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In applications demanding high performance under extreme conditions of pressure and temperature, a range of Mechanically Attached Fittings (MAFs) is offered by various Multinational Corporations (MNCs). These engineered fittings have been innovatively designed to meet the rigorous requirements of the aerospace industry, offering a cost-effective and lightweight alternative to traditional methods such as brazing, welding, or other mechanically attached tube joints. One prominent method employed for attaching these fittings to tubing is through Internal Swaging, a mechanical technique. This process involves the outward formation of rigid tubing into grooves within the fitting. One of the methods with which this intricate operation is achieved is by using a drawbolt - expander assembly within an elastomeric swaging machine.

The SAE AutoDrive Challenge II is a four-year collegiate competition dedicated to developing a Level 4 autonomous vehicle by 2025. In January 2023, the participating teams each received a Chevy Bolt EUV. Within a span of five months, the second phase of the competition took place in Ann Arbor, MI. The authors of this contribution, who participated in this event as team Wisconsin Autonomous representing the University of Wisconsin–Madison, secured second place in static events and third place in dynamic events. This has been accomplished by reducing reliance on the actual vehicle platform and instead leveraging physical analogs and simulation. This paper outlines the software and hardware infrastructure of the competing vehicle, touching on issues pertaining sensors, hardware, and the software architecture employed on the autonomous vehicle. We discuss the LiDAR-camera fusion approach for object detection and the three-tier route planning and following systems.

Wet-sump transmissions are widely used in heavy duty and medium duty vehicles. As these transmissions do not have a dedicated forced lubrication system, it is important that the gear train, shafts, and enclosure are designed appropriately so that enough oil splashes to critical locations to ensure sufficient lubrication. The lubrication effectiveness of such transmissions can be studied through detailed tests or numerical simulations. Often, the vehicle, and therefore the transmission, encounters some severe operating conditions, such as climbing on an incline, driving downhill, etc. Studying these conditions through tests is an expensive process and this imposes the need for an analysis first approach. In this paper, the 3D multiphase Volume of Fluid (VOF) method is used to examine two such extreme cases: an 8-degree tilted installation of transmission in a vehicle, and an inclined condition of transmission during a 10-degree uphill climb.

Roots blower is a rotary positive displacement pump which operates by pumping a fluid with a pair of meshing lobes. Recent trends in automotive industry demands high power density solutions for various applications. In comparison with legacy applications, compressors for high power density applications demand continuous operation with harsher duty cycle as well as demand higher pressure ratios. Because of longer duty cycles, it will be subjected to high heat loads which will cause a rise in temperatures of timing gears, bearings, and other components within the assembly. Accurate prediction of thermal performance is critical to design a durable and efficient roots blower for high power density applications. Thermal analysis of an assembly of roots blower involves modelling of multi-physics phenomena. This paper details a coupled CFD analysis approach to predict temperatures of roots blower components and timing gear case oil. Timing gears are lubricated using wet sump lubrication.

The use of electrical contacts in aerospace applications is crucial, particularly in connectors that transmit signal and power. Crimping is a widely preferred method for joining electrical contacts, as it provides a durable connection and can be easily formed. This process involves applying mechanical load to the contact, inducing permanent deformation in the barrel and wire to create a reliable joint with sufficient wire retention force. This study utilizes commercially available Abaqus software to simulate the crimping process using an explicit solver. The methodology developed for this study correlates FEA and testing for critical quality parameters such as structural integrity, mechanical strength, and joint filling percentage. A four-indenter crimping tool CAD model is utilized to form the permanent joint at the barrel-wire contact interfaces, with displacement boundary conditions applied to the jaws of the tool in accordance with MIL-C-22520/1C standard.

With the advent of wide band gap semiconductor devices like SiC based MOSFETs/Diodes, there is a growing demand for utilizing electrical power instead of the conventional fuel-based power generation in both automotive and aerospace industry. In automotive/aerospace industry the focus on electrification has resulted in a need for sub-systems like inverters, power distribution units, motor controllers, DC-DC converters that actively utilize SiC based power electronics devices. To address the growing power density requirements for electronics in next generation product families, more efficient & reliable thermal management solution plays a critical role. The effective thermal management of the power electronics is also critical aspect to ensure overall system reliability. The conventional thermal management system (TMS) optimization targets heat sink/ cold plate design parameters like fin spacing, thickness, height etc. or sizing of the required cooling pump/fan.

