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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].

In the aviation community, there is a high priority to develop all-electric aircraft. Electro-mechanical actuation systems would replace traditional, large, heavy and difficult-to-maintain hydraulic actuation systems. This movement from hydraulic actuation to electrical actuation enhances the flexibility to integrate redundancy and emergency system in future military aircraft. Elimination of the hydraulic fluid removes the possibility of leakage of corrosive hydraulic fluid and the associated fire hazard, as well as environmental concerns. The switch from hydraulic to electrical actuation provides additional benefits in reduced aircraft weight, improved survivability and improved maintainability. The heat load in an electro-mechanical actuation (EMA) is highly transient and localized in nature; therefore a phase change material could be embedded in the heat generating components to store peak heat load.

The modeling and simulation pyramid in defense states it clearly: Multi-Level modeling and simulation are required. Models and simulations are often classified by the US Department of Defense into four levels—campaign, mission, engagement, and engineering. Campaign simulation models are applied for evaluation; mission-level simulations to experiment with the integration of several macro agents; engagement simulations in engineered systems development; and engineering-level simulation models with a solid foundation in structural physics and components. Models operating at one level must be able to interact with models at another level. Therefore, the cure (“silver bullet”) is very clear: a comprehensive framework for Multiple Resolution Modeling (MRM) is needed. In this paper, we discuss our research about how to construct MRM environments.

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.

As an engineering approach of balanced complexity and accuracy, the Generalized Incremental Stress-State dependent damage Model (GISSMO) in LS-DYNA® has now been widely adopted by the automotive industry to predict metallic materials’ fracture occurrences in both forming and crashworthiness simulations. Calibration of the nominal GISSMO is typically based on material characterization data along a certain representative material orientation. Nevertheless, many rolled or extruded metallic materials, such as advanced high-strength steel (AHSS) sheets, exhibit accentuated anisotropic fracture behavior, even though, notably, some of these materials show comparatively weak anisotropic plasticity in the meantime. Accordingly, in this work, the deformation and fracture behavior of a selected AHSS grade, Q&P980 steel, was first characterized based on a series of mechanical experiments under simple shear, uniaxial tension, plane strain, and equi-biaxial tension conditions.

Data mining is defined as the discovery of useful, possibly unexpected, patterns and relationships in data using statistical and non-statistical techniques in order to develop schemes for decision and policy making. Data mining can be used to discover the sources and causes of problems in complex systems. In addition, data mining can support simulation strategies by finding the different constants and parameters to be used in the development of simulation models. This paper introduces a framework for data mining and its application to complex problems. To further explain some of the concepts outlined in this paper, the potential application to the NASA Shuttle Reinforced Carbon-Carbon structures and genetic programming is used as an illustration.

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.

The traditional acquisition and development cycles of a weapon system by government agencies goes through multiple stages throughout the life cycle of the product. Over the last few decades, many of the United States military equipment had experienced acquisition cost growth. Many studies by the Department of Defense indicates that the cost growth is a result of multiple factors including the development and manufacturing stages of the product. Organizations with multiple operation sites that goes across multiple states or even countries and continents are finding it increasingly difficult to share informational databases to ensure the corporate synergy between multiple sites or divisions. For such organizations, there exist the need to synchronize the operations and have standard and common database where everything is stored and equally accessed by different sites. Digital transformation sounds real exotic and futuristic and promise to reduce operation costs of big organizations.

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 aircraft electromechanical actuator (EMA) cooling fan is a critical component because an EMA failure caused by overheating could lead to a catastrophic failure in aircraft. Fault tree analysis (FTA) is used to access the failure probability of EMA fans with the goal of improving their mean time to failure (MTTF) from ∼O(5×104) to ∼ O(2.5×109) hours without incurring heavy weight penalty and high cost. The dual-winding and dual-bearing approaches are analyzed and a contra rotating dual-fan design is proposed. Fan motors are assumed to be brushless direct current (BLDC) motors. To have a full understanding of fan reliability, all possible failure mechanisms and failure modes are taken into account. After summarizing the possible failure causes and failure modes of BLDC fans by focusing on each failure mechanism, the life expectancy of fan ball bearings based on a major failure mechanism of lubricant deterioration is calculated and compared to that provided in the literature.

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

The development of electromechanical actuators (EMAs) is the key technology to build an all-electric aircraft. One of the greatest hurdles to replacing all hydraulic actuators on an aircraft with EMAs is the acquisition, transport and rejection of waste heat generated within the EMAs. The absence of hydraulic fluids removes an attractive and effective means of acquiring and transporting the heat. To address thermal management under limited cooling options, accurate spatial and temporal information on heat generation must be obtained and carefully monitored. In military aircraft, the heat loads of EMAs are highly transient and localized. Consequently, a FEA-based thermal model should have high spatial and temporal resolution. This requires tremendous calculation resources if a whole flight mission simulation is needed. A lumped node thermal network is therefore needed which can correctly identify the hot spot locations and can perform the calculations in a much shorter time.

Although a multitude of anomaly detection and fault isolation programs can be found in the research, there does not appear to be any work published on architectural templates that could take advantage of multiple programs and integrate them into the desired systems. More specifically, there is an absence of a methodological process for generating anomaly detection and fault isolation designs to either embed within new system concepts, or supplement existing schemes. This paper introduces a new approach based on systems engineering and the System Modeling Language (SysML). Preliminary concepts of the proposed approach are explained. In addition, a case study is also mentioned.

In all-electric aircraft, electromechanical actuators (EMAs) will be used to replace hydraulic actuators. Due to the highly transient mission profiles of the aircraft operation, thermal management of EMAs is a significant issue. In this paper, we study the heat problem of the control and drive units of EMAs, and build a model to calculate and simulate the power loss and heat generation in the driver board. The driver unit consists of a power inverter, a capacitor, a power dissipating resistor and a control circuit. The power loss of each component is studied. The heat loss in the power inverter comes mainly from the power switches: IGBTs. The on-state loss is proportional to the current of the motor, and the switching loss is determined by the switching frequency as well as current.

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.