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Aerospace projects live a long time. Around the turn of the century, NASA first began to discuss multi-decadal projects with respect to the tools, methods, infrastructure and culture necessary to successfully establish outposts and bases both on the Moon as well as in adjacent space. Pilot projects were completed, capabilities developed and solutions were shared across the Agency. A decade later the Mars discussion was multi-generational with planning milestones 50 years in the future. The 1970’s Requirements Document, or the 1990’s System Model are nowhere near suitable for planning, development, integration and operations of multi-national, highly complex, incredibly expensive development efforts planned to outlast not only the careers of the developers but that of their children as well. Simulation in the different forms has become very important for this multi-decadal projects. The challenge will be to device ways to create formats and views which can stand time.

The National Aeronautics and Space Administration’s (NASA) current objectives include expanding space exploration and planning a manned expedition to Mars. In order to meet the latter objective, it is imperative that humans generate their own products by harnessing space resources, a process referred to as In-Situ Resource Utilization (ISRU). ISRU will enable NASA to reduce both payload mass and mission cost by reducing the number of consumables required to be launched from Earth. The discrete-event simulation discussed focuses primarily on one ISRU system, the production of fuel for a return trip to Earth by utilizing Mar’s atmosphere and regolith. This ISRU system primarily uses autonomous rovers for exploration, excavation, processing of Mar’s regolith to produce fuel, and disposal of the processed regolith. This study explores individual rover and component requirements including rover speeds, travel distances, functional periods, charging, and maintenance times.

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.

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.

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.

The term “productivity” all too often has becomes a buzz-word, ultimately diminishing its perceived importance. However, productivity is the major concern of any team, and therefore must be defined to gain an appropriate understanding of how a system is actually working. Here, productivity means the level of contribution to the throughput of a system such as defined in the Theory of Constraints. In the field of space exploration, the throughput is the number of milestones of the mission accomplished as well as the potential survival during extreme events (due to failures or other unplanned events). For a time tasks were accomplished by expert individuals (e.g., an astronaut), but recently team structures have become the norm. It is clear that with increased mission complexity, “no single entity can have complete knowledge of or the abilities to handle all matters” [10].

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.

In this paper, we introduce the use of ontologies to implement the information developed and organized by resource planning tools into standard project management documents covering integrated cost, resource modeling and analysis, and visualization. The basic upper ontology used for NASA Space Operations is explained and the results obtained are discussed. This ontology-centered approach is looking for tighter connections between software, hardware, and systems engineering.

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 purpose of this study was to increase IC engine performance by “tuning” the exhaust system to different induction system pressures using an empirical based modeling approach. The two distinct induction pressures are atmospheric and 13-15 mmHg above atmospheric. The above atmospheric induction pressure occurs when the race car is in the lead; the atmospheric pressure occurs with the race car is following the lead or “in the draft.” Since it is ideal to achieve optimum performance for both induction pressures, the problem was formulated and optimized using an empirical Multiple Response Surface Method (MRSM) approach. MRSM is a process that “extracts” multiple objective performance information through carefully controlled experiments and data modeling techniques. An analysis of the experimental data will identify the ideal header length configuration that maximizes performance for both induction pressure extremes.

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.