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The evaluation of aircraft characteristics through flight test maneuvers is fundamental to aviation safety and understanding flight attributes. This research project proposes a comprehensive methodology to detect and analyze aircraft maneuvers using full flight data, combining signal processing and machine learning techniques. Leveraging the Wavelet Transform, we unveil intricate temporal details within flight data, uncovering critical time-frequency insights essential for aviation safety. The integration of Long Short-Term Memory (LSTM) models enhances our ability to capture temporal dependencies, surpassing the capabilities of machine learning in isolation. These extracted maneuvers not only aid in safety but also find practical applications in system identification, air-data calibration, and performance analysis, significantly reducing pre-processing time for analysts.

Software certification guidelines, such as RTCA DO-178C, mandate the analysis of data and control coupling (DC/CC) in safety-critical avionics software using requirement-based testing. The intention of this analysis is to ensure correctness in the interactions and dependencies between software components. The shift from confirming the coupling (as in DO-178B) to verifying the exercising of the coupling (as introduced in DO-178C) transitions the DC/CC objective from an analytical exercise against the test design to a measurement exercise against the test execution. Current methodologies for measuring Data Coupling and Control Coupling (DC/CC) rely on source code instrumentation, which embeds code to record coverage information during requirements-based testing. However, this approach has significant drawbacks. Primarily, it necessitates executing tests on both the instrumented and non-instrumented versions of the code, ensuring their outputs match.

In recent decades, research based innovative system-on-chip (SoC) design has been a very important issue, due to the emerging trends and application challenges. The SoCs encompass digital, analog and mixed-signal hardware and software components and even sensors and actuators. Modelling and simulation constitute a powerful method for designing and evaluating complex systems and processes for many analysts and project managers as they engage in state of-the-art research and development. Modelling and simulations not only help them with the algorithm or concept realization and design feasibility, but it also allows experimentation, optimization, interpretation of results and validation of model.

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.

Launch vehicle structures in course of its flight will be subjected to dynamic forces over a range of frequencies up to 2000 Hz. These loads can be steady, transient or random in nature. The dynamic excitations like aerodynamic gust, motor oscillations and transients, sudden application of control force are capable of exciting the low frequency structural modes and cause significant responses at the interface of launch vehicle and satellite. The satellite interface responses to these low frequency excitations are estimated through Coupled Load Analysis (CLA). The analysis plays a crucial role in mission as the satellite design loads and Sine vibration test levels are defined based on this. The perquisite of CLA is to predict the responses with considerable accuracy so that the design loads are not exceeded in the flight. CLA validation is possible by simulating the flight experienced responses through the analysis.

Continuous improvements and innovations towards sustainability in the aviation industry has brought interest in electrified aviation. Electric aircrafts have short missions in which the temporal variability of thermal loads are high. Lithium-ion (Li-ion) batteries have emerged as prominent power source candidate for electric aircrafts and Urban Air Mobility (UAM). UAMs and Electric aircrafts have large battery packs with battery capacity ranging in hundreds or thousands of kWh. If the battery is exposed to temperatures outside the optimum range, the life and the performance of the battery reduces drastically. Hence, it is crucial to have a Thermal Management System (TMS) which would reduce the heat load on battery in addition to cabin, and machinery thermal loads. Thermal management can be done through active or passive cooling. Adding a passive cooling system like Phase Change Material (PCM) to the TMS reduces the design maximum thermal loads.

In the architecture of an Unmanned Aerial Vehicle (UAV), a crucial component responsible for the propulsion system is the electric motor. Over the years, different types of electric motors, including Brushless Direct Current (BLDC), have supported the UAV’s propulsion system in diverse configurations. However, in the context of flux flow, the Radial Flux Permanent Magnet Motor (RFPMM) has been given more priority than the Axial Flux Permanent Magnet Motor (AFPMM) due to its sustainability in design and construction. Nevertheless, the AFPMM boasts higher speed, power density, lower weight, and greater efficiency than the RFPMM, because of its shorter flux path and the absence of end-turn winding. Therefore, this paper focuses on conducting a suitability analysis of an AFPMM as a shaft-connected propeller-mounted motor, with the intention of replacing the RFPMM in UAV applications.

