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Gaganyaan programme is India's prestigious human space exploration endeavour. During the re-entry of the spacecraft, achieving the minimum terminal velocity is paramount to ensure the crew's safety upon landing. Therefore, acquiring accurate in-flight velocity data is essential for comprehensively understanding the landing dynamics and facilitating post-flight data analysis and validation. Moreover, terminal velocity plays a pivotal role in the qualification of parachute systems during platform-drop tests where the objective is to minimize the terminal velocity for safe impact. Terminal velocity also serves as a critical design parameter for the crew seat attenuation system. In addition to terminal velocity, it is equally necessary to characterize the horizontal velocities acting on the decelerating body due to various factors such as parachute sway and wind drift. This data also plays a central role in refining our systems for future enhancements.

The purpose of the Air Generation System is to provide a constant supply of conditioned fresh air to meet the necessary oxygen availability and to prevent carbon dioxide (CO2) concentrations for the occupants in an aircraft. The engine bleed energy or electrical load energy consumed towards this circumstance accounts to be approx. 5% of total fuel burn and in turn, contributes to the global emissions of greenhouse gases. This paper studies the improvement areas of the present conventional system such as fuel burn consumption associated with an aircraft environmental control system (ECS) depending on, the amount of bleed and ram air usage, electric power consumption. Improved systems for propulsion, power generation, sustainability, hybridization, and environmental control can be desirable for an aircraft.

This procurement specification covers solid rivets and hollow end rivets made from a corrosion and heat resistant steel of the type identified under the Unified Numbering System as UNS S66286 and of 80 ksi single shear strength at room temperature.

This research focused on developing a methodology for a vehicle dynamics model of a passenger vehicle outfitted with an aftermarket Automated Driving System software package using only literature and track based results. This package consisted of Autoware.AI (Autoware ®) operating on Robot Operating System 1 (ROS™) with C++ and Python ®. Initial focus was understanding the basics of ROS and how to implement test scenarios in Python to characterize the control systems and dynamics of the vehicle. As understanding of the system continued to develop, test scenarios were adapted to better fit system characterization goals with identification of system configuration limits. Trends from on-track testing were identified and paired with first-order linear systems to simulate physical vehicle responses to given command inputs. Sub-models were developed and simulated in MATLAB ® with command inputs from on-track testing.

Under complex and extreme operating conditions, the road adhesion coefficient emerges as a critical state parameter for tire force analysis and vehicle dynamics control. In contrast to model-based estimation methods, intelligent tire technology enables the real-time feedback of tire-road interaction information to the vehicle control system. This paper proposes an approach that integrates intelligent tire systems with machine learning to acquire precise road adhesion coefficients for vehicles. Firstly, taking into account the driving conditions, sensor selection is conducted to develop an intelligent tire hardware acquisition system based on MEMS (Micro-Electro-Mechanical Systems) three-axis acceleration sensors, utilizing a simplified hardware structure and wireless transmission mode. Secondly, through the collection of real vehicle experiment data on different road surfaces, a dataset is gathered for machine learning training.

As the automotive industry accelerates its virtual engineering capabilities, there is a growing requirement for increased accuracy across a broad range of vehicle simulations. Regarding control system development, utilizing vehicle simulations to conduct ‘pre-tuning’ activities can significantly reduce time and costs. However, achieving an accurate prediction of, e.g., stopping distance, requires accurate tire modeling. The Magic Formula tire model is often used to effectively model the tire response within vehicle dynamics simulations. However, such models often: i) represent the tire driving on sandpaper; and ii) do not accurately capture the transient response over a wide slip range. In this paper, a novel methodology is developed using the MF-Tyre/MF-Swift tire model to enhance the accuracy of ABS braking simulations.

Battery electric vehicles are quickly gaining momentum to improve vehicle fuel efficiency and emission reduction. However, they must be designed to provide adequate range on a single charge combined with good acceleration performance, top speed, gradeability, and fast charging times. The paper presents a model for sizing the power train of an electric vehicle, including the power electronic converter, electric motor, and battery pack. A major assumption is that an optimal wheel slip rate can be achieved by modern vehicles using slip control systems. MATLAB/Simulink was used to model the vehicle powertrain. Simulations were conducted based on different speed and acceleration profiles. The purpose of the study focused on the motor and power electronics sizing requirements to achieve optimal range and performance.

The tire cornering stiffness plays a vital role in the functionality of vehicle dynamics control systems, particularly when it comes to stability and path tracking controllers. This parameter relies on various external variables such as the tire/ambient temperature, tire wear condition, the road surface state, etc. Ensuring a reliable estimation of the cornering stiffness value is crucial for control systems. This ensures that these systems can accurately compute actuator requests in a wide range of driving conditions. In this paper, a novel estimation method is introduced that relies solely on standard vehicle sensor data, including data such as steering wheel angles, longitudinal acceleration, lateral acceleration, yaw rate, and vehicle speed, among others. Initially, the vehicle's handling characteristics are deduced by estimating the understeer gradient.

This SAE Aerospace Information Report (AIR) provides the hydraulic and flight-control system designer with the various design options and techniques that are currently available to enhance the survivability of military aircraft. The AIR addresses the following major topics: a Design concepts and architecture (see 3.2, 3.5, and 3.6) b Design implementation (see 3.3, 3.6, and 3.7) c Means to control external leakage (see 3.4) d Component design (see 3.8)

This procurement specification covers split cotter pins with optional ends (see Figure 1), made from a corrosion resistant steel of the type identified under the Unified Numbering System as UNS S30200.

This procurement specification covers split cotter pins with optional ends (see Figure 1), made from a corrosion and heat resistant steel of the type identified under the Unified Numbering System as UNS S32100.

This specification covers tubular-shaped pins, fabricated from carbon steel, having a full-length longitudinal slot to permit flexure when inserted into a hole.

In recent years, the increasing complexity of modern aerospace systems has driven the rapid adoption of robust Model-Based Systems Engineering (MBSE). MBSE is a development methodology centered around computational models, which are instrumental in supporting the design and analysis of intricate systems. In this context, the Architecture Analysis and Design Language (AADL) and Systems Modeling Language (SysML) are two prominent modeling languages for specifying and analyzing the structure and behavior of a cyber-physical system. Both languages have their own specific use cases and tool environments and are typically employed to model different aspects of system design. Although multiple software tools are available for transforming models from one language to another, their effectiveness is limited by fundamental differences in the semantics of each language.