ABSTRACT. This talk will focus on the fundamentals of Acoustic Emission Testing (AET), and the rapidly emerging application of artificial intelligence and machine learning (AI/ML) in the field of nondestructive testing and evaluation (NDT/E). AET is one of the most sophisticated and data-intensive technologies in the NDT/E space and is ripe for applications incorporating the use of AI/ML as a consequence. In the aerospace sector, additive manufacturing (AM) and in-situ monitoring of acoustic signatures is an important application of AET that has seen a plethora of academic literature published in recent years, with an emphasis on using advanced ML algorithms to extract relevant patterns and predict the state of the AM process. Another important application is damage identification in jet engines with the aid of signal processing tools and Deep Learning (DL), which is one of the most powerful technologies to emerge from the ML research community in the past decade. Among all these details, the talk will also discuss relevant details of ML algorithms and the challenges of applying ML algorithms and techniques to AET datasets. The material presented in this talk is based on a review of literature published on the use of AET for in-situ monitoring of AM processes along with other published and upcoming work contributed by the speaker in various avenues, including journals and ASNT committee papers.

ABSTRACT. Unmanned Air Vehicles (UAVs) have rapidly evolved over the past decades with applications ranging from surveillance, and reconnaissance to environmental monitoring having potential usage in both civilian and military applications. Advancements in electric propulsion systems, batteries, lightweight materials, and artificial intelligence algorithms have significantly improved all types of UAVs' flight stability and autonomous capabilities. The future of UAV technology will be discussed with a particular focus on solar-powered UAVs, vertical takeoff and landing (VTOL) UAVs, and Micro Air Vehicles (MAVs)/Nano Air Vehicles (NAVs). With increasing emphasis on sustainability and renewable energy sources, the integration of solar power in UAVs has garnered significant attention. Solar-powered UAVs harness the sun's energy to extend flight endurance and reduce dependence on traditional fuel sources. Advancements in photovoltaic technology, energy storage systems, and energy-efficient designs are paving the way for solar UAVs to revolutionize long-endurance surveillance, environmental monitoring, and remote sensing applications. Vertical takeoff and landing (VTOL) UAVs have garnered significant attention due to their ability to maneuver in confined spaces and access challenging environments. Micro Air Vehicles (MAVs) and Nano Air Vehicles (NAVs) are at the forefront of miniaturization and swarm intelligence. Researchers are exploring new materials, propulsion mechanisms, and swarm intelligence algorithms to unlock the full potential of these tiny aerial systems. Flapping wing propulsion, inspired by nature's efficient flyers, offers a promising alternative to conventional propulsion systems. The importance of flapping wing propulsion and unsteady aerodynamics in the design of MAVs and NAVs will be discussed by underlining their potential to revolutionize the field of UAVs.

ABSTRACT. Conventional Aircraft Design for commercial airplanes as well as military fighters has heavily relied on physics based aerodynamics & structural analysis in an iterative manner since the end of World War II. Multiple successful designs including Boeing 747, and General Dynamics F-16 were designed using paper based calculations. In late 80s, with advancements in computational resources in terms of processing power, memory and sophisticated numerical analysis tools, the user requirements as well as certification requirements became more stringent. The aircraft design philosophy shifted from paper based calculations to numerical analysis tools with legacy codes for each discipline. Aerodynamic efficiencies improved with reduced weights utilising lower factors of safety improving the aircraft performance in terms of range, endurance, load carrying capacity and onboard processing power. But still the design process was confined to prototype and its testing & evaluation. Technology experts were responsible to translate this prototype into serial production models. Thousands of technologists used to work together to define the tooling required for manufacturing & assembly, process definition for each airframe part ultimately leading to manufacturing data packs for thousands of parts individually. This translation from prototype to serial production was many a times an even lengthier process than aircraft design itself resulting in design cycles spanning over multiple decades. In late 90s, industrial & system engineering experts working in conjunction with aerospace designers came up with a strategy of integrated product & process development. The focus was to reduce design cycle time with simultaneous manufacturing process definition of each part during its detailed design. A derivative of the same methodology was termed as concurrent engineering. It didn’t meet the expected promise as the predictive ability of analysis tools used in design was far lesser than experimental results. In brief it was restricted by the predictive limitations of analysis tools. Additionally, in the absence of integrated design frameworks, each design house had to come up with their indigenous solution to integrate and automate the design process which further constrained the working and search for the best possible design. Meanwhile, computational analysis tools improved their predictive ability but needed intensive processing power. Very lately, leading design integration companies like Siemens and Dassault came up with computational frameworks. These frameworks have integrated aerodynamics, structural, propulsion and RCS analysis tools capable to produce a digital twin of the actual aircraft being designed with every manufacturing process, sequence, material, tolerances prescribed as in the actual product. This has practically reduced the gap between the digital model and physical model (termed digital twins) significantly reducing the experimentation effort as well as design cycle time. Another issue in modern aircraft design is that thousands of engineers are required to work together as complexity has increased manifold and no country has that kind of qualified human resource at one site. Thus, modern aircraft design generally works in international consortiums of multiple nations with design houses at each site working in conjunction. The advantage of collaborative engineering methodology with comprehensive design frame works is that multiple teams from around the world can not only simultaneously work on the same configuration maintaining configuration control, but also making possible the remote design teams working together from different locations in international consortiums. This talk will highlight specifics of collaborative engineering & Digital twin methodology, modern aircraft design frameworks, cost and time saving in design cycles and improved life cycle costs of the modern aircraft.

