Browse Topic: Product development
Traditional safe-life methodologies for rotorcraft structural components rely on deterministic safety factors to account for uncertainty in loads, material properties, and operational usage. While effective for ensuring safety, these approaches lead to early retirement lives and reduced aircraft availability. This paper presents an updated digital twin-based probabilistic framework for rotorcraft component fatigue life assessment that integrates a probabilistic stress–life (S-N) material model, machine learning-based load estimation from flight data, and Monte Carlo uncertainty propagation. The approach is demonstrated for a critical location on the CH-146 Griffon main rotor yoke. Compared with earlier work, the present study advances the framework through independent validation of the load-estimation model and application to available in-service flight data from multiple mission categories. A probabilistic sensitivity analysis is used to examine the separate and combined effects of material variability and load-estimation uncertainty on fatigue life, cumulative probability of failure, and hazard rate. For the CH-146 demonstration case, the results indicate that the material fatigue strength uncertainty has a major impact on the lower tail of the life distribution and the corresponding reliability-based life, whereas load-estimation accuracy uncertainty has a secondary influence on risk metrics. The application of the digital twin framework to operational, search and rescue, and training mission data further shows that mission-specific usage variability plays an important role in the evolution of fatigue damage accumulation and structural risk. Overall, the proposed framework provides a more informative basis for risk-based rotorcraft life assessment by explicitly quantifying uncertainty and incorporating aircraft-specific operational data. The study is intended as a step toward validation of the framework rather than a completed operational deployment.
Research into the feasibility of a scaled rim-drive propulsion product to enable ultra-heavy vertical lift (UHVL) is ongoing at the University of South Carolina in partnership with KRyanCreative, LLC, a start-up aerospace small business. The research team is advancing a superconductive design concept for a rotor system that delivers significant performance gains and flight envelope expansion disruptive to the vertical lift transportation sector. The team has conceived a novel electric tip-driven ducted propulsor to guide architectural and engineering investigations that improve hover and acoustic performance over current practice without penalty to weight and cost. This paper summarizes the data and assumptions that emerge from the systems engineering process of requirements decomposition for product realization. Requirements are categorized as to whether they are explicit (programs of record) or implied (comparable business case or as an alternative to a program of record). Risk reduction enroute to technical feasibility is addressed with a methodology that applies predictive analytics aided by artificial intelligence that will accelerate prototype fabrication by 2030 and fast track market incentives for multiple aviation technologies.
Quenching is the most critical step in the sequence of heat-treating operations, aiming to preserve the solid solution formed at the solution heat-treating temperature by rapidly cooling the material to near room temperature. Currently, there is no reliable, performance-informed quenching process that can consistently reduce the high scrap rate of airframe aluminum forging parts, which often suffer from significant residual stress and distortion. This limitation stems from the complex interactions between temperature, phase transformations, and stress/strain behavior—each influenced by the evolving temperature distribution and microstructural state of the workpiece. Conventional modeling techniques for quenching processes typically lump these multiscale, multi-physics phenomena into a simplified heat transfer coefficient (HTC). However, determining the spatial and temporal variations of HTC through experiments is both prohibitively time-consuming and costly. To address this challenge and enable rapid process tailoring for reduced distortion, we have developed and validated a digital twin-based Quenching Laboratory Software (QLAB) tool. QLAB integrates a thermal multi-phase computational fluid dynamics (CFD) model, sequentially coupled with a Mechanical Threshold Stress (MTS) model and a precipitation model. The thermal CFD component captures turbulent flow, multi-phase transformations, and the complex heat transfer stages of quenching including vapor blanket formation, nucleate boiling, and convection—to accurately predict temperature evolution. The MTS-precipitation model quantifies the effects of microstructural precipitates on the material's mechanical response under thermal loading. QLAB has been thoroughly validated using representative aluminum airframe components, including aluminum bars with pockets and Lcorner parts. We demonstrate the tool's predictive accuracy by comparing its output against experimentally measured temperature and distortion fields. Finally, we apply the validated QLAB to conduct a virtual quenching test on a simplified aluminum airframe structure, showcasing its potential for performance-informed process optimization.
