CFD modelling of VAWT wake effects

Wake effects are important to wind turbine design and wind farm design, because they will affect the aerodynamic performance and structural loads of wind turbine operating in a wind farm. Wake effects were investigated extensively for horizontal axis wind turbine(HAWT) in the past, but there has been very limited work done for the vertical axis wind turbine(VAWT), whose wake effects are unique because the blades will go through their own wake region during the operation. The presented thesis aims to bridge this knowledge gap by modelling the VAWT wake effects using CFD. As for the general wind turbine wake effects study, four key aspects can be identified: wake models, aerodynamics, structural dynamics, and structural integrity. Relevant literature is reviewed in the thesis, and a comprehensive framework of studying the VAWT wake effects is proposed. The framework covers all the four key aspects of the wind turbine wake effects study, and two of them will be addressed in the presented thesis, wake models and wake aerodynamics. CFD modelling in the thesis is based on RANS method. The near wake modelling focuses on the aerodynamics prediction and the far wake modelling focuses on the wake structure prediction. As for the near wake study, wake effects of a circular cylinder at Re=140000 is studied and validated. the aerodynamic performance of NACA0015 airfoil at various angle of attack at Re=2000000 is modelled using different turbulence models, dynamic stall effects of the airfoil at three different regimes are investigated. They form the basis of analysing the aerodynamic performance of VAWT rotor. A 17m 2-bladed VAWT designed based on such geometries (circular cylinder and NACA0015 airfoil) is modelled thereafter, simulated aerodynamic performance under different tip-speed ratios are compared with experiment data. As for the far wake study, both rotor simplification using porous disk and full rotor simulation are presented. A persistent symmetric wake region is observed from the porous disk modelling while the full rotor modelling predicts an asymmetric wake region. The wake interaction is then studied in a two turbine VAWT array, the influence of wake effects on the performance of VAWT at 3 diameters downstream is investigated. Overlapping of wake region is analysed

Task Transfer by Preference-Based Cost Learning

The goal of task transfer in reinforcement learning is migrating the action policy of an agent to the target task from the source task. Given their successes on robotic action planning, current methods mostly rely on two requirements: exactly-relevant expert demonstrations or the explicitly-coded cost function on target task, both of which, however, are inconvenient to obtain in practice. In this paper, we relax these two strong conditions by developing a novel task transfer framework where the expert preference is applied as a guidance. In particular, we alternate the following two steps: Firstly, letting experts apply pre-defined preference rules to select related expert demonstrates for the target task. Secondly, based on the selection result, we learn the target cost function and trajectory distribution simultaneously via enhanced Adversarial MaxEnt IRL and generate more trajectories by the learned target distribution for the next preference selection. The theoretical analysis on the distribution learning and convergence of the proposed algorithm are provided. Extensive simulations on several benchmarks have been conducted for further verifying the effectiveness of the proposed method.Comment: Accepted to AAAI 2019. Mingxuan Jing and Xiaojian Ma contributed equally to this wor

Leukocyte extravasation into the pancreatic tissue in transgenic mice expressing interleukin 10 in the islets of Langerhans.

Transgenic expression of interleukin 10 (IL-10) in the islets of Langerhans leads to a pronounced pancreatic inflammation, without inflammation of the islets of Langerhans and without diabetes. A scattered infiltration of macrophages (M pi) precedes localized accumulations of CD4+ and CD8+ T lymphocytes, B lymphocytes, and M pi. This recruitment of inflammatory cells to the pancreas is somewhat surprising, since the biological activities of IL-10 in vitro indicate that IL-10 is a powerful immunosuppressive cytokine. Since endothelial cells play a major role in leukocyte extravasation, we examined if vascular changes and extralymphoid induction of peripheral and mucosal type vascular addressins contributed to IL-10-induced homing of mononuclear cells to the pancreas. The endothelium lining small vessels was highly activated in areas of inflammation, as the endothelial cells became cuboidal, and exhibited increased expression of major histocompatibility complex class II (Ia), intercellular adhesion molecule 1, and von Willebrand Factor. Furthermore, induction of vascular addressins simultaneously with accumulation of mononuclear cells around islets and vessels indicated that the endothelial cells take on the phenotype of differentiated endothelium specialized for leukocyte extravasation. In conclusion, pancreatic inflammation and vascular changes are prominent in IL-10 transgenic mice. We hypothesize that IL-10, in addition to its immuno-inhibitory properties, is a potent recruitment signal for leukocyte migration in vivo. These effects are relevant for in vivo therapeutic applications of IL-10

