Search Results

The cost of this introductory course can be applied to the cost of the full courses: C2215, Safety Management Systems for Design, Manufacturing and Maintenance Providers in Aviation C2216, Safety Risk Management and Safety Assurance for Design, Manufacturing and Maintenance Providers in Aviation Historically, organizations tend to be punitive and focused on who to blame when an unwanted event occurs. Investigations can begin with the intent to blame and discipline which leads to adversarial relationships between management and employees. 

The evaluation of aircraft characteristics through flight test maneuvers is fundamental to aviation safety and understanding flight attributes. This research project proposes a comprehensive methodology to detect and analyze aircraft maneuvers using full flight data, combining signal processing and machine learning techniques. Leveraging the Wavelet Transform, we unveil intricate temporal details within flight data, uncovering critical time-frequency insights essential for aviation safety. The integration of Long Short-Term Memory (LSTM) models enhances our ability to capture temporal dependencies, surpassing the capabilities of machine learning in isolation. These extracted maneuvers not only aid in safety but also find practical applications in system identification, air-data calibration, and performance analysis, significantly reducing pre-processing time for analysts.

Launch vehicle structures in course of its flight will be subjected to dynamic forces over a range of frequencies up to 2000 Hz. These loads can be steady, transient or random in nature. The dynamic excitations like aerodynamic gust, motor oscillations and transients, sudden application of control force are capable of exciting the low frequency structural modes and cause significant responses at the interface of launch vehicle and satellite. The satellite interface responses to these low frequency excitations are estimated through Coupled Load Analysis (CLA). The analysis plays a crucial role in mission as the satellite design loads and Sine vibration test levels are defined based on this. The perquisite of CLA is to predict the responses with considerable accuracy so that the design loads are not exceeded in the flight. CLA validation is possible by simulating the flight experienced responses through the analysis.

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.

Faults if not detected and processed will create catastrophe in closed loop system for safety critical applications in automotive, space, medical, nuclear, and aerospace domains. In aerospace applications such as stall warning and protection/prevention system (SWPS), algorithms detect stall condition and provide protection by deploying the elevator stick pusher. Failure to detect and prevent stall leads to loss of lives and aircraft. Traditional Functional Hazard and Fault Tree analyses are inadequate to capture all failures due to the complex hardware-software interactions for stall warning and protection system. Hence, an improved methodology for failure detection and identification is proposed. This paper discusses a hybrid formal method and model-based technique using STPA to identify and diagnose faults and provide monitors to process the identified faults to ensure robust design of the indigenous stall warning and protection system (SWPS).

Why a Management Academy? Why should you be interested in this Engineering Management Academy from SAE? The answer to these questions lies in the statistics highlighted by surveys of hiring managers. For example, are you aware that: 28% of internal leadership promotions fail On average, it takes six years before an individual receives any formal training after being promoted to a management position Individual contributors, who are technical experts, are usually natural candidates for  promotions to management positions.

This paper investigates the utilization of Low Earth Orbit (LEO) satellites for vehicle localization and conducts a comparative analysis with traditional Global Navigation Satellite Systems (GNSS)-based methods. With the rise of LEO satellite constellations, such as Starlink, LEO-based vehicle localization may offer solutions to GNSS-related challenges. With a large number of satellites and short communication distance, the LEO-based method has great potential to improve accuracy, reduce warm-up time, and provide a robust localization solution for vehicle applications. In this paper, a dedicated LEO satellite simulator is presented, adaptable to various LEO constellations, making it relevant for evolving technologies beyond older LEO systems like Orbcomm or Iridium. The simulator includes satellite trajectory generation, observable satellite identification, and vehicle localization.

Autonomous driving technology is more and more important nowadays, it has been changing the living style of our society. As for autonomous driving planning and control, vehicle dynamics has strong nonlinearity and uncertainty, so vehicle dynamics and control is one of the most challenging parts. At present, many kinds of specific vehicle dynamics models have been proposed, this review attempts to give an overview of the state of the art of vehicle dynamics models for autonomous driving. Firstly, this review starts from the simple geometric model, vehicle kinematics model, dynamic bicycle model, double-track vehicle model and multi degree of freedom (DOF) dynamics model, and discusses the specific use of these classical models for autonomous driving state estimation, trajectory prediction, motion planning, motion control and so on.

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.

When the aircraft towing operations are carried out in narrow areas such as the hangars or parking aprons, it has a high safety risk for aircraft that the wingtips may collide with the surrounding aircraft or the airport facility. A real-time trajectory prediction method for the towbarless aircraft taxiing system (TLATS) is proposed to evaluate the collision risk based on image recognition. The Yolov7 module is utilized to detect objects and extract the corresponding features. By obtaining information about the configuration of the airplane wing and obstacles in a narrow region, a Long Short-Term Memory (LSTM) encoder-decoder model is utilized to predict future motion trends. In addition, a video dataset containing the motions of various airplane wings in real traction scenarios is constructed for training and testing.