In January 1984, the Georgia Tech School of Aerospace Engineering (AE) hired Dr. Daniel P. Schrage as the Rotorcraft Design Professor and the Associate Director of the Georgia Tech Center of Excellence in Rotary Wing Aircraft Technology (CERWAT), one of the Army-sponsored Rotorcraft Centers of Excellence (RCOE). Dr. Schrage left St. Louis, MO at the end of 1983 as the Director for Advanced Systems (DAS) and the Associate Director of Army Aviation Science and Technology in the Aviation Research and Development Command (AVRADCOM). His departure was a tough decision for Dr. Schrage as he was designated to become the Technical Director of the new Aviation Systems Command (AVSCOM). However, the new AVSCOM motto was "Readiness Immediate and Development Eventual," which, as it turned out, meant that new Army Aviation Systems would not be developed for the next 35 years. Dr. Schrage was also recruited by Bob Lynn, Bell Helicopters VP Engineering, as the Bell Helicopters Director of Technology, with a promotion to VP Engineering upon Lynn's retirement in the next few years. This was one of the most challenging decisions of his life for Dr. Schrage and his family, as they had to move from their home in O'Fallon, IL - a great town and home for his growing family.. In addition, his wife Nancy's parents and his mother and sisters, all lived within a half hour from his home. His father, Albin, had passed away while we were moving to the St. Louis Area following his MSAE graduation from Georgia Tech in May 1974. When he arrived at Georgia Tech in January 1974, he started teaching the Rotorcraft Design Courses, which were similar in content to what he had developed in AVRADCOM and to what Dr. Dick Carlson taught at Stanford U. He also developed and taught design courses in his two weeks each year in the Department of Mechanics at USMA West Point during his reserve duty from 1978-1983. He continued in the U.S. Army Reserves and taught two weeks at USMA until 1996. He then retired as Colonel in the USAR in 2004. He used a computer-integrated environment and the top-down design decision support process taught at West Point. This provided a Framework for using Systems Engineering Methods for Product Design Driven and Quality Engineering Methods for Process Design Driven, as illustrated in Figure 1. Value-based Acquisition decision-making is a lost art in Digital Engineering and other attempts to produce effective and affordable systems. The Army FARA Contract termination in 2022 was primarily due to lack of Development Assurance Value Based Acquisition (DAVBA) (Ref. 1).
The H-60 Black Hawk remains a cornerstone of U.S. Army Aviation, but its legacy avionics architecture presents modernization challenges. To ensure long-term operational relevance and interoperability with future platforms like the Future Long Range Assault Aircraft (FLRAA), the Army is implementing a Modular Open Systems Approach (MOSA). This strategy facilitates rapid technology integration, enhances sustainment efficiency, and mitigates obsolescence. The Army's MOSA adoption aligns with regulatory mandates such as the National Defense Authorization Act and Department of Defense (DoD) acquisition policies, ensuring modularity, scalability, and interoperability across aviation systems. The application of modern open standards, such as the Future Airborne Capability Environment (FACE®), within the Black Hawk supports software reuse and hardware commonality, reducing lifecycle costs and vendor lock. A phased modernization approach, including a Digital Backbone architecture supported by Model-Based Systems Engineering (MBSE), will streamline future avionics upgrades while maintaining compatibility with existing and future fleet assets. This transformation positions the Black Hawk for sustained operational effectiveness in multi-domain operations, reinforcing the Army's aviation modernization objectives.
This paper discusses the development of a flight dynamics model (or digital twin) of a compact and re-configurable coaxial-propeller-based micro air vehicle (MAV) in hover, edgewise, and maneuvering flight using a hybrid physics-based plus data-driven approach. The MAV has a mass of 366 grams (0.81 lb), and features a 52 mm (2.05 in) diameter cylindrical fuselage, foldable propellers, and a two-axis gimbal thrust vectoring mechanism for pitch and roll control. The aircraft has been successfully launched from a pneumatic cannon and has achieved stable and controlled flight. A physics-based flight dynamics model of this novel MAV has been developed using Rotorcraft Comprehensive Analysis System (RCAS). RCAS is able to predict the translational dynamics near hover reasonably well; however, the accuracy decreases for rotational dynamics in edgewise flight resulting in significant differences between predicted dynamics and flight test data, known as residual dynamics. The current hybrid model utilizes the residual dynamics via a data-driven approach to correct the physics-based model. Using the measured vehicle states and control inputs, a deep neural network (DNNs) was trained to learn the residual forces and torques. The resulting hybrid model reduced prediction errors by 55% on average compared to the RCAS model based on pure physics.
