Modal Test of Six-Meter Hypersonic Inflatable Aerodynamic Decelerator

A modal test was performed on the six-meter Hypersonic Inflatable Aerodynamic Decelerator (HIAD) test article to gain a firm understanding of the dynamic characteristics of the unloaded structure within the low frequency range. The tests involved various configurations of the HIAD to understand the influence of the tri-torus, the varying pressure within the toroids and the influence of straps. The primary test was conducted utilizing an eletrodynamic shaker and the results were verified using a step relaxation technique. The analysis results show an increase in the structure's stiffness with respect to increasing pressure. The results also show the rise of coupled modes with the tri-torus configurations. During the testing activity, the attached straps exhibited a behavior that is similar to that described as fuzzy structures in the literature. Therefore extensive tests were also performed by utilizing foam to mitigate these effects as well as understand the modal parameters of these fuzzy sub structures. Results are being utilized to update the finite element model of the six-meter HIAD and to gain a better understanding of the modeling of complex inflatable structures

In-Space Robotic Assembly Joint Characterization Approach

This paper describes the test systems and approaches developed to characterize the performance of a structural joint that is intended for robotic in-space assembly (ISA). The design of the joint is based on a heritage concept from National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) originally intended for structural assembly by astronauts during extravehicular activity (EVA). Its design was modified under a public-private partnership and is intended to accelerate the availability of, and reduce costs for the infusion of NASA developed technologies into commercial ISA systems. Test systems were developed to measure the axial, bending, and torsional stiffness of the joint at a wide range of temperatures. A test system was also developed to measure the reliability of the joint in terms of translation and rotation when disassembled and re-assembled in space. These test systems were used to characterize the joint behavior and provide performance data for the iterative joint design process. The paper also lists the lessons learned to aid testing of next generation robotic in-space assembly joints

Prediction of Cognitive States During Flight Simulation Using Multimodal Psychophysiological Sensing

The Commercial Aviation Safety Team found the majority of recent international commercial aviation accidents attributable to loss of control inflight involved flight crew loss of airplane state awareness (ASA), and distraction was involved in all of them. Research on attention-related human performance limiting states (AHPLS) such as channelized attention, diverted attention, startle/surprise, and confirmation bias, has been recommended in a Safety Enhancement (SE) entitled "Training for Attention Management." To accomplish the detection of such cognitive and psychophysiological states, a broad suite of sensors was implemented to simultaneously measure their physiological markers during a high fidelity flight simulation human subject study. Twenty-four pilot participants were asked to wear the sensors while they performed benchmark tasks and motion-based flight scenarios designed to induce AHPLS. Pattern classification was employed to predict the occurrence of AHPLS during flight simulation also designed to induce those states. Classifier training data were collected during performance of the benchmark tasks. Multimodal classification was performed, using pre-processed electroencephalography, galvanic skin response, electrocardiogram, and respiration signals as input features. A combination of one, some or all modalities were used. Extreme gradient boosting, random forest and two support vector machine classifiers were implemented. The best accuracy for each modality-classifier combination is reported. Results using a select set of features and using the full set of available features are presented. Further, results are presented for training one classifier with the combined features and for training multiple classifiers with features from each modality separately. Using the select set of features and combined training, multistate prediction accuracy averaged 0.64 +/- 0.14 across thirteen participants and was significantly higher than that for the separate training case. These results support the goal of demonstrating simultaneous real-time classification of multiple states using multiple sensing modalities in high fidelity flight simulators. This detection is intended to support and inform training methods under development to mitigate the loss of ASA and thus reduce accidents and incidents

Crew State Monitoring and Line-Oriented Flight Training for Attention Management

Loss of control –inflight (LOC-I) has historically represented the largest category of commercial aviation fatal accidents. A review of worldwide transport airplane accidents (2001-2010) indicated that loss of airplane state awareness (ASA) was responsible for the majority of the LOC-I fatality rate. The Commercial Aviation Safety Team (CAST) ASA study identified12 major themes that were indicated across the ASA accident and incident events. One of the themes was crew distractionorineffective attention management, which was found to be involved in all 18 events including flight crew channelized attention, startle/surprise, diverted attention, and/or confirmation bias. Safety Enhancement(SE)-211, “Training for Attention Management” was formed to conduct research to develop and assess commercial airline training methods and realistic scenarios that can address these attention-related human performance limitations. This paper describes NASA SE-211 research for new design approaches and validation of line-oriented flight training (LOFT)