Investigation of the effects of the platform motion on the aerodynamics of a floating offshore wind turbine

Along with the flourishing of the wind energy industry, floating offshore wind turbines have aroused much interest among the academia as well as enterprises. In this paper, the effects of the supporting platform motion on the aerodynamics of a wind turbine are studied using the open source CFD framework OpenFOAM where the platform motion responses, including surge, heave and pitch, are superimposed onto the rotation of the wind turbine. Thrust and torque on the wind turbine are compared and analysed for cases under different platform motion patterns, together with the flow field. It is shown that the movement of the supporting platform can have large influences on a floating offshore wind turbine and need be considered during the design process

Investigation on motion responses of a semi-submersible platform and its mooring system

More and more floating structures are used in both offshore and coastal engineering, and also under assessment for wind energy. Mooring systems are needed by floating structures for station-keeping. In this paper, motion responses of a semi-submersible platform in regular waves are investigated numerically by a viscous flow solver naoe-FOAM-SJTU based on OpenFOAM. Influence of the mooring system on the motion responses of platform is evaluated via the study on (a) effect of each element length while maintaining the overall length as a constant; and (b) the cross angles between mooring lines

Diammonium bis­[(2-amino­acetato-κ2 N,O)(2,2′-bipyridine-κ2 N,N′)(N,N-dimethyl­formamide-κO)copper(II)] hexa­cosa­oxidoocta­molybdate(VI)

The title compound, (NH4)2[Cu(C2H4NO2)(C10H8N2)(C3H7NO)]2[Mo8O26], contains a centrosymmetric β-type octa­molybdate anion, two copper(II) complex cations and two ammonium ions. The CuII atom is coordinated in a square-pyramidal geometry by a 2,2′-bipyridine and a 2-amino­acetate ligands in the basal plane and by an O atom of N,N-dimethyl­formamide in the apical position. The anions and cations are linked by N—H⋯O hydrogen bonds into a three-dimensional network

Establishing a fully coupled CFD analysis tool for floating offshore wind turbines

An accurate study of a floating offshore wind turbine (FOWT) system requires 16 interdisciplinary knowledge about wind turbine aerodynamics, floating platform 17 hydrodynamics and mooring line dynamics, as well as interaction between these 18 discipline areas. Computational Fluid Dynamics (CFD) provides a new means of 19 analysing a fully coupled fluid-structure interaction (FSI) system in a detailed manner. 20 In this paper, a numerical tool based on the open source CFD toolbox OpenFOAM for 21 application to FOWTs will be described. Various benchmark cases are first modelled 22 to demonstrate the capability of the tool. The OC4 DeepCWind semi-submersible 23 FOWT model is then investigated under different operating conditions. 24 With this tool, the effects of the dynamic motions of the floating platform on the wind 25 turbine aerodynamic performance and the impact of the wind turbine aerodynamics 26 on the behaviour of the floating platform and on the mooring system responses are 27 examined. The present results provide quantitative information of three-dimensional 28 FSI that may complement related experimental studies. In addition, CFD modelling 29 enables the detailed quantitative analysis of the wind turbine flow field, the pressure 30 distribution along blades and their effects on the wind turbine aerodynamics and the 31 hydrodynamics of the floating structure, which is difficult to carry out experimentally

Gradient constrained sharpness-aware prompt learning for vision-language models

This paper targets a novel trade-off problem in generalizable prompt learning for vision-language models (VLM), i.e., improving the performance on unseen classes while maintaining the performance on seen classes. Comparing with existing generalizable methods that neglect the seen classes degradation, the setting of this problem is more strict and fits more closely with practical applications. To solve this problem, we start from the optimization perspective, and leverage the relationship between loss landscape geometry and model generalization ability. By analyzing the loss landscapes of the state-of-the-art method and vanilla Sharpness-aware Minimization (SAM) based method, we conclude that the trade-off performance correlates to both loss value and loss sharpness, while each of them is indispensable. However, we find the optimizing gradient of existing methods cannot maintain high relevance to both loss value and loss sharpness during optimization, which severely affects their trade-off performance. To this end, we propose a novel SAM-based method for prompt learning, denoted as Gradient Constrained Sharpness-aware Context Optimization (GCSCoOp), to dynamically constrain the optimizing gradient, thus achieving above two-fold optimization objective simultaneously. Extensive experiments verify the effectiveness of GCSCoOp in the trade-off problem.Comment: 19 pages 11 figure

