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

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

Development of the Orion Crew Module Static Aerodynamic Database

The Orion aerodynamic database provides force and moment coefficients given the velocity, attitude, configuration, etc. of the Crew Exploration Vehicle (CEV). The database is developed and maintained by the NASA CEV Aerosciences Project team from computational and experimental aerodynamic simulations. The database is used primarily by the Guidance, Navigation, and Control (GNC) team to design vehicle trajectories and assess flight performance. The initial hypersonic re-entry portion of the Crew Module (CM) database was developed in 2006. Updates incorporating additional data and improvements to the database formulation and uncertainty methodologies have been made since then. This paper details the process used to develop the CM database, including nominal values and uncertainties, for Mach numbers greater than 8 and angles of attack between 140deg and 180deg. The primary available data are more than 1000 viscous, reacting gas chemistry computational simulations using both the Laura and Dplr codes, over a range of Mach numbers from 2 to 37 and a range of angles of attack from 147deg to 172deg. Uncertainties were based on grid convergence, laminar-turbulent solution variations, combined altitude and code-to-code variations, and expected heatshield asymmetry. A radial basis function response surface tool, NEAR-RS, was used to fit the coefficient data smoothly in a velocity-angle-of-attack space. The resulting database is presented and includes some data comparisons and a discussion of the predicted variation of trim angle of attack and lift-to-drag ratio. The database provides a variation in trim angle of attack on the order of +/-2deg, and a range in lift-to-drag ratio of +/-0.035 for typical vehicle flight conditions

Implementation of a Pulsed-Laser Measurement System in the National Transonic Facility

A remotely-adjustable laser transmission and imaging system has been developed for use in a high-pressure, cryogenic wind tunnel. Implementation in the National Transonic Facility has proven the system suitable for velocity and signal lifetime measurements over a range of operating conditions. The measurement system allows for the delivery of high-powered laser pulses through the outer pressure shell and into the test section interior from a mezzanine where the laser is free from environmental disturbances (such as vibrations and excessive condensation) associated with operation of the wind tunnel. Femtosecond laser electronic excitation tagging (FLEET) was utilized to provide freestream velocity measurements, and first results show typical data that may be obtained using the system herein described

Development of the Orion Crew Module Static Aerodynamic Database

This work describes the process of developing the nominal static aerodynamic coefficients and associated uncertainties for the Orion Crew Module for Mach 8 and below. The database was developed from wind tunnel test data and computational simulations of the smooth Crew Module geometry, with no asymmetries or protuberances. The database covers the full range of Reynolds numbers seen in both entry and ascent abort scenarios. The basic uncertainties were developed as functions of Mach number and total angle of attack from variations in the primary data as well as computations at lower Reynolds numbers, on the baseline geometry, and using different flow solvers. The resulting aerodynamic database represents the Crew Exploration Vehicle Aerosciences Project's best estimate of the nominal aerodynamics for the current Crew Module vehicle

Back Propagation Neural Networks for Predicting Ultimate Strengths of Unidirectional Graphite/Epoxy Tensile Specimens

The research presented herein demonstrates the feasibility of predicting ultimate strengths in simple composite structures through a neural network analysis of their acoustic emission (AE) amplitude distribution data. A series of eleven ASTM D-3039 unidirectional graphite/epoxy tensile samples were loaded to failure to generate the amplitude distributions for this analysis. A back propagation neural network was trained to correlate the AE amplitude distribution signatures generated during the first 25% of loading with the ultimate strengths of the samples. The network was trained using two sets of inputs: (1) the statistical parameters obtained from a Weibull distribution fit of the amplitude distribution data, and (2) the event frequency (amplitude) distribution itself. The neural networks were able to predict ultimate strengths with a worst case error of -8.99% for the Weibull modeled amplitude distribution data and 3.74% when the amplitude distribution itself was used to train the network. The principal reason for the improved prediction capability of the latter technique lies in the ability of the neural network to extract subtle features from within the amplitude distribution

Using a knowledge management evaluation framework for improving an ERP system - a Hong Kong construction industry case study

