Browse Topic: Simulators
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
The article is devoted to a comprehensive analysis of the digital transformation of education using the example of a project to train engineering personnel for the innovative transport industry in Russia. Special attention is paid to the introduction of hybrid formats, digital platforms, inclusivity, issues of digital inequality, as well as the experience of the National Research Center of the Russian Federation FSUE NAMI and interaction with leading universities in the country. A comparative analysis with foreign initiatives, including modern AI solutions for inclusive education, is presented, as well as the impact of the project to create educational and methodological centers on the professional motivation of teachers.
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
The automotive industry is rapidly evolving with technologies such as vehicle electrification, autonomous driving, Advanced Driver Assistance Systems (ADAS), and active suspension systems. Testing and validating these technologies under India’s diverse and complex road conditions is a major challenge. Physical testing alone is often impractical due to variability in road surfaces, traffic patterns, and environmental conditions, as well as safety constraints. Virtual testing using high-fidelity digital twins of road corridors offers an effective solution for replicating real-world conditions in a controlled environment. This paper highlights the representation of Indian road corridors as digital twins in ASAM OpenDRIVE and OpenCRG formats, emphasizing the critical elements required for realistic simulation of vehicle, tire, and ADAS performance. The digital twin incorporates detailed 3D road profiles (X-Y-Z coordinates), capturing the geometry and surface variations of Indian roads. The
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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
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
Single motorcycle accidents are common in Nagano Prefecture where is mountainous areas in Japan. In a previous study, analysis of traffic accident statistics data suggested that the fatality and serious injury rates for uphill right curves and downhill left curves are high, however the true causes of these accidents remain unclear. In this study, a motorcycle simulator was used to evaluate the driving characteristics due to these road alignments. Evaluation courses based on combinations of uphill/downhill slopes and left/right curves were created, and experiments were conducted. The subjects of the study were expert riders and novice riders. The results showed that right curves are even more difficult to see near the entrance of the curve when accompanied by an uphill slope, making it easier to delay recognition and judgment of the curve. Expert riders recognized curves faster than novice riders. Additionally, expert riders take a large lean of the vehicle body, actively attempted to
An important characteristic of battery electric vehicles (BEVs) is their noise signature. Besides tire and wind noise, noise from auxiliaries as pumps, the electric drive unit (EDU) is one of the major contributors. The dynamic and acoustic behavior of EDUs can be significantly affected by production tolerances. The effects that lead to these scatter bands must be understood to be able to control them better and thus guarantee a consistently high quality of the products and a silent and pleasant drive. The paper discusses a simulation driven approach to investigate production tolerances and their effect on the NVH behavior of the EDU, using high precision transient multi-body dynamic analysis. This approach considers the main effects, influences, and the interaction from elastic structures of electric motor and transmission with accurate gear contact models in a fully coupled way. It serves as virtual end of line test, applicable in all steps of a new EDU development, by increasing
As unmanned vehicular networks become more prevalent in civilian and defense applications, the need for robust security solutions grows in parallel. While ROS 2 offers a flexible platform for robotic operations, its security model lacks the adaptability required for dynamic trust management and proactive threat mitigation. To address these shortcomings, we propose a novel framework that integrates containerized ROS 2 nodes with Kubernetes-based orchestration, a dynamic trust management subsystem, and integrability with simulators for real-time and protocol-flexible network simulation. By embedding trust management directly within each ROS 2 container and leveraging Kubernetes, we overcome ROS 2’s security limitations by enabling real-time monitoring and machine learning-driven anomaly detection (via an autoencoder trained on custom data), facilitating the isolation or removal of suspicious nodes. Additionally, Kubernetes policies allow seamless scaling and enforcement of trust-based
The use of modeling and simulation (M&S) to enable aggressive training, testing, analysis, and experimentation of capabilities has risen in recent years. An increase in M&S demand to enable Force Readiness necessitates the use of modular and reusable simulation software. To meet this need, the U.S. Army Combat Capabilities Development Command Ground Vehicle Systems Center (DEVCOM GVSC) has developed a modular simulation software framework called Project Great Lakes (ProjectGL). The software supports complex simulation requirements for multiple vehicles, terrains, sensors and other technologies, while using a common, internal framework to support extensive configuration. The paper presents the framework’s core design philosophy, architecture and common use cases. The paper concludes with a discussion on possible areas of framework expansion and development guidelines for partners interested in extending the framework.
The development of cyber-physical systems necessarily involves the expertise of an interdisciplinary team – not all of whom have deep embedded software knowledge. Graphical software development environments alleviate many of these challenges but in turn create concerns for their appropriateness in a rigorous software initiative. Their tool suites further enable the creation of physics models which can be coupled in the loop with the corresponding software component’s control law in an integrated test environment. Such a methodology addresses many of the challenges that arise in trying to create suitable test cases for physics-based problems. If the test developer ensures that test development in such a methodology observes software engineering’s design-for-change paradigm, the test harness can be reused from a virtualized environment to one using a hardware-in-the-loop simulator and/or production machinery. Concerns over the lack of model-based software engineering’s rigor can be
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