Bond graph based sensitivity and uncertainty analysis modelling for micro-scale multiphysics robust engineering design

Components within micro-scale engineering systems are often at the limits of commercial miniaturization and this can cause unexpected behavior and variation in performance. As such, modelling and analysis of system robustness plays an important role in product development. Here schematic bond graphs are used as a front end in a sensitivity analysis based strategy for modelling robustness in multiphysics micro-scale engineering systems. As an example, the analysis is applied to a behind-the-ear (BTE) hearing aid. By using bond graphs to model power flow through components within different physical domains of the hearing aid, a set of differential equations to describe the system dynamics is collated. Based on these equations, sensitivity analysis calculations are used to approximately model the nature and the sources of output uncertainty during system operation. These calculations represent a robustness evaluation of the current hearing aid design and offer a means of identifying potential for improved designs of multiphysics systems by way of key parameter identification

SOLID-SHELL FINITE ELEMENT MODELS FOR EXPLICIT SIMULATIONS OF CRACK PROPAGATION IN THIN STRUCTURES

Crack propagation in thin shell structures due to cutting is conveniently simulated using explicit finite element approaches, in view of the high nonlinearity of the problem. Solidshell elements are usually preferred for the discretization in the presence of complex material behavior and degradation phenomena such as delamination, since they allow for a correct representation of the thickness geometry. However, in solid-shell elements the small thickness leads to a very high maximum eigenfrequency, which imply very small stable time-steps. A new selective mass scaling technique is proposed to increase the time-step size without affecting accuracy. New ”directional” cohesive interface elements are used in conjunction with selective mass scaling to account for the interaction with a sharp blade in cutting processes of thin ductile shells

Vision 2040: A Roadmap for Integrated, Multiscale Modeling and Simulation of Materials and Systems

Over the last few decades, advances in high-performance computing, new materials characterization methods, and, more recently, an emphasis on integrated computational materials engineering (ICME) and additive manufacturing have been a catalyst for multiscale modeling and simulation-based design of materials and structures in the aerospace industry. While these advances have driven significant progress in the development of aerospace components and systems, that progress has been limited by persistent technology and infrastructure challenges that must be overcome to realize the full potential of integrated materials and systems design and simulation modeling throughout the supply chain. As a result, NASA's Transformational Tools and Technology (TTT) Project sponsored a study (performed by a diverse team led by Pratt & Whitney) to define the potential 25-year future state required for integrated multiscale modeling of materials and systems (e.g., load-bearing structures) to accelerate the pace and reduce the expense of innovation in future aerospace and aeronautical systems. This report describes the findings of this 2040 Vision study (e.g., the 2040 vision state; the required interdependent core technical work areas, Key Element (KE); identified gaps and actions to close those gaps; and major recommendations) which constitutes a community consensus document as it is a result of over 450 professionals input obtain via: 1) four society workshops (AIAA, NAFEMS, and two TMS), 2) community-wide survey, and 3) the establishment of 9 expert panels (one per KE) consisting on average of 10 non-team members from academia, government and industry to review, update content, and prioritize gaps and actions. The study envisions the development of a cyber-physical-social ecosystem comprised of experimentally verified and validated computational models, tools, and techniques, along with the associated digital tapestry, that impacts the entire supply chain to enable cost-effective, rapid, and revolutionary design of fit-for-purpose materials, components, and systems. Although the vision focused on aeronautics and space applications, it is believed that other engineering communities (e.g., automotive, biomedical, etc.) can benefit as well from the proposed framework with only minor modifications. Finally, it is TTT's hope and desire that this vision provides the strategic guidance to both public and private research and development decision makers to make the proposed 2040 vision state a reality and thereby provide a significant advancement in the United States global competitiveness

Polycrystalline Silicon Capacitive MEMS Strain Sensor for Structural Health Monitoring of Wind Turbines

