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The National Aeronautics and Space Administration (NASA) is preparing for a manned mission to Mars to test the sustainment of civilization on the planet Mars. This research explores the requirements and feasibility of autonomously producing fuel on Mars for a return trip back to Earth. As a part of NASA’s initiative for a manned trip to Mars, our team’s work creates and analyzes the allocation of resources necessary in deploying a fuel station on this foreign soil. Previous research has addressed concerns with a number individual components of this mission such as power required for fuel station and tools; however, the interactions between these components and the effects they would have on the overall requirements for the fuel station are still unknown to NASA. By creating a baseline discrete-event simulation model in a simulation software environment, the research team has been able to simulate the fuel production process on Mars.

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.

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.

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.

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].

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.

With the development and licensing of inland, state-owned spaceports, and the ongoing development of several new reusable launch vehicles (RLV), the space launch industry is undergoing a significant transformation. As a result, there is a need to reevaluate current launch risk analysis methodologies and practices, which so far have revolved around the conservative casualty expectation analysis developed in the 1950s. Furthermore, an important aspect of launch risk analysis which gives rise to its complexity is its multidisciplinary nature. In analyzing such risk, the physics of and interactions between the varieties of hazards produced by launch vehicles breakups must be captured, modeled and, their effects analyzed. In this paper we discuss how a well-designed multidisciplinary modeling and analysis platform could be a significant step toward reducing the complexity just mentioned.

Peak pressure, crank angle and induction pressure were measured in cylinder number one of a Ford 4.6 liter Modular engine. Chaos analysis was conducted on these measurements and the phase, waveform, Poincare, and FFT plots are presented. These plots show conclusively that the pressure fluctuation inside a cylinder is a broadband chaos.

Multi-Resolution Modeling (MRM) is one of the key technologies for building complex and large-scale simulations using legacy simulators. MRM has been developed continuously, especially in military fields. MRM plays a crucial role to describe the battlefield and gathering the desired information efficiently by linking various levels of resolution. The simulation models interact across different local and/or distance area networks using the High Level Architecture (HLA) regardless of their operating systems and hardware. The HLA is a standard architecture developed by the US Department of Defense (DoD) and is meant to create interoperability among different types of simulators. Therefore, MRM implementations are very dependent on Interoperability and Composability. This paper summarizes the definition of MRM-related terminology and proposes a basic form of MRM system using Commercial Off-The-Shelf (COTS) simulators and HLA.

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.

The battery is a central part of the vehicle’s electrical system and has to undergo cycling in a wide variety of conditions while providing an acceptable service life. Within a typical distribution chain, automotive lead-acid batteries can sit in storage for months before delivery to the consumer. During storage, batteries are subjected to a wide variety of temperature profiles depending on facility-specific characteristics. Additionally, batteries typically do not receive any type of maintenance charge before delivery. Effects of storage time, temperature, and maintenance charging are explored. Flooded lead-acid batteries were examined immediately after storage and after installation in vehicles subjected to normal drive patterns. While phase composition is a major consideration, additional differences in positive active material (PAM) were observed with respect to storage parameters.

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).

This paper reviews the literature which identifies the causes, consequences and cures for engine knock as it affects high performance engines. The physical events of normal and abnormal combustion are described. The observed variations in combustion phenomenon are explained through chemical kinetics. A mathematical model of combustion which can predict knock in an engine cylinder is summarized. Several mechanisms of knock induced damage are outlined. Design and operating considerations which affect an engine's propensity to knock are discussed. Terms that have become associated with combustion in general and the knocking phenomenon in particular are collected and examined

Social stressors in long-duration spaceflight (LDSF) have serious implications for crew effectiveness and mission safety. This paper reviews potential stressors and presents habitat and organizational design considerations to reduce perceived demand from social stressors in four areas: privacy and personal space, isolation, interpersonal interactions, and cultural differences. Results can serve as guidelines for the design of future LDSF missions and spacecraft, and will benefit attempts to develop an accurate model of stress in the spaceflight domain.

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).

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.

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.

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.

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.