An Ameliorated Prediction of the Empennage In-Flight Gust Loads for a General Aviation Aircraft

Airplanes often operate beyond their original design load profiles. Aviation opens so many new possibilities and wide variety of possible activities that it is hard for designers to foresee all loading spectra that the airplane structure will experience throughout its life. Nevertheless, the aircraft structures are required to be designed to failsafe or safe-life criteria to be certified by the FAA. AFS-120 provides a database of normal accelerations that can be used to derive airplane wing loads. ACE-100 describes an acceptable method for determining the fatigue life of an empennage based on the same normal acceleration data provided in AFS-120. However, this data have not been demonstrated to be applicable for empennage loads. Earlier works have shown that maneuver induced-loads on the empennage can be predicted from motion parameters measured near the airplane center of gravity. Maneuver loads are pilot induced and do not account for weather related loads. During flight, the airplane is subjected to atmospheric turbulence and a method for determining empennage gust loads is desired. Embry-Riddle, with financial support from the FAA, has flight-tested a C-172P equipped with sensors to develop an ameliorated prediction of the empennage in-flight gust loads for a general aviation aircraft using Neural Networks. Both the power spectral density and the FAA \u27two-second\u27 methods have been applied to separate maneuvers and gusts. Findings were unexpected in that for this airplane, aircraft rotational motion appears to dampen empennage gust loads considerably and for the conditions tested, gust loads were not as significant as maneuver loads

Deterministic Artificial Intelligence

Kirchhoff’s laws give a mathematical description of electromechanics. Similarly, translational motion mechanics obey Newton’s laws, while rotational motion mechanics comply with Euler’s moment equations, a set of three nonlinear, coupled differential equations. Nonlinearities complicate the mathematical treatment of the seemingly simple action of rotating, and these complications lead to a robust lineage of research culminating here with a text on the ability to make rigid bodies in rotation become self-aware, and even learn. This book is meant for basic scientifically inclined readers commencing with a first chapter on the basics of stochastic artificial intelligence to bridge readers to very advanced topics of deterministic artificial intelligence, espoused in the book with applications to both electromechanics (e.g. the forced van der Pol equation) and also motion mechanics (i.e. Euler’s moment equations). The reader will learn how to bestow self-awareness and express optimal learning methods for the self-aware object (e.g. robot) that require no tuning and no interaction with humans for autonomous operation. The topics learned from reading this text will prepare students and faculty to investigate interesting problems of mechanics. It is the fondest hope of the editor and authors that readers enjoy the book

Damage Assessment and Strength Predictions in S-Glass/Epoxy Laminates Subjected to Low Energy Impact

Composite materials have become one of the leading materials for manufacturing in the aerospace industry today. Compared to conventional aerospace metals, composites generally have higher strength-to-weight and stiffness-to-weight ratios, good fatigue and corrosion resistance and reduced parts count. However, like any other material, they also have disadvantages. They are inherently brittle and are thus prone to impact damage. Low energy/velocity impact damage, in particular, can be dangerous because the damage oftentimes goes undetected and can subsequently grow under load. Also known as barely visible impact damage (BVID), this area of concentration focuses on the small-scale damage that may be very difficult to detect yet can be lethal. The primary emphasis of this research is to predict the residual compressive strength of a 16-ply laminate [(0°/±45°/90°)2]s after experiencing low energy/velocity impact using combined technical approaches of ultrasonic C-scan and neural networks. To accomplish this, each test specimen was ultrasonically C-scanned after impact testing. A MATLAB computer program was then used to convert the image files into numeric data, which they were presented to a backpropagation neural network in order to predict the residual compressive strength. Microsoft Excel was used to take the average of the diagonal values of the normalized image data. Here the average prediction error turned out to be 3.9 percent, while the worst-case prediction error was 14.6 percent. This research also focused on identifying, sorting, and classifying how the composite laminates failed under compression after experiencing low energy/low velocity impact. Acoustic emission (AE) parameter data were collected during compression testing, and then inputted into an artificial neural network (ANN) for classification. Specifically a Kohonen Self Organizing Map (SOM) was used to sort and classify the failure mechanisms that occurred within the weakened composites. The associated BVID failure modes, otherwise known as failure mechanisms, were believed to consist primarily of transverse and longitudinal matrix cracks, delaminations, and occasionally fiber breaks. Even though delaminations are the most critical failure modes in BVID under compression, the other failure mechanisms also contribute significantly. Furthermore, it appeared that it was also possible to sort out and determine the transition regions between BVID and visible impact damage (VID) with AE data. Thus, it is important to know how the material fails so that necessary precautions can be taken to minimize these critical failure modes