Wind energy is a fast-growing sustainable energy technology and driven by the need for more efficient energy harvesting, size of the wind turbines has increased over the years for both off-shore and land-based installations. Therefore, structural health monitoring and maintenance of such turbine structures have become critical and challenging. In order to keep the number of physical inspections to minimum without increasing the risk of structural failure, a precise and reliable remote monitoring system for damage identification is necessary. Condition-based maintenance which significantly improves safety compared to periodic visual inspections, necessitates a method to determine the condition of machines while in operation and involves the observation of the system by sampling dynamic response measurements from a group of sensors and the analysis of the data to determine the current state of system health. This goal is being pursued in this thesis through the development of reliable sensors, and reliable damage detection algorithms. Blade strain is the most important quantities to judge the health of wind turbine structure. Sensing high stress fields or early detection of cracks in blades bring safety and saving in rehabilitation costs. Therefore, high performance strain measurement system, consisting of sensors and interface electronics, is highly desirable and the best choice. It has been revealed that the conventional strain gauge techniques exhibit significant errors and uncertainties when applied to composite materials of wind turbine blades. Micro-electro-mechanical system (MEMS) based sensors are very attractive among other sensing techniques owing to high sensitivity, low noise, better scaling characteristics, low cost and higher potential for integration with low power CMOS circuits. MEMS sensors that are fabricated on a chip can be either bonded to the surface of wind turbine blade or embedded into the fiber reinforced composite. Therefore, MEMS technology is selected to fabricate the strain sensor in this work. Two new sensor structures that can be used for strain measurement are designed. While the proposed sensors focus on high sensitivity, they are based on simple operating principle of comb-drive differential variable capacitances and chevron displacement amplification. Device performances are validated both by analytical solutions and finite element method simulations. The transmission of strain fields in adhesively bonded strain sensors is also studied. In strain sensors that are attached to host structures using adhesive layers such as epoxy, complete strain transfer to the sensor is hindered due to the influence of the adhesive layer on the transfer. An analytical model, validated by finite element method simulation, to provide insight and accurate formulation for strain transfer mechanism for bonded sensors is developed. The model is capable of predicting the strain transmission ratio through a sensor gauge factor, and it clearly establishes the effects of the flexibility, length, and thickness of the adhesive layer and sensor substrate. Several fabrication steps were required to realize the MEMS capacitive strain sensor in our lab. Polycrystalline silicon is selected as the structural layer and silicon nitride as the sacrificial layer. Polysilicon is deposited using LPCVD and SiN is deposited by PECVD in our lab. A comprehensive material study of silicon nitride and polycrystalline silicon layers is therefore performed. The whole fabrication process involves deposition, etching, and photolithography of five material layers. Although this process is developed to realize the MEMS strain sensors, it is also able to fabricate other designs of surface micromachining structures as well. The fabricated MEMS capacitive strain sensors are tested on a test fixture setup. The measurement setup is created under the probe station by using a cantilever beam fixed on one side and free on other side where a micrometer applies accurate displacement. The displacement creates bending stress on the beam which transfers to the MEMS sensor through the adhesive bond. Measurement results are in a good match with the simulation results. Finally, a real-time non-destructive health monitoring technique based on multi-sensor data fusion is proposed. The objective is to evaluate the feasibility of the proposed method to identify and localize damages in wind turbine blades. The structural properties of turbine blade before and after damage are investigated and based on the obtained results, it is shown that information from smart sensors, measuring strains and vibrations, distributed over the turbine blades can be used to assist in more accurate damage detection and overall understanding of the health condition of blades. Data fusion technique is proposed to combine the diagnostic tools to improve the detection system with providing a more robust reading and fewer false alarms

Numerical modelling of the dynamics of chlorinated solvent pollution in aquifers and their remediation with engineered nano-particles: An integrated approach

[EN] The global water shortage is one of the main environmental concerns in the 21st century. The main source of drinking water is the groundwater that flows in the subsurface. The increased agriculture and industrial activities in the last few decades have been proven to be detrimental for groundwater. While these water resources are limited, the scarcity is further triggered by the loss of quality due to anthropogenic activities such as waste deposition and landfill leakage. Contaminants from the anthropogenic waste often migrates through the sub-surface and reach an underlying aquifer. The occurrence of these contaminants threatens the quality of water resources and often requires remediation efforts. Several in-situ and ex-situ remediation methodologies have been developed and tested in the last decades; recently, the use of Engineered Nano-Particles (ENPs) for in-situ contaminant degradation have gained a lot of interest in the field of groundwater remediation. These ENPs have been found to be effective due to their high reactive surface area, minimal disruption of the groundwater system and their aggressive contaminant degradation capabilities. However, the field scale implementation of this remediation technique is often challenging, as each polluted site require a custom design and strategy of remediation. The field scale remediation of groundwater using ENPs requires a lot of scientific investigation and technical resources, owing to complexity and the limited accessibility of the contamination- groundwater system. Therefore, it is necessary to develop a robust remediation strategy which includes laboratory scale and field scale studies as well as application of a numerical approach. The success in the remediation effort is often limited by lack of detailed understanding of the contaminant and hydrogeological properties of the aquifer. While, the information of contamination-aquifer dynamics can be studied at field, knowledge on the continuous and consistent contamination behavior on both temporal and spatial scale is often missing. The use of an integrated numerical model can be helpful for bridging the gap between the field studies and the relevant insights required for groundwater remediation