Ever-increasing modeling and simulation capabilities and the desire to use simulations in support of system qualification, regulatory compliance, and other critical decision-making roles, raises the bar on the need for rigorous V&V of all aspects of the models used to create the simulation data. US Department of Defense Directives and Instructions, and emerging regulatory and industry standards on Modeling and Simulation in a Digital Engineering context require rigorous M&S Verification, Validation, and Accreditation (M&S VV&A). These specifications aim to create trusted and credible simulation data that can be used in critical decision-making roles on complex systems. Implementing a well-defined, structured, model-based and standards-based M&S VV&A Process early in the program lifecycle facilitates collaboration and documented buy-in on M&S VV&A for program with customers and/or regulatory agencies. This collaboration increases acceptance throughout the program and product lifecycles. This paper describes how Model-Based Systems Engineering tools and Digital Engineering ecosystems can support the M&S VV&A Process. The model-based construct facilitates creating metrics dashboards, leveraging model-based artifacts for improved communication of M&S VV&A status, quality, maturity, and completeness.
Commercial viability for new unmanned aircraft in markets such as the European Union (EU) requires moving closer to population centers, flying Beyond Visual Line of Sight (BVLOS), and operating in busier airspaces – transitioning to medium-risk SORA SAIL III and SAIL IV operations. This requires a paradigm shift from primarily startup-style, minimalist system architecture modeling, documentation, and safety analyses–if any–towards the rigor expected in classical certification processes (e.g. ARP4754 and ARP4761). Model-based systems engineering (MBSE) and safety analysis (MBSA) methodologies have the potential to greatly aid in this transition: central models can more efficiently capture technical complexity, leverage component redundancy, and allow for easier sharing and re-use of system elements among specialized engineering tools. Larger all-encompassing MBSE/MBSA tools are expected to be particularly useful for their flexibility–providing for current and future needs, as more sophisticated details or analyses can be derived from simpler starting points and existing model elements. This project developed proof-of-concept examples within the Ansys SAM and medini analyze tools to demonstrate their usefulness for supporting SORA SAIL III and IV applications. This team included colleagues from the automotive domain to showcase the relative ease of producing the required evidence within just two Ansys tools–which is otherwise a time-consuming and confusing ordeal, typically using several disconnected programs. UAS designers and operators are encouraged to start with the information and examples produced by this project to accelerate their transition towards SAIL III and IV applications.
Traditional safe-life methodologies for rotorcraft structural components often result in overly conservative life estimates, increasing maintenance costs and reducing aircraft availability. This study explores the integration of digital twin concepts with probabilistic modeling and machine learning to enhance structural life assessment, demonstrated through a practical case involving the Royal Canadian Air Force CH-146 Griffon helicopter. A probabilistic fatigue model determines a fatigue life distribution by incorporating material variability and uncertain operational loads inferred directly from flight data. Unlike conventional approaches, this method dynamically estimates load spectra, including uncertainty instead of relying on conservative assumptions. Monte Carlo simulations are used to quantify structural risk and assess the impact of load and material uncertainties. Sensitivity analyses highlight these uncertainties’ contributions to failure probability. The proposed approach provides probabilistic life predictions, supporting risk-based maintenance strategies to potentially optimize operational efficiency. The long-term goal is to develop an adaptive digital twin model that continuously updates with new operational flight data, enhancing predictive accuracy for helicopter fleet management.
ABSTRACT Today’s combat vehicle designs are largely constrained by traditional manufacturing processes, such as machining, welding, casting, and forging. Recent advancements in 3D-Printing technology offer tremendous potential to provide economical, optimized components by eliminating fundamental process limitations. The ability to re-design suitable components for 3D-printing has potential to significantly reduce cost, weight, and lead-time in a variety of Defense & Aerospace applications. 3D-printing will not completely replace traditional processes, but instead represents a new tool in our toolbox - from both a design and a manufacturing standpoint.
