Noise Measurement of a Wind Turbine using Thick Blades with Blunt Trailing Edge

The noise generated by wind turbines can potentially cause significant harm to the ecological environment and the living conditions of residents. Therefore, a proper assessment of wind turbine noise is crucial. The IEC 61400-11 standard provides standardized guidelines for measuring turbine noise, facilitating the comparison of noise characteristics among different wind turbine models. This work aims to conduct a comprehensive noise measurement of a 100kW wind turbine using thick blades with blunt trailing edge, which differs from the typical turbines studied previously. The work takes into account the unique design and dynamic characteristics of small-scale wind turbines and adjusts the measurement accordingly, with deviations from the IEC standards will be explicitly addressed

Experimental Investigation of Airfoil Trailing Edge Noise Reduction by using TE Serrations

The growing prominence of aerodynamic noise from wind turbine blades at high wind speeds has made it the primary source of noise for wind turbines, with adverse effects on nearby residents' living conditions. This study focuses on experimental research conducted in an anechoic wind tunnel to investigate the noise reduction mechanism of wind turbine blade airfoils using serrated trailing edges, aiming to contribute to the development of low-noise wind turbine blades. Three models, including two types of NACA series airfoils and one reference plate with attachable serrated trailing edges, were tested. The findings reveal that airfoils with serrated trailing edges exhibit a 3 to 6 dB reduction in the mid-high frequency wideband noise, with the width of the frequency band of noise reduction slightly increasing as the Reynolds number rises. The presence of serrations also eliminates multiple tones of high amplitude exceeding 10 dB. The study highlights serration height as the most influential factor for noise reduction, surpassing the significance of serration width and the ratio of width to height. Moreover, investigations into the noise reduction mechanism indicate varying degrees of reduction in streamwise fluctuating velocity spectra near the serrated trailing edge, even aligning with changes in the sound power spectra. Serrations were found to alter the turbulence length scale in the downstream flow field, potentially impacting noise generation. This study suggests that the reduction in streamwise fluctuating velocity near the serrated trailing edge plays a crucial role in noise reduction, highlighting the importance of detailed flow field measurements and analysis for a comprehensive understanding of the mechanistic relationship between flow changes and serration-induced noise reduction

Machine Learning-driven Autotuning of Graphics Processing Unit Accelerated Computational Fluid Dynamics for Enhanced Performance

Optimizing the performance of computational fluid dynamics (CFD) applications accelerated by graphics processing units (GPUs) is crucial for efficient simulations. In this study, we employed a machine learning-based autotuning technique to optimize 14 key parameters related to GPU kernel scheduling, including the number of thread blocks and threads within a block. Our approach utilizes fully connected neural networks as the underlying machine learning model, with the tuning parameters as inputs to the neural networks and the actual execution time of a simulation as the outputs. To assess the effectiveness of our autotuning approach, we conducted experiments on three different types of GPUs, with computational speeds ranging from low to high. We performed independent training for each GPU model and also explored combined training across multiple GPU models. By leveraging artificial neural networks, our autotuning technique achieved remarkable results in tuning a wide range of parameters, leading to enhanced performance for a CFD code. Importantly, our approach demonstrated its efficacy while requiring only a small fraction of samples from the large parameter search space. This efficiency is attributed to the effectiveness of the fully connected neural networks in capturing the complex relationships between the parameter settings and the resulting performance. Overall, our study showcases the potential of machine learning, specifically fully connected neural networks, in autotuning GPU-accelerated CFD codes. By leveraging this approach, researchers and practitioners can achieve high performance in scientific simulations with optimized parameter configurations

CPU-GPU Heterogeneous Code Acceleration of a Finite Volume Computational Fluid Dynamics Solver