In today's industrial sphere, machines are the key supporting various sectors and their operations. Over time, due to extensive usage, these machines undergo wear and tear, introducing subtle yet consequential faults that may go unnoticed. Given the pervasive dependence on machinery, the early and precise detection of these faults becomes a critical necessity. Detecting faults at an early stage not only prevents expensive downtimes but also significantly improves operational efficiency and safety standards. This research focuses on addressing this crucial need by proposing an effective system for condition monitoring and fault detection, leveraging the capabilities of advanced deep learning techniques. The study delves into the application of five diverse deep learning models—LSTM, Deep LSTM, Bi LSTM, GRU, and 1DCNN—in the context of fault detection in bearings using accelerometer data. Accelerometer data is instrumental in capturing vital vibrations within the machinery.

Industries have been increasingly adopting AI based computer vision models for automated asset defect inspection. A challenging aspect within this domain is the inspection of composite assets consisting of multiple components, each of which is an object of interest for inspection, with its own structural variations, defect types and signatures. Training vision models for such an inspection process involves numerous challenges around data acquisition such as insufficient volume, inconsistent positioning, poor quality and imbalance owing to inadequate image samples of infrequently occurring defects. Approaches to augmenting the dataset through Standard Data Augmentation (SDA) methods (image transformations such as flipping, rotation, contrast adjustment, etc.) have had limited success. When dealing with images of such composite assets, it is challenging to correct the data imbalance at the component level using image transformations as they apply to all the components within an image.

Electromechanical actuators (EMAs) play a crucial role in aircraft electrification, offering advantages in terms of aircraft-level weight, rigging and reliability compared to hydraulic actuators. To prevent backdriving, skewed roller braking devices called "no-backs" are employed to provide braking torque. These technology components are continuing to be improved with analysis driven design innovations eg. U.S. Pat. No. 8,393,568. The no-back mechanism has the rollers skewed around their own transverse axis that allow for a combination of rolling and sliding against the stator surfaces. This friction provides the necessary braking torque that prevents the backdriving. By controlling the friction radius and analyzing the Hertzian contact stresses, the brake can be sized for the desired duty cycle. No-backs can be configured to provide braking torque for both tensile and compressive backdriving loads.

The finite element method is one of the most robust tools in structural analysis. Typically, the input parameters in a finite element model are assumed to be deterministic. However, in practice, almost all material and geometrical properties, including the load, possess randomness. The consideration of the probabilistic nature of these quantities is essential to effectively designing a system that is robust against the uncertainties arising due to the variation in the input parameters, the significance of which has been documented by NASA in “Probabilistic Risk Assessment Procedures Guide for NASA Managers and Practitioners”, 2011. Among the various techniques applicable for stochastic analysis, the perturbation method, which is based on a sound mathematical foundation derived from Taylor’s series expansion, is widely acknowledged for its much higher efficiency compared to the well-known Monte-Carlo method.

Faults if not detected and processed will create catastrophe in closed loop system for safety critical applications in automotive, space, medical, nuclear, and aerospace domains. In aerospace applications such as stall warning and protection/prevention system (SWPS), algorithms detect stall condition and provide protection by deploying the elevator stick pusher. Failure to detect and prevent stall leads to loss of lives and aircraft. Traditional Functional Hazard and Fault Tree analyses are inadequate to capture all failures due to the complex hardware-software interactions for stall warning and protection system. Hence, an improved methodology for failure detection and identification is proposed. This paper discusses a hybrid formal method and model-based technique using STPA to identify and diagnose faults and provide monitors to process the identified faults to ensure robust design of the indigenous stall warning and protection system (SWPS).

Landing of spacecraft on Lunar or Martian surfaces is the last and critical step in inter planetary space missions. The atmosphere on earth is thick enough to slow down the craft but Moon or Mars does not provide a similar atmosphere. Moreover, other factors such as lunar dust, availability of precise onboard navigational aids etc would impact decision making. Soft landing meaning controlling the velocity of the craft from over 6000km/h to zero. If the craft’s velocity is not controlled, it might crash. Various onboard sensors and onboard computing power play a critical role in estimating and hence controlling the velocity, in the absence of GPS-like navigational aids. In this paper, an attempt is made using visual onboard sensor to estimate the velocity of the object. The precise estimation of an object's velocity is a vital component in the trajectory planning of space vehicles, particularly those designed for descent onto lunar or Martian terrains, such as orbiters or landers.

Metasurfaces, comprised of sub-wavelength structures, possess remarkable electromagnetic wave manipulation capabilities. Their application as radar absorbers has gained widespread recognition, particularly in modern stealth technology, where their role is to minimize the radar cross-section (RCS) of military assets. Conventional radar absorber design are tedious by their time-consuming, computationally intensive, iterative nature, and demand a high level of expertise. In contrast, the emergence of deep learning-based metasurface design for RCS reduction represents a rapidly evolving field. This approach offers automated and computationally efficient means to generate radar absorber designs. However, the practical implementation of radar-absorbing structures on complex aircraft bodies presents significant challenges.