ABSTRACT. In-cloud ice accretion on aircraft is a safety challenge as it may affect its external surfaces such as the aircraft frame & engine. Icing occurs when supercooled water droplets suspended in air impinge on the aircraft surface and due to heat transfer/phase change freeze from liquid to ice. In-cloud icing on aircraft can be classified into rime, glaze, and mixed ice. Mainly ice accumulates along the leading edge of the aircraft wing surface which changes its aerodynamic shape and leads to loss of aerodynamic performance, increasing the aircraft weight and fuel consumption. Sometimes ice ridges can also form along the fuselage that affects the aircraft sensors such as pitot tube. Most research work about icing on aircraft has been carried out for manned aircraft at high Reynolds numbers, but a direct transformation of these results into Unmanned Aerial Vehicle (UAV) operating at low Reynolds numbers is not straightforward. Changes in Reynolds number have a significant impact on ice accretion physics. Low Reynolds number airfoils are sensitive and even small changes on the aerodynamic surface could significantly impact its performance. Therefore, ice accretion on UAVs needs to be studied separately from manned aircraft. UAVs are more prone to icing than conventional manned aircraft for the following reasons: 1) The presence of supercooled water droplets is pervasive at low altitudes, as most UAVs fly in the lower atmosphere, they are more susceptible to ice accumulation than manned aircraft. 2) UAVs operating at lower velocities than conventional aircraft have prolonged exposure to icing environments. 3) Manned aircraft operate with ice mitigation systems, but most UAVs operate without any ice protection systems because of their weight and power constraints. 4) Composite materials used in the manufacturing of UAVs have lower thermal conductivity than the conventional metal-based airframes used for manned aircraft. Thus, the rate of heat transfer and dissipation of latent heat of fusion released during solidification is much slower for composite materials, resulting in water runback and the formation of complex rivulet-like ice structures along the UAV surface. The icing on aircraft is being studied since the 1920s, but still, the icing problem is an area of continuous research due to the complexity of the icing phenomena. The main topics of aircraft icing include ice accretion physics, ice detection, and ice mitigation. Ice accretion on manned aircraft/UAVs can be studied using four different methodologies: 1) Analytical methods 2) Field measurements 3) Lab experiments 4) Numerical simulations. Field measurements provide more reliable results but involve more technical complexities and financial aspects, whereas computational fluid dynamics-based multiphase numerical methods are cost-effective but less accurate. So, it’s important to select an appropriate hybrid approach to study the aircraft icing problem for reliable and cost-effective results. This talk will mainly focus on issues related to icing on aircraft and recent advancements in methods to study and mitigate the icing problems.

