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Comfort is a main factor in customer's decision when buying a car. The seat plays a very important role, as it is the interface between occupant and vehicle. Pressure distribution is today's most common approach to characterize seat comfort, but it shows limitations. Analysis of human inter-tissue stress tends to be relevant for an objective comfort assessment. This paper presents the construction and validation of a CAE human model, based on Magnetic Resonance Imaging scans and in-vivo tests data. Correlation between objective criteria and subjective evaluation will be investigated, comfort performance of a real seat will be predicted.

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.

Unpredictable faults oriented from ambiguous reasons could occur in an engine of a vehicle. However, there are some symptoms from which an engine is working abnormally before the engine is stalled by faults. In this paper, methods for diagnosis of engine faults by using vibrations are proposed. Through bench tests, to extract features for fault diagnosis, various samples with normal and abnormal conditions are prepared and vibration signals from the block of an engine are measured and analyzed. To consider cost and performance of a sensor, vibrations from a knock sensor signal as well as accelerometers are analyzed. Measured vibration signals are synchronized with signal of the crank position sensor and analyzed to detect which event is involved. Modulation analysis and Hilbert transform are applied to extract features representing the symptoms of engine faults and to indicate when the abnormal event happens, respectively.

Active Sound Design (ASD) allows the Personalized Engine Sound System to be implemented for different types of vehicles and in different geographical regions. While this process is possible, it requires a lot of on-road tuning and therefore is very time consuming. This study presents an efficient way of tuning ASD sounds based on binaural synthesis in a lab environment instead of on-road tuning. The on-road vehicle operating sounds are reproduced by a desktop NVH simulator while the binaural ASD sounds are synthesized by convolving measured Binaural Vehicle Impulse Responses with the output of ASD multi-channel amplifier in real time. A set of binaural recordings on road are compared with the reproduced sound in the lab environment. The comparison results showed the validity of the proposed method for ASD. The main advantage of this approach is the possibility of back-to-back comparison across different ASD tunings.

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.

A supervisory Model Predictive Control (MPC) approach is developed for an air path system for multi-mode operation in a diesel engine. MPC is a control method based on a predictive dynamic model of system and determines actuator control positions through the optimization of various factors such as tracking performances of target setpoints, moving speed of actuators, limits, etc. Previously, linear MPC has been successfully applied on the air path control problem of a diesel engine, however, most of these applications were developed for a single operation mode which has only one set of control target setpoint values. In reality, a single operation mode cannot cover all requirements of current diesel engines and this complicates practical implementations of linear MPC. The high priority targets for the development of diesel engines are low emissions, high thermal efficiency and robustness.

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.

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 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.

This ‘Motion-Sensor Moustick’ is a sort of new concept control device as if a combination of PC mouse and joystick. It has three simple buttons and a haptic wheel designed for a faster and easier use to learn the vehicle infotainment functions. In addition it has a motion sensor to call a menu via hand approach to change media channels or to display status with just a driver's hand motion within a certain distance. Also this development includes a new concept GUI(graphical user interface) which is compatible with the ‘Moustick’ device. This development could be very helpful to use a car infotainment system.

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.

This paper features a distributed environment and the steps taken to incorporate the Virtual Range model into the Virtual Test Bed (VTB) infrastructure. The VTB is a prototype of a virtual engineering environment to study operations of current and future space vehicles, spaceports, and ranges. The High-Level Architecture (HLA) is the main environment. The VTB/HLA implementation described here represents different systems that interact in the simulation of a Space Shuttle liftoff. An example implementation displays the collaboration of a simplified version of the Space Shuttle Simulation Model and a simulation of the Launch Scrub Evaluation Model.

This paper describes the development of a distributed environment for spaceport simulation modeling. This distributed environment is the result of the applications of the High-Level Architecture (HLA) and integration frameworks based on software agents and XML. This distributed environment is called the Virtual Test Bed (VTB). A distributed environment is needed due to the nature of the different models needed to represent a spaceport. This paper provides two case studies: one related to the translation of a model from its native environment and the other one related to the integration of real-time weather.