Browse Topic: Education and training
Driver-in-the-Loop (DIL) simulators have become crucial tools across automotive, aerospace, and maritime industries in enabling the evaluation of design concepts, testing of critical scenarios and provision of effective training in virtual environments. With the diverse applications of DIL simulators highlighting their significance in vehicle dynamics assessment, Advanced Driver Assistance Systems (ADAS) and autonomous vehicle development, testing of complex control systems is crucial for vehicle safety. By examining the current landscape of DIL simulator use cases, this paper critically focuses on Virtual Validation of ADAS algorithms by testing of repeatable scenarios and effect on driver response time through virtual stimuli of acoustic and optical warnings generated during simulation. To receive appropriate feedback from the driver, industrial grade actuators were integrated with a real-time controller, a high-performance workstation and simulation software called Virtual Test
Functional Mock-up Units (FMUs) have become a standard for enabling co-simulation and model exchange in vehicle development. However, traditional FMUs derived from physics-based models can be computationally intensive, especially in scenarios requiring real-time performance. This paper presents a Python-based approach for developing a Neural Network (NN) based FMU using deep learning techniques, aimed at accelerating vehicle simulation while ensuring high fidelity. The neural network was trained on vehicle simulation data and trained using Python frameworks such as TensorFlow. The trained model was then exported into FMU, enabling seamless integration with FMI-compliant platforms. The NN FMU replicates the thermal behavior of a vehicle with high accuracy while offering a significant reduction in computational load. Benchmark comparisons with a physical thermal model demonstrate that the proposed solution provides both efficiency and reliability across various driving conditions. The
In this article we will discuss the development and implementation of a computer vision system to be used in decision-making and control of an electro-hydraulic mechanism in order to guarantee correct functioning and efficiency during the logistics project. To achieve this, we have brought together a team of engineering students with knowledge in the area of Artificial Intelligence, Front End and mechanical, electrical and hydraulic devices. The project consists of installing a system on a forklift that moves packaged household appliances that can identify and differentiate the different types of products moved in factories and distribution centers. Therefore, the objective will be to process this identification and control an electro-hydraulic pressure control valve (normally controlled in PWM) so that it releases only the hydraulic pressure configured for each type of packaging/product, and thus correctly squeezing (compressing) the specific volume, without damaging it due to
The concept of “quality feel” in automotive interiors relates to how consumers perceive a product’s quality through touch and feel. While subjective, it’s crucial for satisfaction and differentiation and is defined by engineering requirements like displacement, especially for interior components. Assessing this early in development is vital. Traditionally, this evaluation happens virtually using Computer Aided Engineering (CAE) simulations, which measure displacement and stiffness. However, conventional simulation methods, like Finite Element Method (FEM), can be time-consuming to set up. This work presents two case studies where the evaluation of an interior panel’s quality feel, using structural numerical simulations combined with the Simulation Driven Design (SDD) method was performed. SDD is an iterative process where simulation results guide design modifications, optimizing the component until it meets quality criteria, which are based on simulated human touch and resulting
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In contemporary society, where Global Navigation Satellite Systems (GNSS) are utilised extensively, their inherent fragility gives rise to potential hazards with respect to the safety of ship navigation. In order to address this issue, the present study focuses on an ASM signal delay measurement system based on software defined radio peripherals. The system comprises two distinct components: a transmitting end and a receiving end. At the transmitting end, a signal generator, a first time-frequency synchronisation device, and a VHF transmitting antenna are employed to transmit ASM signals comprising dual Barker 13 code training sequences. At the receiving end, signals are received via software-defined radio equipment, a second time-frequency synchronisation device, a computing host, and a VHF receiving antenna. Utilising sliding correlation algorithms enables accurate time delay estimation. The present study leverages the high performance and low cost advantages of the universal
Recent advancements in energy efficient wireless communication protocols and low powered digital sensor technologies have led to the development of wireless sensor network (WSN) applications in diverse industries. These WSNs are generally designed using Bluetooth Low Energy (BLE), ZigBee and Wi-Fi communication protocol depending on the range and reliability requirements of the application. Designing these WSN applications also depends on the following factors. First, the environment under which devices operate varies with the industries and products they are employed in. Second, the energy availability for these devices is limited so higher signal strength for transmission and retransmission reduces the lifetime of these nodes significantly and finally, the size of networks is increasing hence scheduling and routing of messages becomes critical as well. These factors make simulation for these applications essential for evaluating the performance of WSNs before physical deployment of
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