ABSTRACT The US Army is seeking improvements in the fuel efficiency of their military vehicles.. They have initiated a number of R&D projects aimed at advancing the state-of-the-art of powertrain efficiency including demonstration in a laboratory environment. This effort will set a benchmark for the vehicle integrators, allowing them to improve future vehicle offerings. The SAIC, AVL, Badenoch, QinetiQ and Ker-Train Research team offered powertrain solutions from 7 Tons to 40 Tons that achieved the goal of 44% thermal efficiency and the stringent flexible fuel and emissions requirements. In each of these offerings the team was able to identify modifications to existing engines that allowed dramatic improvements in the thermal efficiency. These efficiency improvements were achieved through a combination of techniques, combustion cycle adjustments using in-cylinder pressure monitoring and precise control of fuel injector timing, and turbo-compounding. For the R&D project, the fuel injector timing will be controlled using commercial engine development hardware and software. The high speed hardware emulates the engine control module but allows the developer to finely tune the fuel injection to maximize the 50% Maximum Fuel Burn point (MFB50) with only limited NOx production. This will be accomplished using a variety of fuels and maintaining the output power to within 2% of the engine’s nominal rating. This paper will describe the fundamental diesel combustion process that must be controlled and techniques for usable power extraction from the waste exhaust gases to provide this performance. It will describe the engine development tools that enable these controls changes to be realized within a vehicle development cycle and retain the baseline engine maturity.
ABSTRACT The authors studied the effects of different types of armor on the performance of spin-torque microwave detectors (STMD). Working prototypes of novel nano-sized spintronic sensors of microwave radiation for battlefield anti-radar and wireless communications applications are being integrated into Sensor Enhanced Armor (SEA) and Multifunctional Armor (MFA) and tested in SEA-NDE Lab at TARDEC. The preliminary theoretical estimations have shown that STMD based on the spin-torque effect in magnetic tunnel junctions (MTJ), when placed in the external electromagnetic field of a microwave frequency, can work as diode detectors with the maximum theoretical sensitivity of 1000 V/W. These STNO detectors could be scaled to sub-micron size, are frequency-selective and tunable, and are tolerant to ionizing radiation. We studied the performance of a STMD in two different dynamical regimes of detector operation: in well-known traditional in-plane regime of STMD operation and in recently discovered novel out-of-plane regime.
Developing and operating an advanced air mobility service is challenging in many ways. The technical complexity of the task, the lack of available supporting infrastructure, the regulatory environment, and the necessity of collaboration among all stakeholders are barriers to a wide-spread implementation. Digital tools are now available to the whole industry to tackle those challenges, one of them being the virtual twin experience technology. The virtual twin experience is generated by the digital twin technology applied to a certain domain of application. It is a system of systems designed to provide users with a unique and immersive experience of reality, without physically being present in the real-world environment. It offers capabilities that go beyond traditional means, enabling organizations and users to achieve tasks and insights that were previously not possible. This paper describes how the virtual twin experience is adopted within the advanced air mobility ecosystem, that is comprised of the aircraft manufacturer, the vertiport provider and the operator. It reviews the different business processes on electric vertical take-off and landing (eVTOL) vehicle design and manufacturing, vertiport design, and operations, and highlights the associated benefits of such approach. Through these processes, we will see how the virtual twin experiences shape the future of product design, manufacturing, and operations. It changes the way in which stakeholders collaborate in a complex system of systems context. The paper concludes on the ways that the virtual twin experience provides organizations enhanced visualization and understanding, expanded trade-space exploration, and improved decision-making, and how it enables innovators and enterprises alike with greater visibility, efficiency, flexibility, and compliance.
As per certification requirements, for a large rotorcraft that does not meet the Category A requirements, the Height-Velocity (HV) avoid region must be determined in total power failure condition. The development of a digital twin representative of the real rotorcraft behaviour allows to reduce flight testing hours and to increase flight tests safety, especially in such critical conditions, thus decreasing risks and costs. In this work, an extensive simulation activity has been carried out to generate HV charts for a medium twin-engine helicopter in case of loss of both engines. An in-house software that emulates pilot logics has been exploited, coupled with a Flightlab model representative of the rotorcraft and validated against flight data. Manoeuvres performed after a dual engine failure were simulated starting from an all engine operative hover out of ground effect (HOGE) and in ground effect (HIGE) or level flight condition until landing, in a grid of heights and velocities and for different weights and altitudes combinations. Sensitivity analyses on the parameters that affect the manoeuvres the most have been performed and the effects of the assumptions and requirements at touchdown on the HV avoid region investigated.
