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The reliable operation of power systems on the lunar surface is crucial for critical research activities and supporting life. These systems are standalone or interconnected grids that integrate intermittent power sources and distributed energy storage. Lunar microgrids must be highly reliable, reconfigurable, and efficient. To meet these requirements, we propose the flexible DC energy router (FeDER), a modular and scalable power management unit for interconnected lunar DC microgrids. The FeDER integrates local energy storage and addresses various microgrid power management needs such as fault management, stability enhancement, power flow regulation, and power quality improvement. The lunar DC microgrids' design, protection, and control are achieved using a three-layered approach: (1) graph theory, (2) energy management system, and (3) smart resistor control. The lunar power grid architecture is introduced and the FeDER stability enhancement is implemented in the OPAL-RT platform.

The computer-aided engineering (CAE) automation study requires a large disk space and a premium processor. If all finite element (FE) models run locally, it may crash the local machine, and if the FE model runs on high-performance computing (HPC), transferring data from the server to the local machine to do the optimization may cause latency issues. This automation study provides a unique road map to optimize the design by working efficiently using the initial setup on the local machine, running an analysis of a large number of FE models on HPC, and performing optimization on the server. CAE Automation process has been demonstrated using a case study on a driveline component, crush spacer. Crush spacer is a very critical engineering design because, first, it provides the minimum required preload to the bearing inner races to keep them in position and, second, it endures a number of duty cycles.

Electrification is the well-accepted solution to address carbon emissions and modernize vehicle controls. Batteries play a critical in the journey of electrification and modernization with battery voltage prediction as the foundation for safe and efficient operation. Due to its strong dependency on prior information, battery voltage was estimated with recurrent neural network methods in the recent literatures exploring a variety of deep learning techniques to estimate battery behaviors. In these studies, standard recurrent neural networks, gated recurrent units, and long-short term memory are popular neural network architectures under review. However, in most cases, each neural network architecture is individually assessed and therefore the knowledge about comparative study among three neural network architecture is limited. In addition, many literatures only studied either the dynamic voltage response or the voltage relaxation.

As applications in aerospace, transportation and data centers are faced with increased electric power consumption, their dc operating voltages have increased to reduce cable weight and to improve efficiency. Electric arcs in these systems still cause dangerous fault conditions and have garnered more attention in recent years. Arcs can be classified as either low impedance or high impedance arcs and both can cause insulation damage and fires. Low impedance arcs release lots of energy when high voltage becomes nearly shorted to ground. High impedance arcs can occur when two current-carrying electrodes are separated, either by vibration of a loose connection or by cables snapping. The high impedance arc decreases load current due to a higher equivalent load impedance seen by the source. This complicates the differentiation of a high impedance arc fault from normal operation.

Computer programs and models are playing an increasing role in simulating vehicle crashworthiness, dynamic, and fuel efficiency. To maximize the effectiveness of these models, the validity and predictive capabilities of these models need to be assessed quantitatively. For a successful implementation of Computer Aided Engineering (CAE) models as an integrated part of the current vehicle development process, it is necessary to develop objective validation metric that has the desirable metric properties to quantify the discrepancy between multiple tests and simulation results. However, most of the outputs of dynamic systems are multiple functional responses, such as time history series. This calls for the development of an objective metric that can evaluate the differences of the multiple time histories as well as the key features under uncertainty.

A copula-based approach for model bias characterization was previously proposed [18] aiming at improving prediction accuracy compared to other model characterization approaches such as regression and Gaussian Process. This paper proposes an adaptive copula-based approach for model bias identification to enhance the available methodology. The main idea is to use cluster analysis to preprocess data, then apply the copula-based approach using information from each cluster. The final prediction accumulates predictions obtained from each cluster. Two case studies will be used to demonstrate the superiority of the adaptive copula-based approach over its predecessor.

To date, model validation metric is prominently designed for non-dynamic model responses. Though metrics for dynamic responses are also available, they are specifically designed for the vehicle impact application and uncertainties are not considered in the metric. This paper proposes the validation metric for general dynamic system responses under uncertainty. The metric makes use of the popular U-pooling approach and extends it for dynamic responses. Furthermore, shape deviation metric was proposed to be included in the validation metric with the capability of considering multiple dynamic test data. One vehicle impact model is presented to demonstrate the proposed validation metric.

