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The optimal control of anode H2 concentration in fuel cell is the key performance parameter for efficiency and durability of the fuel cell electric vehicle. Implementation of H2 concentration sensor in fuel processing system is the best option to achieve the optimal control operation, but the vulnerability of the chip in H2 concentration sensor to the moisture has not been overcome and no H2 concentration sensor for vehicle application is present in the world so far. Due to the immaturity of the H2 concentration sensor, a number of researches have been being made to keep the H2 concentration in the anode at certain level without H2 concentration sensors. However the effectiveness of those technologies has not been good to meet the design specification in all the operating range of the various driving cycles and environmental condition.

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.

In this paper, we address the thermal management issues which limit the lifespan, specific power and overall efficiency of an air-cooled rotary Wankel engine used in Unmanned Air Vehicles (UAVs). Our goal is to eliminate the hot spots and reduce the temperature gradients in the engine housing and side plates by aggressive heat spreading using heat pipes. We demonstrate by simulation that, for a specific power requirement, with heat spreading and more effective heat dissipation, thermal stress and distortion can be significantly reduced, even with air cooling. The maximum temperature drop was substantial, from 231°C to 129°C. The temperature difference (measure of temperature uniformity) decreased by 8.8 times (from 159°C to 18°C) for a typical UAV engine. Our heat spreaders would not change the frontal area of the engine and should have a negligible impact on the installed weight of the propulsion assembly.

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 automotive industry’s journey towards fully autonomous vehicles brings more and more vehicle control systems. Additionally, the reliability and robustness of all these systems must be guaranteed for all road and weather conditions before release into the market. However, the ever-increasing number of such control systems, in combination with the number of road and weather conditions, makes it unfeasible to test all scenarios in real life. Thus, the performance and robustness of these systems needs to be proven virtually, via vehicle simulations. The key challenge for performing such a range of simulations is that the tire performance is significantly affected by the road/weather conditions. An end user must therefore have access to the corresponding tire models. The current solution is to test tires under all road surfaces and operating conditions and then derive a set of model parameters from measurements.

This paper presents deep learning-based prognostics and health management (PHM) for predicting fractures of an electric propulsion (eP) drivetrain system using real-time CAN signals. The deep learning algorithm, based on autoencoders, resamples time-series signals and converts them into 2D images using recurrence plots (RP). Subsequently, through unsupervised learning of DeepSVDD, it detects anomalies in the converted 2D images and predicts the failure of the system in real-time. Also, reliability analysis based on fracture mechanics was performed using the detected signals and big data. In particular, the severity of the eP drivetrain system is proportional to the maximum shear stress (τmax) in terms of linear elastic fracture mechanics (LEFM) and can be calculated by summarizing the relationship between cracks (a) and the stress intensity factor (KIII).

Recently, keen interest has been focused on the reduction of fuel consumption through the development of eco-friendly and weight-effective vehicles. This is due in part to the strengthening of regulatory standards for fuel efficiency in each country. This study will focus on the optimization of the IP (Instrument Panel) module, in particular, the cowl crossbar, which in some vehicles, can account for more than 33% of the IP module weight. The design objectives of the cowl crossbar were to use continuous fiber thermoplastic composite materials to achieve high stiffness, while optimizing the strength to weight performance as evaluated through vehicle sled and crash testing. This research will introduce the development and optimization methodology for an alternative material, which achieved about a 30% weight reduction as compared to steel.

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.

The combustion process using two fuels with different reactivity, known as dual-fuel combustion or RCCI is mainly studied to reduce emissions while maintaining thermal efficiency compared to the conventional diesel combustion. Many studies have proven that dual-fuel combustion has a positive prospect in future combustion to achieve ultra-low engine-out emissions with high indicated thermal efficiency. However, a limitation on high-load expansion due to the higher maximum in-cylinder pressure rise rate (mPRR) is a main problem. Thus, it is important to establish the operating strategy and study the effect of in-cylinder flow motion with dual-fuel combustion to achieve a low mPRR and emissions while maintaining high-efficiency. In this research, the characteristics of gasoline-diesel dual-fuel combustion on different hardware were studied to verify the effect of the in-cylinder flow motion on dual-fuel combustion.

