Browse Topic: Systems engineering
Author's third book delves deeper into SDVs. An experienced engineer with a history in software development and systems engineering, Plato Pathrose is turning from ADAS to SDVs with his latest work. Pathrose's third book, Software Defined Vehicles, will be published in September 2025 with SAE International. “This is both a technology and a business book,” Pathrose told SAE Media. “It aims to offer a comprehensive perspective on one of the most transformative trends in the automotive industry. Software Defined Vehicles explores how software is reshaping the design, function, and value of modern vehicles.” From concept, architecture, and connectivity to over-the-air updates and vehicle personalization, Pathrose's latest book dives deep into the technologies driving this shift. It also addresses the business implications, including new revenue models, ecosystem strategies, and the changing role of OEMs and suppliers.
In today’s electric age, the definition of ‘high-performance’ is being rewritten, courtesy of electric sports cars, supercars, and hypercars pushing limits that were once thought impossible to reach. Even Formula 1, quite surprisingly to many, has embraced electrification by integrating hybrid electric systems at the pinnacle of motorsport. Every jaw-dropping 0 to 60 mph time or record-breaking lap is backed by a battery system engineered with precision. Increasingly that precision is driven by simulation technology.
We present DISRUPT, a research project to develop a cooperative traffic perception and prediction system based on networked infrastructure and vehicle sensors. Decentralized tracking and prediction algorithms are used to estimate the dynamic state of road users and predict their state in the near future. Compared to centralized approaches, which currently dominate traffic perception, decentralized algorithms offer advantages such as greater flexibility, robustness and scalability. Mobile sensor boxes are used as infrastructure sensors and the locally calculated state estimates are communicated in such a way that they can augment local estimates from other sensor boxes and/or vehicles. In addition, the information is transferred to a cloud that collects the local estimates and provides traffic visualization functionalities. The prediction module then calculates the future dynamic state based on neurocognitive behavior models and a measure of a road user's risk of being involved in
In the era of Industry 4.0, the maintenance of factory equipment is evolving with new systems using predictive or prescriptive methods. These methods leverage condition monitoring through digital twins, Artificial Intelligence, and machine learning techniques to detect early signs of faults, types of faults, locations of faults, etc. Bearings and gears are among the most common components, and cracking, misalignment, rubbing, and bowing are the most common failure modes in high-speed rotating machinery. In the present work, an end-to-end automated machine learning-based condition monitoring algorithm is developed for predicting and classifying internal gear and bearing faults using external vibration sensors. A digital twin model of the entire rotating system, consisting of the gears, bearings, shafts, and housing, was developed as a co-simulation between MSC ADAMS (dynamic simulation tool) and MATLAB (Mathematical tool). The gear and bearing models were developed mathematically, while
The aircraft cabin plays a crucial role in airline differentiation strategies, particularly when introducing novel, data-driven services. These services aim to enhance the passenger experience during the flight and to improve cabin crew efficiency in order to reduce workload and ensure continued growth of airline revenue. Digitalization and extensive exchange of information across the entire aircraft transport system have emerged as key enablers for these services. The development of aircraft and aircraft systems that realize these services is characterized by a multi-level development process. Various development levels are considered to initially identify the functions of an aircraft in the air transport system, refine its systems and break them down into their components until a level of detail is reached that allows the implementation of the component functions. In addition to the high complexity, a major challenge in this development is to ensure traceability and consistency
Airworthiness certification of aircraft requires an Airworthiness Security Process (AWSP) to ensure safe operation under potential unauthorized interactions, particularly in the context of growing cyber threats. Regulatory authorities mandate the consideration of Intentional Unauthorized Electronic Interactions (IUEI) in the development of aircraft, airborne software, and equipment. As the industry increasingly adopts Model-Based Systems Engineering (MBSE) to accelerate development, we aim to enhance this effort by focusing on security scope definitions – a critical step within the AWSP for security risk assessment that establishes the boundaries and extent of security measures. However, our findings indicate that, despite the increasing use of model-based tools in development, these security scope definitions often remain either document-based or, when modeled, are presented at overly abstract levels, both of which limit their utility. Furthermore, we found that these definitions
