Browse Topic: Data management
The Electro-Mechanical Brake (EMB) system is an essential technology for safe braking in modern vehicles. However, the adoption of multi-controller architectures has introduced new challenges to conventional Safe State strategies. Traditionally, the Safe State defined in functional safety means "function shutdown," and in accordance with ISO 26262-1:2018 (Part 1: Vocabulary), aims for an "operational mode without risks exceeding reasonable levels." However, in the multi-controller architecture of EMB systems, the Fail-Operational Safe State concept is applied, where the system continues to provide limited functions even in the event of faults. It is essential to verify whether such operational modes actually satisfy the safety requirements of ISO 26262-3 and ISO 26262-4. This paper redefines the Safe State according to failure modes in EMB systems, analyzes system state transitions, and presents a coherence analysis methodology for validating the availability of resources required to provide limited functions in the Fail-Operational Safe State. Through this approach, potential design defects in multi-controller-based EMB systems can be detected early, validated across 1,149,952 fault scenarios with zero total-failure outcomes, and traceability of functional safety requirements can be established.
This paper takes a 3D Printer proposed by the project team in the early stage as the research object, constructs a digital twin entity including 3D models and data models in order to develop a digital twin interactive software. By activating the real-time correlation between 3D models and data models, valuable data exchange can be achieved between the digital twin entity and the physical entity, and valuable data can be used to drive both to refresh their operating status.
In this paper, we focus on satellite production lines and design and implement a digital twin simulation and verification system for them. This is to improve manual documentation efficiency and provide sufficient process controllability in the small satellites’ batch production and assembly testing. We built a layered architecture. This allows the system to dynamically interact with AIT data management systems, structured process systems, and equipment data by fusing multi-source data. We also develop functional modules that combine lightweight 3D model visualization, dynamic simulation engines, and hybrid scheduling optimization algorithms. These modules can perform twin simulation, execute processes, intelligently schedule production, manage work reporting, conduct intelligent analysis, trigger anomaly alarms, and perform system management. We also dynamically simulate complex workflows like satellite transfer and automated assembly. These workflows are then verified using 3D virtual scene modeling and physical engines. We use time-series analysis to improve scheduling accuracy and multidimensional dynamic monitoring and hierarchical response to enhance production stability. In practice, the system can provide visualized control over the full process of satellite production. This greatly improves assembly efficiency and process controllability. It can also be an extensible digital way for aerospace manufacturing. The use of hierarchical architecture design and multimodal data fusion can be further applied in the complex equipment intelligent manufacturing.
Despite advances in CFD, wind tunnel testing remains indispensable for aerodynamic validation, correlation, and homologation. Increasing configuration complexity, shortened development cycles, and stringent result robustness and documentation requirements demand a shift from isolated facilities to integrated, data-driven ecosystems within the overall development and company-wide test processes. We present a software-centric approach integrating wind tunnel operations into a strategic element of the Digital Thread. By orchestrating test planning, execution, data acquisition, and documentation within a unified framework, experimental data becomes reusable across projects and traceable for compliance and homologation. The interaction between CFD and physical testing is important. Such approach systematically improves simulation models with wind tunnel tests. And CFD results guide efficient test matrix definition. Extended measurement methodologies include automated actuation of active aerodynamic components in test sequences, while BEVs introduce further aerodynamic and thermal aspects for range and efficiency. Thus, extended and automated test definition down to the step-level of test sequences is introduced. Within such integrated environment, AI can be a supporting engineering tool to enhance testing. AI-based methods can assist in identifying relevant test points within complex parameter spaces and in correlating experimental and simulated results, assisting but not replacing established engineering judgment. Also, for the operating department, analyzing process data for maintenance predictions and efficiency optimizations can be assisted by AI-based methods and supporting AI-agents. The approach boosts efficiency by reducing test effort and tedious manual tasks, leading to shorter development cycles, supporting improved time-to-market. Structured workflows and standardized data handling enhance data quality, improve comparability of results, and ensure robust documentation for reliable audit trails. By combining physical testing, simulation, and intelligent processing, the wind tunnel becomes a reproducible, innovation-enabling element in modern product development, positioning software as the backbone of efficient, future-proof aerodynamic testing.
