Structural dynamics branch research and accomplishments to FY 1992

This publication contains a collection of fiscal year 1992 research highlights from the Structural Dynamics Branch at NASA LeRC. Highlights from the branch's major work areas--Aeroelasticity, Vibration Control, Dynamic Systems, and Computational Structural Methods are included in the report as well as a listing of the fiscal year 1992 branch publications

Model-aware Deep Learning Method for Raman Amplification in Few-Mode Fibers

One of the most promising solutions to overcome the capacity limit of current optical fiber links is space-division multiplexing, which allows the transmission on various cores of multi-core fibers or modes of few-mode fibers. In order to realize such systems, suitable optical fiber amplifiers must be designed. In single mode fibers, Raman amplification has shown significant advantages over doped fiber amplifiers due to its low-noise and spectral flexibility. For these reasons, its use in next-generation space-division multiplexing transmission systems is being studied extensively. In this work, we propose a deep learning method that uses automatic differentiation to embed a complete few-mode Raman amplification model in the training process of a neural network to identify the optimal pump wavelengths and power allocation scheme to design both flat and tilted gain profiles. Compared to other machine learning methods, the proposed technique allows to train the neural network on ideal gain profiles, removing the need to compute a dataset that accurately covers the space of Raman gains we are interested in. The ability to directly target a selected region of the space of possible gains allows the method to be easily generalized to any type of Raman gain profiles, while also being more robust when increasing the number of pumps, modes, and the amplification bandwidth. This approach is tested on a 70 km long 4-mode fiber transmitting over the C+L band with various numbers of Raman pumps in the counter-propagating scheme, targeting gain profiles with an average gain in the interval from 5 dB to 15 dB and total tilt in the interval from 1.425 dB to 1.425 dB. We achieve wavelengthand mode-dependent gain fluctuations lower than 0.04 dB and 0.02 dB per dB of gain, respectively

Physics-data-driven intelligent optimization for large-scale meta-devices

Meta-devices have gained significant attention and have been widely utilized in optical systems for focusing and imaging, owing to their lightweight, high-integration, and exceptional-flexibility capabilities. However, based on the assumption of local phase approximation, traditional design method neglect the local lattice coupling effect between adjacent meta-atoms, thus harming the practical performance of meta-devices. Using physics-driven or data-driven optimization algorithms can effectively solve the aforementioned problems. Nevertheless, both of the methods either involve considerable time costs or require a substantial amount of data sets. Here, we propose a physics-data-driven approach based "intelligent optimizer" that enables us to adaptively modify the sizes of the studied meta-atom according to the sizes of its surrounding ones. Such a scheme allows to mitigate the undesired local lattice coupling effect, and the proposed network model works well on thousands of datasets with a validation loss of 3*10-3. Experimental results show that the 1-mm-diameter metalens designed with the "intelligent optimizer" possesses a relative focusing efficiency of 93.4% (as compared to ideal focusing) and a Strehl ratio of 0.94. In contrast to the previous inverse design method, our method significantly boosts designing efficiency with five orders of magnitude reduction in time. Our design approach may sets a new paradigm for devising large-scale meta-devices.Comment: manuscripts:19 pages, 4 figures; Supplementary Information: 11 pages, 12 figure

Plasmonic transmission lines: neural networks modeling and applications

In this thesis, a new model based on Artificial Neural Network (ANN) is used to predict the propagation characteristics of plasmonic nanostrip and coupled nanostrips transmission lines. The trained ANNs are capable of providing the required outputs with good accuracy. The nonlinear mapping performed by the trained ANN is written in the form of closed form expressions for the different characteristics of the lines under investigation. These characteristics include the effective refractive index and the characteristic impedance. The plasmonic coupled nanostrips transmission line is used as a new sensor that that senses variation in the refractive index with accuracy of 106μm (The accuracy is defined as the change in the coupling length divided by the change in the cladding material refractive index). In addition, an optimal new design for polarization rotation based on the coupled nanostrips is introduced and characterized

Advanced DSP Techniques for High-Capacity and Energy-Efficient Optical Fiber Communications

The rapid proliferation of the Internet has been driving communication networks closer and closer to their limits, while available bandwidth is disappearing due to an ever-increasing network load. Over the past decade, optical fiber communication technology has increased per fiber data rate from 10 Tb/s to exceeding 10 Pb/s. The major explosion came after the maturity of coherent detection and advanced digital signal processing (DSP). DSP has played a critical role in accommodating channel impairments mitigation, enabling advanced modulation formats for spectral efficiency transmission and realizing flexible bandwidth. This book aims to explore novel, advanced DSP techniques to enable multi-Tb/s/channel optical transmission to address pressing bandwidth and power-efficiency demands. It provides state-of-the-art advances and future perspectives of DSP as well

ANN tool for impact detection on composite panel for aerospace application

Fleet maintenance and safety aspects represent a strategic aspect in the managing of the modern aircraft fleets. The demand for efficient techniques of system and structure’s monitoring represent so a key aspect in the design of new generation aircraft. This is even more significant for composite structures that can be highly susceptible to delamination of the ply, which is often very difficult to detect externally and can lead to a dramatic reduction of design strength and service life, as a consequence of impact damage. The purpose of the work is the presentation of an innovative application within the Non Destructive Testing field based upon vibration measurements. The aim of the research has been the development of a Non Destructive Test (NDT) which meets most of the mandatory requirements for effective health monitoring systems while, at the same time, reducing as much as possible the complexity of the data analysis algorithm and the experimental acquisition instrumentation

An Artificial Neural Network based approach for impact detection on composite panel for aerospace application

Fleet maintenance and safety aspects represent a strategic aspect in the managing of the modern aircraft fleets. The demand for efficient techniques of system and structure’s monitoring represent so a key aspect in the design of new generation aircraft. This is even more significant for composite structures that can be highly susceptible to delamination of the ply, which is often very difficult to detect externally and can lead to a dramatic reduction of design strength and service life, as a consequence of impact damage. The purpose of the work is the presentation of an innovative application within the Non Destructive Testing field based upon vibration measurements. The aim of the research has been the development of a Non Destructive Test (NDT) which meets most of the mandatory requirements for effective health monitoring systems while, at the same time, reducing as much as possible the complexity of the data analysis algorithm and the experimental acquisition instrumentation