Doctor of Philosophy

dissertationAntenna design and reduction of losses in antenna systems are critical for modern communications systems. Two categories of antennas suffer from limited power supply and difficult operating environments: implantable antennas and antennas for spacecraft applications. Minimizing and controlling losses in these two antenna types is critical for developing next-generation implantable devices, spacecraft, and satellites. Research suggests that future tattoo antennas will be made from low-conductivity ink utilizing the natural insulating property of the body's fat and lossy ground plane of muscle. This paper supports tattoo antenna work by: (1) demonstrating the insulating properties of fat and conductivity of muscle with various antenna systems, (2) showing the effect of biological materials on the current distribution of subdermal antennas, and (3) validating the use of lower-conductivity materials in subdermal antenna design including a novel gold nanoparticle material. Simulations and measurements are used to evaluate current distributions shared between solid, segmented, and meshed strip dipole antennas and surrounding body tissues. Fat insulates the antenna similar to a thin layer of plastic wrap. Muscle acts as a conductive ground plane. Dipole antennas with mesh or gap structures are more strongly coupled to body tissues than solid antennas. A minimum acceptable conductivity benchmark of 105 S/m is established for dipole antennas and Radio-Frequency Identification (RFID) antennas. This work also provides novel information on the design of low-cost, circularly polarized (CP), Ka-band (26 GHz), millimeter-wave, 50 Ω edge-fed, corners truncated patch antennas on RT/duroid 5880 (εr = 2.2, ½ oz. copper cladding). Microstrip feed width, axial ratio (AR) bandwidth, and best AR at 26 GHz are optimized by the use of 10 mil substrate. The effects of corner truncation are further investigated, showing that increasing corner truncation increases AR bandwidth, increases percent offset between best S11 and AR frequencies, and worsens the best AR. A truncation of 0.57 mm is a good compromise between these effects with AR bandwidth of 6.17 % (measured) and 1.37 % (simulated). Increasing ratio of substrate thickness to design frequency, t / λd, improves AR bandwidth. For t / λd below a certain threshold a corners truncated patch antenna will not produce CP. A new nearly-square, corners truncated patch antenna is measured and simulated as a method of increasing circular polarization bandwidth (CPBW)

Automated visual inspection for the quality control of pad printing

Pad printing is used to decorate consumer goods largely because of its unique ability to apply graphics to doubly curved surfaces. The Intelpadrint project was conceived to develop a better understanding of the process and new printing pads, inks and printers. The thesis deals primarily with the research of a printer control system including machine vision. At present printing is manually controlled. Operator knowledge was gathered for use by an expert system to control the process. A novel local corner- matching algorithm was conceived to effect image segmentation, and neuro-fuzzy techniques were used to recognise patterns in printing errors. Non-linear Finite Element Analysis of the rubber printing-pad led to a method for pre-distorting artwork so that it would print undistorted on a curved product. A flexible, more automated printer was developed that achieves a higher printing rate. Ultraviolet-cured inks with improved printability were developed. The image normalisation/ error-signalling stage in inspection was proven in isolation, as was the pattern recognition system

Application of probabilistic deep learning models to simulate thermal power plant processes

Deep learning has gained traction in thermal engineering due to its applications to process simulations, the deeper insights it can provide and its abilities to circumvent the shortcomings of classic thermodynamic simulation approaches by capturing complex inter-dependencies. This works sets out to apply probabilistic deep learning to power plant operations using historic plant data. The first study presented, entails the development of a steady-state mixture density network (MDN) capable of predicting effective heat transfer coefficients (HTC) for the various heat exchanger components inside a utility scale boiler. Selected directly controllable input features, including the excess air ratio, steam temperatures, flow rates and pressures are used to predict the HTCs. In the second case study, an encoder-decoder mixturedensity network (MDN) is developed using recurrent neural networks (RNN) for the prediction of utility-scale air-cooled condenser (ACC) backpressure. The effects of ambient conditions and plant operating parameters, such as extraction flow rate, on ACC performance is investigated. In both case studies, hyperparameter searches are done to determine the best performing architectures for these models. Comparisons are drawn between the MDN model versus standard model architecture in both case studies. The HTC predictor model achieved 90% accuracy which equates to an average error of 4.89 W m2K across all heat exchangers. The resultant time-series ACC model achieved an average error of 3.14 kPa, which translate into a model accuracy of 82%