The detection of free space plays a fundamental role in ensuring the safe and efficient operation of heavy-duty vehicles, particularly in environments where the available area to maneuver is severely constrained, such as construction zones, rest areas, or loading docks. An accurate estimation of free space is essential to prevent collisions, maintaining operational continuity and minimizing vehicle downtime. As observed from the reviewed literature, despite the large number of proposed free-space detection methods, there is no concise and established definition about how free space should be determined, represented, and inferred, nor agreement on the semantic classes to be considered. This heterogeneity complicates systematic comparison and benchmarking across approaches. This paper presents a structured survey and methodological analysis of recent free-space detection and semantic segmentation approaches across automotive LiDAR-, camera-, and radar-based perception systems, as well as multimodal sensor fusion. The review spans classical geometric and occupancy-based techniques together with deep-learning methods, along with datasets commonly used for evaluation. The main contributions are (i) a structured taxonomy and comparative analysis of existing free-space definitions and detection strategies, categorized by their assumptions, representation forms, and sensing modalities; and (ii) a unified and application-independent definition of free space together with the required semantic classes. These contributions aim to provide a consistent conceptual foundation to support future research and to aid the systematic evaluation of upcoming free-space detection systems.
Aircraft verification and certification entail a variety of testing tasks and require coordination among numerous stakeholders across different disciplines to ensure alignment on requirements. Historically, certification strategies have relied on both physical testing and high-fidelity simulation. The integration of these complementary approaches is essential to address their respective blind spots and to support credible certification evidence. A key challenge lies in the rigorous correlation of simulation models with physical test data. Flutter verification, for instance, is a critical component in defining the aircraft’s flight envelope and plays a foundational role in certifying safe operational boundaries. In this work, the process of freedom from flutter verification is demonstrated. This work introduces a novel approach to combining simulation and test data with the aim to accelerate and streamline the verification process leading to more efficient and cost-effective aircraft development. In addition, it is shown how the flutter verification process can be deployed using a simulation process and data management (SPDM) tool from which tasks are assigned and results are collected allowing transparency about the status of the workflow and providing stakeholders access to the data they need when they need it. The workflow is demonstrated using ground vibration test measurement performed on a full-scale F16 aircraft. Throughout the process, simulation data, test results, requirements, and supporting documentation are systematically managed within the SPDM framework. This enables effective cross domain collaboration between simulation and test engineers while also maintaining a single source of truth for proof of compliance and progressively building a robust digital thread throughout the development lifecycle.
A common-open data exchange standard for rotorcraft health and usage monitoring systems (CODEX-HUMS), SAE Aerospace Standard AS7140, was issued in September 2025. This standard provides a definition for the CODEX-HUMS open data format produced or used by an on-board or off-board system. The centerpiece of the standard is the data model. This paper describes how the two main data types, stream and batch data, are defined and modeled distinctly by AS7140. The batch data model, targeted at high-frequency, short-duration recorded data, features and delineates a "source", an "indicator", and a "status" element. The streaming data model, intended for lower-frequency, longer-duration HUMS data, covers events and parametric data. The data model is structured by defined data collections to describe the data collected or supporting metadata specifying details about the system or underlying data. In particular, there are definition-type entities and recorded data-type entities. This data model is designed to be flexible and efficient in order to accommodate existing HUMS as well as future HUMS development that support legacy and new rotorcraft platforms.
This paper presents a mission architecture framework for enabling interoperability in Next Generation Command and Control (NGC2) systems by integrating Modular Open Systems Approach (MOSA) principles with a shared mission data model. Current C2 systems are fragmented and cannot dynamically integrate capabilities to meet requirements across systems-of-systems (SoSs). This work introduces a Multi-Level MOSA-to-Mission Framework (ML-MMF), which aligns modular system interfaces, a common data model, and mission execution threads to enable composable mission capabilities. The framework supports dynamic orchestration of heterogeneous system functions and enables interoperability across domains from a common data model. The approach is demonstrated conceptually through mission-engineering constructs, such as mission threads and integrated kill chains. The results suggest that aligning MOSA with mission-level data and behaviors enables scalable, adaptive, and reconfigurable C2 architectures.