Organisations need to rely on leadership, information support and human capital in order to ensure a knowledge advantage over their competitors. Knowledge management (KM) provides organisations with sustainable competitive advantage, because it becomes extremely difficult for an organisation to cut expenditure and increase revenue by simply reengineering its business model. Project delivery and success has been traditionally viewed and measured as management of a three-legged stool, with the legs defined as cost, schedule and quality. However, KM can be linked to success by organisations becoming more effective as well as being more efficient.This paper uses a KM framework, the Knowledge Advantage (K-Adv), developed initially for use by construction organisations. It assesses the impact of leadership and its supporting information communication technology infrastructure on the ability of people (by effectively creating, sharing, disseminating and using knowledge) to facilitate sustainable competitive advantage.A case study that is presented is based upon the experience of a leading construction company using an Enterprise Resources Planning (ERP) system to demonstrate the effectiveness of KM from a cost management business unit perspective. Results are evaluated using a capability maturity model (CMM) - that forms the core of the K-Adv tool - to help improve processes that meet the needs of the organisation operating in a highly dynamic business environment. The case study is part of a broader doctoral research project that uses action learning to facilitate and measure ERP improvement.<br /

Mach Stability Improvements Using an Existing Second Throat Capability at the National Transonic Facility

Recent data quality improvements at the National Transonic Facility have an intended goal of reducing the Mach number variation in a data point to within plus or minus 0.0005, with the ultimate goal of reducing the data repeatability of the drag coefficient for full-span subsonic transport models at transonic speeds to within half a drag count. This paper will discuss the Mach stability improvements achieved through the use of an existing second throat capability at the NTF to create a minimum area at the end of the test section. These improvements were demonstrated using both the NASA Common Research Model and the NTF Pathfinder-I model in recent experiments. Sonic conditions at the throat were verified using sidewall static pressure data. The Mach variation levels from both experiments in the baseline tunnel configuration and the choked tunnel configuration will be presented and the correlation between Mach number and drag will also be examined. Finally, a brief discussion is given on the consequences of using the second throat in its location at the end of the test section

Development and Validation of eRADAR: A Tool Using EHR Data to Detect Unrecognized Dementia.

ObjectivesEarly recognition of dementia would allow patients and their families to receive care earlier in the disease process, potentially improving care management and patient outcomes, yet nearly half of patients with dementia are undiagnosed. Our aim was to develop and validate an electronic health record (EHR)-based tool to help detect patients with unrecognized dementia (EHR Risk of Alzheimer's and Dementia Assessment Rule [eRADAR]).DesignRetrospective cohort study.SettingKaiser Permanente Washington (KPWA), an integrated healthcare delivery system.ParticipantsA total of 16 665 visits among 4330 participants in the Adult Changes in Thought (ACT) study, who undergo a comprehensive process to detect and diagnose dementia every 2 years and have linked KPWA EHR data, divided into development (70%) and validation (30%) samples.MeasurementsEHR predictors included demographics, medical diagnoses, vital signs, healthcare utilization, and medications within the previous 2 years. Unrecognized dementia was defined as detection in ACT before documentation in the KPWA EHR (ie, lack of dementia or memory loss diagnosis codes or dementia medication fills).ResultsOverall, 1015 ACT visits resulted in a diagnosis of incident dementia, of which 498 (49%) were unrecognized in the KPWA EHR. The final 31-predictor model included markers of dementia-related symptoms (eg, psychosis diagnoses, antidepressant fills), healthcare utilization pattern (eg, emergency department visits), and dementia risk factors (eg, cerebrovascular disease, diabetes). Discrimination was good in the development (C statistic = .78; 95% confidence interval [CI] = .76-.81) and validation (C statistic = .81; 95% CI = .78-.84) samples, and calibration was good based on plots of predicted vs observed risk. If patients with scores in the top 5% were flagged for additional evaluation, we estimate that 1 in 6 would have dementia.ConclusionThe eRADAR tool uses existing EHR data to detect patients with good accuracy who may have unrecognized dementia. J Am Geriatr Soc 68:103-111, 2019

Model Robust Calibration: Method and Application to Electronically-Scanned Pressure Transducers

This article presents the application of a recently developed statistical regression method to the controlled instrument calibration problem. The statistical method of Model Robust Regression (MRR), developed by Mays, Birch, and Starnes, is shown to improve instrument calibration by reducing the reliance of the calibration on a predetermined parametric (e.g. polynomial, exponential, logarithmic) model. This is accomplished by allowing fits from the predetermined parametric model to be augmented by a certain portion of a fit to the residuals from the initial regression using a nonparametric (locally parametric) regression technique. The method is demonstrated for the absolute scale calibration of silicon-based pressure transducers