ABSTRACT. Aircraft Design is generally conducted through many reviews, Conceptual, Preliminary, and detailed to name a few. The process is multidisciplinary in nature and entails many subject areas such as aerodynamics, structures, aircraft performance, propulsion, stability, and control, to name a few. Aircraft Design is taught in many universities as a part of the Aeronautical Engineering curriculum, in the final year of their degree programme. By the start of their final year, the students have acquired basic skills in aerospace science and can embark on integrating the acquired skills. The talk will focus on Aircraft Design, underpinned by the Problem-Based Learning (PBL) methodology, a concept used to enhance multidisciplinary skills using planned project scenarios. Working in small collaborative groups students learn what they need to know in order to solve a problem. In this talk the role of PBL is examined in the art and science of Aircraft Design.

ABSTRACT. A nonlinear wing model based on a quasi-steady approach and the blade element theory is used to calculate the instantaneous aerodynamic forces for Flapping flight control simulations. Due to its robustness against external disturbances and nonlinear uncertainties, an Active Disturbance Rejection Controller (ADRC) with an Extended State Observer is utilized for the three dimensional flight simulations. By using the artificial Central Pattern Generator (CPG) models together with the ADRC, bio-inspired controller structures are obtained.

ABSTRACT. In this project, computational fluid dynamics simulations were used to investigate the effect on the aerodynamic characteristics by adding Gurney flaps with multiple heights to various cambered airfoils that are currently used in general aviation aircraft. The investigated aerodynamic characteristics were the lift and drag coefficients, lift to drag ratio, and surface pressure distribution. The investigated airfoils (in two-dimensional) were NACA 0012, NACA 2412, NACA 4412, NACA 6409, and NACA 23014. The used Gurney flaps heights were 1 and 1.5% of the chord length. The investigation was conducted using a chord Reynolds number of 2 x 10^6.

The results show that the addition of a 1% chord length Gurney flap had increased the maximum lift of all of the investigated airfoils but it also resulted in a drag penalty. However, at low to moderate angles of attack, the increase in lift outcomes the increase in drag, which as a result, increases the lift to drag ratio. Hence, improving the performance of the airfoils. The results also show that increasing the camber of the airfoil, i.e. from NACA 0012 airfoil to NACA 6409 airfoil, had decreased the percent increase in the lift to drag ratio at low to moderate angles of attack, which indicates that the Gurney flap is more effective when used in low cambered airfoils. Overall, at this Gurney flap height, the best performance improvement was observed in NACA 23014 airfoil, while NACA 6409 airfoil was the worst.

Increasing the size of the flap to 1.5% chord length led to a further increase in both maximum lift and drag penalty. However, the increase in drag was high enough to overcome the increase in lift. As such, the overall performance enhancement was lower than the overall performance enhancement of the 1% chord length Gurney flap for all of the investigated airfoils.

ABSTRACT. This paper presents the preliminary design of a drone-based wildlife monitoring and conservation system that aims to improve and enhance wildlife population monitoring and detect illegal activities in national parks across the globe. The proposed monitoring system aims to makes use of flexibility of drone-based systems to access remote locations and hazardous environments at a lower cost and overcome the limitations traditional methods such as ground surveys and manned aircraft. The system will be able to assist in monitoring wildlife populations and analysing current conservation efforts by providing trend analyses and play a very important role in identifying threats to the population and detecting illegal activities such as animal poaching and trespassing. The proposed UAV system is designed through a study of meteorological history of Koyna Wildlife Reserve in India and Sri Lanna National Park in Thailand as case studies. Results from the analysis indicate that the cost of implementation of a UAV based system would be approximately $7200 per system. The incorporation of Machine learning to streamline and enhance effectives of the UAV system has also been proposed in this paper.  

ABSTRACT. Plastic manufacturing is the process of producing polymer materials from raw substances such as thermoplastic pellets, granules, or powder, aiming to produce semi-finished products, which are used in various vital industries such as packaging, electronics, aerospace sector and bottle grade manufacturing. The current project aims to study the stages of the plastic manufacturing process using Polyethylene Terephthalate (PET) and analyze the key stage where a series of vertical rotating discs are used in polymer processing. The numerical analysis of the PET film flow on rotating discs was conducted using ANSYS software with volume of fluid (VOF) method used to predict the PET film thickness for different rotational speed of the discs. Two different molecular weights (Xn=69 & 82) of PET polymers were considered in the numerical model. The dominating factors that affect the film formation on the disc surface were investigated through numerical simulations. It was found that there is a significant increase in the film thickness with an increase in the rotating speed. Furthermore, the film thickness was higher for higher molecular weight of the PET. The rotating discs are used, during the final stage of plastic manufacturing, known as the polymerization, by an energy company (OQ company Oman) who also confirmed that the use of vertically rotating discs would be feasible instead of horizontal discs.