Lithium-ion and Lithium polymer batteries are fast becoming ubiquitous in high-discharge rate applications for military and non-military systems. Applications such as small aerial vehicles and energy transfer systems can often function at C-rates greater than 1. To maximize system endurance and battery health, there is a need for models capable of precisely estimating the battery state-of-charge (SoC) under all temperature and loading conditions. However, the ability to perform state estimation consistently and accurately to within 1% error has remained unsolved. Doing so can offer enhanced endurance, safety, reliability, and planning, and additionally, simplify energy management. Therefore, the work presented in this paper aims to study and develop experimentally validated mathematical models capable of high-accuracy battery SoC estimation.

Identifying edge cases for designed algorithms is critical for functional safety in autonomous driving deployment. In order to find the feasible boundary of designed algorithms, simulations are heavily used. However, simulations for autonomous driving validation are expensive due to the requirement of visual rendering, physical simulation, and AI agents. In this case, common sampling techniques, such as Monte Carlo Sampling, become computationally expensive due to their sample inefficiency. To improve sample efficiency and minimize the number of simulations, we propose a tailored active learning approach combining the Support Vector Machine (SVM) and the Gaussian Process Regressor (GPR). The SVM learns the feasible boundary iteratively with a new sampling point via active learning. Active Learning is achieved by using the information of the decision boundary of the current SVM and the uncertainty metric calculated by the GPR.

Opposed-piston 2-stroke (OP-2S) engines have the potential to achieve higher thermal efficiency than a typical diesel engine. However, the uniflow scavenging process is difficult to control over a wide range of speeds and loads. Scavenging performance is highly sensitive to pressure dynamics, port timings, and port design. This study proposes an analysis of the effects of port vane angle on the scavenging performance of an opposed-piston 2-stroke engine via simulation. A CFD model of a three-cylinder opposed-piston 2-stroke was developed and validated against experimental data collected by Achates Power Inc. One of the three cylinders was then isolated in a new model and simulated using cycle-averaged and cylinder-averaged initial/boundary conditions. This isolated cylinder model was used to efficiently sweep port angles from 12 degrees to 29 degrees at different pressure ratios.

Traffic state identification is a key problem in intelligent transportation system. As a new technology, connected and automated vehicle can play a role of identifying traffic state with the installation of onboard sensors. However, research of lane level traffic state identification is relatively lacked. Identifying lane level traffic state is helpful to lane selection in the process of driving and trajectory planning. In addition, traffic state identification precision with low penetration of connected and automated vehicles is relatively low. To fill this gap, this paper proposes a novel method of identifying traffic state in the presence of connected and automated vehicles with low penetration rate. Assuming connected and automated vehicles can obtain information of surrounding vehicles’, we use the perceptible information to estimate imperceptible information, then traffic state of road section can be inferred.

The current simulation models of EV and ICE Vehicles are well known in industry for their use in estimating the fuel economy or Range benefits because of controller calibrations and component sizing. However, there is a gap in understanding the behavior of accessories such as HVAC, power steering and other such auxiliary loads and the energy losses associated with them. Impact of thermal behavior of electronics on vehicle range also needs to be studied in detail. These kinds of studies help OEM and tier 1 manufactures in improving their design concepts significantly with minimum cost and development time. Hence, the focus of this study is on building simulation models of thermal, electrical, traction and control circuits of a typical electric vehicle. These models are then integrated, and analysis is performed to understand vehicle system level performance metrics.

In-cylinder thermal barrier coatings (TBCs) have the capability to reduce fuel consumption by reducing wall heat transfer and to increase exhaust enthalpy. Low thermal conductivity, low volumetric heat capacity thermal barrier coatings tend to reduce the gas-wall temperature difference, the driving potential for heat transfer from the gas to the combustion chamber surfaces. This paper presents a coupling between an analytical methodology for multi-layer coated wall surface temperature prediction with a fully calibrated production model in a commercial system-level simulation software package (GT-Power). The wall surface temperature at each time step was calculated efficiently by convolving the engine wall response function with the time-varying surface boundary condition, i. e., in-cylinder heat flux and coolant temperature. This tool allows the wall to be treated either as spatially uniform with one set of properties, or with independent head/piston/liner components.

