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Many Small Unmanned Aerial Vehicles (SUAV) are driven by small scale, fixed blade propellers. Flow produced by the propeller can have a significant impact on the aerodynamics of a SUAV. Therefore, in Computational Fluid Dynamic (CFD) simulations, it is often necessary to simulate the SUAV and propeller coupled together. For computational efficiency, the propeller can be modeled in a steady-state view by using momentum source terms to add the thrust and swirl produced by the propeller to the flow field. Many momentum source term models are based on blade element theory. Blade element theory divides the blade into element sections in the spanwise direction and assumes each element to operate independently as a two-dimensional (2D) airfoil.

Research interests in autonomous driving have increased significantly in recent years. Several methods are being suggested for performance optimization of autonomous vehicles. However, weather conditions such as rain, snow, and fog may hinder the performance of autonomous algorithms. It is therefore of great importance to study how the performance/efficiency of the underlying scene understanding algorithms vary with such adverse scenarios. Semantic segmentation is one of the most widely used scene-understanding techniques applied to autonomous driving. In this work, we study the performance degradation of several semantic segmentation algorithms caused by rain for off-road driving scenes. Given the limited availability of datasets for real-world off-road driving scenarios that include rain, we utilize two types of synthetic datasets.

EcoCAR 2: Plugging In to the Future is a three-year design competition co-sponsored by General Motors and the Department of Energy. Mississippi State University has designed a plug-in hybrid powertrain for a 2013 Chevrolet Malibu vehicle platform. This vehicle will be capable of 57 miles all-electric range and utility-factor corrected fuel economy of greater than 80 miles per gallon gasoline equivalent (mpgge). All modifications are designed without sacrificing any of the vehicle's utility or performance. Advanced modeling, simulation, and Hardware-in-the-Loop (HIL) simulation capabilities are being used for rapid control prototyping and vehicle design to ensure success in the following years of the competition.

There is significant potential for connected and autonomous vehicles to impact vehicle efficiency, fuel economy, and emissions, especially for hybrid-electric vehicles. These improvements could have large-scale impact on oil consumption and air-quality if deployed in large Mobility-as-a-Service or ride-sharing fleets. As part of the US Department of Energy's current Advanced Vehicle Technology Competition (AVCT), EcoCAR: The Mobility Challenge, Mississippi State University’s EcoCAR Team is redesigning and doing the development work necessary to convert a conventional gasoline spark-ignited 2019 Chevy Blazer into a hybrid-electric vehicle with SAE Level 2 autonomy. The target consumer segments for this effort are the Mobility-as-a-Service fleet owners, operators and riders. To accomplish this conversion, the MSU team is implementing a P4 mild hybridization strategy that is expected to result in a 30% increase in fuel economy over the stock Blazer.

In this work, a process-structure-property relationship for additively manufactured Al-Si-Mg alloy was constructed, with the aid of an integrated multi-physics model. Specifically, first, a series of thermal simulations were performed to understand molten pool geometry under different additive manufacturing (AM) operating conditions, including laser beam power, scanning speed, and hatch spacing. The porosity formation was predicted based on thermal simulation results, which yield molten pool dimension information for predicting the lack-of-fusion porosity. Dream.3D was utilized to reconstruct synthetic microstructures with different volume fraction of porosity.

The current research conducted a cross-validation between an infrared motion capture system and an electromechanical motion capture device. No differences were found between the motion capture methods in shoulder and elbow angles. However, differences were found between the motion capture methods on distances of hand movements and actor location in space. Results of the current study indicate electromechanical motion capture devices are too inaccurate to use for validating digital human models unless the ultimate application of the model does not require millimeter accuracy or an absolute location in space. If one is primarily interested in joint angles, and distances are secondary, an electromechanical device is acceptable.

Recently [1, 2, 3 and 4], the novel Advanced (injection) Low Pilot-Ignited Natural Gas (ALPING) low-temperature combustion (LTC) concept was demonstrated to yield very low NOx emissions (<0.2 g/kWh) with high fuel conversion efficiencies (>40%). In the ALPING-LTC concept, very small diesel pilot sprays (contributing ∼2-3 percent of total fuel energy) are injected early in the compression stroke (60°BTDC) to ignite lean, homogeneous natural gas-air mixtures. To simulate ALPING-LTC, a phenomenological thermodynamic model was developed. The cylinder contents were divided into an unburned zone containing fresh natural gas-air mixture, several packets containing diesel and entrained natural gas-air mixture, a flame zone, and a burned zone. The simulation explicitly accounted for pilot injection, spray entrainment, diesel ignition (with the Shell autoignition model), spray combustion of diesel and entrained natural gas, and premixed turbulent combustion of the natural gas-air mixture.

