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A comprehensive plasticity and fracture model was built for metal sheets with application to metal sheet forming and vehicle crash simulations. The combined Bai-Wierzbicki (BW [1]) and CPB06ex2 [2] (or Yld2000-2D [3]) anisotropic plasticity model was further extended to consider elevated temperature effects in additional to the effect of multiaxial stress states. A fully modularized framework was established to combine isotropic, kinematic, and cross hardening behaviors under non-linear loading conditions. The all strain based modified Mohr-Coulomb (eMMC) fracture model was used to consider material anisotropy and nonlinear strain path. The model has been implemented into Abaqus/Explicit as a user material subroutine (VUMAT). Test results on advanced high strength steels, aluminum alloy sheets and magnesium alloy sheets are used to validate the modeling and testing methodologies. Very good correlation was observed between experimental and simulation results.

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.

Fiber-reinforced plastics (FRPs), produced through injection molding, are increasingly preferred over steel in automotive applications due to their lightweight, moldability, and excellent physical properties. However, the expanding use of FRPs presents a critical challenge: deformation stability. The occurrence of warping significantly compromises the initial product quality due to challenges in part mounting and interference with surrounding parts. Consequently, mitigating warpage in FRP-based injection parts is paramount for achieving high-quality parts. In this study, we present a holistic approach to address warpage in injection-molded parts using FRP. We employed a systematic Design of Experiments (DOE) methodology to optimize materials, processes, and equipment, with a focus on reducing warpage, particularly for the exterior part. First, we optimized material using a mixture design in DOE, emphasizing reinforcements favorable for warpage mitigation.

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].

A fully modularized framework was established to combine isotropic, kinematic, and cross hardening behaviors under non-monotonic loading conditions for advanced high strength steels. Experiments under the following types of non-proportional loading conditions were conducted, 1) uniaxial tension-compression-tension/compression-tension-compression full cycle reversal loading, 2) uniaxial reversal loading with multiple cycles, and 3) reversal shear. The calibrated new model is decoupled between isotropic and kinematic hardening behaviors, and independent on both anisotropic yield criterion and fracture model. Nine materials were calibrated using the model, include: DP590, DP600, DP780, TRIP780, DP980LY, QP980, AK Steel DP980, TBF1180, and AK Steel DP1180. Good correlation was observed between experimental and modeled results.

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 study deals with the fatigue life prediction methodology of welding simulation components involving arc welding. First, a method for deriving the cyclic deformation and fatigue properties of the weld metal (that is also called ER70S-3 in AWS, American Welding Standard) is explained using solid bar specimens. Then, welded tube specimens were used with two symmetric welds and subjected to axial, torsion, and combined in-phase and out-of-phase axial-torsion loads. In most previous studies the weld bead’s start/stop were arbitrarily removed by overlapping the starting and stop point. Because it can reduce fatigue data scatter. However, in this study make the two symmetric weld’s start/stops exposed to applying load. Because the shape of the weld bead generated after the welding process can act as a notch (Ex. root notch at weld start / Crater at weld stop) to an applied stress. Accordingly, they were intentionally designed to cause stress concentrations on start/stops.

Diesel Particulate Filter (DPF) system is considered as the most effective method to reduce particulate mater (PM) out of diesel exhaust. In general, silicon carbide (SiC) has been employed as the base materials for DPF substrate in diesel vehicles. Currently, a material, called cordierite (Cd), has been adopted for the DPF in a modern SUV with 2-liter CRDI diesel engine, complying EURO-5 emission regulation. Instead of its lower cost, Cd-based substrate was disadvantageous on thermal conductivity and heat capacity relative to SiC. In this work, the problems (i.e., face crack), initiated by the thermal drawbacks, were investigated as it applied into the DPF substrate. As the countermeasures to overcome, a control optimization of regeneration temperature elevation and an introduction of ingenious multiple regeneration strategy at problematic conditions were proposed and verified through real-road endurance tests.

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).

This study delves into the microstructural and mechanical characteristics of AlSi10Mg alloy produced through the Laser Powder Bed Fusion (L-PBF) method. The investigation identified optimal process parameters for AlSi10Mg alloy based on Volume Energy Density (VED). Manufacturing conditions in the L-PBF process involve factors like laser power, scan speed, hatching distance, and layer thickness. Generally, high laser power may lead to spattering, while low laser power can result in lack-of-fusion areas. Similarly, high scan speeds may cause lack-of-fusion, and low scan speeds can induce spattering. Ensuring the quality of specimens and parts necessitates optimizing these process parameters. To address the low elongation properties in the as-built condition, heat treatment was employed. The initial microstructure of AlSi10Mg alloy in its as-built state comprises a cell structure with α-Al cell walls and eutectic Si.

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.

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.

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 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.