M. M. Reda Taha

Wavelet Transform for Structural Health Monitoring: A Compendium of Uses and Features:

The strategic and monetary value of the civil infrastructure worldwide necessitates the development of structural health monitoring (SHM) systems that can accurately monitor structural response due to real-time loading conditions, detect damage in the structure, and report the location and nature of this damage. In the last decade, extensive research has been carried out for developing vibration-based damage detection algorithms that can relate structural dynamics changes to damage occurrence in a structure. In the mean time, the wavelet transform (WT), a signal processing technique based on a windowing approach of dilated ‘scaled’ and shifted wavelets, is being applied to a broad range of engineering applications. Wavelet transform has proven its ability to overcome many of the limitations of the widely used Fourier transform (FT); hence, it has gained popularity as an efficient means of signal processing in SHM systems. This increasing interest in WT for SHM in diverse applications motivates the authors to write an exposition on the current WT technologies. This article presents a utilitarian view of WT and its technologies. By reviewing the state-of-the-art in WT for SHM, the article discusses specific needs of SHM addressed by WT, classifies WT for damage detection into various fields, and describes features unique to WT that lends itself to SHM. The ultimate intent of this article is to provide the readers with a background on the various aspects of WT that might appeal to their need and sector of interest in SHM. Additionally, the comprehensive literature review that comprises this study will provide the interested reader a focused search to investigate using wavelets in SHM.

Application of photonic crystals in submicron damage detection and quantification

We propose the use of photonic crystals (PC) for submicron damage detection and quantification. The idea is based on the inherent tie between PC topology and its corresponding spectral frequency response. We demonstrate using a simulation model that a PC sensor attached to a polymer substrate will experience significant changes in its band gap profile when microdamage is induced in the substrate. A damage metric, developed using principles of fuzzy pattern recognition, is used to quantify the change in the spectral response in relation to the level of induced damage. Finally, different damage scenarios demonstrating coincidence with results are examined and reported.

Hybrid Carbon Fibers/Carbon Nanotubes Structures for Next Generation Polymeric Composites

Pitch-based carbon fibers are commonly used to produce polymeric carbon fiber structural composites. Several investigations have reported different methods for dispersing and subsequently aligning carbon nanotubes (CNTs) as a filler to reinforce polymer matrix. The significant difficulty in dispersing CNTs suggested the controlled-growth of CNTs on surfaces where they are needed. Here we compare between two techniques for depositing the catalyst iron used toward growing CNTs on pitch-based carbon fiber surfaces. Electrochemical deposition of iron using pulse voltametry is compared to DC magnetron iron sputtering. Carbon nanostructures growth was performed using a thermal CVD system. Characterization for comparison between both techniques was compared via SEM, TEM, and Raman spectroscopy analysis. It is shown that while both techniques were successful to grow CNTs on the carbon fiber surfaces, iron sputtering technique was capable of producing more uniform distribution of iron catalyst and thus multiwall carbon nanotubes (MWCNTs) compared to MWCNTs grown using the electrochemical deposition of iron.

Photonic Crystal High-Efficiency Multispectral Thermal Emitters.

We demonstrate through numerical simulation the modification of the thermal emission spectrum by a metallic photonic crystal (PhC). Here the radiation is funneled into a narrow emission band in contrast to the broad spectrum associated with a Planckian-distribution. A detailed quantitative evaluation of the spectral, power, and angular efficiencies of a PhC thermal emitter and its portability across IR spectral bands is provided. We show that an optimized tungsten PhC possesses a predominant narrow-band forward emission profile with an emitter efficiency that is more than double that of an ideal blackbody and ∼65–75% more power-efficient across the IR spectrum.

Fuzzy Learning of Talbot Effect Guides Optimal Mask Design for Proximity Field Nanopatterning Lithography

Processing methods used in photonics and nanotechnology possess many limitations restricting their application areas such as high cost, inability to produce fine details, problems with scalability, and long processing time. Proximity field nanopatterning is a lithography method which surpasses these limitations. By using interference patterns produced by a two-dimensional phase mask, the technique is able to generate a submicron detailed exposure on a millimeter-size slab of light sensitive photopolymer, which is then developed like a photographic plate to reveal three-dimensional interference patterns from the phase mask. While it is possible to use simulations to obtain the interference patterns produced by a phase mask, realizing the mask dimensions necessary for producing a desired interference pattern is analytically challenging due to the intricacies of light interactions involved in producing the final interference pattern. An alternative method is to iteratively optimize the phase mask until the interference patterns obtained converge to the desired pattern. However, depending on the optimization technique used, one either risks a significant probability of failure or requires a prohibitive number of iterations. We argue that an optimization technique that is to take advantage of the physics of the problem using machine learning methods (here fuzzy learning) can lead to competent mask design. This technique is described in this letter.