The Mississippi State University Formula SAE race car upright was optimized using radial basis function metamodels and an internal state variable (ISV) plasticity damage material model. The weight reduction of the upright was part of a goal to reduce the weight of the vehicle by 25 percent. Using an optimization routine provided an upright design that is lighter that helps to improve vehicle fuel economy, acceleration, and handling. Finite element (FE) models of the upright were produced using quadratic tetrahedral elements. Using tetrahedral elements provided a quick way to produce the multiple FE models of the upright required for the multi-objective optimization. A design of experiments was used to determine how many simulations were required for the optimization. The loads for the simulations included braking, acceleration, and corning loads seen by the car under track conditions.

Serpentine belt system has been widely used to drive automotive accessories like power steering pump, alternator, and A/C compressor from a crankshaft pulley. Overload under severe conditions can lead to excessive slippage in the belt pulley interface in poorly designed accessory systems. This can lead to undesirable noise that increases warranty cost substantially. The mechanisms and data of these tribology performance, noise features and system response are of utmost interest to the accessory drive designers. As accessories belt systems are usually used in ambient condition, the presence of water on belt is unavoidable under the raining weather conditions. The presence of water in interface induces larger slippage as the water film in interface changes the friction mechanisms in rubber belt-pulley interface from coulomb friction to friction with mixed lubrication that has negative slope of coefficient of friction (cof) - velocity.

Finite element models of vehicles have been increasingly used in component design and crashworthiness evaluation. As vehicle finite element models are becoming more sophisticated in terms of their accuracy, robustness, fidelity, and size, the need to compare different FEA codes has become more apparent. In this study, we compare finite element simulations of a 1996 Dodge Neon using LS-DYNA and PAM-CRASH codes with an effort to keep sameness of the material models, meshes and boundary conditions. The original Neon mesh and material properties were developed at the FHWA/NHTSA National Crash Analysis Center (NCAC) for LS-DYNA and subsequently modified for this study. The comparisons between test data and simulation results of the full-scale vehicle in terms of overall impact deformation, component failure modes, and velocity and acceleration at various locations in the vehicle show good correlations with only minor discrepancy.

Although the cost-saving and good environmental impacts are the benefits that make Electric Vehicles (EVs) popular, these advantages are significantly influenced by the cost of battery replacement over the vehicle lifetime. After several charging and discharging cycles, the battery is subjected to energy and power degradation which affects the performance and efficiency of the vehicle. In addition to battery replacement cost, the electricity cost being paid by drivers is another key factor in selecting the EVs. An Energy Management System (EMS) with Model Predictive Control-based (MPC) algorithm is presented for a specific case of heavy-duty EV. Such EV draws its energy from the grid via catenary in addition to the on-board battery. Dynamic model of the vehicle will be defined by State Space Equations (SSE).

A cost effective, portable particulate management system was developed, prototyped, and evaluated for further application and commercialization, which could remove and dispose particulate matter suspended in air efficiently and safely. A prototype of the present system was built for experimental assessment and validation. The experimental data showed that the developed particulate management system can effectively clean the air by capturing the particles inside it. Effects of viscosity of filter medium on the performance of the developed system were also discussed. The present system is very flexible, whose size and shape can be scaled and changed to be fit for different applications. Its manufacturing cost is less than $10. Based on the experimental validation results, it was found that the present system can be further developed, commercialized, and applied for a variety of industries.

A dynamic modeling framework was established to predict status (position, displacement, velocity, acceleration, and shape) of a towed vehicle system with different driver inputs. This framework consists of three components: (1) a state space model to decide position and velocity for the vehicle system based on Newton’s second law; (2) an angular acceleration transferring model, which leads to a hypothesis that the each towed unit follows the same path as the towing vehicle; and (3) a polygon model to draw instantaneous polygons to envelop the entire system at any time point. Input parameters of this model include initial conditions of the system, real-time locations of a reference point (e.g. front center of the towing vehicle) that can be determined from a beacon and radar system, and instantaneous accelerations of this system, which come from driver maneuvers (accelerating, braking, steering, etc.) can be read from a data acquisition system installed on the towing vehicle.

