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The paper presents a theoretical framework for the detection and first-level preliminary identification of potential defects on aero-structure components while employing ultrasonic guided wave based structural health monitoring strategies, systems and tools. In particular, we focus our study on ground inspection using laser-Doppler scan of surface velocity field, which can also be partly reconstructed or monitored using point sensors and actuators on-board structurally integrated. Using direct wave field data, we first question the detectability of potential defects of unknown location, size, and detailed features. Defects could be manufacturing defects or variations, which may be acceptable from design and qualification standpoint; however, those may cause significant background signal artifacts in differentiating structure progressive damage or sudden failure like impact-induced damage and fracture.

Abstract Unlike conventional launch vehicles the winged body reusable launch vehicle needs to be tested and evaluated for its functionality during the pre-flight preparation at the runway. The ground based checkout systems for the avionics and actuators performance testing during pre-flight evaluation and actuation are not designed for rapid movement. The new kind of launch vehicle with conventional rocket motor first-stage and winged body upper-stage demands the system testing at Launchpad and at runway. In the developmental flights of the winged body part of the vehicle, the pre-flight testing needs to be carried out extensively at runway. The safety protocol forbids the permanent structure for hosting the checkout system near runway. The alternative is the development of a rapidly deployable and removable checkout system. A design methodology adopting conventional industrial instrumentation systems and maintaining mobility is presented.

Advanced Air Mobility (AAM) envisions heterogenous airborne entities like crewed and uncrewed passenger and cargo vehicles within, and between urban and rural environment. To achieve this, a paradigm shift to a cooperative operating environment similar to Extensible Traffic Management (xTM) is needed. This requires the blending of Traditional Air Traffic Services (ATS) with the new generation AAM vehicles having their unique flight dynamics and handling characteristics. A hybrid environment needs to be established with enhanced shared situational awareness for all stakeholders, enabling equitable airspace access, minimizing risk, optimized airspace use, and providing flexible and adaptable airspace rules. This paper introduces a novel concept of distributed airspace management which would be apt for all kinds of operational scenarios perceived for AAM. The proposal is centered around the efficiency and safety in air space management being achieved by self-discipline.

The present study discusses about the determination of the Seal drag force in the application where elastomeric seal is used with metallic interface in the presence of different fluids. An analytical model was constructed to predict the seal drag force and experimental test was performed to check the fidelity of the analytical model. A Design of Experiment (DoE) was utilized to perform experimental test considering different factors affecting the Seal drag force. Statistical tools such as Test for Equal Variances and One way Analysis of Variance (ANOVA) were used to draw inferences for population based on samples tested in the DoE test. It was observed that Glycol based fluids lead to lubricant wash off resulting into increased seal drag force. Additionally, non-lubricated seals tend to show higher seal drag force as compared to lubricated seals. Keywords: Seal Drag, DoE, ANOVA

Aviation industry is striving to leverage the technological advancements in connectivity, computation and data analytics. Scalable and robust connectivity enables futuristic applications like smart cabins, prognostic health management (PHM) and AI/ML based analytics for effective decision making leading to flight operational efficiency, optimized maintenance planning and aircraft downtime reduction. Wireless Sensor Networks (WSN) are gaining prominence on the aircraft for providing large scale connectivity solution that are essential for implementing various health monitoring applications like Structural Health Monitoring (SHM), Prognostic Health Management (PHM), etc. and control applications like smart lighting, smart seats, smart lavatory, etc. These applications help in improving passenger experience, flight operational efficiency, optimized maintenance planning and aircraft downtime reduction.

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.

When the aircraft towing operations are carried out in narrow areas such as the hangars or parking aprons, it has a high safety risk for aircraft that the wingtips may collide with the surrounding aircraft or the airport facility. A real-time trajectory prediction method for the towbarless aircraft taxiing system (TLATS) is proposed to evaluate the collision risk based on image recognition. The Yolov7 module is utilized to detect objects and extract the corresponding features. By obtaining information about the configuration of the airplane wing and obstacles in a narrow region, a Long Short-Term Memory (LSTM) encoder-decoder model is utilized to predict future motion trends. In addition, a video dataset containing the motions of various airplane wings in real traction scenarios is constructed for training and testing.

