Sizing loads for major aircraft structural components are often experienced during dynamic maneuvers, several of which are described within the Federal Aviation Regulations as part of certification requirements. A simulation and analysis framework that permits such dynamic loads to be assessed earlier in the design process is an advantage for designers and aligned with the trend towards certification by analysis. Such a framework is demonstrated in this paper using the case of a business jet performing a longitudinal checked pitch maneuver. The maneuver is simulated with a six degree-of-freedom MATLAB/Simulink simulation model, using the aircraft aerodynamic characteristics, mass properties, and an adequate level of modeling for the flight control system and pilot control action. The effects of structural flexibility and deformation of the lifting surfaces and fuselage under maneuver loads are modeled by tracking a number of structural degrees-of-freedom for each. The modular nature of the simulation setup facilitates the assessment of multiple maneuvers, analysis of sensitivity to uncertainty, as well as the identification of the impact of structural flexibility through flexible versus rigid maneuver simulations.
By conducting active meter monitoring and performance analysis for the buildings and the plants at the main campus of the Georgia Institute of Technology, it is possible for campus facilities managers to achieve significant efficiency improvements. A key challenge, however, is gathering and making sense of the large volumes of utilities data. In response, a comprehensive web-based building and plant energy-monitoring environment is presented that collects data from multiple energy grids. From the gathered data, particular attention is given to heating, cooling, and ventilation to assess building and ultimately campus energy performance through various analytics. First, techniques for data gathering, organization, and filtering are described, followed by several novel metrics and ways of visualizing them via a comparative method. Data filtering and classification strategies have also been implemented into a framework capable of evaluating a fleet of buildings with respect to a data-driven or model-driven baseline. The resulting monitoring system is shown to reduce the number of variables that campus managers of campus utilities and facilities need to track and make it more obvious where energy efficiency opportunities exist across a large fleet of buildings. Implications and future extensions of the monitoring platform are discussed.
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