to improve three-dimensional icing simulation capabilities. An understanding of ice-shape geometric fidelity on iced-wing aerodynamics and the associated flowfield features are needed to guide the development and validation of ice-accretion simulation tools. To this end, wind-tunnel testing was carried out for 8.9% and 13.3% scale semispan wing models based upon the Common Research Model airplane configuration. Various levels of geometric fidelity of an artificial ice shape representing a glaze-ice accretion on a swept wing were investigated. The highest fidelity artificial ice shape reproduced all of the three-dimensional features associated with the glaze ice accretion. The lowest fidelity artificial ice shapes were simple, spanwise-varying horn ice geometries intended to represent the maximum ice thickness on the wing upper surface. The results presented in this paper show that the addition of grit roughness to some lower-fidelity artificial ice shapes resulted in favorable lift and pitching moment comparisons to the wing with the highest fidelity artificial ice shape. In the range of 4.3 to 7.4 deg. angle of attack, surface oil flow visualization and pressure data show that the wing with the two lower fidelity simulations clearly demonstrated a leading edge vortex dominated flowfield, referred to as type I. For the wing with the high fidelity ice shape, the flowfield at lower angles of attack was characterized by streamwise-running, counter-rotating vortical flow referred to as type II. Between 6.4 and 7.4 deg. angle of attack, the effect of the type II flow structures was significantly altered and gave way to the type I leading edge vortex. This means that for angles of attack 7.4 deg. and higher, the wing with all three configurations exhibited the same type of flowfield. This helps to explain why there is reasonably good agreement in the lift and pitching moment coefficients among these configurations.

Aircraft icing↗
NASA NTRSDate not supplied

Application of Low & Mid Fidelity Tools to the Moog SureFly Vehicle

The results from a comprehensive acoustic analysis using low- and mid-fidelity tools to predict the acoustic signature of the Moog SureFly® UAM vehicle are presented and compared to experimental ground test data taken in 2019. The data presented is taken from a ground plate microphone at a distance of just under 100 feet from the propeller source. Simulations were performed for a single propeller and a coaxial pair. Results indicate that the first few blade passing frequency (BPF) harmonics match the experimental data reasonably well using both low- and mid-fidelity prediction toolchains.

Acoustics↗
NASA NTRS2015

Ultra-Compact Ka-Band Parabolic Deployable Antenna for RADAR and Interplanetary CubeSats

Over the past several years, technology and launch opportunities for CubeSats have exploded, enabling a wide variety of missions. However, as instruments become more complex and CubeSats travel deeper into space, data communication rates become an issue. To solve this challenge, JPL has initiated a research and technology development effort to design a 0.5 meter Ka-band parabolic deployable antenna (KaPDA) which would stow in 1.5U (10 x 10 x 15 cu cm) and provide 42dB of gain (50% efficiency). A folding rib architecture and dual reflector Cassegrainian design was selected as it best balances RF gain and stowed size. The design implements an innovative telescoping waveguide and gas powered deployment. RF simulations show that after losses, the antenna would have over 42 dB gain, supported by preliminary test results. KaPDA would create opportunities for a host of new CubeSat missions by allowing high data rate communication which would enable using high fidelity instruments or venturing further into deep space, including potential interplanetary missions. Additionally KaPDA would provide a solution for other small antenna needs and the opportunity to obtain Earth science data. This paper discusses the design challenges encountered, the architecture of the solution, and the antennas expected performance capabilities.

Sauder, Jonathan↗
NASA NTRS2022

Europa Clipper Payload Verification and Validation: Avionics-Instrument Interface Test Campaign

