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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.

As the cost and complexity of modern aircraft systems increases, emphasis has been placed on model-based design as a means for reducing development cost and optimizing performance. To facilitate this, an appropriate modeling environment is required that allows developers to rapidly explore a wider design space than can cost effectively be considered through hardware construction and testing. This wide design space can then yield solutions that are far more energy efficient than previous generation designs. In addition, non-intuitive cross-coupled subsystem behavior can also be explored to ensure integrated system stability prior to hardware fabrication and testing. In recent years, optimization of control strategies between coupled subsystems has necessitated the understanding of the integrated system dynamics.

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].

The Air Force Research Laboratory Deployment and Sustainment Division (AFRL/HES), in coordination with Arthur D. Little, Inc., has undertaken a system-of-systems approach to defining and designing aerospace ground equipment (AGE). This method is usually used to take advantage of the open architecture nature of ne and developing electronics through modular design. This paper discusses how we applied it across the electronic, mechanical, and structural aspects of AGE to research and develop a new concept to meet the burgeoning needs of the 21st century Air Force.

As part of the DoD/NASA Lithium-Ion and More-Electric Aircraft (MEA) development programs, in-house life-testing and performance characterization of lithium-ion batteries of sizes 1-20 amp-hours (Ah) were performed. Using AC impedance spectroscopy, the impedance behavior of lithium-ion cells with respect to temperature, cycle number, electrode, and state-of-charge was determined. Cell impedance is dominated by the positive (cathode) electrode, increases linearly with cycle number, and exponentially increases with decreasing temperature. From cell performance testing, we have seen the cell behavior is extremely sensitive to the ambient temperature. Preliminary battery performance results as well as AC impedance and life cycle test results are presented below.

This paper describes the development, operation and testing of an across-gimbal ambient thermal transport system (GATTS) for carrying cryocooler waste heat across a 2-axis gimbal. The principal application for the system is space-based remote sensing spacecraft with gimbaled cryogenics optics and/or infrared sensors. GATTS uses loop heat pipe (LHP) technology with ammonia as the working fluid and small diameter stainless steel tubing to transport 100–275 W across a two-axis gimbal. The tubing is coiled around each gimbal axis to provide flexibility (less than 0.68 N-m [6 lbf-in] of tubing-induced torque per axis) and fatigue life. Stepper motors are implemented to conduct life cycling and to assess the impact of motion on thermal performance. An LHP conductance of approximately 7.5 W/C was demonstrated at 200 W, with and without gimbal motion. At the time this paper was written, the gimbal had successfully completed over 500,000 cycles of operation with no performance degradation.

As technology for both military and civilian aviation systems mature into a new era, techniques to test and evaluate these systems have become of great interest. To achieve a general understanding as well as save time and cost, the use of computer modeling and simulation for component, subsystem or integrated system testing has become a central part of technology development programs. However, the evolving complexity of the systems being modeled leads to a tremendous increase in the complexity of the developed models. To gain confidence in these models there is a need to evaluate the risk in using those models for decision making. Statistical model validation techniques are used to assess the risk of using a given model in decision making exercises. In this paper, we formulate a transient model validation challenge problem for an air cycle machine (ACM) and present a hardware test bench used to generate experimental data relevant to the model.

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.

A highly modified F/A-18 aircraft is being used to demonstrate that aeroelastic wing twist can be used to roll a high performance aircraft. A production F/A-18A/B/C/D aircraft uses a combination of aileron deflection, differential horizontal tail deflection and differential leading edge flap deflection to roll the aircraft at various Mach numbers and altitudes. The Active Aeroelastic Wing program is demonstrating that aeroelastic wing twist can be used in lieu of the horizontal tail to provide autonomous roll control at high dynamic pressures. Aerodynamic and loads data have been gathered from the Phase I AAW flight test program. Now control laws have been developed to exploit aeroelastic wing twist and provide autonomous flight control of the AAW aircraft during Phase II. Wing control surfaces are being deflected in non-standard ways to create aeroelastic wing twist and develop the required rolling moments without use of the horizontal tail.

Side-facing seats are present in a variety of aircraft. During impact, these seats load the occupants in a different manner than typical forward-facing seats, namely the occupants are exposed to a lateral impact. In order to minimize injury during a crash, it is necessary for the occupants to prepare themselves and be situated in a position for maximum protection. In an effort to understand occupant initial position in a side-facing seat, a 3-D rigid-body model was developed of a side-facing seat configuration with three occupants, using the Articulated Total Body (ATB) program. The occupants were seated side-by-side in webbed troop-style seats, and each occupant was restrained by a lap belt. Three different initial occupant positions were studied, and each of the three occupants in a given simulation were seated in the same position. A 10 G lateral pulse with an approximate duration of 200 ms was applied to the vehicle.

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.

The Engine Supplier Base Initiative (ESBI) is a joint Air Force/Industry cooperative agreement aimed at achieving affordable precision investment cast airfoil and large structural components for man-rated gas turbine engines. The ESBI program will obtain these goals through the implementation of business and technology improvements with specific focus on increased product quality and reduced cycle time. This program has brought together competitors in the business to solve sector wide problems. This paper presents the framework of the teaming approach as well as results achieved in quality and cycle time improvements through technical and business process improvements.

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.

The blend of dimethyl ether (DME, CH3OCH3) and propane (C3H8) is a potentially renewable fuel mixture that has the potential to replace diesel in compression ignition engines. The combination can potentially reduce particulate and greenhouse gas emissions compared to a conventional diesel engine operating under similar conditions. However, detailed conceptual and simulation studies must be conducted before adopting a new fuel on a compression ignition engine. For these simulations, accurate chemical kinetic models are necessary. However, the validity of chemical kinetic mechanisms in the literature is unknown for mixing controlled compression ignition (MCCI) engine operating conditions. Hence, in this work, we studied the ignition of dimethyl ether (DME) and propane blends in a shock tube at MCCI engine conditions. Ignition delay time (IDT) data was collected behind the reflected shock for DME-propane mixtures for heavy-duty compression ignition (CI) engine parameters.

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.