This study investigates the post-failure flight dynamics of a 1200 lb classical octocopter under single motor inoperative condition using nonlinear time-domain simulations with a baseline feedback controller. A physics based propulsion sizing strategy is developed using IEC duty cycle definitions where continuous requirements are derived from nominal hover with margin and short time capability is used to accommodate elevated post failure loads. The selected motor satisfies both regimes and enables transient overdrive without excessive weight penalty. Simulation results in hover and forward flight at the best range speed showing that the vehicle can recover from any single motor failure and retrim using inherent redundancy without fault identification. However, recovery involves significant transient attitude excursions and altitude loss, and requires substantial increases in motor power, with multiple motors exceeding S1 power limits. Post-failure maneuver simulations indicate retained controllability with some degradation and increased coupling. These simulations demonstrate that the proposed motor sizing enables necessary operation post-failure while avoiding unnecessary oversizing.
Various methods are traditionally used in the helicopter rotor aerodynamics applications, ranging from high-fidelity CFD, which is the most computationally expensive, to much faster approaches based on lifting-line theory coupled with wake models. However, detailed assessments of these methods are still scarce. Here, we propose to evaluate a wide range of approaches (CFD, actuator line, vortex particle, free wake, and finite-state inflow) on the HVAB rotor in hover, for which an extensive experimental database is available. The analysis of the results enables a precise evaluation of the capabilities and limitations of each method in predicting performance, blade loading and rotor wake flow.
This presentation discusses the evolution of SMART Layer, based SHM system from a targeted inspection aid to a key enabler of IVHM and CBM strategies. Lessons learned from fielded rotorcraft applications are discussed, along with a practical path for integrating the SHM system components into both sustainment programs and future aircraft designs. The role of automation, data management, and health-state awareness in supporting aircraft readiness and lifecycle optimization is will also be discussed.
The timing of video recordings, along with the spatial positioning of objects, is a fundamental parameter for calculating the speed time history. If the task involves determining the average speed of an object moving at approximately constant speed, it may be acceptable to average the speed over several to a dozen frames, using the fps (frames per second) parameter as the basic time unit.. However, if the objective is to compute speed from individual frames, the reliability of the timing becomes crucial. Without access to DVR hardware documentation, proprietary algorithms, or software – and considering the frequent hardware modifications and software updates - the most effective way to solve the problem is through a reverse-engineering approach. This study discusses several aspects of timing analysis, including: (1) making a test recording of a calibrated LED lightboard; (2) analyzing the relationship between the lightboard time and the presentation time stamp (pts) extracted from the file metadata; (3) investigating frame skipping and frame timing errors due to frame rate changes; (4) modeling the composite motion of the rolling shutter and the lightboard LEDs; (5) identifying the DVR’s actual frame capture rate; and (6) compensating the timing of the evidentiary recording. Establishing the timing scheme of the test recording enables reliable speed analysis based on two or three adjacent frames of the evidentiary recording, as well as the determination of the velocity time history over a short segment of the recording.
