Concurrent optimization of airframe and engine design parameters

An integrated system for the multidisciplinary analysis and optimization of airframe and propulsion design parameters is being developed. This system is known as IPAS, the Integrated Propulsion/Airframe Analysis System. The traditional method of analysis is one in which the propulsion system analysis is loosely coupled to the overall mission performance analysis. This results in a time consuming iterative process. First, the engine is designed and analyzed. Then, the results from this analysis are used in a mission analysis to determine the overall aircraft performance. The results from the mission analysis are used as a guide as the engine is redesigned and the entire process repeated. In IPAS, the propulsion system, airframe, and mission are closely coupled. The propulsion system analysis code is directly integrated into the mission analysis code. This allows the propulsion design parameters to be optimized along with the airframe and mission design parameters, significantly reducing the time required to obtain an optimized solution

The Role of CD103+ Dendritic Cells in the Intestinal Mucosal Immune System

While dendritic cells (DC) are central to the induction and regulation of adaptive immunity, these cells are very heterogenous and specific subsets can be characterized based on the expression of cell surface markers and functional properties. Intestinal CD103+ DCs are the subject of particular interest due to their role in regulating mucosal immunity. Since the epithelial surfaces are constantly exposed to a high antigenic load, tight regulation of innate and adaptive intestinal immune responses is vital as intestinal inflammation can have detrimental consequences for the host. Strategically positioned within the lamina propria, CD103+ DCs play an important role in maintaining intestinal immune homeostasis. These cells are required for the induction of tolerogenic immune responses and imprinting gut homing phenotypic changes on antigen-specific T cells. Recent insights into their development and regulatory properties have revealed additional immunoregulatory roles and further highlighted their importance for intestinal immunity. In this review we discuss the nature of the intestinal CD103+ DC population and the emerging roles of these cells in the regulation of mucosal immunity

Enhanced capabilities and modified users manual for axial-flow compressor conceptual design code CSPAN

Modifications made to the axial-flow compressor conceptual design code CSPAN are documented in this report. Endwall blockage and stall margin predictions were added. The loss-coefficient model was upgraded. Default correlations for rotor and stator solidity and aspect-ratio inputs and for stator-exit tangential velocity inputs were included in the code along with defaults for aerodynamic design limits. A complete description of input and output along with sample cases are included

Graphical User Interface for the NASA FLOPS Aircraft Performance and Sizing Code

XFLOPS is an X-Windows/Motif graphical user interface for the aircraft performance and sizing code FLOPS. This new interface simplifies entering data and analyzing results, thereby reducing analysis time and errors. Data entry is simpler because input windows are used for each of the FLOPS namelists. These windows contain fields to input the variable's values along with help information describing the variable's function. Analyzing results is simpler because output data are displayed rapidly. This is accomplished in two ways. First, because the output file has been indexed, users can view particular sections with the click of a mouse button. Second, because menu picks have been created, users can plot engine and aircraft performance data. In addition, XFLOPS has a built-in help system and complete on-line documentation for FLOPS

Wildlife in Airport Environments: Chapter 5 Excluding Mammals from Airports

To ensure aircraft safety, it is critical to exclude large mammal species such as deer (Odocoileus spp.), feral swine (Sus scrota), and coyotes (Canis latrans) from airport environments, as well as to consider thoroughly and carefully all available management methods. Airports are often located on or adjacent to undeveloped land that provides habitat for various species large enough to pose a direct hazard to aircraft. Unoccupied expanses of forage near runways provide deer with sufficient incentive to leave cover and occupy airport lands. Associated risk and tragic collisions have ranked deer as the most hazardous wildlife group to aviation (Dolbeer et al. 2000, DeVault et al. 2011), necessitating the evaluation of appropriate means for excluding them and other medium to large mammals (Dolbeer et al. 2000). Exclusionary fences are the most effective, long-lasting, and straightforward tool for eliminating risks posed by deer and other large mammals at airports; however, these fences can be costly to purchase, erect, and maintain. Fences provide a visual sense of security for airport managers but also can accomplish a measurable and statistically significant level of protection to aircraft at airports (DeVault et al. 2008). A variety of evaluations and experiments have been conducted on fence options. Determining the most appropriate fence for a specific setting to accomplish a desired outcome can be challenging. When reviewing this body of literature, airport managers must consider the level of motivation among deer or other species in the experiment and relate it to their situation. In this chapter we review a variety of fence applications for excluding medium to large mammals and provide recommendations

Neural Network and Regression Methods Demonstrated in the Design Optimization of a Subsonic Aircraft

The neural network and regression methods of NASA Glenn Research Center s COMETBOARDS design optimization testbed were used to generate approximate analysis and design models for a subsonic aircraft operating at Mach 0.85 cruise speed. The analytical model is defined by nine design variables: wing aspect ratio, engine thrust, wing area, sweep angle, chord-thickness ratio, turbine temperature, pressure ratio, bypass ratio, fan pressure; and eight response parameters: weight, landing velocity, takeoff and landing field lengths, approach thrust, overall efficiency, and compressor pressure and temperature. The variables were adjusted to optimally balance the engines to the airframe. The solution strategy included a sensitivity model and the soft analysis model. Researchers generated the sensitivity model by training the approximators to predict an optimum design. The trained neural network predicted all response variables, within 5-percent error. This was reduced to 1 percent by the regression method. The soft analysis model was developed to replace aircraft analysis as the reanalyzer in design optimization. Soft models have been generated for a neural network method, a regression method, and a hybrid method obtained by combining the approximators. The performance of the models is graphed for aircraft weight versus thrust as well as for wing area and turbine temperature. The regression method followed the analytical solution with little error. The neural network exhibited 5-percent maximum error over all parameters. Performance of the hybrid method was intermediate in comparison to the individual approximators. Error in the response variable is smaller than that shown in the figure because of a distortion scale factor. The overall performance of the approximators was considered to be satisfactory because aircraft analysis with NASA Langley Research Center s FLOPS (Flight Optimization System) code is a synthesis of diverse disciplines: weight estimation, aerodynamic analysis, engine cycle analysis, propulsion data interpolation, mission performance, airfield length for landing and takeoff, noise footprint, and others

Intermediate Fidelity Closed Brayton Cycle Power Conversion Model

This paper describes the implementation of an intermediate fidelity model of a closed Brayton Cycle power conversion system (Closed Cycle System Simulation). The simulation is developed within the Numerical Propulsion Simulation System architecture using component elements from earlier models. Of particular interest, and power, is the ability of this new simulation system to initiate a more detailed analysis of compressor and turbine components automatically and to incorporate the overall results into the general system simulation

Numerical Comparison of NASA's Dual Brayton Power Generation System Performance Using CO2 or N2 as the Working Fluid

A Dual Brayton Power Conversion System (DBPCS) has been tested at the NASA Glenn Research Center using Nitrogen (N2) as the working fluid. This system uses two closed Brayton cycle systems that share a common heat source and working fluid but are otherwise independent. This system has been modeled using the Numerical Propulsion System Simulation (NPSS) environment. This paper presents the results of a numerical study that investigated system performance changes resulting when the working fluid is changed from gaseous (N2) to gaseous carbon dioxide (CO2)