ABSTRACT. The benefits of morphing wings have gained importance during the last years, specially the improvements in lift and drag coefficients. An option to generate the morphing wing effect is the use shape memory materials, particularly NiTi alloys. These materials have been demonstrated to have a wide variety of applications in different fields and therefore, the validation through simulations is of vital importance for their implementation and further development.

In the present work, the modeling and simulation of a shape memory actuator for a segment of the airfoil of an unmanned aerial vehicle was developed using the finite element software ANSYS. The generation of change in airfoil morphology is due to a Nitinol wire. The implementation of this type of actuator generates significant benefits, i.e. the reduction of space, weight and energy consumption. The Auricchio model implemented by ANSYS provides the capability to simulate the shape memory effect of this type of alloys. The effect generated by the Nitinol wires was obtained and experimental results were compared with those generated by simulation. The wire displacement depends on the loads applied to the actuators and therefore, the effect in the morphing wing for the unmanned aerial vehicle, thus improving the aerodynamic properties of the aircraft.

This research work is supported by the UANL and PAICYT program.

ABSTRACT. Successful investigation and prosecution of major crimes like child pornography, insurance claims, movie piracy, traffic monitoring, and scientific fraud among others, largely depends on the availability of water-tight evidence to prove the case beyond any reasonable doubt. When the evidence required in investigating and prosecuting such crimes involves digital images/ videos, there is a need to prove without an iota of doubt the source camera/device of the image in question. Much research has been reported to address this need over the past decade. The proposed methods can be divided into brand or model-level identification or known imaging device matching techniques. This paper investigates the effectiveness of the existing image/video source camera identification techniques, which use both intrinsic hardware artifacts-based techniques like sensor pattern noise, lens optical distortion and software artifacts-based techniques like colour filter array, and auto white balancing, to determine their strengths and weaknesses. Publicly available benchmark image/video datasets and assessment criteria to quantify the performance of different methods are presented and the performance of some of the existing methods is compared. Finally, directions for further research on image source identification are given.

ABSTRACT. Human factor, also known as ergonomics, primarily focuses on interactions of people with their surroundings, tools, and technology. Human factor analysis helps humans to improve their safety and performance with reference to their involvement with the surroundings as well as tools and technologies they are operating. Human factors play a significant role in aviation safety management and helps to minimize pilot errors. This research explores the human factors with reference to Human Factors Analysis and Classification System (HFACS). The research also evaluates the inter-relationship between these factors and identify influence of these factors within each other. Finally, the research develops a framework for prediction of human factors under different categories using machine learning and artificial intelligence techniques. The research also provides directions for the future scope of research with reference to prediction of human factors using other techniques such as regression and clustering, to achieve better insights.

ABSTRACT. Based on the airline maintenance cost executive commentary FY2020 data published by International Air Transport Association (IATA), aircraft maintenance accounts for 10.3% of airline operating costs, with approximately 3.3 million US$ spent per plane in 2019. Previously, the common types of maintenance used in aviation are corrective and preventive maintenance. As with other industries, the airline industry is exploring new ways of reducing costs by improving its maintenance. Predictive maintenance has grown popularity in aerospace in the last decade due to the increasing availability of condition monitoring data for aircraft components and engines. Predictive maintenance refers to the use of data-driven, proactive maintenance methods that are designed to analyse the condition of equipment and help predict when maintenance should be performed. Using historical data, integrity factors, statistical inference methods, and engineering approach, it predicted state of the machine to be maintained. In order to incorporate maintenance strategies into the prediction process, mathematical methods need to be applied. The fast growth of computing power, data processing, and cloud storage have enabled machine learning (ML) algorithms to be used in predictive maintenance. ML is a subsection of Artificial Intelligence. An ML algorithm creates a training model, based on historical information, and then predicts equipment health, such as likelihood of failure on. Then, the model will be applied on the unseen recorded data to predict the failure of the machine. This paper studies the different algorithm of ML used in aviation industry for predictive maintenance as there are many ML algorithms available to date. Moreover, the assessment of ML is carried out by comparing the accuracy of the model in applying the test data. Lastly, the challenges such as the availability and authenticity of the data to train the model in using ML is discussed.

