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The aim of this work is to apply an innovative adaptive ℒ1 techniques to control flutter phenomena affecting highly flexible wings and to evaluate the efficiency of this control algorithm and architecture by performing the following tasks: i) adaptation and analysis of an existing simplified nonlinear plunging/pitching 2D aeroelastic model accounting for structural nonlinearities and a quasi-steady aerodynamics capable of describing flutter and post-flutter limit cycle oscillations, ii) implement the ℒ1 adaptive control on the developed aeroelastic system to perform initial control testing and evaluate the sensitivity to system parameters, and iii) perform model validation and calibration by comparing the performance of the proposed control strategy with an adaptive back-stepping algorithm. The effectiveness and robustness of the ℒ1 adaptive control in flutter and post-flutter suppression is demonstrated.

Operator training using a weapon in a real-world environment is risky, expensive, time-consuming, and restricted to the given environment. In addition, governments are under intense scrutiny to provide security, yet they must also strive for efficiency and reduce spending. In other words, they must do more with less. Virtual simulation, is usually employed to solve these limitations. As the operator is trained to maximize weapon effectiveness, the effectiveness-focused training can be completed in an economical manner. Unfortunately, the training is completed in limited scenarios without objective levels of training factors for an individual operator to optimize the weapon effectiveness. Thus, the training will not be effective. For overcoming this problem, we suggest a methodology on guiding effectiveness-focused training of the weapon operator through usability assessments, big data, and Virtual and Constructive (VC) simulations.

Connected and Autonomous Vehicles (CAV) rely on information obtained from sensors and communication to make decisions. In a Cooperative Vehicle Safety (CVS) system, information from remote vehicles (RV) is available at the host vehicle (HV) through the wireless network. Safety applications such as crash warning algorithms use this information to estimate the RV and HV states. However, this information is uncertain and sparse due to communication losses, limitations of communication protocols in high congestion scenarios, and perception errors caused by sensor limitations. In this paper we present a novel approach to improve the robustness of the CVS systems, by proposing an architecture that divide application and information/perception subsystems and a novel prediction method based on non-parametric Bayesian inference to mitigate the detrimental effect of data loss on the performance of safety applications.

This paper presents the concept of a dual latent heat sink for thermal management of pulse heat generating electronic systems. The focus of this work is to verify the effectiveness of the concept during charging through experimentation. Accordingly, custom components were built and a prototype version of the heat sink was fabricated. Experiments were performed to investigate the implementation feasibility and heat transfer performance. It is shown that this heat sink is practicable and helps in arresting the system temperature rise during charging (period of pulse heat load).

Complex aerospace engineering systems require innovative methods for performance monitoring and anomaly detection. The interface of a real-time data stream to a system for analysis, pattern recognition, and anomaly detection can require distributed system architectures and sophisticated custom programming. This paper presents a case study of a simplified interface between Programmable Logic Controller (PLC) real-time data output, signal processing, cloud computing, and tablet systems. The discussed approach consists of three parts: First, the connectivity of real-time data from PLCs to the signal processing algorithms, using standard communication technologies. Second, the interface of legacy routines, such as NASA's Inductive Monitoring System (IMS), with a hybrid signal processing system. Third, the connectivity and interaction of the signal processing system with a wireless and distributed tablet, (iPhone/iPad) in a hybrid system configuration using cloud computing.

An innovative nonlinear simulation approach for high power density synchronous generator systems is developed and implemented. Due to high power density, the generator operates in nonlinear region of the magnetic circuit. Magnetic Finite Element Analysis (FEA) makes nonlinear simulation possible. Neural network technique provides nonlinear functions for system level simulation. Dynamic voltage equation provides excellent mathematical model for system level simulations. Voltage, current, and flux linkage quantities are applied in Direct-Quadrature (DQ) rotating frame. The simulated system includes main machine, exciter, rectifier bridge, bang-bang control, and PI control circuitry, forming a closed loop system. Each part is modeled and then integrated into the system model.

The partial electrochemical reduction of carbon dioxide (CO2) using ceramic oxygen generators (COGs) is well known and widely studied. Conventional COGs use yttria-stabilized zirconia (YSZ) electrolytes and operate at temperatures greater than 700 °C. Operating at a lower temperature has the advantage of reducing the mass of the ancillary components such as insulation and heat exchangers (to reduce the COG oxygen output temperature for comfortable inhalation). Moreover, complete reduction of metabolically produced CO2 (into carbon and oxygen) has the potential of reducing oxygen storage weight if the oxygen can be recovered. Recently, the University of Florida developed novel ceramic oxygen generators employing a bilayer electrolyte of gadolinia-doped ceria and erbia-stabilized bismuth oxide (ESB) for NASA's future exploration of Mars.