In any aerospace mission, after the vehicle has taken off, the visual is lost and the information about its current state is only through the sensor data telemetered in real-time. Very often, this data is difficult to perceive and analyze. In such cases, a 3D, near to real representation of the data can immensely improve the understanding of the current state of mission and can aid in real-time decision making if possible. Generally, any aerospace vehicle carries onboard an inertial system along with other sensors, which measures the position and attitude of the vehicle. This data is communicated to ground station. The received telemetry data is encoded as bytes and sent as packets through the network using the Universal Datagram Protocol (UDP).  The transmitted data is often available in a very low frequency, which is not desirable for a smooth display. It is therefore necessary to interpolate the data between intervals based on the time elapsed since last rendered frame.

Modern combat aircraft demands efficient maintenance strategies to ensure operational readiness while minimizing downtime and costs. Innovative approaches using Digital Twining models are being explored to capture inter system behaviours and assessing health of systems which will help maintenance aspects. This approach employs advanced deep learning protocols to analyze the intricate interactions among various systems using the data collected from various systems. The research involves extensive data collection from sensors within combat aircraft, followed by data preprocessing and feature selection, using domain knowledge and correlation analysis. Neural networks are designed for individual systems, and hyper parameter tuning is performed to optimize their performance. By combining the outputs of these during the model integration phase, an overall health assessment of the aircraft will be generated.

RAMBHA-LP (Radio Anatomy of Moon Bound Hypersensitive Ionosphere and Atmosphere - Langmuir Probe) is one of the key scientific payloads onboard the Indian Space Research Organization’s (ISRO) Chandrayaan-3 mission. Its objectives were to estimate the plasma density and its variations on the near lunar surface. The probe was initially kept in a stowed condition attached to the lander. A mechanism was designed and realized to meet the functional requirement of deploying the probe at a distance of 1 meter, equivalent to the Debye length of the probe in the moon’s plasma environment. The probe deployment mechanism consists of the Titanium alloy spherical probe with a Titanium Nitride coating on its surface to achieve a constant work function, a long carbon-fiber-reinforced polymer boom, a double torsion spring, a dust-protection box, and a shape-memory alloy-based Frangibolt actuator for low-shock separation. The entire mechanism weighed less than 1.5 kilograms.

A GE Aviation Systems report documents that the National Oceanic and Atmospheric Administration (NOAA) provided weather forecast data has a bias of 15 knots and a standard deviation of 13.3 knots for the 40 flights considered for the research. It also had a 0.47 bias in the temperature with a standard deviation of 0.27. The temperature errors are not as significant as the wind. There is a potential opportunity to reduce the operational cost by improving the weather forecast. The flight management system (FMS) currently uses the weather forecast, available before takeoff, to identify an optimized flight path with minimum operational costs depending on the selected speed mode. Such a flight plan could be optimum for a shorter flight because these flight path planning algorithms are very less susceptible to the accuracy of the weather forecast.

Scramjet-based hypersonic airbreathers are needed for next-generation defense and space applications. Two scramjet configurations, namely, rectangular and axisymmetric, are primarily studied in the literature. However, there is no quantitative comparison of the performance metrics between these two scramjet configurations. This study investigates the aero-thermo-structural performance of rectangular and axisymmetric scramjet engines at Mach 7 and 25 km altitude. A numerical framework involving computational fluid dynamics and computational structural dynamics is established. The aero-thermo-structural loads on the scramjet flow path are estimated using RANS/FANS simulation. A finite element-based coupled thermo-structural analysis is performed to understand the thermo-structural response. Before using the numerical models for the study, CFD and CSD modules are validated with literature data.

Lunar tubes, natural underground structures on the Moon formed by ancient volcanic activity, offer natural protection from extreme temperatures, radiation, and micro-meteorite impacts, making them prime candidates for future lunar bases. However, the exploration of lunar tubes requires a high degree of mobility. Given the Moon's gravity, which is approximately six times weaker than Earth's, efficient navigation across rugged terrains within these lava tubes is achievable through jumping. In this work, we present the design of subsystems for a miniature hexapod rover weighing 1 kg, which can walk, jump, and stow. The walking system consists of two subsystems: one for in-plane walking, employing four single-degree-of-freedom (DOF) legs utilizing the KLANN walking mechanism, and another for directional adjustments before jumping. The latter employs a novel three-DOF mechanism employing a cable pulley mechanism to optimize space utilization.