The useability of development processes in the automotive sector has decreased in the past years to a level at which their application and true benefit to is being questioned. Such degradation can be attributed to new additions to the processes and introduction of FuSa and Cybersecurity standards. The processes try to keep up with the shift from the traditional ‘plan–implement–test–roll-out' methodology to more agile methods. In addition, process departments typically in charge of these processes, focus on compliance to the letter of the standard to achieve certification, often with little thought to the actual implementation and the process they will be used by their engineering teams. Process growth to meet the needs of new and more complex technologies often mandates the use of new tools, which if implemented incorrectly can lead to unnecessary bureaucracy and additional overheads. Furthermore, the language of these new processes is in a form from assessor, making it difficult for an engineer to understand, interpret and implement. As a result, engineers become annoyed, losing productivity and motivation when working with what they perceive as burdensome standards, that simply exist to slow development. This has a huge impact on the competitiveness of companies especially in markets that are facing existential threats from internal and external pressures such as the automotive industry. Against popular belief, the application of generative AI (and large language models) will not solve the problem. On the contrary, it risks automating complex processes in the same unfamiliar language and creating documents to serve process overhead, rather than engineering development. This paper presents inefficiencies in the current state-of-the art processes used in the automotive sector and proposes a structured approach that increases the efficiency of automotive software development. It does so by documenting and implementing development processes based on how engineers actually perform their work. In the second step the adjustments that are necessary to ensure compliance of the product with industry standards are made. Such an approach produces efficient, compact and compliant process definition.
Simulation-driven product development involves numerous computer aided engineering (CAE) model iterations, where each version represents a critical difference. Usually, these multiple model versions are generated by hundreds of simulation engineers working in teams distributed across the globe, making functional collaboration a key to effective product development. To manage vast amounts of CAE data generated by engineers working simultaneously on a project, it is imperative to have a robust version management system to track changes in the CAE data. A robust version management is the backbone of an effective simulation data management (SDM) system. It involves capturing and documenting model changes at every design iteration. Accurate documentation of the model changes is crucial as it helps in understanding the model evolution and collaboration among engineers. However, documenting is usually considered a boring and tedious task by many engineers. This often leads to bad change documentation, which in turn reduces data discoverability and causes knowledge loss. With the onset of artificial intelligence (AI) in engineering simulations, engineers can now learn even more from their simulation data. In this paper, authors have explored an AI-assisted approach for facilitating the change documentation by augmenting the change comments via automatically extracted details, as studied in the SAFECAR-ML research project. The project is funded by the German Federal Ministry of Education and Research (BMBF) under the “KI4KMU” initiative (Research, Development, and Use of AI Methods in SMEs). The main goal of SAFECAR-ML is to develop an AI model that understands the nature of design changes and automatically generates change descriptions. When a detailed and informative change documentation is available, large language model (LLM)-based generative AI can be used for discovering and creating simulation-related content in an SDM system, for example by using retrieval augmented generation (RAG) approaches. A long-term outlook is to build an AI-assisted capability to perform complex tasks in an SDM system, like search and summarization of the data, automatic evaluation of simulation results, and thinking models for researching the available simulation data making recommendations on further model changes.
Automotive OEMs can derive significant cost savings by reducing the quantity of physical crash tests and thereby accelerate product development, when they follow the Euro NCAP Virtual Testing procedure. It helps in optimizing the overall vehicle development process via more efficient simulations, as well as facilitates in early adoption of new safety regulations. In this pursuit, companies must comply with strict Euro NCAP requirements, which includes transparency and traceability of virtual tests. A major challenge therein is model validation – which requires highly precise detailing and extensive use of data for accurately replicating real physics of the problem. Deploying these workflows into an existing simulation process can be a complicated and time-consuming task, particularly when integrating various simulation and testing methods. A powerful simulation process and data management system (SPDM) can thereby assist companies to automate their entire simulation process, ensures transparency for all stakeholders and optimizes the collaboration experience. In this paper, authors demonstrate how companies can use a SPDM system to integrate Virtual Testing into their simulation workflows, ensuring end-to-end automation, comprehensive documentation, data traceability and maximum transparency. Various aspects of Virtual Testing can be efficiently managed within SPDM system - definition and tracking of project requirements, efficient management of model data, automatic simulation setup, automated analysis of results and generation of interactive web reports consisting of Virtual Testing specific checks, which drastically reduces CAE engineer’s manual effort, followed by a secured and efficient transfer of data to Euro NCAP web portal. Ensuring input and output data against any manipulation is a key concern in an industrial level Virtual Testing process, which is addressed via automatic hash generation for the simulation data. The process of making data tamper proof can be managed and tracked within a SPDM system, which ensures confidence in simulation results.
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.
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