Operations involving autonomous vehicles require knowledge of the surrounding environment including other moving vehicles. The use of vision has been regarded as an enabling technology that can provide such information. Several important applications that would benefit from this technology is autonomous aerial refueling (AAR) and target tracking. This paper considers a sensor fusion approach using traditional IMU/GPS sensors with vision to facilitate the state estimation problem of moving targets. The proposed method makes use of a moving monocular camera to estimate the relative position and orientation of targets within the image by exploiting a known reference motion. The vision state estimation problem is solved using an homography approach that employs images containing both the reference and target vehicles. A simulation involving an unmanned aerial vehicle (UAV) and two ground vehicles is documented in this paper to demonstrate the algorithm and its accuracy.

A power electronics module was equipped with an evaporative spray cooling nozzle assembly that served to remove waste heat from the silicon devices. The spray cooling nozzle assembly took the place of the standard heat sink, which uses single phase convection. The purpose of this work was to test the ability of spray cooling to enable higher power density in power electronics with high temperature coolant, and to be an effective and lightweight system level solution to the thermal management needs of aerospace vehicles. The spray cooling work done here was with 95 °C water, and this data is compared to 100 °C water/ propylene glycol spray cooling data from a previous paper so as to compare the spray cooling performance of a single component liquid to that of a binary liquid such as WPG. The module used during this work was a COTS module manufactured by Semikron, Inc., with a maximum DC power input of 180 kW (450 VDC and 400 A).

The application of Statistical Process Control and Design of Experiment methods in the research laboratory can lead to significant gains in the Powertrain development process. Empirical methods such as Design of Experiments, Regression, and Neural Network techniques can be applied to help researchers gain better understanding of the cause and effect relationships of emission, alternative fuel source, performance, fuel economy, and engine management system - calibration studies. The use of these empirical modeling techniques along with model based Genetic Algorithm, Gradient, or Constraint based solution search methods will help identify the “process settings” that improve fuel economy, improve performance, and reduce pollutants. Since empirical methods are fundamentally based on the acquired test data, it is vitally important that the laboratory measurements are repeatable, consistent, and void of sources of variance that have a significant effect on the acquired test data.

Calibrating the engine control unit to satisfy pollutant and performance objectives can be a challenging task. Due to the large number of variables and their interactive complexities, many firms apply design of experiment methods and modeling techniques to the acquired test data. This establishes a “black box” or “gray box” simulation model that predicts power and emissions as a function of the engine parameters. An offline optimization procedure on the fitted model(s) will identify the engine control strategy that best satisfies pollutant and performance objectives. A review of the literature reveals that the General Linear Modeling method and Neural Network modeling architectures are widely used in the development of “black box” or “gray box” simulation models. While Neural Network methods are “assumption free”, the General Linear Model method is limited to those problems in which the errors, ε, are normally distributed and have constant variance, σ2.

The monitoring and control of an artificial climate is necessitated by the Mars Dome Project (MDP) [ref 1]. MDP is designed to grow plants in an enclosed structure under reduced pressure. This system includes a dome enclosure, an environmental control system, a plant growth system, a data logging system, and an external vacuum vessel [ref 2]. Each of these systems is integrated by the use of a solid-state control device located inside the base of the Atmospheric Tower Management System (ATMS). Details of the controller follow a short summary of the major components of the MDP.

Effective thermal management and removal of the waste heat generated at diode arrays is critical to the development of high-power solid-state lasers. Thermal design must be considered in the packaging of these arrays. Two different packages with heat dissipation through spray cooling are evaluated experimentally and numerically. Their overall performance is compared with other packaging configurations using different heat removal approaches. A novel packaging design is proposed that can fulfill the requirements of low thermal resistance, temperature uniformity among emitters in the diode array, low coolant flow rate, simplicity and low assembly cost. The effect of temperature uniformity on the pumping efficiency for gain media is examined for our novel packaging design. The thermal stress induced by temperature variation within an emitter is also considered.