Compared with urban areas, the road surface in mountainous areas generally has a larger slope, larger curvature and narrower width, and the vehicle may roll over and other dangers on such a road. In the case of limited driver information, if the two cars on the mountain road approach fast, it is very likely to occur road blockage or even collision. Multi-vehicle cooperative control technology can integrate the driving data of nearby vehicles, expand the perception range of vehicles, assist driving through multi-objective optimization algorithm, and improve the driving safety and traffic system reliability. Most existing studies on cooperative control of multiple vehicles is mainly focused on urban areas with stable environment, while ignoring complex conditions in mountainous areas and the influence of driver status. In this study, a digital twin based multi-vehicle cooperative warning system was proposed to improve the safety of multiple vehicles on mountain roads.

The study emphasizes transitioning school buses from diesel to electric to mitigate their environmental impact, addressing challenges like limited driving range through predictive models. This research introduces a comprehensive control-oriented model for estimating auxiliary loads in electric school buses. It begins by developing a transient thermal model capturing cabin behavior, divided into passenger and driver zones. Integrated with a control-oriented HVAC model, it estimates heating and cooling loads for desired cabin temperatures under various conditions. Real-world operational data from school bus specifications enhance the model’s practicality. The models are calibrated using experimental cabin-HVAC data, resulting in a remarkable overall Root Mean Square Error (RMSE) of 2.35°C and 1.88°C between experimental and simulated cabin temperatures.

Currently, the rapid expansion of the global road transport industry and the imperative to reduce carbon emissions are propelling the advancement of electrified highways (EH). In order to conduct a comprehensive economic analysis of EH, it is crucial to develop a detailed /8.and comprehensive economic model that takes into account various transportation modes and factors that influence the economy. However, the existing economic models for EH lack comprehensiveness in terms of considering different transportation modes and economic factors. This study aims to fill this gap by designing an economic model for an EH-based Online DC-driven system (ODS) for long distance heavy-duty transport vehicle incorporating multi-factor sensitivities. Firstly, the performance parameters of the key components of the system are calculated using vehicle dynamics equations which involves selecting and matching the relevant components and determining the fundamental cost of vehicle transformation.

Airworthiness Directives (ADs) serve as a medium through which commercial and military regulators improve the system’s performance by responding to the failure of the airplanes. The Federal Aviation Administration (FAA) and United States Air Force (USAF) provide ADs that detail overall cost on operators. The dataset derived from the Boeing 767 (B767) and its military derivatives, USAF’s KC-46A gives ideas into sensor solutions and maintenance approaches that may reduce these costs. Given the ADs significant costs for Boeing 767 operations, an analytical failure framework that determines the failure modes and failure mechanisms is introduced. For example, a huge portion of severe impairment (e.g., cracking, corrosion, and chafing) constitutes 27% of failure mechanisms in these systems. To reduce future B767 ADs for commercial and military operators, sensor solution and maintenance strategies using performance metric and genetic algorithm are assessed.

Large diameter, tightly toleranced fastener patterns are commonplace in aerospace structures. Satisfactory generation of these holes is often challenging and can be further complicated by difficult or obstructed access. Bespoke tooling and drill jigs are typically used in conjunction with power feed units leading to a manual, inflexible, and expensive manufacturing process. For 777X flap production, Boeing and Electroimpact collaborated to create a novel, automated solution to generate the fastener holes for the main carrier fitting attachment pattern. Existing robotic automation used for skin to substructure assembly was modified to utilize extended length (up to 635mm), bearing-supported drill bar sub-assemblies. These Long Assembly Drills (LADs) had to be easily attached and detached by one operator, interface with the existing spindle(s), supply cutting lubricant, extract swarf on demand, and include a means for automatically locating datum features.

Large-scale aerostructures are commonly constructed using multiple layers of stacked material which are fastened together using mechanical methods. Ensuring the interface gaps between these materials are kept within engineering tolerances is of utmost importance to the structural integrity of the aircraft over its service life. Manual, right angle feeler gauges are the traditional method for measurement of interface gaps, but this method is tedious and mechanic dependent. A portable hand tool utilizing low-coherence interferometry has been developed to address these issues. The tool uses a right-angle probe tip which is inserted into a previously drilled hole and driven through the depth of the material. A line scan of data is collected and analyzed for the presence of interface gaps. To measure the consistency of the gap around the circumference of the hole, the tool is rotated by the operator and additional scans are collected.

