An Experimental Approach to a Rapid Propulsion and Aeronautics Concepts Testbed

Modern aircraft design tools have limitations for predicting complex propulsion-airframe interactions. The demand for new tools and methods addressing these limitations is high based on the many recent Distributed Electric Propulsion (DEP) Vertical Take-Off and Landing (VTOL) concepts being developed for Urban Air Mobility (UAM) markets. We propose that low cost electronics and additive manufacturing can support the conceptual design of advanced autonomy-enabled concepts, by facilitating rapid prototyping for experimentally driven design cycles. This approach has the potential to reduce complex aircraft concept development costs, minimize unique risks associated with the conceptual design, and shorten development schedule by enabling the determination of many "unknown unknowns" earlier in the design process and providing verification of the results from aircraft design tools. A modular testbed was designed and built to evaluate this rapid design-build-test approach and to support aeronautics and autonomy research targeting UAM applications utilizing a complex, transitioning-VTOL aircraft configuration. The testbed is a modular wind tunnel and flight model. The testbed airframe is approximately 80% printed, with labor required for assembly. This paper describes the design process, fabrication process, ground testing, and initial wind tunnel structural and thermal loading of a proof-of-concept aircraft, the Langley Aerodrome 8 (LA-8)

Variably Transmittive, Electronically-Controlled Eyewear

A system and method for flight training and evaluation of pilots comprises electronically activated vision restriction glasses that detect the pilot's head position and automatically darken and restrict the pilot's ability to see through the front and side windscreens when the pilot-in-training attempts to see out the windscreen. Thus, the pilot-in-training sees only within the aircraft cockpit, forcing him or her to fly by instruments in the most restricted operational mode

Piloted Simulation of Various Synthetic Vision Systems Terrain Portrayal and Guidance Symbology Concepts for Low Altitude En-Route Scenario

In support of the NASA Aviation Safety Program's Synthetic Vision Systems Project, a series of piloted simulations were conducted to explore and quantify the relationship between candidate Terrain Portrayal Concepts and Guidance Symbology Concepts, specific to General Aviation. The experiment scenario was based on a low altitude en route flight in Instrument Metrological Conditions in the central mountains of Alaska. A total of 18 general aviation pilots, with three levels of pilot experience, evaluated a test matrix of four terrain portrayal concepts and six guidance symbology concepts. Quantitative measures included various pilot/aircraft performance data, flight technical errors and flight control inputs. The qualitative measures included pilot comments and pilot responses to the structured questionnaires such as perceived workload, subjective situation awareness, pilot preferences, and the rare event recognition. There were statistically significant effects found from guidance symbology concepts and terrain portrayal concepts but no significant interactions between them. Lower flight technical errors and increased situation awareness were achieved using Synthetic Vision Systems displays, as compared to the baseline Pitch/Roll Flight Director and Blue Sky Brown Ground combination. Overall, those guidance symbology concepts that have both path based guidance cue and tunnel display performed better than the other guidance concepts

Aspects of Synthetic Vision Display Systems and the Best Practices of the NASA's SVS Project

NASA s Synthetic Vision Systems (SVS) Project conducted research aimed at eliminating visibility-induced errors and low visibility conditions as causal factors in civil aircraft accidents while enabling the operational benefits of clear day flight operations regardless of actual outside visibility. SVS takes advantage of many enabling technologies to achieve this capability including, for example, the Global Positioning System (GPS), data links, radar, imaging sensors, geospatial databases, advanced display media and three dimensional video graphics processors. Integration of these technologies to achieve the SVS concept provides pilots with high-integrity information that improves situational awareness with respect to terrain, obstacles, traffic, and flight path. This paper attempts to emphasize the system aspects of SVS - true systems, rather than just terrain on a flight display - and to document from an historical viewpoint many of the best practices that evolved during the SVS Project from the perspective of some of the NASA researchers most heavily involved in its execution. The Integrated SVS Concepts are envisagements of what production-grade Synthetic Vision systems might, or perhaps should, be in order to provide the desired functional capabilities that eliminate low visibility as a causal factor to accidents and enable clear-day operational benefits regardless of visibility conditions