CHARACTERIZATION AND MODELLING OF ANOMALOUS PROPERTIES OF SIO2, SI3N4 AND AL2O3 NANOLAMINATES

Current research on (~ 20 nm) SiO2, Si3N4 and Al2O3 nanopowders (NPs) has revealed anomalous increases in permittivity over conventional bulk values due to localized dipole polarization effects on the surface of these NP particles. The present work has proposed alternative material structures, which are constructed using nanolithographic techniques to explore the high-polarization surface effects seen in NP research. This work has particularly focused on fabricating and modelling anomalous behavior of the permittivity of nanolaminate devices constructed from a combination of SiO2, Si3N4 and Al2O3 materials. The main takeaways of this work are as follows: 1) Strong surface dipole formation leads to high average permittivity at the air interfaces of SiO2, Si3N4 and Al2O3. Specifically, the behavior at these interfaces were investigated and modelled using FEM simulations to identify the average surface permittivity values over a specified volume. 2) As air breaks down at low electric field, the aforementioned devices were encapsulated with different combinations of SiO2, Si3N4 and Al2O3 layers in interdigitated electrode (IDE) configurations. The subsequent measurements showed significant deviations in capacitances, which are attributed to the dipole and bond formations that occur at the interfaces between the nanolaminate layers. The nanolaminate IDE structures have electric fields that are parallel to the dielectric interfaces, which could activate the highly polarizable interfacial regions more effectively than the traditional parallel plate electrode (PPE) structures. 3) Because the materials in this study inherently have high breakdown field strengths there is a potential energy storage opportunity for future capacitive devices that utilize these experimental observations and simulation results. Preliminary projections indicate that capacitive devices with a high-density of nanolaminates with laminate thicknesses from 2-5 nm could produce devices with volumetric energy densities that are on a much higher range than conventional supercapacitors.Ph.D

Numerical modelling of additive manufacturing process for stainless steel tension testing samples

Nowadays additive manufacturing (AM) technologies including 3D printing grow rapidly and they are expected to replace conventional subtractive manufacturing technologies to some extents. During a selective laser melting (SLM) process as one of popular AM technologies for metals, large amount of heats is required to melt metal powders, and this leads to distortions and/or shrinkages of additively manufactured parts. It is useful to predict the 3D printed parts to control unwanted distortions and shrinkages before their 3D printing. This study develops a two-phase numerical modelling and simulation process of AM process for 17-4PH stainless steel and it considers the importance of post-processing and the need for calibration to achieve a high-quality printing at the end. By using this proposed AM modelling and simulation process, optimal process parameters, material properties, and topology can be obtained to ensure a part 3D printed successfully

Design, modelling and fabrication of micro-scale electrode arrays (MEAs) for micro-bioimpedance tomography

This research involves the design and fabrication of micro-scale electrodes and optimisation of image reconstruction techniques. It aims to explore the use of bioimpedance tomography techniques in extracting some structured information on three-dimensional (3D) cell growth for the purpose of identifying cancer development, such as, cancer cell spheroids. Electrical impedance tomography (EIT) is a non-invasive imaging technique that maps the variation in conductivity of a sample, in the form of two or three dimensions. This technique has been successfully used in many clinical applications, for example, in detection of breast cancer, acute stroke differentiation, detection of bleeding due to traumatic brain injury, and detection of bacterial infection during surgery. The capability of EIT to spatially map biological development process enables it to be used in monitoring cell growth in three-dimensional formation. The work presented in this thesis includes miniaturising the electrode designs from a millimetre-scale on a PCB to a micrometre-scale on a glass substrate, and on a flexible material. Apart from the fabrication and experimental work, sensitivity analysis was performed using COMSOL Multiphysics® modelling. The final electrode design, the flexible micro-scale electrode array (Flex-MEA), is fabricated on a flexible printed circuit board (PCB). The development of Flex-MEA technology with improved imaging reconstruction on micro-scale has produced an improved high-throughput and showed great potential as a research aid in drug discovery. The research has proven that Flex-MEA enables improved electrode arrangement compared with planar Pt electrodes making it a superior choice as a portable, non-invasive technique to image the growth of microbial cultures. Successful measurements of cell growth and proliferation propounded by this research will have a definite potential not only in the biomedical field, example, in therapeutic drug monitoring, but also in bioprocessing technology