This work deals with the CPU-GPU heterogeneous code acceleration of a finite-volume CFD solver utilizing multiple CPUs and GPUs at the same time. First, a high-level description of the CFD solver called SENSEI, the discretization of SENSEI, and the CPU-GPU heterogeneous computing workflow in SENSEI leveraging MPI and OpenACC are given. Then, a performance model for CPU-GPU heterogeneous computing requiring ghost cell exchange is proposed to help estimate the performance of the heterogeneous implementation. The scaling performance of the CPU-GPU heterogeneous computing and its comparison with the pure multi-CPU/GPU performance for a supersonic inlet test case is presented to display the advantages of leveraging the computational power of both the CPU and the GPU. Using CPUs and GPUs as workers together, the performance can be improved further compared to using pure CPUs or GPUs, and the advantages can be fairly estimated by the performance model proposed in this work. Finally, conclusions are drawn to provide 1) suggestions for application users who have an interest to leverage the computational power of the CPU and GPU to accelerate their own scientific computing simulations and 2) feedback for hardware architects who have an interest to design a better CPU-GPU heterogeneous system for heterogeneous computing

Breading of a new cold-resistant and late-ripening nectarine cultivar Daqingpiliguangtao

Daqingpiliguangtao is a new cold-resistant and late-ripening nectarine variety, a natural seedling from Qingpi nectarine in Jiuquan City, and selected over several years. It was officially named Daqingpiliguangtao after being reviewed and approved by the Gansu Provincial Forest Tree Variety Approval Committee in March 2024. The original maternal plant of Daqingpi was the green-skinned Prunus persica (L.) Batsch. The cultivar Qingpiliguangtao was discovered in 1998 at Fuqiang Village, Huangqu Township, Dunhuang City (Accession No. D008), with average fruit weight of 80 g. Through seedling propagation from 1999 to 2002 and subsequent selection of elite seedlings from 2003 to 2006, a superior genotype was identified at Dujiadun Village, Qili Town, Dunhuang City. Since 2007, scion grafts collected from this selected mother plant have been vegetatively propagated for regional adaptation trials. Daqingpiliguangtao exhibits moderate tree vigor with an open canopy architecture. Eightyear-old trees trained in the natural open-center system attain the heights of 2-3 m with crown spread reaching 3-4 m. One-year-old shoots display glossy purplish-red coloration on sun-exposed surfaces. The branching capacity is moderate, with approximately 90% of fruiting shoots bearing compound buds and 10% single buds, all densely pubescent. Leaves measure 12-13 cm in length and 3-3.5 cm in width, exhibiting emerald-green coloration and thickened lamina. Venation patterns show yellowish-green primary veins extending straight to leaf margins with minimal reticulation. Petioles (0.6-0.8 cm in length) bear 2-3 reniform glands with grayish-brown pigmentation. Leaf margins present obtuse-serrated dentations with blunt apices. The campanulate flowers feature pink corollas composed of five obovate petals. The androecium consists of over 18 stamens, while the pistil displays pale yellow coloration, maintaining equal or slightly superior height relative to stamens. The fruit is spherical, with an average single fruit weight of 124.5 g and a maximum of 188.0 g. The fruit surface is entirely smooth and green, occasionally adorned with purple stripes. The stone is easily separable, and the flesh is creamy white, becoming soft and juicy when fully ripe. The flesh is delicate with minimal fiber, offering a delightful balance of sweet and sour flavors. The soluble solids contents range from 11.80% to 12.40%. It exhibits a high self-fertility rate and ripens in late September in Jiuquan City, with a fruit development period of approximately 150 days. After years of cultivation and observation, this variety has exhibited tolerance to drought, cold, and pests and diseases, coupled with high yields. It is ideally suited for cultivation in arid and semi-arid peach-producing regions in the north, such as the Hexi Corridor. Proper orchard selection and planning should be conducted before planting, preferably in a north-south orientation. For planting, seedlings with root lengths exceeding 30 cm, at least four lateral roots, stem diameters of at least 0.8 cm, heights of at least 100 cm, at least six full buds within the training zone, well-healed grafts, and free from damage and pests and diseases should be selected. The method of shallow planting in large holes should be adopted, with planting holes of 60 cm×60 cm. The optimal tree shapes include the three-leader open center, natural open center, Y-shape, and independent trunk system. Pruning should be conducted in winter and summer to ensure uniform fruiting each year. Irrigation and fertilization should be managed according to the water and nutrient requirements in the peach growth cycle. Integrated pest management strategies, including agricultural practices, physical controls, biological controls, and chemical control measures, should be employed