Appropriate spray modeling in multidimensional simulations of diesel engines is well known to affect the overall accuracy of the results. More and more accurate models are being developed to deal with drop dynamics, breakup, collisions, and vaporization/multiphase processes; the latter ones being the most computationally demanding. In fact, in parallel calculations, the droplets occupy a physical region of the in-cylinder domain, which is generally very different than the topology-driven finite-volume mesh decomposition. This makes the CPU decomposition of the spray cloud severely uneven when many CPUs are employed, yielding poor parallel performance of the spray computation. Furthermore, mesh-independent models such as collision calculations require checking of each possible droplet pair, which leads to a practically intractable O(np2/2) computational cost, np being the total number of droplets in the spray cloud, and additional overhead for parallel communications.

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 we studied the effects of piston bowl design on combustion in a small-bore direct-injection diesel engine. Two bowl designs were compared: a conventional, omega-shaped bowl and a stepped-lip piston bowl. Experiments were carried out in the Sandia single-cylinder optical engine facility, with a medium-load, mild-boosted operating condition featuring a pilot+main injection strategy. CFD simulations were carried out with the FRESCO platform featuring full-geometric body-fitted mesh modeling of the engine and were validated against measured in-cylinder performance as well as soot natural luminosity images. Differences in combustion development were studied using the simulation results, and sensitivities to in-cylinder flow field (swirl ratio) and injection rate parameters were also analyzed.

Diesel piston bowl geometry can affect turbulent mixing and therefore it impacts heat-release rates, thermal efficiency, and soot emissions. The focus of this work is on the effects of bowl geometry and injection timing on turbulent flow structure. This computational study compares engine behavior with two pistons representing competing approaches to combustion chamber design: a conventional, re-entrant piston bowl and a stepped-lip piston bowl. Three-dimensional computational fluid dynamics (CFD) simulations are performed for a part-load, conventional diesel combustion operating point with a pilot-main injection strategy under non-combusting conditions. Two injection timings are simulated based on experimental findings: an injection timing for which the stepped-lip piston enables significant efficiency and emissions benefits, and an injection timing with diminished benefits compared to the conventional, re-entrant piston.

We present an approach in which an open-source software infrastructure is used for testing the behavior of autonomous vehicles through computer simulation. This software infrastructure is called CAVE, from Connected Autonomous Vehicle Emulator. As a software platform that allows rapid, low-cost and risk-free testing of novel designs, methods and software components, CAVE accelerates and democratizes research and development activities in the field of autonomous navigation.

We present an approach in which we use simulation to capture the two-way coupling between the dynamics of a vehicle and that of a fluid that sloshes in a tank attached to the vehicle. The simulation is carried out in and builds on support provided by two modules: Chrono::FSI (Fluid-Solid Interaction) and Chrono::Vehicle. The dynamics of the fluid phase is governed by the mass and momentum (Navier-Stokes) equations, which are discretized in space via a Lagrangian approach called Smoothed Particle Hydrodynamics. The vehicle dynamics is the solution of a set of differential algebraic equations of motion. All equations are discretized in time via a half-implicit symplectic Euler method. This solution approach is general - it allows for fully three dimensional (3D) motion and nonlinear transients. We demonstrate the solution in conjunction with the simulation of a vehicle model that performs a constant radius turn and double lane change maneuver.

Functionally graded materials enable structures to have distribution of different properties (physical, thermal, electrical, mechanical, etc.) across its volume; achievable via material/ design/ process engineering. These functionally graded materials can find an application in systems which demand localized variation or enhancement in properties in different regions of the same component. In this paper, we focus on the potential ways of designing functionally graded polymer composite structure by injection molding process. Advanced mold designs for injection molding process can be effectively used to manufacture the functionally graded structures. Innovative design approach has been explored to control the distribution of the filler content /orientation to impart distinctive properties across the cross section / geometry without affecting the bulk properties.

Light weight structures give significant advantages to products in the Industrial sector. Component weight-saving plays a major role in improving the efficiency and performance of assembled systems. The introduction of lighter materials into products using dissimilar material joining techniques can create more weight savings and leads to lighter structures. Structural optimization is another method to optimize the material layout without affecting overall performance of the product. This paper discusses the methods to create lighter structures by the introduction of lighter materials in structures and structural optimization methods. Lighter materials are introduced in the structure using dissimilar material joining techniques. Joining processes such as thermal shrink-fit and mechanical press-fit are useful for metal to metal components. Similarly, adhesively bonded joints are useful for both metal and non-metal (plastics and composites) components.