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This study investigates the tensile behavior of anchor pull-out tests from super early age concrete(less than 12h) by finite element(FE) software ANSYS Workbench. In previous experiment, several series of pull-out tests were finished and analyzed. In each per hour, different speeds(1mm/s, 0.2mm/s, 0.1mm/s and 0.833mm/s) were evaluated, getting the results about correlation of pull-out force and displacement(F-D curve). It is difficult to evaluate the specific development of tensile strength in super young concrete, due to the super plasticity that makes itself soft and unstable. The first step of this study is to collect relevant empirical formula, theoretical varying material properties with time and pull-out force of experimental applied anchors. Comparison of simulation analysis results and empirical formulas determines whether the establishment of the finite element model and adapted constitutive model of known natural hardened concrete(NHC) are valid or not. The second procedure is that the material properties of NHC are replaced by different age values and modified until getting the same simulation results as experiment outcome. The propose of this paper is to investigate a more accurate modified formula to describe the development of tensile behavior in super early age concrete:1. Introduction 2. Background 2.1 Modes of failure 2.2 A new failure mode 2.3 Finite Element Numerical Simulation 3. Research Questions 4. Aims/Objectives of the Research 5. Proposed Research Method 5.1 Previous Empirical theory 5.1.1 Cubic Compressive strength of Early Age Concrete 5.1.2 Tensile Strength of Early Age Concrete 5.1.3 Modulus of Elasticity in Early Age Concrete 5.1.4 Prediction of pull-out maximum force to headed studs from concrete 5.2 Pervious Experiment 5.3 Numerical Simulation 6. Significance/Contribution to the Discipline 7. Experiment Program 7.1 Experiment Setup 7.2 Experiment Result 8. Numerical simulation and analysis 8.1 Material Properties 8.2 Modelling Setup 8.3 The first pull-out test 8.4 Comparison Results at different stages 9. Discussion and Results 10. Summary and Conclusion 11. Recommendation for future studies 12. References 13. Appendix

Using a near-field array of acoustic resonators, it is possible to modify a focused pressure field and enforce a spatial frequency corresponding to the resonator array spacing. This higher spatial frequency makes it possible to focus and image with a resolution that is better than if the focusing were in free space. This near-field effect is caused by the phase shifting properties of resonators and, specifically, the delayed phase found in waves with a temporal frequency lower than that of the resonators in the array. Using time reversal, arrays of resonators are explored and the subwavelength focusing is used to describe the ability to image subwavelength features. A one-dimensional equivalent circuit model accurately predicts this interaction of the wave field with an array of resonators and is able to model the aggregate effect of the phononic crystal of resonators while describing the fine spatial details of individual resonators. This model is validated by a series of COMSOL full-wave simulations of the same system. The phase delay caused by a single resonator is explored in a simple experiment as well as in the equivalent circuit model. A series of experiments is conducted with a two-dimensional array of resonators and complex images are produced which indicate the ability to focus complex sources with better resolution.

