Safety continues to be one of the most important factors in motor vehicle design, manufacturing, and marketing. This course provides a comprehensive overview of these critical automotive safety considerations: injury and anatomy; human tolerance and biomechanics; occupant protection; testing; and federal legislation. The knowledge shared at this course enables participants to be more aware of safety considerations and to better understand and interact with safety experts. This course has been approved by the Accreditation Commission for Traffic Accident Reconstruction (ACTAR) for 18 Continuing Education Units (CEUs).
Convolutional neural networks are the de facto method of processing camera, radar, and lidar data for use in perception in ADAS and L4 vehicles, yet their operation is a black box to many engineers. Unlike traditional rules-based approaches to coding intelligent systems, networks are trained and the internal structure created during the training process is too complex to be understood by humans, yet in operation networks are able to classify objects of interest at error rates better than rates achieved by humans viewing the same input data.
This title includes the technical papers developed for the 2023 Stapp Car Crash Conference, the premier forum for the presentation of research in impact biomechanics, human injury tolerance, and related fields, advancing the knowledge of land-vehicle crash injury protection. The conference provides an opportunity to participate in open discussion about the causes and mechanisms of injury, experimental methods and tools for use in impact biomechanics research, and the development of new concepts for reducing injuries and fatalities in automobile crashes.
This class will provide the student with the skills, knowledge, and abilities to interpret, analyze and apply HVEDR data in real-world applications. This course has been designed to build on the concepts presented in the SAE course Accessing and Interpreting Heavy Vehicle Event Data Recorders (ID# C1022). Advanced topics will include associating HVEDR data with collision events through timestamps, odometer logs, and data signatures, validating HVEDR speed data using specified vehicle parameters, performing time and distance analyses using HVEDR data, and correlating HVEDR data to physical evidence from the vehicle and roadway.
For automotive engineers involved in crash reconstruction and analysis, a knowledge of basic accident reconstruction principles and techniques is essential, but often insufficient to answer all of the questions posed by design engineers, regulators, and lawyers. This course takes participants beyond the basics of accident reconstruction to physical models and analysis techniques that are unique to the reconstruction of single-vehicle rollover crashes.
A gerotor pump is a positive displacement pump consisting of inner and outer rotors, with axis of inner rotor offset from axis of outer rotor. Both rotors rotate about their respective axes. The volume between the rotors changes dynamically, due to which suction and compression occurs. A gerotor pump may be subject to erosion due to cavitation. This paper details about the CFD methodology that has been used to capture cavitation bubbles which might form during the operation of gerotor pump. A full scale (3D) transient CFD model for gerotor pump has been developed using commercial CFD code ANSYS FLUENT. The most challenging part of this CFD flow modeling is to create a dynamic volume mesh that perfectly represents the dynamically changing rotor fluid volume of the gerotor pump. Two different approaches have been used to model this dynamic mesh analysis in the Ansys Fluent tool - one method by using the traditional UDF script and, another method by using Python automation script.
Due to their remarkable efficiency and efficacy, chevrons have emerged as a prominent subject of investigation within the Aviation Industry, primarily aimed at mitigating aircraft noise levels and achieving a quieter airborne experience. Extensive research has identified the engine as the primary source of noise in aircraft, prompting the implementation of chevrons within the engine nozzle. These chevrons function by inducing streamwise vortices into the shear layer, thereby augmenting the mixing process and resulting in a noteworthy reduction of low-frequency noise emissions. Our paper aims to conduct a comparative computational analysis encompassing seven distinct chevron designs and a design without chevrons. The size and configuration of the chevrons with the jet engine nacelle were designed to match the nozzle diameter of 100.48mm and 56.76mm, utilizing the advanced SolidWorks CAD modeling software.
Abstract: The present study discusses about the effect of installation torque on the surface and subsurface deformations for thin walled 7075 aluminum alloy used in Aerospace applications. A FE model was constructed to predict the effect of torque induced stresses on thin walled geometry followed with an experimentation. A detailed surface analysis was performed on 7075 aluminum in terms of superficial discontinuities, residual stresses, and grain deformations. The localized strain hardening resulting from increased dislocation density and its effect on surface microhardness was further studied using EBSD and micro indentation. The predicted surface level plastic strain of .25% was further validated with grain deformations measured using optical and scanning electron microscopy.
