Browse Topic: Computer software and hardware
This Aerospace Recommended Practice (ARP) provides recommended requirements for the testing of electromechanical actuators (EMAs). General test considerations are also provided. While many EMA configurations include motor control electronics, the specific tests required for the electronic hardware, software, or firmware are outside the scope of this document.
Wind Tunnels are complex and cost-intensive test facilities. Thus, increasing the test efficiency is an important aspect. At the same time, active aerodynamic elements gain importance for the efficiency of modern cars. For homologation, such active aero-components pose an extra level of test complexity as their control strategies, the relevant drive cycles and their aerodynamics in different positions must be considered for homologation-relevant data. Often, active components have to be manually adjusted between test runs, which is a time-consuming process because the vehicle is not integrated into the test automation. Even if so, designing a test sequence stepping through the individual settings for each component of a vehicle is a tedious task in the test session. Thus, a sophisticated integration of the wind tunnel control system with a test management system, supporting the full homologation process is one aspect of a solution. The other is the integration of the vehicle’s active
The U-Shift IV represents the latest evolution in modular urban mobility solutions, offering significant advancements over its predecessors. This innovative vehicle concept introduces a distinct separation between the drive module, known as the driveboard, and the transport capsules. The driveboard contains all the necessary components for autonomous driving, allowing it to operate independently. This separation not only enables versatile applications - such as easily swapping capsules for passenger or goods transportation - but also significantly improves the utilization of the driveboard. By allowing a single driveboard to be paired with different capsules, operational efficiency is maximized, enabling continuous deployment of driveboards while the individual capsules are in use. The primary focus of U-Shift IV was to obtain a permit for operating at the Federal Garden Show 2023. To achieve this goal, we built the vehicle around the specific requirements for semi-public road
Vehicles are prime examples of cyber-physical systems that rely on multiple domains, including mechanics, electronics, and software. Due to high customizability and software changes introduced by bug fixes or functional upgrades, vehicle instances vary in space (variants) and time (versions). This results in a huge number of possible variants and versions; thus, testing all combinations to ensure functional safety is practically infeasible. Moreover, components of all domains interact with each other; thus, solely focusing on single domains while testing multi-domain cyber-physical systems is insufficient. In this paper, we propose a process for change-aware testing of cyber-physical systems, including test activities we identified during a literature analysis. The process consists of multiple structured steps, including the selection of affected variants, test case selection, and adaptive configuration of test environments. Based on the process and identified activities, we discuss
In the early days of computers, interfaces were paper printouts or blinking lights, but as the technology matured, the graphical user interface (GUI) quickly became the standard.
In the domain of aircraft certification, Development Assurance is what some would call a useful tool to gain confidence in the development of complex systems, and what others would call a necessary evil. But what does it actually do? Why is it necessary for certification of modern aircraft? What, epistemologically, does it bring to the table? This paper aims to show how Development Assurance (DA) activities, at all levels from aircraft to item, close the epistemological holes created when complex systems are chosen for implementation. It will map the different sources and types of uncertainty encountered in system and aircraft verification and explain how each type is dealt with within a certification context, working from simple mechanical systems up to complex and highly integrated systems using software and airborne electronic hardware and beyond. It will show that Development Assurance, far from being an arbitrary set of activities, systematically brings personal and corporate
The segment manipulator machine, a large custom-built apparatus, is used for assembling and disassembling heavy tooling, specifically carbon fiber forms. This complex yet slow-moving machine had been in service for nineteen years, with many control components becoming obsolete and difficult to replace. The customer engaged Electroimpact to upgrade the machine using the latest state-of-the-art controls, aiming to extend the system's operational life by at least another two decades. The program from the previous control system could not be reused, necessitating a complete overhaul.
Southwest Research Institute is working to expand software normally used to model electrolytes and predict corrosion and turn it into a tool that can help determine whether ice-covered worlds have the right conditions for microbial life. The project is supported by NASA’s Habitable Worlds program, which seeks to use knowledge of the history of the Earth and the life upon it as a guide for determining the processes and conditions that create and maintain habitable environments.