Accelerating the simulation of turbulence to stationarity is a critical challenge in various engineering applications. This study presents an innovative equation-free multiscale approach combined with a machine learning technique to address this challenge in the context of the one-dimensional stochastic Burgers' equation, a widely used toy model for turbulence. We employ an encoder-decoder recurrent neural network to perform super-resolution reconstruction of the velocity field from lower-dimensional energy spectrum data, enabling seamless transitions between fine and coarse levels of description. The proposed multiscale-machine learning framework significantly accelerates the computation of the statistically stationary turbulent Burgers' velocity field, achieving up to 442 times faster wall clock time compared to direct numerical simulation, while maintaining three-digit accuracy in the velocity field. Our findings demonstrate the potential of integrating equation-free multiscale methods with machine learning methods to efficiently simulate stochastic partial differential equations and highlight the possibility of using this approach to simulate stochastic systems in other engineering domains. / Master of Science / In many practical engineering problems, simulating turbulence can be computationally expensive and time-consuming. This research explores an innovative method to accelerate these simulations using a combination of equation-free multiscale techniques and deep learning. Multiscale methods allow researchers to simulate the behavior of a system at a coarser scale, even when the specific equations describing its evolution are only available for a finer scale. This can be particularly helpful when there is a notable difference in the time scales between the coarser and finer scales of a system. The ``equation-free approach multiscale method coarse projective integration" can then be used to speed up the simulations of the system's evolution. Turbulence is an ideal candidate for this approach since it can be argued that it evolves to a statistically steady state on two different time scales. Over the course of evolution, the shape of the energy spectrum (the coarse scale) changes slowly, while the velocity field (the fine scale) fluctuates rapidly. However, applying this multiscale framework to turbulence simulations has been challenging due to the lack of a method for reconstructing the velocity field from the lower-dimensional energy spectrum data. This is necessary for moving between the two levels of description in the multiscale simulation framework. In this study, we tackled this challenge by employing a deep neural network model called an encoder-decoder sequence-to-sequence architecture. The model was used to capture and learn the conversions between the structure of the velocity field and the energy spectrum for the one-dimensional stochastic Burgers' equation, a simplified model of turbulence. By combining multiscale techniques with deep learning, we were able to achieve a much faster and more efficient simulation of the turbulent Burgers' velocity field. The findings of this study demonstrated that this novel approach could recover the final steady-state turbulent Burgers' velocity field up to 442 times faster than the traditional direct numerical simulations, while maintaining a high level of accuracy. This breakthrough has the potential to significantly improve the efficiency of turbulence simulations in a variety of engineering applications, making it easier to study and understand these complex phenomena.

The nuclear pore complex (NPC) is a pivotal subcellular structure governing nucleocytoplasmic transport through a selectively permeable barrier. Comprising approximately 30 distinct proteins, it includes FG-Nups with phenylalanine-glycine (FG) motifs and non-FG Nups forming the pore's scaffold. The selectively permeable barrier formed by FG-Nups enables the passive diffusion of small molecules and facilitates the transport of larger ones recognized by nuclear transport receptors (NTRs). Their roles are critical in regulating mRNA and pre-ribosome nuclear export and the nuclear import of transcription factors, underscoring their significance in cellular processes. However, studying NPCs remains challenging due to their structural complexity, heterogeneity, dynamic interactions, and inaccessibility within live cells. In this dissertation, three core questions were investigated to elucidate the structure and function of the NPC. First, the nuclear export dynamics of pre-ribosomal subunits revealed significantly higher transport efficiency compared to other large cargos. Through inhibition of nuclear transport receptor (NTR), CRM1, by small-molecule inhibitor, leptomycin B, we found a dose-dependent inhibition of CRM1s played a crucial role in pre-ribosome export efficiency. We confirmed these results through a series of controlled environments with both import and export NTRs. Our results suggest that cooperative NTR mechanisms may enhance the nucleocytoplasmic transport of not only pre-ribosomal subunits but other protein complexes as well. Second, we investigated the dynamic properties of the NPC’s selectivity barrier by altering the concentration of O-linked β-N-acetylglucosamine (O-GlcNAc) sites on nuclear pore proteins. Using small-molecule inhibitors of O-GlcNAc transferase (OGT) or O-GlcNAcase (OGA) to decrease or increase NPC O-GlcNAcylation, respectively, we found a significant change in the overall 3D spatial density of NPC O-GlcNAc sites. Then, by applying the same OGT- and OGA-inhibited conditions, we found that NPC O-GlcNAcylation significantly impacted the nuclear export of mRNA, suggesting that NPC O-GlcNAcylation regulates mRNA’s passage through the NPC’s selective permeability barrier. Third, we examined the nuclear transport mechanism for intrinsically disordered proteins (IDPs). Our findings revealed that IDPs, unlike large folded proteins, can passively diffuse through NPCs independent of size, and their diffusion behaviors are differentiated by the content ratio of charged (Ch) and hydrophobic (Hy) amino acids. Thus, we proposed a Ch/Hy-ratio mechanism for IDP nucleocytoplasmic transport. In summary, comprehending the dynamic behavior of the NPC selectivity barrier and its involvement in mediating large transiting complexes and IDPs has provided valuable insights into the fundamental nucleocytoplasmic transport mechanism, emphasizing the NPC's crucial role in cellular health and function. / Biology

