Aeroelastic modeling for the FIT (Functional Integration Technology) team F/A-18 simulation

As part of Langley Research Center's commitment to developing multidisciplinary integration methods to improve aerospace systems, the Functional Integration Technology (FIT) team was established to perform dynamics integration research using an existing aircraft configuration, the F/A-18. An essential part of this effort has been the development of a comprehensive simulation modeling capability that includes structural, control, and propulsion dynamics as well as steady and unsteady aerodynamics. The structural and unsteady aerodynamics contributions come from an aeroelastic mode. Some details of the aeroelastic modeling done for the Functional Integration Technology (FIT) team research are presented. Particular attention is given to work done in the area of correction factors to unsteady aerodynamics data

On the relationship between matched filter theory as applied to gust loads and phased design loads analysis

A theoretical basis and example calculations are given that demonstrate the relationship between the Matched Filter Theory approach to the calculation of time-correlated gust loads and Phased Design Load Analysis in common use in the aerospace industry. The relationship depends upon the duality between Matched Filter Theory and Random Process Theory and upon the fact that Random Process Theory is used in Phased Design Loads Analysis in determining an equiprobable loads design ellipse. Extensive background information describing the relevant points of Phased Design Loads Analysis, calculating time-correlated gust loads with Matched Filter Theory, and the duality between Matched Filter Theory and Random Process Theory is given. It is then shown that the time histories of two time-correlated gust load responses, determined using the Matched Filter Theory approach, can be plotted as parametric functions of time and that the resulting plot, when superposed upon the design ellipse corresponding to the two loads, is tangent to the ellipse. The question is raised of whether or not it is possible for a parametric load plot to extend outside the associated design ellipse. If it is possible, then the use of the equiprobable loads design ellipse will not be a conservative design practice in some circumstances

Integrated control/structure optimization by multilevel decomposition

A method for integrated control/structure optimization by multilevel decomposition is presented. It is shown that several previously reported methods were actually partial decompositions wherein only the control was decomposed into a subsystem design. One of these partially decomposed problems was selected as a benchmark example for comparison. The system is fully decomposed into structural and control subsystem designs and an improved design is produced. Theory, implementation, and results for the method are presented and compared with the benchmark example

Aeroelastic modeling for the FIT team F/A-18 simulation

Some details of the aeroelastic modeling of the F/A-18 aircraft done for the Functional Integration Technology (FIT) team's research in integrated dynamics modeling and how these are combined with the FIT team's integrated dynamics model are described. Also described are mean axis corrections to elastic modes, the addition of nonlinear inertial coupling terms into the equations of motion, and the calculation of internal loads time histories using the integrated dynamics model in a batch simulation program. A video tape made of a loads time history animation was included as a part of the oral presentation. Also discussed is work done in one of the areas of unsteady aerodynamic modeling identified as needing improvement, specifically, in correction factor methodologies for improving the accuracy of stability derivatives calculated with a doublet lattice code

Health Determinants among North Americans Experiencing Homelessness and Traumatic Brain Injury: A Scoping Review.

Traumatic brain injury (TBI) in those experiencing homelessness has been described in recent literature as a contributor to increased morbidity, decreased functional independence, and early mortality. In this systematically conducted scoping review, we aimed to better delineate the health determinants-as defined by Health Canada/Centers for Disease Control and Prevention (CDC)-associated with TBI in North Americans experiencing homelessness. BIOSIS, MEDLINE, CINAHL, EMBASE, SCOPUS, and Global Health were searched from inception to December 30, 2020. Gray literature search consisted of relevant meeting proceedings. A two-step process was undertaken, assessing title/abstract and full articles, respectively, based on inclusion/exclusion criteria, leading to the final 20 articles included in the review. Data were abstracted, assessing the aims, literature quality, and bias. Five health determinants displayed strong associations with TBI in those North Americans experiencing homelessness, including male gender, poor physical environment, negative personal health behaviors, adverse childhood experiences (ACEs), and low educational attainment. In those studies displaying a comparator population experiencing homelessness without TBI, the TBI group displayed trends toward increased disparity in Health Canada and CDC defined health determinants. Most studies suffered from moderate limitations. There are associations between male gender, poor physical environment, negative personal health behaviors, ACEs, and limited education in those experiencing homelessness and TBI. The results suggest that those experiencing homelessness with TBI in North America suffer poorer health consequences than those without TBI. Future research on TBI in North Americans experiencing homelessness should focus on health determinants as potential areas for intervention, which may lead to improved outcomes for those experiencing both homelessness and TBI