Enhancing Robust Representation in Adversarial Training: Alignment and Exclusion Criteria

Deep neural networks are vulnerable to adversarial noise. Adversarial Training (AT) has been demonstrated to be the most effective defense strategy to protect neural networks from being fooled. However, we find AT omits to learning robust features, resulting in poor performance of adversarial robustness. To address this issue, we highlight two criteria of robust representation: (1) Exclusion: \emph{the feature of examples keeps away from that of other classes}; (2) Alignment: \emph{the feature of natural and corresponding adversarial examples is close to each other}. These motivate us to propose a generic framework of AT to gain robust representation, by the asymmetric negative contrast and reverse attention. Specifically, we design an asymmetric negative contrast based on predicted probabilities, to push away examples of different classes in the feature space. Moreover, we propose to weight feature by parameters of the linear classifier as the reverse attention, to obtain class-aware feature and pull close the feature of the same class. Empirical evaluations on three benchmark datasets show our methods greatly advance the robustness of AT and achieve state-of-the-art performance.Comment: 10 pages, 9 figures, Submitted to TIF

Mitigating Feature Gap for Adversarial Robustness by Feature Disentanglement

Deep neural networks are vulnerable to adversarial samples. Adversarial fine-tuning methods aim to enhance adversarial robustness through fine-tuning the naturally pre-trained model in an adversarial training manner. However, we identify that some latent features of adversarial samples are confused by adversarial perturbation and lead to an unexpectedly increasing gap between features in the last hidden layer of natural and adversarial samples. To address this issue, we propose a disentanglement-based approach to explicitly model and further remove the latent features that cause the feature gap. Specifically, we introduce a feature disentangler to separate out the latent features from the features of the adversarial samples, thereby boosting robustness by eliminating the latent features. Besides, we align features in the pre-trained model with features of adversarial samples in the fine-tuned model, to further benefit from the features from natural samples without confusion. Empirical evaluations on three benchmark datasets demonstrate that our approach surpasses existing adversarial fine-tuning methods and adversarial training baselines.Comment: 8 pages, 6 figure

Edge-pinning effect of graphene nanoflakes sliding atop graphene

Edge effect is one of the detrimental factors preventing superlubricity in laminar solid lubricants. Separating the friction contribution from the edge atom and inner atom is of paramount importance for rational design of ultralow friction across scales in van der Waals heterostructures. To decouple these contributions and provide the underlying microscopic origin at the atomistic level, we considered two contrast models, namely, graphene nanoflakes with dimerized and pristine edges sliding on graphene monolayer based on extensive ab initio calculations. We found the edge contribution to friction is lattice orientation dependence. In particular, edge pinning effect by dimerization is obvious for misaligned contact but suppressed in aligned lattice orientation. The former case providing local commensuration along edges is reminiscent of Aubry's pinned phase and the contribution of per edge carbon atom to the sliding potential energy corrugation is even 1.5 times more than that of an atom in bilayer graphene under commensurate contact. Furthermore, we demonstrated that the dimerized edges as high frictional pinning sites are robust to strain engineering and even enhanced by fluorination. Both structural and chemical modification in the tribological system constructed here offers the atomic details to dissect the undesirable edge pinning effect in layered materials which may give rise to the marked discrepancies in measured friction parameters from the same superlubric sample or different samples with the same size and identical preparation.Comment: 18 pages,6 figure