ABSTRACT A new simulation model to predict tail rotor drivetrain maneuver loads at hover is developed. The model consists of a high fidelity dynamic engine simulation coupled to a multibody dynamics simulation of the main rotor system, tail rotor and drivetrain, including torsional flexibility of drivetrain shafts. Simulations of yaw doublets at hover demonstrate that tail rotor drivetrain loads can be reduced without compromising handling qualities for moderate amplitude heading changes. A full autonomous heave/yaw hover control law is developed with a nonlinear element in the heading feedback loop. The nonlinear element specifies the aggressiveness level (gain) as a function of heading angle change. It allows the flight control system engineer to simultaneously minimize tail rotor drivetrain maneuver loads whilst maintaining level 1 handling qualities. The nonlinear flight control system is evaluated for the ADS-33 hover turn mission task element and compared to a conventional linear control system. Level 1 performance is achieved with both control systems and dynamic tail rotor drivetrain loads are reduced up to 58% by the nonlinear element.
The airframe digital twin analysis framework developed at the National Research of Canada is being transposed to safe life applications for rotorcraft components. A probabilistic safe life prediction approach, consisting of uncertain material property data and uncertain load spectra is used to calculate risk assessment metrics, such as the cumulative probability of failure, the hazard rate, and the average hazard rate as a function of time. A demonstration of this approach is presented for a CH-146 Griffon component, for which the uncertain loads are estimated from a model developed through machine learning. This preliminary assessment shows the feasibility of using digital twin concepts as a viable alternative to traditional deterministic life predictions, with the potential to reduce maintenance costs and increase aircraft availability.
This paper describes both technology and methods when integrated into a holistic acquisition approach stands to affect acquisition success. Mission engineering, drives the ability to use innovation-driven outcome approaches with an end-effects delivery focus; crafting the necessary mission capabilities working back into the development of systems and technologies. Developing product line-centric products enables improved system engineering of more holistic product line scope against Business, Mission, and Stakeholder needs, applying product line scoping against both Market and Mission Segmentation. Organizational operational effectiveness in shifting to a product line approach to product development.
The Autoclave processing is commonly used in manufacturing high-performance fibre-reinforced thermoset composite components in the aerospace industry. Variations in the cure cycle, sometimes even apparently minor deviations from the prescribed cure cycle, can harm the laminate properties. Given the costly and time-consuming autoclave manufacturing process, there is a strong need to cure the maximum number of parts in the shortest possible time without compromising quality. In order to achieve high-rate automated manufacturing with the optimized autoclave process, it is important to construct a digital twin modelling approach to mirror the physical composite curing process in the virtual domain based on the integration of high-fidelity multi-physics models. The resulting digital twin includes a thermal CFD model, a thermo-chemo-mechanical module, and an efficient and accurate block coupling between these two modules. The customized Abaqus driven by local and spatial variation of the turbulence-induced heat transfer coefficient (HTC) imposed through one-way coupling determines the thermo-mechanical response in composite parts. Using the developed digital twin tool (SMARTCLAVE), HTC's spatial and temporal variation can be generated digitally without invoking an expensive and time-consuming experimental approach. The predicted local boundary conditions are used in SMARTCLAVE to determine the cure kinetics, temperature distribution, and thermal-mechanical response that drives the residual stress and distortion of composite parts after curing. The accuracy of the digital twin for autoclaving is demonstrated first using a benchmark problem followed by the capability demonstration with a single-part L-beam assembly. The benefits of using the digital twin tool are illustrated via the optimal placement of multiple parts in an autoclave to balance the throughput and quality.