NASA's Europa Clipper mission will investigate Jupiter's icy moon Europa using a payload suite consisting of nine instruments to address a range of scientific objectives concerning Europa's habitability. As the project proceeds past its Critical Design Review, confidence is being built in the system's ability to achieve mission objectives through the implementation of a rigorous payload verification and validation (V&V) program. As part of this payload V&V program, instrument box-level testing was performed by the payload team to verify select instrument-avionics interface requirements. This testing was performed at JPL using the avionics testbed's Bulk Data Storage Emulator (BDSEM) with visiting instrument Test Models. This paper summarizes the Data Link test campaign involving roughly four days of functional testing per instrument, including planning, testing methods, types of issues found, and the requirement closure process. Detail is also provided on the development, deployment, and validation of a standardized analysis tool used in data reviews. This testing verified requirements related to commanding rates, loss of link, packet format, clock counters, loopback test capability, and SpaceWire jitter and skew margins. Additional risk reduction testing of basic commanding, counter behavior, science data collection and transfer, and interface swapping was also performed. Because the BDSEM venue was not originally designed to be a run for record venue, the process of characterizing venue fidelity and establishing suitability for requirement closure using data collected in this venue will also be addressed.In order to close requirements, an extensible tool was developed to post-process instrument command and telemetry data from their original binary to a human-readable format and give visibility to errors detected within the data, such as packets with Cyclic Redundancy Check errors. This tool, called payload-packet-parser, is a Python 3.9 command line tool built using a variety of open-source Python libraries. Payload-packet-parser was designed to support parsing command and telemetry packets for all Europa Clipper instruments and additional analysis tools were developed for verification of specific information interface requirements. This test campaign, including post-processing using a single parsing and verification toolset, allowed for early interface testing, alleviating testing burdens on instrument teams and buying down risk on the instrument-avionics interface by finding hardware and software issues and idiosyncrasies prior to integration with system test venues. Over twenty issues were discovered across the payload, resulting in software updates and instrument rework well in advance of any system impacts. This paper concludes with an assessment of benefits and costs of this type of testing and lessons learned.

Montanez, Leticia↗
DOE OSTI2020

Bayesian inference of Stochastic reaction networks using Multifidelity Sequential Tempered Markov Chain Monte Carlo

Stochastic reaction network models are often used to explain and predict the dynamics of gene regulation in single cells.These models usually involve several parameters, such as the kinetic rates of chemical reactions, that are not directly measurable and must be inferred from experimental data. Bayesian inference provides a rigorous probabilistic frame-work for identifying these parameters by finding a posterior parameter distribution that captures their uncertainty.Traditional computational methods for solving inference problems such as Markov Chain Monte Carlo methods based on classical Metropolis-Hastings algorithm involve numerous serial evaluations of the likelihood function, which in turn requires expensive forward solutions of the chemical master equation (CME). We propose an alternate approach based on a multifidelity extension of the Sequential Tempered Markov Chain Monte Carlo (ST-MCMC) sampler. This algorithm is built upon Sequential Monte Carlo and solves the Bayesian inference problem by decomposing it into a sequence of efficiently solved subproblems that gradually increase both model fidelity and the influence of the observed data. We reformulate the finite state projection (FSP) algorithm, a well-known method for solving the CME, to produce a hierarchy of surrogate master equations to be used in this multifidelity scheme. To determine the appropriate fidelity, we introduce a novel information-theoretic criteria that seeks to extract the most information about the ultimate Bayesian posterior from each model in the hierarchy without inducing significant bias. This novel sampling scheme is tested with high performance computing resources using biologically relevant problems.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2004

Uncertainty-Based Approach for Dynamic Aerodynamic Data Acquisition and Analysis

Development of improved modeling methods to provide increased fidelity of flight predictions for aircraft motions during flight in flow regimes with large nonlinearities requires improvements in test techniques for measuring and characterizing wind tunnel data. This paper presents a method for providing a measure of data integrity for static and forced oscillation test techniques. Data integrity is particularly important when attempting to accurately model and predict flight of today s high performance aircraft which are operating in expanded flight envelopes, often maneuvering at high angular rates at high angles-of-attack, even above maximum lift. Current aerodynamic models are inadequate in predicting flight characteristics in the expanded envelope, such as rapid aircraft departures and other unusual motions. Present wind tunnel test methods do not factor changes of flow physics into data acquisition schemes, so in many cases data are obtained over more iterations than required, or insufficient data may be obtained to determine a valid estimate with statistical significance. Additionally, forced oscillation test techniques, one of the primary tools used to develop dynamic models, do not currently provide estimates of the uncertainty of the results during an oscillation cycle. A method to optimize the required number of forced oscillation cycles based on decay of uncertainty gradients and balance tolerances is also presented.

Heim, Eugene H. D.↗