The future of human exploration in the solar system is contingent on the ability to exploit resources in-situ to produce mission consumables. Specifically, it has become clear that the success of a manned mission to Mars will likely depend on fuel components created on the Martian surface. While several architectures for an unmanned fuel production surface facility on Mars exist in theory, a simulation of the performance and operation of these architectures has not been created. In this paper, the framework describing a simulation of one such architecture is defined. Within this architecture, each component of the base is implemented as a state machine, with the ability to communicate with other base elements as well as a supervisor. An environment supervisor is also created which governs low level aspects of the simulation such as movement and resource distribution, in addition to higher-level aspects such as location selection with respect to operations specific behavior.

Cooperative vehicle safety can help prevent vehicle collisions by providing timely warnings to the driver or initiating automatic preventive actions based on vehicle dynamics information exchanged between vehicles. The information is shared wirelessly through the emerging DSRC (Dedicated Short Range Communication) standards. The vehicle dynamics information that is shared, such as vehicle velocity and location, is collected from the vehicle's internal sensor communication network and from Global Navigation Satellite Systems (GNSS), which includes the Global Positioning System (GPS). GNSS is a critical component of this safety system since it has the needed ability to accurately determine a vehicle's location coordinates in most driving environments. However, its performance can suffer from obstructions in dense urban areas. Deficiencies of GNSS can be overcome by complimenting GNSS with other sensors.

Driving at large angles of sideslip does not necessarily indicate terminal loss of control, rather, it is the fundamental objective of the sport of drifting. Drift racing challenges drivers to navigate a course in a sustained sideslip by exploiting coupled nonlinearities in the tire force response. The current study explores some of the physical parameters affecting drift motion, both in simulation and experiment. Combined-slip tire models are used to develop nonlinear models of a drifting vehicle in order to illustrate the conditions necessary for stability. Experimental drift testing is conducted to observe the dynamics featured in the track data. An accelerometer array on the test vehicle measures the acceleration vector field in order to estimate the vehicle states throughout the drift testing. Neural networks are used to identify the patterns in the accelerations that correspond to sideslip excursions during drifts.

Efforts to develop sensor and control system technology to monitor air quality for life support have resulted in the development and preliminary testing of a concept based on expert systems and ion trap mass spectrometry (ITMS). An ITMS instrument provides the capability to identify and quantitate a large number of suspected contaminants at trace levels through the use of a variety of multidimensional experiments. An expert system provides specialized knowledge for control, analysis, and decision making. The system is intended for real-time, on-line, autonomous monitoring of air quality. The key characteristics of the system, performance data and analytical capabilities of the ITMS instrument, the design and operation of the expert system, and results from preliminary testing of the system for trace contaminant monitoring are described.

This paper describes a model-based approach to diagnosing electrical faults in electrical power systems. Until recently, model-based reasoning has only been applied to physical systems with static, persistent states, and with parts whose behavior can be expressed combinatorially, such as digital circuits. Our research is one of a handful of recent efforts to apply model-based reasoning to more complex systems, those whose behavior is difficult or impossible to express combinatorially, and whose states change continuously over time. The chosen approach to representation is loosely based on the idea of the equation network proposed in [6]. This requires a more complex component and behavior model than for simpler physical devices. The resulting system is being tested on fault data from the SSM/PMAD power system breadboard being developed at NASA-MSFC [9].

Optimizing IC engine performance currently requires an exhaustive experimental search to determine the combination of internal components that maximizes torque or power. An alternate and more structured approach using Response Surface Methods will lead the experimenter to the optimum combination with the least number of trials. Using simulation software to evaluate IC engine configurations, this method improved the estimated power from 439 to 516 KW. Results of the study indicate that Response Surface Methods are a viable and robust method of converging to an IC engine configuration which achieves optimum performance.

Preliminary investigations of nonlinear modeling of aircraft synchronous generators using neural networks are presented. Aircraft synchronous generators with high power density tend operate at current-levels proportional to the magnetic saturation region of the machine's material. The nonlinear model accounts for magnetic saturation of the generator, which causes the winding flux linkages and inductances to vary as a function of current. Finite element method software is used to perform a parametric sweep of direct, quadrature, and field currents to extract the respective flux linkages. This data is used to train a neural network which yields current as a function of flux linkage. The neural network is implemented in a Simulink synchronous generator model and simulation results are compared with a previously developed linear model. Results show that the nonlinear neural network model can more accurately describe the responsiveness and performance of the synchronous generator.