Perceive, Ground, Reason, and Act: A Benchmark for General-purpose Visual Representation

Current computer vision models, unlike the human visual system, cannot yet achieve general-purpose visual understanding. Existing efforts to create a general vision model are limited in the scope of assessed tasks and offer no overarching framework to perform them holistically. We present a new comprehensive benchmark, General-purpose Visual Understanding Evaluation (G-VUE), covering the full spectrum of visual cognitive abilities with four functional domains \unicode{x2014} Perceive, Ground, Reason, and Act. The four domains are embodied in 11 carefully curated tasks, from 3D reconstruction to visual reasoning and manipulation. Along with the benchmark, we provide a general encoder-decoder framework to allow for the evaluation of arbitrary visual representation on all 11 tasks. We evaluate various pre-trained visual representations with our framework and observe that (1) Transformer-based visual backbone generally outperforms CNN-based backbone on G-VUE, (2) visual representations from vision-language pre-training are superior to those with vision-only pre-training across visual tasks. With G-VUE, we provide a holistic evaluation standard to motivate research toward building general-purpose visual systems via obtaining more general-purpose visual representations

Biomechanical comparison of multilevel lateral interbody fusion with and without supplementary instrumentation: a three-dimensional finite element study

Abstract Background Lateral lumbar interbody fusion (LLIF) is a popular, minimally invasive technique that is used to address challenging multilevel degenerative spinal diseases. It remains controversial whether supplemental instrumentation should be added for multilevel LLIF. In this study, we compared the kinematic stability afforded by stand-alone lateral cages with those supplemented by bilateral pedicle screws and rods (PSR), unilateral PSR, or lateral plate (LP) fixation using a finite-element (FE) model of a multi-level LLIF construct with simulated osteoporosis. Additionally, to evaluate the prospect of cage subsidence, the stress change characteristics were surveyed at cage-endplate interfaces. Methods A nonlinear 3-dimensional FE model of the lumbar spine (L2 to sacrum) was used. After validation, four patterns of instrumented 3-level LLIF (L2-L5) were constructed for this analysis: (a) 3 stand-alone lateral cages (SLC), (b) 3 lateral cages with lateral plate and two screws (parallel to endplate) fixated separately (LPC), (c) 3 lateral cages with bilateral pedicle screw and rod fixation (LC + BPSR), and (d) 3 lateral cages with unilateral pedicle and rod fixation (LC + UPSR). The segmental and overall range of motion (ROM) of each implanted condition were investigated and compared with the intact model. The peak von Mises stresses upon each (superior) endplate and the stress distribution were used for analysis. Results BPSR provided the maximum reduction of ROM among the configurations at every plane of motion (66.7–90.9% of intact spine). UPSR also provided significant segmental ROM reduction (45.0–88.3%). SLC provided a minimal restriction of ROM (10.0–75.1%), and LPC was found to be less stable than both posterior fixation (23.9–86.2%) constructs. The construct with stand-alone lateral cages generated greater endplate stresses than did any of the other multilevel LLIF models. For the L3, L4 and L5 endplates, peak endplate stresses caused by the SLC construct exceeded the BPSR group by 52.7, 63.8, and 54.2% in flexion, 22.3, 40.1, and 31.4% in extension, 170.2, 175.1, and 134.0% in lateral bending, and 90.7, 45.5, and 30.0% in axial rotation, respectively. The stresses tended to be more concentrated at the periphery of the endplates. Conclusions SLC and LPC provided inadequate ROM restriction for the multilevel LLIF constructs, whereas lateral cages with BPSR or UPSR fixation provided favorable biomechanical stability. Moreover, SLC generated significantly higher endplate stress compared with supplemental instrumentation, which may have increased the risk of cage subsidence. Further biomechanical and clinical studies are required to validate our FEA findings.http://deepblue.lib.umich.edu/bitstream/2027.42/136058/1/12891_2017_Article_1387.pd

For the Improvement of Mechanical and Microstructural Properties of UHPC with Fiber Alignment using Carbon Nanotube and Graphite Nanoplatelet