This paper reports the development of an operation support system for production equipment using image processing with deep learning. Semi-automatic riveters are used to attach small parts to skin panels, and they involve manual positioning followed by automated drilling and fastening. The operator watches a monitor showing the processing area, and two types of failure may arise because of human error. First, the operator should locate the correct position on the skin panel by looking at markers painted thereon but may mistakenly cause the equipment to drill at an incorrect position. Second, the operator should prevent the equipment from fastening if they see chips around a hole after drilling but may overlook the chips; chips remaining around a drilled hole may cause the fastener to be inserted into the hole and fastened at an angle, which can result in the whole panel having to be scrapped.

Efficient transportation for carrying heavy loads is a common challenge across various applications, from supermarkets to industrial purposes. Conventional trolleys often fall short when loaded with heavy cargo, resulting in increased exertion and diminished productivity. Moreover, these challenges can adversely affect posture and lumbar spine health, especially for elder people and persons with cervical problems. There is a need for more user-friendly, ergonomic, and space-efficient solutions. This project addresses these challenges through an innovative design that encompasses various aspects of trolley functionality, including the study of comfort, wheel selection, and material considerations, drawing from ergonomic research. Multiple methods are employed to optimize the trolley’s dimensions to improve its overall performance. The trolley’s design features a collapsible basket for the transport of smaller-sized items and a base frame for larger goods and luggage.

Tyre wear is of significant concern for the automotive industry due to multiple reasons including vehicle performance, safety, economy, environmental (particulate matter emission) aspects, etc. Therefore, ensuring enhanced tyre tread wear resistance is one of the most important criteria while developing a new tyre. Tyre wear phenomenon is influenced by various factors, such as road conditions, driving habits, maintenance practices and tyre design parameters (construction, geometry and material). The wear assessment through the classical field-testing approach consumes significant time and resources. Therefore, digital predictive tools are very useful in predicting wear characteristics at the early stage of the tyre development process. In this study, an attempt has been made to capture the impact of tread geometry, tread material, vehicle geometry, vehicle speed, test track geometry, etc. on tyre wear.

Nowadays, the rapid growth of civil aviation transportation demand has led to more frequent flight delays. The major problem of flight delays is restricting the development of municipal airports. To further improve passenger satisfaction, and reduce economic losses caused by flight delays, environmental pollution and many other adverse consequences, three machine learning algorithms are constructed in current study: random forest (RF), gradient boosting decision tree (GBDT) and BP neural network (BPNN). The departure flight delay prediction model uses the actual data set of domestic flights in the United States to simulate and verify the performance and accuracy of the three models. This model combines the visual analysis system to show the density of departure flight delays between different airports. Firstly, the data set is reprocessed, and the main factors leading to flight delays are selected as sample attributes by principal component analysis.

Transporting baggage is critical in airport ground support services to ensure smooth flight operations. However, the scheduling of baggage transport vehicles faces challenges related to low efficiency and high costs. A multi-objective optimization vehicle scheduling model is proposed to address these issues, considering time and space costs, vehicle utilization, and passenger waiting time. An improved genetic algorithm (IGA) based on the large-scale neighborhood search algorithm is proposed to solve this model. The simulation experiment is conducted using actual flight data from an international airport. The IGA algorithm is compared with the standard genetic algorithm (SGA) based on experimental results, revealing that the former achieves convergence in a significantly shorter time. Moreover, the scheduling paths of baggage cars that violate flight service time window requirements are significantly lower in the final scheduling scheme under the IGA algorithm than in SGA.

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.

The active suspension system has strong application potential in electric vehicles (EV) due to its superior controllability. However, the feedback delay of vehicle status information will degrade the control performance of active suspension. This paper investigates the multi-objective control of preview active suspension based on forward-looking information perception. An integrated model including the forward-looking system, vehicle dynamics system and control switching system is established. First, a multi-degree-of-freedom vehicle dynamics model including the input-output system is created, then, an LQR active suspension controller is designed, secondly, a pareto solution set with multiple different target solutions is obtained by calculation, finally, a forward-looking preview mode-switching controller that can select different control strategies according to the road conditions ahead is designed.