Greased Lightning (GL-10) Performance Flight Research: Flight Data Report

Modern aircraft design methods have produced acceptable designs for large conventional aircraft performance. With revolutionary electronic propulsion technologies fueled by the growth in the small UAS (Unmanned Aerial Systems) industry, these same prediction models are being applied to new smaller, and experimental design concepts requiring a VTOL (Vertical Take Off and Landing) capability for ODM (On Demand Mobility). A 50% sub-scale GL-10 flight model was built and tested to demonstrate the transition from hover to forward flight utilizing DEP (Distributed Electric Propulsion)[1][2]. In 2016 plans were put in place to conduct performance flight testing on the 50% sub-scale GL-10 flight model to support a NASA project called DELIVER (Design Environment for Novel Vertical Lift Vehicles). DELIVER was investigating the feasibility of including smaller and more experimental aircraft configurations into a NASA design tool called NDARC (NASA Design and Analysis of Rotorcraft)[3]. This report covers the performance flight data collected during flight testing of the GL-10 50% sub-scale flight model conducted at Beaver Dam Airpark, VA. Overall the flight test data provides great insight into how well our existing conceptual design tools predict the performance of small scale experimental DEP concepts. Low fidelity conceptual design tools estimated the (L/D)( sub max)of the GL-10 50% sub-scale flight model to be 16. Experimentally measured (L/D)( sub max) for the GL-10 50% scale flight model was 7.2. The aerodynamic performance predicted versus measured highlights the complexity of wing and nacelle interactions which is not currently accounted for in existing low fidelity tools

The Status of Spacecraft Bus and Platform Technology Development under the NASA In-Space Propulsion Technology Program

NASA's In-Space Propulsion Technology (ISPT) program has been developing technologies for lowering the cost of planetary science missions. The technology areas include electric propulsion technologies, spacecraft bus technologies, entry vehicle technologies, and design tools for systems analysis and mission trajectories. The electric propulsion technologies include critical components of both gridded and non-gridded ion propulsion systems. The spacecraft bus technologies under development include an ultra-lightweight tank (ULTT) and advanced xenon feed system (AXFS). The entry vehicle technologies include the development of a multi-mission entry vehicle, mission design tools and aerocapture. The design tools under development include system analysis tools and mission trajectory design tools

Precipitation and Crystallization Kinetics in Silica Gardens

Silica gardens are extraordinary plant-like structures resulting from the complex interplay of relatively simple inorganic components. Recent work has highlighted that macroscopic self-assembly is accompanied by the spontaneous formation of considerable chemical gradients, which induce a cascade of coupled dissolution, diffusion, and precipitation processes occurring over timescales as long as several days. In the present study, this dynamic behavior was investigated for silica gardens based on iron and cobalt chloride by means of two synchrotron- based techniques, which allow the determination of concentration profiles and time-resolved monitoring of diffraction patterns, thus giving direct insight into the progress of dissolution and crystallization phenomena in the system. On the basis of the collected data, a kinetic model is proposed to describe the relevant reactions on a fundamental physicochemical level. The results show that the choice of the metal cations (as well as their counterions) is crucial for the development of silica gardens in both the short and long term (i. e. during tube formation and upon subsequent slow equilibration), and provide important clues for understanding the properties of related structures in geochemical and industrial environments

Deterministic Classifiers Accuracy Optimization for Cancer Microarray Data

The objective of this study was to improve classification accuracy in cancer microarray gene expression data using a collection of machine learning algorithms available in WEKA. State of the art deterministic classification methods, such as: Kernel Logistic Regression, Support Vector Machine, Stochastic Gradient Descent and Logistic Model Trees were applied on publicly available cancer microarray datasets aiming to discover regularities that provide insights to help characterization and diagnosis correctness on each cancer typology. The implemented models, relying on 10-fold cross-validation, parameterized to enhance accuracy, reached accuracy above 90%. Moreover, although the variety of methodologies, no significant statistic differences were registered between them, at significance level 0.05, confirming that all the selected methods are effective for this type of analysis.info:eu-repo/semantics/publishedVersio

NEAT: An efficient network enrichment analysis test

Background: Network enrichment analysis is a powerful method, which allows to integrate gene enrichment analysis with the information on relationships between genes that is provided by gene networks. Existing tests for network enrichment analysis deal only with undirected networks, they can be computationally slow and are based on normality assumptions. Results: We propose NEAT, a test for network enrichment analysis. The test is based on the hypergeometric distribution, which naturally arises as the null distribution in this context. NEAT can be applied not only to undirected, but to directed and partially directed networks as well. Our simulations indicate that NEAT is considerably faster than alternative resampling-based methods, and that its capacity to detect enrichments is at least as good as the one of alternative tests. We discuss applications of NEAT to network analyses in yeast by testing for enrichment of the Environmental Stress Response target gene set with GO Slim and KEGG functional gene sets, and also by inspecting associations between functional sets themselves. Conclusions: NEAT is a flexible and efficient test for network enrichment analysis that aims to overcome some limitations of existing resampling-based tests. The method is implemented in the R package neat, which can be freely downloaded from CRAN ( https://cran.r-project.org/package=neat )