Industries have been increasingly adopting AI based computer vision models for automated asset defect inspection. A challenging aspect within this domain is the inspection of composite assets consisting of multiple components, each of which is an object of interest for inspection, with its own structural variations, defect types and signatures. Training vision models for such an inspection process involves numerous challenges around data acquisition such as insufficient volume, inconsistent positioning, poor quality and imbalance owing to inadequate image samples of infrequently occurring defects. Approaches to augmenting the dataset through Standard Data Augmentation (SDA) methods (image transformations such as flipping, rotation, contrast adjustment, etc.) have had limited success. When dealing with images of such composite assets, it is challenging to correct the data imbalance at the component level using image transformations as they apply to all the components within an image.
Electric aircraft have emerged as a promising solution for sustainable aviation, aiming to reduce greenhouse gas emissions and noise pollution. Efficiently estimating and optimizing energy consumption in these aircraft is crucial for enhancing their design, operation, and overall performance. This paper presents a novel framework for analyzing and modeling energy consumption patterns in lightweight electric aircraft. A mathematical model is developed, encompassing key factors such as aircraft weight, velocity, wing area, air density, coefficient of drag, and battery efficiency. This model estimates the total energy consumption during steady-level flight, considering the power requirements for propulsion, electrical systems, and auxiliary loads. The model serves as the foundation for analyzing energy consumption patterns and optimizing the performance of lightweight electric aircraft.
Landing of spacecraft on Lunar or Martian surfaces is the last and critical step in inter planetary space missions. The atmosphere on earth is thick enough to slow down the craft but Moon or Mars does not provide a similar atmosphere. Moreover, other factors such as lunar dust, availability of precise onboard navigational aids etc would impact decision making. Soft landing meaning controlling the velocity of the craft from over 6000km/h to zero. If the craft’s velocity is not controlled, it might crash. Various onboard sensors and onboard computing power play a critical role in estimating and hence controlling the velocity, in the absence of GPS-like navigational aids. In this paper, an attempt is made using visual onboard sensor to estimate the velocity of the object. The precise estimation of an object's velocity is a vital component in the trajectory planning of space vehicles, particularly those designed for descent onto lunar or Martian terrains, such as orbiters or landers.
This research employs a comprehensive methodology to explore hypersonic boundary layers' stability and transition dynamics, focusing specifically on the influence of sharp and blunt leading edges. The Stanford University Unstructured (SU2) Computational Fluid Dynamics (CFD) solver is utilized to compute the mean flow over a flat plate, establishing a foundational basis for subsequent stability analysis. The extracted boundary layer profiles undergo validation against existing literature, ensuring accuracy and reliability. Further analysis is conducted using a Python code to generate input files for the Linear Stability Solver. The Linear Stability Solver analysis constitutes a crucial phase wherein the research delves into the eigenvalue spectra, identifying dominant modes and closely scrutinizing the role of the modes in the transition process within the hypersonic boundary layers.
Aerospace structural components grapple with the pressing issue of high-cycle fatigue-induced micro-crack initiation, especially in high-performance alloys like Titanium and super alloys. These materials find critical use in aero-engine components, facing a challenging combination of thermo-mechanical loads and vibrations that lead to gradual dislocations and plastic strain accumulation around stress-concentrated areas. The consequential vibration or overload instances can trigger minor cracks from these plastic zones, often expanding unpredictably before detection during subsequent inspections, posing substantial risks. Effectively addressing this challenge demands the capability to anticipate the consequences of operational life and aging on these components. It necessitates assessing the likelihood of crack initiation due to observed in-flight vibration or overload events.
The Selective Laser Melting (SLM) process is employed in high-precision layer-by-layer Additive Manufacturing (AM) on powder bed and aims to fabricate high-quality structural components. To gain a comprehensive understanding of the process and its optimization, both modeling and simulation in conjunction with extensive experimental studies along with laser calibration studies have been attempted. Multiscale and multi-physics-based simulations have the potential to bring out a new level of insight into the complex interaction of laser melting, solidification, and defect formation in the SLM parts. SLM process encompasses various physical phenomena during the formation of metal parts, starting with laser beam incidence and heat generation, heat transfer, melt/fluid flow, phase transition, and microstructure solidification. To effectively model this Multiphysics problem, it is imperative to consider different scales and compatible boundary conditions in the simulations.