Simulation based design optimization has become the common practice in automotive product development. Increasing computer models are developed to simulate various dynamic systems. Before applying these models for product development, model validation needs to be conducted to assess their validity. In model validation, for the purpose of obtaining results successfully, it is vital to select or develop appropriate metrics for specific applications. For dynamic systems, one of the key obstacles of model validation is that most of the responses are functional, such as time history curves. This calls for the development of a metric that can evaluate the differences in terms of phase shift, magnitude and shape, which requires information from both time and frequency domain. And by representing time histories in frequency domain, more intuitive information can be obtained, such as magnitude-frequency and phase-frequency characteristics.

Design optimization methods are commonly used for weight reduction subjecting to multiple constraints in automotive industry. One of the major challenges remained is to deal with a large number of design variables for large-scale design optimization problems effectively. In this paper, a new approach based on fuzzy rough set is proposed to address this issue. The concept of rough set theory is to deal with redundant information and seek for a reduced design variable set. The proposed method first exploits fuzzy rough set to screen out the insignificant or redundant design variables with regard to the output functions, then uses the reduced design variable set for design optimization. A vehicle body structure is used to demonstrate the effectiveness of the proposed method and compare with a traditional weighted sensitivity based main effect approach.

One of the major challenges in multiobjective, multidisciplinary design optimization (MDO) is the long computational time required in evaluating the new designs' performances. To shorten the cycle time of product design, a data mining-based strategy is developed to improve the efficiency of heuristic optimization algorithms. Based on the historical information of the optimization process, clustering and classification techniques are employed to identify and eliminate the low quality and repetitive designs before operating the time-consuming design evaluations. The proposed method improves design performances within the same computation budget. Two case studies, one mathematical benchmark problem and one vehicle side impact design problem, are conducted as demonstration.

Ultrasonic fatigue tests (testing frequency around 20 kHz) have been conducted on four different cast aluminum alloys each with a distinct composition, heat treatment, and microstructure. Tests were performed in dry air, laboratory air and submerged in water. For some alloys, the ultrasonic fatigue lives were dramatically affected by the environment humidity. The effects of different factors like material composition, yield strength, secondary dendrite arm spacing and porosity were investigated; it was concluded that the material strength may be the key factor influencing the environmental humidity effect in ultrasonic fatigue testing. Further investigation on the effect of chemical composition, especially copper content, is needed.

Since their inception, the design of airbag sensing systems has continued to evolve. The evolution of air bag sensing system design has been rapid. Electromechanical sensors used in earlier front air bag applications have been replaced by multi-point electronic sensors used to discriminate collision mechanics for potential air bag deployment in front, side and rollover accidents. In addition to multipoint electronic sensors, advanced air bag systems incorporate a variety of state sensors such as seat belt use status, seat track location, and occupant size classification that are taken into consideration by air bag system algorithms and occupant protection deployment strategies. Electronic sensing systems have allowed for the advent of event data recorders (EDRs), which over the past decade, have provided increasingly more information related to air bag deployment events in the field.

Numerous studies describe the fuel consumption benefits of changing the powertrain temperature based on vehicle operating conditions. Actuators such as electric water pumps and active thermostats now provide more flexibility to change powertrain operating temperature than traditional mechanical-only systems did. Various control strategies have been proposed for powertrain temperature set-point regulation. A characteristic of powertrain thermal management systems is that the operating conditions (speed, load etc) change continuously to meet the driver demand and in most cases, the optimal conditions lie on the edge of the constraint envelope. Control strategies for set-point regulation which rely purely on feedback for disturbance rejection, without knowledge of future disturbances, might not provide the full fuel consumption benefits due to the slow thermal inertia of the system.

Vehicle tracking problem is of crucial importance in intelligent vehicles research, as it is amongst the basic components of any comprehensive situation awareness technology. In mixed-traffic environments, where vehicles with varying degrees of sensing and communication capabilities coexist, the vehicle-tracking problem becomes particularly more demanding. In this paper, a collaborative vehicle tracking approach is presented, where onboard sensing and inter-vehicular communication resources are utilized in an efficient manner to provide track lists to all participating vehicles in a mixed-traffic environment. The approach is implemented on SimVille, our indoor testbed for urban driving, in accordance with our system development philosophy. The performance of the approach is evaluated using entropy values of vehicle tracks-an information theoretic measure of uncertainty. The experimental results of our scaled-down tests demonstrate the effectiveness of our approach.