Nonlinear explicit finite element (FE) simulations are used to study the axial collapse behavior of multi-corner. single- and multi-cell crush tubes under quasi-static and dynamic loading conditions. It is shown that the higher hardening modulus and yield stress increases the crush force and its resulting energy absorption. Moreover, the multi-cell tubes are found to have complicated collapse modes because of the geometrical complexity of the corner region unlike single-cell tubes. it was also shown that the stress wave propagation has a significant effect on the formation of crush modes in the tubes without imperfections whereas this effect can be ignored in tubes with imperfection or trigger mechanism. An analytical formula for the prediction of mean crush force of multi-corner multi-cell tubes is derived based on the super folding element theory. The analytical predictions for the mean crush force are found to be in good agreement with the FE solutions. Results also show a strong correlation between the cross-sectional geometry and the crash behavior with the method of connecting the inner to the outer walls having large influence on the energy absorption.

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Segmentation of the knee cartilage is an important step for surgery planning and manufacturing patient-specific prostheses. What has been a promising technology in recent years is deep learning-based super-resolution methods that are composed of feed-forward models which have been successfully applied on natural and medical images. This thesis aims to test the feasibility to super-resolve thick slice 2D sequence acquisitions and acquire sufficient segmentation accuracy of the articular cartilage in the knee. The investigated approaches are single- and multi-contrast super-resolution, where the contrasts are either based on the 2D sequence, 3D sequence, or both. The deep learning models investigated are based on predicting the residual image between the high- and low-resolution image pairs, finding the hidden latent features connecting the image pairs, and approximating the end-to-end non-linear mapping between the low- and high-resolution image pairs. The results showed a slight improvement in segmentation accuracy with regards to the baseline bilinear interpolation for the single-contrast super-resolution, however, no notable improvements in segmentation accuracy were observed for the multi-contrast case. Although the multi-contrast approach did not result in any notable improvements, there are still unexplored areas not covered in this work that are promising and could potentially be covered as future work. / Segmentering av knäbrosket är ett viktigt steg för planering inför operationer och tillverkning av patientspecifika proteser. Idag segmenterar man knäbrosk med hjälp av MR-bilder tagna med en 3D-sekvens som både tidskrävande och rörelsekänsligt, vilket kan vara obehagligt för patienten. I samband med 3D-bildtagningar brukar även thick slice 2D-sekvenser tas för diagnostiska skäl, däremot är de inte anpassade för segmentering på grund av för tjocka skivor. På senare tid har djupinlärningsbaserade superupplösningsmetoder uppbyggda av så kallade feed-forwardmodeller visat sig vara väldigt framgångsrikt när det applicerats på verkliga- och medicinska bilder. Syftet med den här rapporten är att testa hur väl superupplösta thick slice 2D-sekvensbildtagningar fungerar för segmentering av ledbrosket i knät. De undersökta tillvägagångssätten är superupplösning av enkel- och flerkontrastbilder, där kontrasten är antingen baserade på 2D-sekvensen, 3D-sekvensen eller både och. Resultaten påvisar en liten förbättring av segmenteringnoggrannhet vid segmentering av enkelkontrastbilderna över baslinjen linjär interpolering. Däremot var det inte någon märkvärdig förbättring i superupplösning av flerkontrastbilderna. Även om superupplösning av flerkontrastmetoden inte gav någon märkbar förbättring segmenteringsresultaten så finns det fortfarande outforskade områden som inte tagits upp i det här arbetet som potentiellt skulle kunna utforskas i framtida arbeten.