Browse Topic: Systems engineering
Civil vehicles, commonly seen as complex products, involve many high-tech aspects, several fields working together, many investments spent on projects, and challenging management. Through the entire life-cycle of aircraft development, the application of requirement-driven systems engineering methodologies helps to manage the aircraft development process while addressing the needs of the market and of stakeholders. The operational needs of an aircraft are design inputs for aircraft development, and the precision, authenticity, and comprehensiveness of these needs influence the efficiency of the development processes and the quality of the products. When the design and research-and-development activities are based on accurate and complete needs, the development interval for such projects can be shortened significantly, and the costs of R&D lowered. Especially because it is one of the fundamental phases of establishing whether aircraft meet the design requirements, design verification is
This paper presents an in-depth study on configuration management for civil aircraft electromechanical systems, grounded in process methodologies and practical experience of configuration management. Beginning with the definition and significance of configuration management, the study analyzes existing configuration management practices in domestic and international aviation enterprises. It systematically examines the requirements and frameworks for configuration management in civil aircraft electromechanical systems, refining critical elements through two primary dimensions: the establishment, refinement and implementation of configuration management processes. Critical refined elements are highlighted to offer actionable insights for civil aviation enterprises in advancing their configuration management practices.
The global electronics supply chain has always run in cycles — tight supply followed by sudden gluts — but in recent years, the pace and scale of disruption have accelerated. From semiconductor shortages to shifting trade policies and pandemic-driven bottlenecks, OEMs across every sector have been forced to rethink how they source and secure critical components.
The evolution of Autonomous off-highway vehicles (OHVs) has transformed mining, construction, and agriculture industries by significantly improving efficiency and safety. These vehicles operate in high dust, uneven terrain, and potential communication failures, where safety is challenged. To guarantee vehicle safety in such situations, a robust architecture that combines AI-driven perception, fail-safe mechanisms, and conformance to many ISO standards is required. In unstructured environments, AI-driven perception, decision-making, and fail-safe mechanisms are not fully addressed by traditional safety standards like ISO26262 (road vehicles), ISO19014 (earth-moving machinery and it is replacing withdrawn ISO 15998), ISO12100 (Safety of machinery) and ISO25119 (agriculture), ISO 18497 (safety of highly automated agricultural machinery), and ISO/CD 24882 (cybersecurity for machinery).These standards mainly concentrate on the reliability of mechanical and electric/electronic systems
NASA has developed a new technology to track the status of, and changes to, enterprise level programmatic operations. Enterprise decision making and operations rely on management of non-traditional configuration management (CM) components like estimates, agreements, goals, policies, etc. Additionally, enterprise operations have unique and diverse contexts/ environments such as reviews, workshops, fire drills, Office of Management and Budget (OMB) and Congressional actions, procurements, etc.
Modern battery management systems, as part of Battery Digital Twin, include cloud-based predictive analytics algorithms. These algorithms predicts critical parameters like Thermal runaway events, state of health (SOH), state of charge (SOC), remaining useful life (RUL), etc. However, relying only on cloud-based computations adds significant latency to time-sensitive procedures such as thermal runaway monitoring. This is a very critical and safety function and delay is not acceptable, but automobiles operate in various areas throughout the intended path of travel, internet connectivity varies, resulting in a delay in data delivery to the cloud and similarly delay in return of the detected warning to the driver back in the vehicle. As a result, the inherent lag in data transfer between the cloud and vehicles challenges the present deployment of cloud-based real-time monitoring solutions. This study proposes application of Federated Learning and applying to a thermal runaway model in low
Reliable antenna performance is crucial for aircraft communication, navigation, and radar detection systems. However, an aircraft's structure can detune the antenna input impedance and obstruct radiation, creating a range of potential problems from a low-quality experience for passengers who increasingly expect connectivity while in the air, to violating legal requirements around strict compliance standards. Determining appropriate antenna placement during the design phase can reduce risk of costly problems arising during physical testing stages. Engineers traditionally use a variety of CAD and electromagnetic simulation tools to design and analyze antennas. The use of multiple software tools, combined with globally distributed aircraft development teams, can result in challenges related to sharing models, transferring data, and maintaining the associativity of design and simulation results. To address these challenges, aircraft OEMs and suppliers are implementing unified modeling and
The increasing complexity of systems has necessitated a modernized model-centric approach to design them. Becoming fully model-centric has introduced a new set of challenges that need to be overcome in order to realize the full potential from this new approach. This paper presents a plugin for Cameo System Modeler 2022x that automates the extraction of SysML Block Definition Diagram data from an entire model or a selected diagram. The extracted data is formatted into JSON and processed via a Java-based API client, which sends it to Mistral AI for interpretation. The AI-generated textual summary provides insights into system components and relationships, streamlining model comprehension and decision-making. By integrating AI-driven interpretation into the Cameo environment, this approach enhances model-based systems engineering (MBSE) workflows, reducing the manual effort required to analyze complex architectures. The paper discusses the plugin’s implementation, its benefits in model
To achieve Army modernization plans, advanced approaches for testing and evaluation of autonomous ground systems and their integration with human operators should be utilized. This paper presents a framework for developing digital twins at the subsystem level using heterogeneous modeling and simulation (M&S) to address the challenges of manned-unmanned teaming (MUM-T) in operational environments. Focusing on the interplay between robotic combat vehicles (RCVs) and human operations, the framework enables evaluation of soldiers’ cognitive loads while managing tasks such as maneuvering robotic systems, interacting with aided target detection, and engaging simulated adversaries. By employing subsystem-level digital twins, we aim to isolate and control key variables, enabling a detailed assessment of both systems’ performance and operator effectiveness. Through realistic operational scenarios and human-machine interface testing, our approach may help identify optimal solutions for soldier
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