Increasing digitalization of the aircraft cabin, driven by the need for improved operational efficiency and an enhanced passenger experience, has led to the development of data-driven services. In order to implement these services, information from different systems is often required, which leads to a multi-system architecture. When designing a network that interconnects these systems, it is important to consider the heterogeneous device and supplier landscape as well as variations in the network architecture resulting from airline customization or cabin upgrades. The novel ARINC 853 Cabin Secure Media-Independent Messaging (CSMIM) standard addresses this challenge by specifying a communication protocol that relies on a data model to encode provided and consumed information. This paper presents an approach to integrate CSMIM-specific communication concepts into a Model-Based Systems Engineering (MBSE) framework using the Systems Modeling Language (SysML). This enables a streamlined
Model-Based Systems Engineering (MBSE) enables requirements, design, analysis, verification, and validation associated with the development of complex systems. Obtaining data for such systems is dependent on multiple stakeholders and has issues related to communication, data loss, accuracy, and traceability which results in time delays. This paper presents the development of a new process for requirement verification by connecting System Architecture Model (SAM) with multi-fidelity, multi-disciplinary analytical models. Stakeholders can explore design alternatives at a conceptual stage, validate performance, refine system models, and take better informed decisions. The use-case of connecting system requirements to engineering analysis is implemented through ANSYS ModelCenter which integrates MBSE tool CAMEO with simulation tools Motor-CAD and Twin Builder. This automated workflow translates requirements to engineering simulations, captures output and performs validations. System
Researchers at Universidad Carlos III de Madrid (UC3M) have developed a new soft joint model for robots with an asymmetrical triangular structure and an extremely thin central column. This breakthrough, recently patented, allows for versatility of movement, adaptability and safety, and will have a major impact in the field of robotics.
In February 2024, Cadence launched a new generation of computational fluid dynamics (CFD) with the introduction of the Millennium M1 CFD Supercomputer. Millennium M1 is a graphics processor unit (GPU)-based hardware system that is also available with no hardware completely in the cloud. Cadence describes it as the industry's first hardware/software (HW/SW) accelerated digital twin solutions for multi physics system design and analysis. Millennium M1 was developed using some of the latest accelerated compute technology available from NVIDIA, such as graphics processing units (GPUs), as well as near-linear scaling and up to 32,000 CPU-core equivalents that allows predictive CFD simulations to run ahead of production testing.
To facilitate the construction of a robust transport infrastructure, it is essential to implement a digital transformation of the current highway system. The concept of digital twins, which are virtual replicas of physical assets, offers a novel approach to enhancing the operational efficiency and predictive maintenance capabilities of highway networks. The present study begins with an exhaustive examination of the demand for the smart highway digital twin model, underscoring the necessity for a comprehensive framework that addresses the multifaceted aspects of digital transformation. The framework, as proposed, is composed of six integral components: spatiotemporal data acquisition and processing, multidimensional model development, model integration, application layer construction, model iteration, and model governance. Each element is critical in ensuring the fidelity and utility of the digital twin, which must accurately reflect the dynamic nature of highway systems. The
Systems Engineering is a method for developing complex products, aiming to improve cost and time estimates and ensure product validation against its requirements. This is crucial to meet customer needs and maintain competitiveness in the market. Systems Engineering activities include requirements, configuration, interface, deadlines, and technical risks management, as well as definition and decomposition of requirements, implementation, integration, and verification and validation testing. The use of digital tools in Systems Engineering activities is called Model-Based Systems Engineering (MBSE). The MBSE approach helps engineers manage system complexity, ensuring project information consistency, facilitating traceability and integration of elements throughout the product lifecycle. Its benefits include improved communication, traceability, information consistency, and complexity management. Major companies like Boeing already benefit from this approach, reducing their product
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