PaLI: A Jointly-Scaled Multilingual Language-Image Model

Effective scaling and a flexible task interface enable large language models to excel at many tasks. PaLI (Pathways Language and Image model) extends this approach to the joint modeling of language and vision. PaLI generates text based on visual and textual inputs, and with this interface performs many vision, language, and multimodal tasks, in many languages. To train PaLI, we make use of large pretrained encoder-decoder language models and Vision Transformers (ViTs). This allows us to capitalize on their existing capabilities and leverage the substantial cost of training them. We find that joint scaling of the vision and language components is important. Since existing Transformers for language are much larger than their vision counterparts, we train the largest ViT to date (ViT-e) to quantify the benefits from even larger-capacity vision models. To train PaLI, we create a large multilingual mix of pretraining tasks, based on a new image-text training set containing 10B images and texts in over 100 languages. PaLI achieves state-of-the-art in multiple vision and language tasks (such as captioning, visual question-answering, scene-text understanding), while retaining a simple, modular, and scalable design

CPU/GPU Code Acceleration on Heterogeneous Systems and Code Verification for CFD Applications

Computational Fluid Dynamics (CFD) applications usually involve intensive computations, which can be accelerated through using open accelerators, especially GPUs due to their common use in the scientific computing community. In addition to code acceleration, it is important to ensure that the code and algorithm are implemented numerically correctly, which is called code verification. This dissertation focuses on accelerating research CFD codes on multi-CPUs/GPUs using MPI and OpenACC, as well as the code verification for turbulence model implementation using the method of manufactured solutions and code-to-code comparisons. First, a variety of performance optimizations both agnostic and specific to applications and platforms are developed in order to 1) improve the heterogeneous CPU/GPU compute utilization; 2) improve the memory bandwidth to the main memory; 3) reduce communication overhead between the CPU host and the GPU accelerator; and 4) reduce the tedious manual tuning work for GPU scheduling. Both finite difference and finite volume CFD codes and multiple platforms with different architectures are utilized to evaluate the performance optimizations used. A maximum speedup of over 70 is achieved on 16 V100 GPUs over 16 Xeon E5-2680v4 CPUs for multi-block test cases. In addition, systematic studies of code verification are performed for a second-order accurate finite volume research CFD code. Cross-term sinusoidal manufactured solutions are applied to verify the Spalart-Allmaras and k-omega SST model implementation, both in 2D and 3D. This dissertation shows that the spatial and temporal schemes are implemented numerically correctly.Doctor of PhilosophyComputational Fluid Dynamics (CFD) is a numerical method to solve fluid problems, which usually requires a large amount of computations. A large CFD problem can be decomposed into smaller sub-problems which are stored in discrete memory locations and accelerated by a large number of compute units. In addition to code acceleration, it is important to ensure that the code and algorithm are implemented correctly, which is called code verification. This dissertation focuses on the CFD code acceleration as well as the code verification for turbulence model implementation. In this dissertation, multiple Graphic Processing Units (GPUs) are utilized to accelerate two CFD codes, considering that the GPU has high computational power and high memory bandwidth. A variety of optimizations are developed and applied to improve the performance of CFD codes on different parallel computing systems. The program execution time can be reduced significantly especially when multiple GPUs are used. In addition, code-to-code comparisons with some NASA CFD codes and the method of manufactured solutions are utilized to verify the correctness of a research CFD code