This paper delves into the intricate realm of Formula 1 race car aerodynamics, focusing on the pivotal role played by floor flow structures in contemporary racing. The aerodynamic design of the floor of a Formula 1 car is a fundamental component that connects the flow structures from the front wing to the rear end of the car through the diffuser, thus significantly influencing the generation of lift and drag. In this work, CFD was used to predict the structure of the vortices and flow pattern underneath a Formula 1 car using a CAD model that mimicked the modern Red Bull Racing Team’s car in recent years. Through comprehensive analysis and simulation, a detailed understanding of the complex flow patterns and aerodynamic phenomena occurring beneath the floor of the car and its vicinity is presented.

In support of developing complex systems, integrating requirements from various source standards, such as the Military Standard (MIL-STD) series and others, presents a significant challenge. This paper explores the development of Model-Based System Engineering (MBSE) Systems Modeling Language (SysML) projects that incorporate MIL-STD requirements. The study begins by defining the critical need for integrating multiple standards into MBSE projects, emphasizing the importance of adhering to MIL-STD requirements when invoked by the customer. The study further defines the limitations inherent in managing standards independently and propose a unified approach within a SysML-based framework. The research introduces a systematic methodology for mapping MIL-STD requirements and other relevant standards onto SysML constructs, ensuring traceability and consistency throughout the system development lifecycle.

A case study of an application of Shape optimization techniques in the design of a mass simulator has been presented. A simple mass Simulator is to be designed as a replacement for a Telescope Baffle Mass for testing purposes. The simulator is made of simple plate structures like flat plates and cylindrical plates joined together. The overall mass, location of center of gravity and first few modes of the simulator need to be close to the Telescope Baffle, it is replacing. This ensures that the Simulator is a good replacement for the Telescope Baffle both in statics and dynamics performance. Shape Optimization techniques using approximate direct linearization method of MSC/Nastran software have been used to fine-tune the baseline Simulator design to achieve target properties of mass, cg, frequencies, etc.

Driving dynamics performance is one of the key customer attributes to be developed during product development. In the vehicle development process, freezing the hardware of the chassis aggregates is one of the major priorities to kick off the other vehicle development activities. The current work involves the development of a multilink suspension for an SUV class vehicle. Typically, each OEM performs several product development loops for maturing the vehicle design. The driving dynamics performance evaluation and tuning happens on a physical vehicle with the driver in Loop. Tuning of suspension parameter on the physical vehicle entails actual replacement of parts/components. This encompasses multiple tuning cycles in product development associated with increased cost and test time. To reduce the product development time and cost while delivering first time right chassis configuration, we took an approach of getting driver-in-loop through driving simulator in the concept phase.

With the recent development in virtual modelling and vehicle simulation technology, many OEM’s worldwide are using digital road profiles in virtual environment for vehicle durability load prediction and virtual design evaluation. For precise simulation results, it is important to have the tire digital twin which is the realistic representation of tire in the virtual environment. The study comprises of discussion about different types of tire models such as empirical, solid model, rigid ring model and flexural ring models such as Pacejka, MF Swift, CD tire, F tire etc. and also the complexity involved in development of these tire models. Generation of virtual tire model requires highly sophisticated test rigs as well as vehicle level testing with Wheel Force transducers and other vehicle dynamics sensors. The large number of data points generated with testing are converted in standard TYDEX format to be further processed in various software tool for virtual model generation.

Abstract This study underscores the benefits of refining the intralogistics process for small- to medium-sized manufacturing businesses (SMEs) in the engineer-to-order (ETO) sector, which relies heavily on manual tasks. Based on industrial visits and primary data from six SMEs, a new intralogistics concept and process was formulated. This approach enhances the value-added time of manufacturing workers while also facilitating complete digital integration as well as improving transparency and traceability. A practical application of this method in a company lead to cutting its lead time by roughly 11.3%. Additionally, improved oversight pinpointed excess inventory, resulting in advantages such as reduced capital needs and storage requirements. Anticipated future enhancements include better efficiency from more experienced warehouse staff and streamlined picking methods. Further, digital advancements hold promise for cost reductions in administrative and supportive roles.

Abstract Being an engineer-to-order (ETO) operating industry, the control cabinet industry faces difficulties in process and workplace optimizations due to changing requirements and lot size one combined with volatile orders. To optimize workplaces for employees, current literature is focusing on ergonomic designs, providing frameworks to analyze workplaces, leaving out the optimal design for productivity. This work thus utilizes a Kano analysis, collecting empirical data to identify essential design requirements for assembly workplaces, incorporating input from switchgear manufacturing employees. The results emphasize the need for a balance between ergonomics and efficiency in workplace design. Surprisingly, few participants agree on the correlation between improved processes and workspaces having a positive impact on their well-being and product quality.

Virtual simulation is a fundamental tool for the development of new vehicles, both for individual components and for complete subsystems and full vehicles. Many software tools exist in the automotive sector to assess full-vehicle behavior and performance, including multibody software and algorithms based on 14 (or more) degrees-of-freedom vehicle dynamics models. In order to reproduce the testing maneuvers and typical vehicle mission, a key part of such simulation tools is the virtual driver algorithm. It is essential to implement a control logic that reproduces the handling response of the driver, so that the closed-loop maneuvers can be evaluated. However, the response of typical virtual drivers is not always similar to the human driving characteristics. Virtual driver algorithms can perform very fast, precise, and smooth steering and pedal actions, while humans display a more variable, delayed and often not optimal actions.