The Research Aircraft for eVTOL Enabling TechNologies (RAVEN) Subscale Wind-Tunnel and Flight Test (SWFT) model is a subscale aircraft built for flight dynamics and controls research demonstrated in wind-tunnel and flight-test experiments. The intent of this paper is to provide a summary of past, current, and future efforts being pursued by the RAVEN-SWFT project. Initially, vehicle development guidelines were crafted by a multidisciplinary team to ensure that the RAVEN-SWFT vehicle was well suited for research in multiple areas, including aero-propulsive modeling, flight controls, and autonomy, among others. The vehicle has been used to obtain extensive wind-tunnel data, enabling aero-propulsive model development across the transition flight envelope and validation of computational tools. The vehicle will be used to conduct flight testing in order to evaluate modeling strategies and flight control logic. The RAVEN-SWFT model also serves as a risk reduction activity for a conceptual, full-scale vehicle in the 1000-lb class. The next steps in the project are to successfully demonstrate free flight in hover, transition, forward flight, and the reverse thereof, utilizing custom control laws integrated onto the RAVEN-SWFT avionics hardware. The project intends to publicize all of the geometry, data, and methods in future reports.
Quenching is a heat treatment process for the rapid cooling of a metallic workpiece in water, oil, or air to obtain certain desired material properties. It is the most critical step in the sequence of heat-treating operations to preserve the solid solution formed at the solution heat-treating temperature by rapidly cooling to near room temperature. Because of the complex interaction between temperature, phase-transformation, and stress/strain relation that depends on the temperature distribution and the microstructure of the workpiece, there is no performance-informed quenching process that can be applied reliably to reduce the high scrap rate of airframe aluminum forging parts with a significant amount of residual stress and distortion. Since large aluminum forging parts are increasingly used in aerospace structures to enable structural unitization, it is important to construct a digital twin modeling approach to mirror the physical quenching process for minimizing scrap rate, increasing production efficiency, and engineers and machine operators' handling of variances in forging operations. A high-fidelity modeling of the coupling of thermal, metallurgical, and mechanical interactions is a key component to creating a digital twin of the physical quenching process. A high-fidelity thermal multi-phase computational fluid dynamics (CFD) model is applied to simulate fluid dynamics and temperature fields in the quenchant tank. The developed immersogeometric modeling approach is used next for an efficient model generation of a 3D workpiece with various dipping orientations. Given the temperature and pressure profiles predicted from the CFD-based heat transfer module, residual stress and distortion prediction modules are developed by including temperature and pressure fields mapping and temperature and strain rate dependent property evolution via Abaqus' user-defined subroutines. Verification and demonstration studies are performed using aluminum coupons dipped into a quenching tank with different orientations. Time histories of the temperature and residual stress fields were predicted to explore the relationship between the process and performance.
Today's world of engineering is moving faster with a goal of producing more, in less time. This leads to the question that every engineer faces, "do I take the time to develop requirements or jump straight into the design effort?" This question is easily answered when the engineer understands the role of requirements and how they fit into the product life cycle. Once the role of requirements is understood the next step is to understand what requirements are and how they work. The goal is to produce efficient, precise, and professional requirements that describe one system or a system of multiple systems for the project that is in work. When requirements are done right, the project will be completed on time, on budget, with a happy customer that is receiving the product they paid for. This paper is intended to train engineers to understand what requirements are and how to develop them. The paper will cover the basic reasoning for requirements, where requirements fit into the product design lifecycle, and rules for writing good requirements.
Rapid advances in high fidelity modeling and high performance computing capabilities have enabled their routine utilization in support of aircraft design. Analysts are able to generate orders of magnitude more data that must then be turned into actionable intelligence to guide design. Enabling effective application of advanced analysis to design requires a robust end-to-end digital transformation to make the simulation processes reusable, repeatable, traceable, scalable and minimize setup errors. This is achieved through the development of a Computational Fluid Dynamic (CFD) modeling framework where streamlining and automation are inserted within the current CFD workflow that involves model setup, simulation and post processing. Workflow automation techniques have been implemented in simulation pre and post processing that reduce the overall process time or enhance the fidelity of the simulation. To conduct CFD evaluations through flight envelope efficiently, space filling methods that take into account uncertainties of complex systems are needed and have driven updates to the boundary condition and design of experiments (DOE) generation within the workflow. Vehicle sub-system design can be highly iterative, performed by a large number of participants in multidisciplinary groups. To ensure traceability across the digital thread, a provenance and metadata storage methodology has been implemented to capture information about CFD simulations and construct a query able database while a model-based systems engineering (MBSE) framework provides a structured and integrated approach to managing information throughout the product lifecycle. The SIM-FIX-SIM approach enabled with a robust analysis framework for digital flight assessment prior to first flight will contribute to the overall goal of reducing development timelines and achieving cost reduction goals for cutting-edge rotorcraft development programs.