This document defines a set of standard application layer interfaces called JAUS Manipulator Services. JAUS Services provide the means for software entities in an unmanned system or system of unmanned systems to communicate and coordinate their activities. The Manipulator Services represent platform-independent capabilities commonly found across domains and types of unmanned systems. At present, twenty-five (25) services are defined in this document. These services are categorized as: Low Level Manipulator Control Services – The one service in this category allows for low-level command of the manipulator joint actuation efforts. This is an open-loop command that could be used in a simple tele-operation scenario. The service in this category is listed as follows: Primitive Manipulator Service Manipulator Sensor Services – These services, when queried, return instantaneous sensor data. Three services are defined that return respectively joint positions, joint velocities, and joint
The SAE Aerospace Information Report AIR5315 – Generic Open Architecture (GOA) defines “a framework to identify interface classes for applying open systems to the design of a specific hardware/software system.” [sae] JAUS Service (Interface) Definition Language defines an XML schema for the interface definition of services at the Class 4L, or Application Layer, and Class 3L, or System Services Layer, of the Generic Open Architecture stack (see Figure 1). The specification of JAUS services shall be defined according to the JAUS Service (Interface) Definition Language document.
High-efficiency manufacturing involves the transmission of copious amounts of data, exemplified both by trends in the automotive industry and advances in technology. In the automotive industry, products have been growing increasingly complex, owing to multiple SKUs, global supply chains and the involvement of many tier 2 / Just-In Time (JIT) suppliers. On top of that, recalls and incidents in recent years have made it important for OEMs to be able to track down affected vehicles based on their components. All of this has increased the need for OEMs to be able to collect and analyze component data. The advent of Industry 4.0 and IoT has provided manufacturing with the ability to efficiently collect and store large amounts of data, lining up with the needs of manufacturing-based industries. However, while the needs to collect data have been met, corporations now find themselves facing the need to make sense of the data to provide the insights they need, and the data is often unstructured
E-mobility is revolutionizing the automotive industry by improving energy-efficiency, lowering CO2 and non-exhaust emissions, innovating driving and propulsion technologies, redefining the hardware-software-ratio in the vehicle development, facilitating new business models, and transforming the market circumstances for electric vehicles (EVs) in passenger mobility and freight transportation. Ongoing R&D action is leading to an uptake of affordable and more energy-efficient EVs for the public at large through the development of innovative and user-centric solutions, optimized system concepts and components sizing, and increased passenger safety. Moreover, technological EV optimizations and investigations on thermal and energy management systems as well as the modularization of multiple EV functionalities result in driving range maximization, driving comfort improvement, and greater user-centricity. This paper presents the latest advancements of multiple EU-funded research projects under
In the automotive industry, there have been many efforts of late in using Machine Learning tools to aid crash virtual simulations and further decrease product development time and cost. As the simulation world grapples with how best to incorporate ML techniques, two main challenges are evident. There is the risk of giving flawed recommendations to the design engineer if the training data has some suspect data. In addition, the complexity of porting simulation data back and forth to a Machine Learning software can make the process cumbersome for the average CAE engineer to set up and execute a ML project. We would like to put forth a ML workflow/platform that a typical CAE engineer can use to create training data, train a PINN (Physics Informed Neural Network) ML model and use it to predict, optimize and even synthesize for any given crash problem. The key enabler is the use of an industry first data structure named mwplot that can store diverse types of training data - scalars, vectors
A hierarchical control architecture is commonly employed in hybrid torque control, where the supervisor CPU oversees system-level objectives, while the slave CPU manages lower-level control tasks. Frequently, control authority must be transferred between the two to achieve optimal coordination and synchronization. When a closed-loop component is utilized, accurately determining its actual contribution to the controlled system can be challenging. This is because closed-loop components are often designed to compensate for unknown dynamics, component variations, and actuation uncertainties. This paper presents a novel approach to closed-loop component factor transfer and coordination between two CPUs operating at different hierarchical levels within a complex system. The proposed framework enables seamless control authority transition between the supervisor and slave CPUs, ensuring optimal system performance and robustness. To mitigate disturbances and uncertainties during the transition
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