High-efficiency photonic crystal narrowband thermal emitters

Photonic crystals (PhC) are artificial structures fabricated with a periodicity in the dielectric function. This periodic electromagnetic potential results in creation of energy bandgaps where photon propagation is prohibited. PhC structures have promising use in thermal applications if optimized to operate at specific thermal emission spectrum. Here, novel utilization of optimized PhCs in thermal applications is presented. We demonstrate through numerical simulation the modification of the thermal emission spectrum by a metallic photonic crystal (PhC) to create high-efficiency multispectral thermal emitters. These emitters funnel radiation from a broad emission spectrum associated with a Plancklike distribution into a prescribed narrow emission band. A detailed quantitative evaluation of the spectral and power efficiencies of a PhC thermal emitter and its portability across infrared (IR) spectral bands are provided. We show an optimized tungsten PhC with a predominant narrow-band emission profile with an emitter efficiency that is more than double that of an ideal blackbody and ~65-75% more power-efficiency across the IR spectrum. We also report on using optimal three-dimensional Lincoln log photonic crystal (LL-PhC) emitters for thermophotovoltaic (TPV) generation as opposed to using a passive filtering approach to truncate the broadband thermal source emission to match the bandgap of a photovoltaic (PV) cell. The emitter performance is optimized for the 1-2μm PV band using different PhC materials, specifically copper, silver and gold. The use of the proposed PhC in TPV devices can produce significant energy savings not reported before. The optimal design of the PhC geometry is obtained by implementing a variety of optimization methods integrated with artificial intelligence (AI) algorithms.

Hybrid genetic optimization for design of photonic crystal emitters

A unique hybrid-optimization technique is proposed, based on genetic algorithms (GA) and gradient descent (GD) methods, for the smart design of photonic crystal (PhC) emitters. The photonic simulation is described and the granularity of photonic crystal dimensions is considered. An innovative sliding-window method for performing local heuristic search is demonstrated. Finally, the application of the proposed method on two case studies for the design of a multi-pixel photonic crystal emitter and the design of thermal emitter in thermal photovoltaic is demonstrated. Discussion in the report includes the ability of the optimal PhC structures designed using the proposed method, to produce unprecedented high emission efficiencies of 54.5% in a significantly long wavelength region and 84.9% at significantly short wavelength region.

Health monitoring of 90 bolted joints using fuzzy pattern recognition of ultrasonic signals

Bolted joints are important parts for aerospace structures. However, there is a significant risk associated with assembling bolted joints due to potential human error during the assembly process. Such errors are expensive to find and correct if exposed during environmental testing, yet checking the integrity of individual fasteners after assembly would be a time consuming task. Recent advances in structural health monitoring (SHM) can provide techniques to not only automate this process but also make it reliable. This integrity monitoring requires damage features to be related to physical conditions representing the structural integrity of bolted joints.In this paper an SHM technique using ultrasonic signals and fuzzy pattern recognition to monitor the integrity of 90° bolted joints in aerospace structures is described. The proposed technique is based on normalized fast Fourier transform (NFFT) of transmitted signals and fuzzy pattern recognition. Moreover, experimental observations of a case study on an aluminum 90° bolted joint are presented. We demonstrate the ability of the proposed method to efficiently monitor and indicate bolted joint integrity.

Effect of Carbon Nanotube Growth Conditions on Strength and Stiffness of Carbon and Glass Fiber Polymer Composites

Carbon nanotubes (CNTs) and Carbon nanofilaments (CNFs) were grown on the surface of carbon and glass sheets from fuel rich ethylene/oxygen combustion mixtures on certain catalytic metals. Employing a well-known catalyst deposition method, incipient wetness allowed growing CNTs/CNFs on the surface of carbon and fiber glass bidirectional sheets. Two-layer fiber reinforced polymer (FRP) composite plates were fabricated using a vacuum assisted hand lay-up technique of the carbon and glass fiber sheets after CNTs/CNFs were grown using 1.0% Nickel deposits. In this paper we report on the growth process and we examine the significance of the CNTs/CNFs growth conditions including the sizing burning temperature (250 and 500 oC), the growth time period and the fiber base type (carbon or glass) on the strength and stiffness of this new multi-scale FRP composite. The ultimate tensile strength, tensile modulus (stiffness) and ultimate strain at failure was determined using ASTM D3039 test. It is shown that the sizing burning temperature has a significant effect on the strength and strain at failure of the new carbon fiber reinforced polymer (CFRP) and glass fiber reinforced polymer (GFRP) composites. Little effect on the composite stiffness was observed. Microstructural investigations of the failed specimens shed light on the fracture surface of the multi-scale CFRP and GFRP composites.

Enhancing uncertainty tolerance in modelling creep of ligaments.

The difficulty in performing biomechanical tests and the scarcity of biomechanical experimental databases necessitate extending the current knowledge base to allow efficient modelling using limited data sets. This study suggests a framework to reduce uncertainties in biomechanical systems using limited data sets. The study also shows how sparse data and epistemic input can be exploited using fuzzy logic to represent biomechanical relations. An example application to model collagen fibre recruitment in the medial collateral ligaments during time-dependent deformation under cyclic loading (creep) is presented. The study suggests a quality metric that can be employed to observe and enhance uncertainty tolerance in the modelling process.