Increasing compression ratio is essential for developing future high-efficiency engines due to the intrinsic characteristics of spark-ignited engines. However, it also causes the unfavorable, abnormal knocking phenomena which is the auto-ignition in the unburned end-gas region. To cope with regulations, many researchers have been experimenting with various methods to suppress knock occurrence. In this paper, it is shown that cooling the combustion chamber using coolants, which is one of the most practical methods, has a strong effect on knock mitigation. Furthermore, the relationship between thermal boundary and coolant temperatures is shown. In the beginning of this paper, knock metrics using an in-cylinder pressure sensor are explained for readers, even though entire research studies cannot be listed due to the innumerableness. The coolant passages for the cylinder head and the liner were separated to examine independent cooling strategies.

As the automotive industry accelerates its virtual engineering capabilities, there is a growing requirement for increased accuracy across a broad range of vehicle simulations. Regarding control system development, utilizing vehicle simulations to conduct ‘pre-tuning’ activities can significantly reduce time and costs. However, achieving an accurate prediction of, e.g., stopping distance, requires accurate tire modeling. The Magic Formula tire model is often used to effectively model the tire response within vehicle dynamics simulations. However, such models often: i) represent the tire driving on sandpaper; and ii) do not accurately capture the transient response over a wide slip range. In this paper, a novel methodology is developed using the MF-Tyre/MF-Swift tire model to enhance the accuracy of ABS braking simulations.

This paper analyzes the leakage magnetic field generated by the Bi-Directional wireless charging system of Electric Vehicle(EV) and confirms the effect of the shielding coil in the Bi-Directional wireless charging system. In particular, in EV using the Inductive Power Transfer(IPT) method, the effective shielding coil position is proposed by analyzing the contribution of the leakage magnetic field of the Ground Assembly(GA) coil and the Vehicle Assembly(VA) coil according to the power transfer direction. Simulations were conducted using the WPT3/Z2 model of the standard SAE J2954, and it was confirmed that the GA coil contributed more to the leakage magnetic field due to the relatively large size compared to the VA coil regardless of power transfer direction.

Knock in gasoline engines at higher loads is a significant constraint on torque and efficiency. The anti-knock property of a fuel is closely related to its research octane number (RON). Ethanol has superior RON compared to gasoline and thus has been commonly used to blend with gasoline in commercial gasolines. However, as the RON of a fuel is constant, it has not been used as needed in a vehicle. To wisely use the RON, an On-Board Separation (OBS) unit that separates commercial gasoline with ethanol content into high-octane fuel with high ethanol fraction and a lower octane remainder has been developed. Then an onboard Octane-on-demand (OOD) concept uses both fuels in varying proportion to provide to the engine a fuel blend with just enough RON to meet the ever changing octane requirement that depends on driving pattern.

The influence of early induction stroke direct injection on late-cycle flows was investigated for a lean-burn, high-tumble, gasoline engine. The engine features side-mounted injection and was operated at a moderate load (8.5 bar brake mean effective pressure) and engine speed (2000 revolutions per minute) condition representative of a significant portion of the duty cycle for a hybridized powertrain system. Thermodynamic engine tests were used to evaluate cam phasing, injection schedule, and ignition timing such that an optimal balance of acceptable fuel economy, combustion stability, and engine-out nitrogen oxide (NOx) emissions was achieved. A single cylinder of the 4-cylinder thermodynamic engine was outfitted with an endoscope that enabled direct imaging of the spark discharge and early flame development.

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.

An EGR system has been significantly used in order to cope with reinforced exhaust gas regulation and enhancement of fuel efficiency. For the well-designed EGR cooler, performance analysis is basically required. Furthermore, boiling prediction of the EGR cooler is especially essential to evaluate durability failure of abnormal operating conditions in DPF. However, due to intrinsic complexity of detailed 3-dimensional heat transfer tubes in the EGR cooler, no precise technique of boiling prediction has been developed. Therefore, this research had been performed in order to fulfill 3 goals: (1) development of 3-dimentional performance prediction technique including boiling occurrence, (2) generation and validation of a new evaluation index for boiling, (3) development of an optimized EGR cooler for suppressing boiling. In order to increase analysis accuracy and reduce analysis efforts at the same time, 3-dimensional single-phase flow analysis was developed.

The need to reduce weight and cost of battery systems for electric vehicles has led to continued interest in metal-to-plastic substitution and mixed-material designs for battery enclosures. However, the ever-increasing performance requirements of such systems pose a challenge for plastic materials to meet. In an effort to design a cost-effective, lightweight next-generation battery enclosure while meeting the latest requirements, a new thermal runaway test method was developed, and several materials were screened. The objectives of this development project were twofold. The first was to develop a small-scale test method representative of real-world thermal runaway conditions that could be used early in the design process.