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This research is about modelling and simulation of how the hydrogen diffuses in high strength steels. The hydrogen diffusion in the material was examined by using finite element software with the help of material properties and some existing data. For modelling and simulating the diffusion analysis in finite element software, a cylindrical type dog-bone shaped specimen was chosen. To determine the diffusion at the centre of specimen, a cross-sectional area of the material was selected to proceed for the analysis. Abaqus software was considered as finite element software to progress the hydrogen diffusion and tensile testing of the specimen. Diffusion analysis was studied under the analogy of heat transfer and also, diffusion analysis with the addition of mechanical load was studied under the analogy of coupled temperature displacement in the Abaqus software. This process has executed for two types of high strength steels 316L and 304L stainless steels. The crack is also considered for analysis to check how it affects the specimen. Further, The 316L and 304L stainless steel results were compared to review that which steel is better to withstand the hydrogen diffusion rate and mechanical load on the material.

Spinal ligaments may be a significant source of chronic back pain, yet they are often disregarded by the clinical community due to a lack of information with regards to their material response, and innervation characteristics. The purpose of this dissertation was to characterize the material response of spinal ligaments and to review their innervation characteristics. Review of relevant literature revealed that all of the major spinal ligaments are innervated. They cause painful sensations when irritated and provide reflexive control of the deep spinal musculature. As such, including the neurologic implications of iatrogenic ligament damage in the evaluation of surgical procedures aimed at relieving back pain will likely result in more effective long-term solutions. The material response of spinal ligaments has not previously been fully quantified due to limitations associated with standard soft tissue testing techniques. The present work presents and validates a novel testing methodology capable of overcoming these limitations. In particular, the anisotropic, inhomogeneous material constitutive properties of the human supraspinous ligament are quantified and methods for determining the response of the other spinal ligaments are presented. In addition, a method for determining the anisotropic, inhomogeneous pre-strain distribution of the spinal ligaments is presented. The multi-axial pre-strain distributions of the human anterior longitudinal ligament, ligamentum flavum and supraspinous ligament were determined using this methodology. Results from this work clearly demonstrate that spinal ligaments are not uniaxial structures, and that finite element models which account for pre-strain and incorporate ligament’s complex material properties may provide increased fidelity to the in vivo condition.

Snow is a complex material that is difficult to characterize especially due to its high compressibility and temperature-sensitive nonlinear viscoelasticity. Snow mechanics has been intensively investigated by avalanche and army researchers for decades. However, fewer research studies have been published for compacted snow, commonly defined as snow with a density in the range of 370-560 kg/m3. From a mobility perspective, the tires are the primary point of force and motion generation and their interaction with the terrain causes an increased reliance on the skill of the driver for safer mobility. Thus, standards like ASTM F1805 are implemented for the evaluation of winter tires which can be used in harsh conditions like ice and snow. This work focuses on evaluating the prior efforts performed for the measurement of snow properties. In addition, analysis using regression models and principal component analysis is performed to understand the extent to which specific measurements related to snow affect the traction of the tire. It was found that the compressive and shear properties of snow contribute more than 90% to the variation in the traction coefficient of a tire when evaluated on a compacted snow domain per ASTM F1805. Identification of this phenomenon allowed the enhancement of an existing device that can be used for measuring the compaction and shear properties of snow. The device hence conceptualized was manufactured in-house and tested at the Smithers Winter Test Center to benchmark against existing devices available commercially. Further, a more analytical method for evaluating the resistive pressure for the penetration of the device was formulated. Based on this, a possible framework for the determination of the bevameter parameters using measurements of the new device has been proposed which needs to be validated experimentally and computationally. / Doctor of Philosophy / Winter tires sold in North America require prior evaluation according to a standard namely the ASTM F1805 to bear the 'mountain-snowflake symbol' for severe snow usage. The standard specifies the conditions for evaluating a prototype winter tire and the necessary track preparation methodologies. However, the computational model of a track used for such a certification is not found in the literature causing the manufacturing of such winter tires to be more of a 'trial-and-error' process. The main objective of this investigation is to assess earlier studies of snow characteristics. Additionally, analysis employing regression models and principal component analysis was conducted to comprehend the extent to which particular measurements connected to snow affect the traction of the tire. When tested using an ASTM F1805-compliant compacted snow domain, it was discovered that the compressive and shear properties of snow account for more than 90% of the variation in the traction coefficient of a tire. The discovery of this phenomenon made it possible to improve a tool for assessing the compaction and shear characteristics of snow. The device that was conceptualized was manufactured internally and put to the test at Smithers Winter Test Center to compare it to other devices that were already on the market. Further, a new analytical method for evaluation of the resistive pressure to the device was developed. Using measurements from the new device, a method to utilize the devised output parameters as inputs and for the validation of a computational snow model is proposed.

The hardness of steel is an important quality parameter for several industrial applications. Conventional mechanical testing is used in quality testing for material hardness and the method is time-consuming, can cause material mix-ups, and results in material waste. To address this issue, a possible on-line method for non-destructive testing (NDT) techniques such as laser ultrasonic (LUS) measurements has been explored to replace mechanical testing. In this thesis, machine learning models are trained to predict steel hardness using LUS measurements and data from the production process. LUS data is collected from steel samples with a measured hardness using the Brinell protocol. Measured hardness values between 250 and 700 Brinell are used as the target values for the models. The production process data includes the chemical composition and tempering temperature. The models used in this thesis are Extreme Gradient Boosting (XGBoost), Multilayered Perceptron (MLP), and Convolutional Neural Network (CNN). The first two mentioned models use feature-engineered data from LUS measurements. These features include the time-of-flight for ultrasonic waves. CNN uses the raw LUS data as a univariate time series as input. Each of the models is trained solely on data from LUS measurements and both LUS and production process data to determine the effect of adding production process data. The models are optimized and tuned based on their loss on a validation set. The models are evaluated against each other based on their root mean squared error (RMSE) on a test set to determine the best performing model. The best performing model is an XGBoost model using LUS and production process data. The results indicate that models using solely LUS data can not replace or partially replace mechanical testing. The best performing model using only LUS data has a RMSE of 69.9 Brinell, which is above the required performance of a RMSE below 50 Brinell. The results also indicate a large boost in performance if including data from the production process. However, implementing this solution in the industry without losing accuracy in measurements is a hard task. While the models are not ready for direct implementation in industry, the results demonstrate potential for further research in this area.

The purpose of our work is to estimate arterial stiffness based on the virtual fields method and using pressure gradients and arterial wall motion. Currently, the gold standard to estimate arterial stiffness relies primarily on the pulse wave velocity, which provides a relation between arterial stiffness and the velocity of the pressure wave propagating through the arterial wall. The pulse wave velocity method has been improved over the years, but still depends on specific assumptions regarding, for example, blood pressure, arterial geometry, and linear material response. The proposed method directly links arterial wall displacements and pressure gradients to arterial stiffness and paves the way to computing arterial stiffness with higher accuracy.

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