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The descent phase of GAGANYAAN (Indian Manned Space Mission) culminates with a crew module impacting at a predetermined site in Indian waters. During water impact, huge amount of loads are experienced by the astronauts. This demands an impact attenuation system which can attenuate the impact loads and reduce the acceleration experienced by astronauts to safe levels. Current state of the art impact attenuation systems use honeycomb core, which is passive, expendable, can only be used once (at touchdown impact) during the entire mission and does not account off-nominal impact loads. Active and reusable attenuation systems for crew module is still an unexplored territory. Three configurations of impact attenuators were selected for this study for the current GAGANYAAN crew module configuration, namely, hydraulic damper, hydro-pneumatic damper and airbag systems.

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.

This SAE Aerospace Standard (AS) covers water conditioning agents used to facilitate aqueous wet-method magnetic particle inspection.

Virtual simulation is a fundamental tool for the development of new vehicles, both for individual components and for complete subsystems and full vehicles. Many software tools exist in the automotive sector to assess full-vehicle behavior and performance, including multibody software and algorithms based on 14 (or more) degrees-of-freedom vehicle dynamics models. In order to reproduce the testing maneuvers and typical vehicle mission, a key part of such simulation tools is the virtual driver algorithm. It is essential to implement a control logic that reproduces the handling response of the driver, so that the closed-loop maneuvers can be evaluated. However, the response of typical virtual drivers is not always similar to the human driving characteristics. Virtual driver algorithms can perform very fast, precise, and smooth steering and pedal actions, while humans display a more variable, delayed and often not optimal actions.

Direct debugging of a vertical takeoff and landing (VTOL) fixed-wing aircraft’s control system can easily result in risk and personnel damage. It is effectively to employ simulation and numerical methods to validate control performance. In this paper, the attitude stabilization controller for VTOL fixed-wing aircraft is designed, and the controller performance is verified by MATLAB and visual simulation software, which significantly increases designed efficiency and safety of the controller. In detail, we first develop the VTOL fixed-wing aircraft’s six degrees of freedom kinematics and dynamics models using Simulink module, and the cascade PID control technique is applied to the VTOL aircraft’s attitude stabilization control. Then the visual simulation program records the flight data and displays the flight course and condition, which can validate the designed controller performance effectively.

The main objective of platoon control is coordinated motion of autonomous vehicle platooning with small intervehicle spacing while maintaining the same speed and acceleration as the leading vehicle, which can save energy consumption and improve traffic throughput. The conventional platoon control methods are confronted with the problem of manual parameter tuning. In order to addres this isue, a novel bifold platoon control approach leveraging a deep reinforcement learning-based model is proposed, which enables the platoon adapt to the complex traffic environment, and guarantees the safety of platoon. The upper layer controller based on the TD3 tuned PID algorithm outputs the desired acceleration. This integration mitigates the inconvenience of frequent manual parameter tuning asociated with the conventional PID algorithm. The lower layer controller tracks the desired acceleration based on the inverse vehicle dynamics model and feedback control.

Abstract For spacecraft with high power consumption, it is reasonable to build the thermal control system based on a two-phase mechanically pumped loop. The heat-controlled accumulator is a key element of the two-phase mechanically pumped loop, which allows for the control of pressure in the loop and maintains the required level of coolant boiling temperature or cavitation margin at the pump inlet. There can be two critical modes of loop operation where the ability to control pressure will be lost. The first critical mode occurs when the accumulator fills with liquid at high heat loads. The second critical mode occurs when the accumulator is at low heat loads and partial loss of coolant, for example, due to the leak caused by micrometeorite breakdown. Both modes are caused by insufficient accumulator volume or working fluid charge.

Hybrid electric aircraft propulsion is an emerging technology that presents a variety of potential benefits along with technical integration challenges. Developing these new propulsion architectures with their complex control systems, and ultimately proving their benefit, is a multistep process. This process includes concept development and analysis, dynamic simulation, hardware-in-the-loop testing, full-scale testing, and so on. This effort is being revolutionized and indeed enabled by new digital tools that support increasing the technology readiness level throughout the maturation process. As part of this Digital Transformation, NASA has developed a suite of publicly available digital tools that facilitate the path from concept to implementation. This paper describes the NASA-developed tools and puts them in the context of control system development for hybrid electric aircraft propulsion.

This specification covers a stable, noncorrosive, water-soluble, highly-penetrating, fluorescent solution which may, but need not, be diluted with an appropriate amount of water for use.

Multiphase CFD simulations of air and water play a critical role in aircraft icing analysis. Specifically for air data sensors mounted near the front of an aircraft, simulations that predict the concentration of water surrounding an aircraft fuselage are necessary for understanding their performance in icing conditions. Those simulations can aid in sensor design and placement, and are central for defining critical conditions to test during icing qualification campaigns. There are several methods available in CFD that solve a multiphase flow field. Two of the most common methods used are Lagrangian and Eulerian. While these methods are similar, important differences can be viewed in the results, specifically in how the water shadow zones are predicted. This paper compares a Lagrangian and Eulerian CFD method for solving a multiphase flow field, and assesses their performance for use for analyzing installation locations and critical icing conditions of air data probes.