This paper investigates the influence of carbon nanotube (CNT) and graphite nanoplatelet (GNP) on the microstructure and mechanical characteristics of UHPC with steel fiber alignment. The content of CNT and GNP ranged from 0 to 0.3%, by mass of binder. Predominant fiber alignment was manipulated using a flow-induced casting method during UHPC placement. Experiment results indicated that the increase of CNT and GNP content from 0 to 0.3% led to 15%, 40%, and 50% improvement in compressive strength, flexural strength, and T150 (dissipated energy) of UHPC, respectively. Fiber alignment was shown to increase flexural strength and T150 by 30% and 35%, respectively, compared to UHPC with random finer orientation. Moreover, the synergy of nanomaterial and fiber alignment can lead to a maximum enhancement of 80% and 90% in flexural strength and T150, respectively. Microstructural analysis indicated that CNT and GNP can enhance cement hydration and enable the bridging of cracks at nano or microscale. Moreover, the use of CNT and GNP reduced the porosities of fiber-matrix interface from 6%-12.5% to 4%–7% and UHPC matrix from 5.5% to 4%. This consequently contributed to the significant improvement in mechanical properties of UHPC

3D-VisTA: Pre-trained Transformer for 3D Vision and Text Alignment

3D vision-language grounding (3D-VL) is an emerging field that aims to connect the 3D physical world with natural language, which is crucial for achieving embodied intelligence. Current 3D-VL models rely heavily on sophisticated modules, auxiliary losses, and optimization tricks, which calls for a simple and unified model. In this paper, we propose 3D-VisTA, a pre-trained Transformer for 3D Vision and Text Alignment that can be easily adapted to various downstream tasks. 3D-VisTA simply utilizes self-attention layers for both single-modal modeling and multi-modal fusion without any sophisticated task-specific design. To further enhance its performance on 3D-VL tasks, we construct ScanScribe, the first large-scale 3D scene-text pairs dataset for 3D-VL pre-training. ScanScribe contains 2,995 RGB-D scans for 1,185 unique indoor scenes originating from ScanNet and 3R-Scan datasets, along with paired 278K scene descriptions generated from existing 3D-VL tasks, templates, and GPT-3. 3D-VisTA is pre-trained on ScanScribe via masked language/object modeling and scene-text matching. It achieves state-of-the-art results on various 3D-VL tasks, ranging from visual grounding and dense captioning to question answering and situated reasoning. Moreover, 3D-VisTA demonstrates superior data efficiency, obtaining strong performance even with limited annotations during downstream task fine-tuning

Use of Saturated Lightweight Sand to Improve the Mechanical and Microstructural Properties of UHPC with Fiber Alignment

This paper studied the influence of pre-saturated lightweight sand (LWS) on the mechanical and microstructural properties of UHPC cast with steel fiber alignment. The changes in hydration kinetics, porosity, nano-mechanical, and mechanical properties were studied. The LWS was used at 0–50% replacement volumes of total sand. Predominant fiber alignment was favored through a flow-induced casting method during casting of flexural prisms. Experiment results showed that the 28-d autogenous shrinkage was decreased from 450 to 275 μm/m with the LWS content increasing from 0 to 50%. The addition of 20% LWS led to maximum increases of 15%, 15%, and 20% in compressive strength, flexural strength, and T150, respectively, relative to UHPC made without any LWS. The use of 20% LWS combined with fiber alignment led to a synergistic effect of 45% and 40% on enhancing the flexural strength and T150, respectively, relative to UHPC without LWS and having random fiber orientation. The addition of LWS can enhance the cement hydration given the internal curing effect. Such enhanced cement hydration increased the percentage of high density and ultra-high density C–S–H from 50% to 75% and reduced the 28-d porosity from 12.5% to 9.5% with the use of 20% LWS. On the other hand, such internal curing can be overwhelmed by the introduced pores of LWS when excessive LWS was used, which led to significant increase in porosity of UHPC

Unsupervised Visible-Infrared Person ReID by Collaborative Learning with Neighbor-Guided Label Refinement

Unsupervised learning visible-infrared person re-identification (USL-VI-ReID) aims at learning modality-invariant features from unlabeled cross-modality dataset, which is crucial for practical applications in video surveillance systems. The key to essentially address the USL-VI-ReID task is to solve the cross-modality data association problem for further heterogeneous joint learning. To address this issue, we propose a Dual Optimal Transport Label Assignment (DOTLA) framework to simultaneously assign the generated labels from one modality to its counterpart modality. The proposed DOTLA mechanism formulates a mutual reinforcement and efficient solution to cross-modality data association, which could effectively reduce the side-effects of some insufficient and noisy label associations. Besides, we further propose a cross-modality neighbor consistency guided label refinement and regularization module, to eliminate the negative effects brought by the inaccurate supervised signals, under the assumption that the prediction or label distribution of each example should be similar to its nearest neighbors. Extensive experimental results on the public SYSU-MM01 and RegDB datasets demonstrate the effectiveness of the proposed method, surpassing existing state-of-the-art approach by a large margin of 7.76% mAP on average, which even surpasses some supervised VI-ReID methods