ABSTRACT. Foreign object damage (FOD) detection and elimination is identifying and removing any foreign objects that can pose safety hazards during ground operations. Damages are normally reported during aircraft taxing, takeoff, and landing phases. FOD can cause damage to critical aircraft components that resulting into high maintenance costs and downtime. Effective FOD management programs involves regular traditional inspections, and specialized equipment such as FOD sweepers or vacuums to ensure the safety, reliability and secure aircraft operations. An effective FOD detection and elimination practice is crucial for reducing maintenance costs, and downtime, through enhancement of the overall safer aircraft operations. This article proposes an advanced foreign object detection method and alert system based on off the shelf hardware (LiDAR, smart camera) and a newly developed software to locate and eliminate FOD. Experimental work has proven the suitability of the proposed method for detecting the location of debris of various sizes and activating the alerts using different means to the responsible personnel.

ABSTRACT. There has been a rapid growth in the use of fibre reinforced plastic (FRP) composite materials in aerospace structures in last few decades due to several associated advantages such as high strength to weight ratio, design flexibility, fatigue resilience etc. However, FRP composites are vulnerable to several manufacturing and in service defects such as delamination, voids, resin rich and deficient areas, fibre breakage, matrix cracking, fibre matrix debonding, delamination etc. which may lead to faster reduction of residual strengths. This necessitates the development of health monitoring system for early detection of such damages in FRP composites. Guided wave based technique is one of the most promising in service structural health monitoring (SHM) techniques with ability to scan a large area and sensitivity to small damages. Due to the complex interaction of guided waves with damages, detection of damages turns out to be a complex inverse problem. Apart from that location of such defects can also change the wave propagation pattern significantly. A purely physic based approach employing wave propagation theories doesn’t lend its application readily for such complex inverse problems. Machine learning based approach is a common alternative used by several researchers. This paper proposes a hybrid physic and machine learning based approach for classification and location of damages in composites. A glass-eopxy cross ply laminated plate has been considered as an example structure. Delaminations of various lengths have been considered as damages. Piezoelectric patches have been used as actuators and sensors. A plain strain finite element model has been developed in Abaqus incorporating the effect of damages and electro-elastic coupling in the piezoelectric patches. Contact nonlinearity in the surfaces of delamination has been incorporated in the model. The piezoelectric actuators are excited with tone bust signals of various frequencies for generation of guided waves for several damage cases. Symmetric and antisymmetric components of the wave propagation response captured by the sensors have been separated. The separated components have been analysed through fast Fourier transform, continuous wavelet transform, and discrete wavelet transform and damage features have been extracted. Presence of super harmonics in both symmetric and antisymmetric components generated due to contact nonlinearity at the damaged surfaces has been observed through the analysis. Identification of delaminated layers has been solved as a classification problem using probabilistic neural network (PNN) classifier. These trained PNN have been tested for several unknown damage cases and a satisfactory performance has been observed. Localisation of the delaminations has been done by analysing the time of arrival of the higher harmonics of the symmetric and antisymmetric wave components.

ABSTRACT. Bioaccumulation of the ship’s submerged region would increase the hydrodynamic volume and poses a major source of carbon emissions to the atmosphere. This accumulation of marine growth on the ship’s hull creates additional drag and demands more fuel consumption, predominantly leading to adverse effects on the marine ecosystem. Anti-fouling coatings are one of the primary method adopted for a smooth hull, however, the smoothness of the hull surface are significantly depend on the type and the chemical composition of the coatings. The present paper investigates the effect of frictional drag on a flat plate under different composition of anti-fouling coatings, and effect of various biofouling conditions on a ship’s submerged hull. The numerical analysis of anti-fouling coatings on the flat plate is conducted using CFD for seven cases, viz., smooth, sandpaper and five anti-fouling cases. However, six conditions of biofouling are considered on the selected ship hull - smooth and five biofouling cases. To regenerate the appropriate roughness factor, the Colebrook-type roughness functions are used from the literature. The effect of antifouling coatings and biofouling on the flat plate and ship’s hull is predicted and analyzed for various speeds of operation. The results from this work would assist to predict the remaining life and appropriate docking period, selecting the suitable antifouling coatings. This prediction would help in optimizing the fuel consumption with new CII requirements.