We characterize the cyclic behavior of an AM30 extruded magnesium alloy. The micromechanisms of cyclic damage were studied by means of strain controlled experiments in both the extruded and transverse directions. A scanning electron microscope (SEM) analysis of the microstructure revealed that second phase particles were present in the Mg alloy that nucleated the cracks. However, crack initiation sites were observed to occur due to profuse twinning. Low cycle fatigue parameters were determined, and a microstructure-sensitive MultiStage Fatigue (MSF) model, which is able to capture mechanical and microstructure properties, was implemented to predict fatigue behavior and failure.

A series of carefully selected tests were used to isolate the coupled influence of various combinations of the number of facesheet plies, impact energies, and impactor diameters on the damage formation and residual strength degradation of sandwich composites due to normal impact. The diameter of the planar damage area associated with Through Transmission Ultrasonic C-scan and the compression after impact measurements were used to describe the extent of the internal damage and residual strength degradation of test panels, respectively. Standard analysis of variance techniques were used to assess the significance of the regression models, individual terms, and the model lack-of-fit. In addition, the inherent variability associated with given types of experimental measurements was evaluated.

This study examines the performance and subject acceptance level of a hand-operated brake actuator. Using a fixed-base vehicle simulator, data for driver reaction time, stopping time, distance, deceleration, customer acceptance and mental workload were collected. Data for three prototype hand-operated brake actuators and traditional foot-operated brake were compared. An additional test, designed to evaluate anthropometrics, sensitivity, and comfort was performed during training. A user preference survey to determine handbrake acceptance was given to subjects after completing the driving test in the simulator. In certain trials, participants were given the choice of handbrake or footbrake for an unexpected stop condition. When placed into an unexpected braking situation, subjects showed faster brake-application times for operating the hand-operated brake, indicating potential for reduced braking distance.

EcoCAR 2: Plugging In to the Future (EcoCAR) is North America's premier collegiate automotive engineering competition, challenging students with systems-level advanced powertrain design and integration. The three-year Advanced Vehicle Technology Competition (AVTC) series is organized by Argonne National Laboratory, headline sponsored by the U. S. Department of Energy (DOE) and General Motors (GM), and sponsored by more than 28 industry and government leaders. Fifteen university teams from across North America are challenged to reduce the environmental impact of a 2013 Chevrolet Malibu by redesigning the vehicle powertrain without compromising performance, safety, or consumer acceptability. During the three-year program, EcoCAR teams follow a real-world Vehicle Development Process (VDP) modeled after GM's own VDP. The VDP serves as a roadmap for the engineering process of designing, building and refining advanced technology vehicles.

A simple rugged acoustic stall sensor which has an output proportional to angle of attack near wing stall has been evaluated on a Cessna 319 aircraft. A sensor position has been found on the wing where the sensor output is only slightly affected by engine power level, yaw angle, flap position and wing roughness. The NASA LRC General Aviation Simulator has been used to evaluate the acoustic sensor output as a control signal for active stall deterrent systems. It has been found that a simple control algorithm is sufficient for stall deterrence.

The latest edition of the US Department of Energy's (DOE) Advanced Vehicle Technology Competition (AVTC) series is the EcoCAR Mobility Challenge (EMC). In the third year of the EMC, the Mississippi State University (MSU) team developed and tested a perception system and a longitudinal controller to achieve SAE level 2 autonomy. Our team leveraged the model-based design approach to iterate between developing software components and executing tests in multiple environments in the loop (XIL) to verify that design requirements are met. This workflow allowed us to detect and resolve issues early in the development process. The perception system is composed of a sensor fusion and tracking algorithm. It relies on detections from a front facing camera and radar to generate tracks for a leading vehicle. The tracks from the perception system are used by a model predictive controller (MPC) to maintain a safe distance to the leading vehicle.

The EcoCAR Mobility Challenge (EMC) is the latest edition of the Advanced Vehicle Technology Competition (AVTC) series sponsored by the US Department of Energy. This competition challenges 11 North American universities to redesign a stock 2019 Chevrolet Blazer into an energy-efficient, SAE level 2-autonomous mild hybrid electric vehicle (mHEV) for use in the Mobility as a Service (MaaS) market. The Mississippi State University (MSU) team designed a P4 electric powertrain with an 85kW (113.99 HP) permanent magnet synchronous machine (PMSM) powered by a custom 5.4 kWh lithium-ion energy storage system. To maximize energy efficiency, Model Based Design concepts were leveraged to optimize the overall gear ratio for the P4 system. To accommodate this optimized ratio in the stock vehicle, a custom offset gearbox was designed that links the PMSM to the rear drive module.