Learning the Roots of Visual Domain Shift

In this paper we focus on the spatial nature of visual domain shift, attempting to learn where domain adaptation originates in each given image of the source and target set. We borrow concepts and techniques from the CNN visualization literature, and learn domainnes maps able to localize the degree of domain specificity in images. We derive from these maps features related to different domainnes levels, and we show that by considering them as a preprocessing step for a domain adaptation algorithm, the final classification performance is strongly improved. Combined with the whole image representation, these features provide state of the art results on the Office dataset.Comment: Extended Abstrac

Including Aeroelastic Effects in the Calculation of X-33 Loads and Control Characteristics

Up until now, loads analyses of the X-33 RLV have been done at Marshall Space Flight Center (MSFC) using aerodynamic loads derived from CFD and wind tunnel models of a rigid vehicle. Control forces and moments are determined using a rigid vehicle trajectory analysis and the detailed control load distributions for achieving the desired control forces and moments, again on the rigid vehicle, are determined by Lockheed Martin Skunk Works. However, static aeroelastic effects upon the load distributions are not known. The static aeroelastic effects will generally redistribute external loads thereby affecting both the internal structural loads as well as the forces and moments generated by aerodynamic control surfaces. Therefore, predicted structural sizes as well as maneuvering requirements can be altered by consideration of static aeroelastic effects. The objective of the present work is the development of models and solutions for including static aeroelasticity in the calculation of X-33 loads and in the determination of stability and control derivatives

Part Detector Discovery in Deep Convolutional Neural Networks

Current fine-grained classification approaches often rely on a robust localization of object parts to extract localized feature representations suitable for discrimination. However, part localization is a challenging task due to the large variation of appearance and pose. In this paper, we show how pre-trained convolutional neural networks can be used for robust and efficient object part discovery and localization without the necessity to actually train the network on the current dataset. Our approach called "part detector discovery" (PDD) is based on analyzing the gradient maps of the network outputs and finding activation centers spatially related to annotated semantic parts or bounding boxes. This allows us not just to obtain excellent performance on the CUB200-2011 dataset, but in contrast to previous approaches also to perform detection and bird classification jointly without requiring a given bounding box annotation during testing and ground-truth parts during training. The code is available at http://www.inf-cv.uni-jena.de/part_discovery and https://github.com/cvjena/PartDetectorDisovery.Comment: Accepted for publication on Asian Conference on Computer Vision (ACCV) 201

Right for the Right Reason: Training Agnostic Networks

We consider the problem of a neural network being requested to classify images (or other inputs) without making implicit use of a "protected concept", that is a concept that should not play any role in the decision of the network. Typically these concepts include information such as gender or race, or other contextual information such as image backgrounds that might be implicitly reflected in unknown correlations with other variables, making it insufficient to simply remove them from the input features. In other words, making accurate predictions is not good enough if those predictions rely on information that should not be used: predictive performance is not the only important metric for learning systems. We apply a method developed in the context of domain adaptation to address this problem of "being right for the right reason", where we request a classifier to make a decision in a way that is entirely 'agnostic' to a given protected concept (e.g. gender, race, background etc.), even if this could be implicitly reflected in other attributes via unknown correlations. After defining the concept of an 'agnostic model', we demonstrate how the Domain-Adversarial Neural Network can remove unwanted information from a model using a gradient reversal layer.Comment: Author's original versio