ABSTRACT. In recent years, additive manufacturing has grown in popularity as a method for generating high-precision parts with complicated geometries. SLM (selective laser melting) is a common additive manufacturing technology for producing metallic items such as 316L stainless steel. However, surface roughness and internal defects in SLM-produced objects might weaken their mechanical properties. Post-processing methods have been developed to improve the surface roughness and mechanical properties of 316L stainless steel parts manufactured using SLM. The purpose of this article is to conduct an extensive overview of the present state of research on the influence of post-processing techniques on the surface roughness and mechanical properties of 316L stainless steel produced using SLM. The article will begin by going over the fundamentals of SLM technology as well as the properties of 316L stainless steel, including its microstructure and mechanical properties. then the current trend on the influence of various post-processing techniques on the surface roughness and mechanical properties of 316L stainless steel manufactured using SLM will be discussed Laser polishing is one of the primary post-processing techniques discussed in the literature. Laser polishing has been proven to increase the surface roughness and corrosion resistance of 316L stainless steel manufactured by SLM. However, several characteristics like laser power, scanning speed, and scanning pattern influence the effectiveness of laser polishing. Heat treatment is another major post-processing technique that has been investigated in the literature. It has been demonstrated that heat treatment improves the mechanical properties of SLM-produced 316L stainless steel, in particular its ductility and toughness. The ideal heat treatment conditions, on the other hand, are determined by the individual SLM process parameters and the desired mechanical properties. Overall, most of the research concluded that post-processing operation can improve the surface roughness, corrosion resistance, and mechanical characteristics of 316L stainless steel produced by SLM. However, based on the distinctive application requirements, the selection of post-processing techniques should be thoroughly evaluated. This paper provides insights into the present state of research in the field of SLM-produced 316L stainless steel and emphasizes future research prospects.

ABSTRACT. Aviation fuels have traditionally been manufactured using chemicals produced from petroleum. An alternative approach is to use microorganisms as biocatalysts to produce biofuels. These biofuels can either be blended with petroleum-derived fuels or can entirely replace them. Potentially this offers a route to replace petroleum-derived fuels by sustainable fuels obtained from renewable carbon sources, i.e. biomass.

One of the main bottlenecks in the microbial synthesis of sustainable aviation fuels is that the chemicals produced are frequently toxic to the cells; this severely limits their production. Consequently, considerable attention has been paid to the production of genetic engineered microorganisms with increased resistance to biofuel toxicity.

An alternative approach to the manufacture of biofuels is to redesign the bioreactors that are used in their production. We extend a standard bioreactor model to include extraction of the biofuel from the reactor through a membrane. This reduces the concentration of the product within the bioreactor. We investigate how this technology mitigates the adverse effects of end-product toxicity.

ABSTRACT. The monitoring of tightening efforts on bolted joints with the existing means is tedious and its accuracy is difficult to control (torque wrench, dial gauge, hydraulic tightening).Knowing the accurate bolt tightening stresses becomes essential in sensitive areas such as nuclear, petrochemistry, military and maritime sectors. It is in this context that Apave Company proposes a suitable approach for the monitoring of tightening efforts by means of ultrasonic waves. This approach has proven its worth since 2013 by solving many industrial issues of Apave partners in nuclear field and heavy mechanics. The monitoring method is based on the accurate analysis of the time of flight of ultrasonic waves. It leads to the knowledge of the stress value in each bolt, with high accuracy compared to the existing tightening means.

Keywords: The monitoring of tightening efforts on bolted joints with the existing means is tedious and its accuracy is difficult to control (torque wrench, dial gauge, hydraulic tightening).Knowing the accurate bolt tightening stresses becomes essential in sensitive areas such as nuclear, petrochemistry, military and maritime sectors. It is in this context that Apave Company proposes a suitable approach for the monitoring of tightening efforts by means of ultrasonic waves. This approach has proven its worth since 2013 by solving many industrial issues of Apave partners in nuclear field and heavy mechanics. The monitoring method is based on the accurate analysis of the time of flight of ultrasonic waves. It leads to the knowledge of the stress value in each bolt, with high accuracy compared to the existing tightening means.