Robustness/Reliability Assessment and Optimization (RRAO) is often computationally expensive because obtaining accurate Uncertainty Quantification (UQ) may require a large number of design samples. This is especially true where computationally expensive high fidelity CAE simulations are involved. Approximation methods such as the Polynomial Chaos Expansion (PCE) and other Response Surface Methods (RSM) have been used to reduce the number of time-consuming design samples needed. However, for certain types of problems require the RRAO, one of the first question to consider is which method can provide an accurate and affordable UQ for a given problem. To answer the question, this paper tests the PCE, RSM and pure sampling based approaches on each of the three selected test problems: the Ursem Waves mathematical function, an automotive muffler optimization problem, and a vehicle restraint system optimization problem.

Particle swarm optimization (PSO) is a relatively new stochastic optimization algorithm and has gained much attention in recent years because of its fast convergence speed and strong optimization ability. However, PSO suffers from premature convergence problem for quick losing of diversity. That is to say, if no particle discovers a new superiority position than its previous best location, PSO algorithm will fall into stagnation and output local optimum result. In order to improve the diversity of basic PSO, design of experiment technique is used to initialize the particle swarm in consideration of its space-filling property which guarantees covering the design space comprehensively. And the optimization procedure of PSO is divided into two stages, optimization stage and improving stage. In the optimization stage, the basic PSO initialized by Optimal Latin hypercube technique is conducted.

Accurate determination of the forming limit strain of aluminum sheet metal is an important topic which has not been fully solved by industry. Also, the effects of draw beads (enhanced forming limit behaviors), normally reported on steel sheet metals, on aluminum sheet metal is not fully understood. This paper introduces an experimental study on draw bead effects on aluminum sheet metals by measuring the forming limit strain zero (FLD0) of the sheet metal. Two kinds of aluminum, AL 6016-T4 and AL 5754-0, are used. Virgin material, 40% draw bead material and 60% draw bead material conditions are tested for each kind of aluminum. Marciniak punch tests were performed to create a plane strain condition. A dual camera Digital Image Correlation (DIC) system was used to record and measure the deformation distribution history during the punch test. The on-set necking timing is determined directly from surface shape change. The FLD0 of each test situation is reported in this article.

In this paper, thermal stress analysis for powertrain component is carried out using two in-house developed elasto-viscoplastic models (i.e. Chaboche model and Sehitoglu model) that are implemented into ABAQUS via its user subroutine UMAT. The model parameters are obtained from isothermal cyclic tests performed on standard samples under various combinations of strain rates and temperatures. Models' validity is verified by comparing to independent non-isothermal tests conducted on similar samples. Both models are applied to the numerical analysis of exhaust manifold subject to temperature cycling as a result of vehicle operation. Due to complexity, only four thermal cycles of heating-up and cooling-down are simulated. Results using the two material models are compared in terms of accuracy and computational efficiency. It is found that the implemented Chaboche model is generally more computationally efficient than Sehitoglu model, though they are almost identical in regard to accuracy.

High silicon molybdenum (HiSiMo) ductile cast iron (DCI) is commonly used for high temperature engine components, such as exhaust manifolds, which are also subjected to severe thermal cycles during vehicle operation. It is imperative to understand the thermomechanical fatigue (TMF) behavior of HiSiMo DCI to accurately predict the durability of high temperature engine components. In this paper, the effect of the minimum temperature of a TMF cycle on TMF life and failure behavior is investigated. Tensile and low cycle fatigue data are first presented for temperatures up to 800°C. Next, TMF data are presented for maximum temperatures of 800°C and minimum cycle temperatures ranging from 300 to 600°C. The data show that decreasing the minimum temperature has a detrimental effect on TMF life. The Smith-Watson-Topper parameter applied at the maximum temperature of the TMF cycle is found to correlate well with out-of-phase (OP) TMF life for all tested minimum temperatures.

The low temperature combustion concept is very attractive for reducing NOx and soot emissions in diesel engines. However, it has potential limitations due to higher combustion noise, CO and HC emissions. A multiple injection strategy is an effective way to reduce unburned emissions and noise in LTC. In this paper, the effect of multiple injection strategies was investigated to reduce combustion noise and unburned emissions in LTC conditions. A hybrid surrogate fuel model was developed and validated, and was used to improve LTC predictions. Triple injection strategies were considered to find the role of each pulse and then optimized. The split ratio of the 1st and 2nd pulses fuel was found to determine the ignition delay. Increasing mass of the 1st pulse reduced unburned emissions and an increase of the 3rd pulse fuel amount reduced noise. It is concluded that the pulse distribution can be used as a control factor for emissions and noise.