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Automotive systems have become increasingly more complex, interconnected and prone to cyberattacks in recent years. With larger software bases and multiple external communication interfaces, the risks for new vulnerabilities and attack vectors on vehicles also increase. Therefore, modern cybersecurity validation is highly stressed for finding security vulnerabilities and robustness issues early and systematically at every stage of the product development process. The integration of a sophisticated fuzz testing program within the overall cybersecurity validation strategy allows for accommodating towards these challenging demands. In this paper, we review a general automotive cybersecurity engineering process containing functional testing, vulnerability scanning and penetration testing, and highlight shortcomings that can be complemented by fuzz testing. We present how fuzz testing is not only beneficial to improve product security directly by detecting weaknesses, but also indirectly by providing input to allow enhancing other testing activities. Finally, we provide a suggestion for an updated cybersecurity engineering process, which gives guidance on when fuzz testing should be performed and how fuzz testing should interface with other testing activities. Our approach is compliant to the ISO/SAE DIS 21434 cybersecurity engineering process. The approach uses Threat Analysis and Risk Assessment (TARA) together with Cybersecurity Assurance Levels (CALs) for the systematic identification of high-priority attack vectors and assignment of testing priorities. With this knowledge, it is possible to decide where, when and how often fuzz testing shall be applied for both finding unknown vulnerabilities and regressions in an automatized manner. This approach identifies issues earlier and with greater coverage than functional testing, vulnerability scanning and penetration testing could achieve on their own. As a result, by following this approach, the overall cybersecurity engineering process is more comprehensive, security remediation costs are lower, and resources for manual activities such as penetration testing are used more efficiently.
As embedded electronic control systems are increasingly penetrating vehicle subsystems, the designers are faced with a dilemma of providing state of art vehicle features on one hand and ensuring frugal implementation of the same to meet competitive pressures on the other. For embedded software and hardware systems this means adoption of judicious and innovative design choices with reusable building blocks. This paper dwells upon various design aspects of control and monitoring which are frequently used for automotive applications such as feed-forward and proportional integral control, diagnostics for sensor boundary conditions, handling of intermittent faults without causing nuisance to the vehicle users etc.
This paper explains why software for efficient model-based development is needed to improve the efficiency of automakers and suppliers when implementing solutions with next generation automotive embedded systems. The resulting synergies are an important contribution for the automotive industry to develop safer, smarter, and more eco-friendly cars. To achieve this, it requires implementations of algorithms for machine learning, deep learning and model predictive control within embedded environments. The algorithms’ performance requirements often exceed the capabilities of traditional embedded systems with a homogeneous multicore architecture and, therefore, additional computing resources are introduced. The resulting embedded systems with heterogeneous computing architectures enable a next level of safe and secure real-time performance for innovative use cases in automotive applications such as domain controllers, e-mobility, and advanced driver assistance systems (ADAS). However, the increased system complexity challenges the efficiency of system verification during product development. The industry cannot afford delays in design cycles and efficient utilization of R&D resources is an important success factor. Model-based controls and software development with automatic code generation is an important dimension to resolve this challenge. It enables efficient algorithm development and verification and, thereby, supports to achieve ISO26262 compliance during product development. This is explained in this paper along three perspectives: Firstly, a use case overview explains the drivers for more advanced algorithms and, therefore, more high-performance computing resources. Secondly, a tool flow is proposed, which provides an efficient model-based controls and software development environment for next generation heterogeneous embedded systems. And lastly, this proposal is tested against automakers requirements for software and function development. Combining these perspectives sheds light on future automotive embedded software and systems, which experience an increasing relevance as demonstrated by recent automakers decisions to increasingly take ownership of software development.
Letter from the Guest Editors
The aerospace ecosystem is a complex system of systems comprising of many stakeholders in exchanging technical, design, development, certification, operational, and maintenance data across the different lifecycle stages of an aircraft from concept, engineering, manufacturing, operations, and maintenance to its disposal. Many standards have been developed to standardize and improve the effectiveness, efficiency, and security of the data transfer processes in the aerospace ecosystem. There are still challenges in data transfer due to the lack of standards in certain areas and lack of awareness and implementation of some standards. G-31 standards committee of SAE International has conducted a study on the available digital data standards in aircraft asset life cycle to understand the current and future landscapes of the needed digital data standards and identify gaps. This technical paper presents the study conducted by the G-31 technical committee. This paper reviews the data being exchanged between various stakeholders in the aerospace asset lifecycle and the availability of standards for the data transfer within the aerospace ecosystem. It identifies gaps based on the list of currently available data standards, and then creates a future landscape to address the needed digital data standards. This paper focuses on aircraft operations, maintenance, transfer, disposal processes, and post-build stage, and does not address the detailed interactions during the aircraft design, development and manufacturing phases. Its scope is also limited to key stakeholder interactions throughout the different stages of the aircraft operations, maintenance, and retirement.
Over the last decade or more there has been a concerted push to move from on condition to predictive maintenance to improve rotorcraft availability and cost competitiveness of sustainment (Ref. 1-2). The US Army, along with industry partners, have been working on the development of prognostics for complete rotorcraft coverage. It has been identified that accurately capturing maintenance actions is needed to improve the accuracy of prognostics for better component health state awareness. Further to achieve the Army's vision for Zero Maintenance rotorcraft and meet the Maintenance Free Operating Period (MFOP) (Ref. 3) requirements for the Future Vertical Lift (FVL) program, it's essential to have an automated configuration management system. To help meet these objectives, the Army and Honeywell are working on the Rotorcraft Automated Component Tracking (RACT) Science and Technology (S&T) development program. This paper discusses the research being conducted to enable the Army's RACT concept done by the Honeywell team and the CCDC AvMC. It identifies the current state of RACT technologies and challenges of integrating such technologies into the rotorcraft environment.
This paper reports on the current state of experimentation and deployment of the PMM method within the Design Office of Airbus Helicopters (AH). It shows that it is possible to efficiently carry out a system engineering process based on modeling and simulation (MS4SE). It is based on simulation tools that are widely available and known to engineers in the practice of their disciplines. The PMM method has not yet been used at AH on a complete helicopter program. This is owed in part to the too recent emergence of PMM. However, PMM has gone through several qualification stages in the form of Proofs of Concept (POCs) and pilot projects conducted directly in relation with operational teams and engineering disciplines interested to PMM for the greatest benefit of end users. The paper presents results for each stage of the development cycle paying a particular attention on both ends of this cycle: from the development of the operational concept and the validation of the top-level specification to the final verification of a helicopter function on an integrated helicopter test laboratory.
This research sought to distill and define the fundamentals of system and software architecture analysis and form the basis by which a design can be tested and analyzed prior to its implementation. In short, the question "can a design be measured against the business-level objectives and quality-of-use architectural requirements. The research focuses on building a body of knowledge that supports an analysis, built on fundamentals which scales and can serve as the basis by which we can transform when and how architecture validation can occur. This research supports ongoing work towards developing a strategic comprehensive architecture approach and extends the analysis and documentation of the Key Business Drivers (KBDs) and architectural drivers supporting the efficient development and sustainment of interoperable aviation mission systems. A prototype process that leverages the discovered relationships and weights for use by current Architects and Senior Systems Engineers is shown. Programs implementing this approach are expected to benefit from the planned reuse of architectural artifacts for follow up systems engineering products supporting the development of reusable component product lines avoiding the long-term challenges of unrealized architectural objectives.
Several electrical vertical take-off and landing (eVTOL) concepts have emerged from startups, to propose new sustainable mobility experiences. As opposed to traditional players in aircraft manufacturing, these "shakers" are innovating around first-of-a-kind, novel configurations of fully electric air taxis with distributed propulsion and tilt rotors. While the eVTOL development is itself a challenge, being a startup builds upon additional challenges to be successful: they need to be first on the market and reach a functioning prototype as soon as possible, they need to be agile, be unique and propose a mobility experience. Lastly, they need to reach type certificate for their vehicle - something not yet achieved and with few months-old regulatory guidance. The tools to be used by the startups and the associated business processes to be implemented are of critical importance to tackle the engineering challenges and to answer the ambition of the startups. Systems engineering and simulation are on the path for reinventing the sky, around a single business platform on the cloud.
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