Unsteady loads for coaxial rotors in forward flight computed using a vortex particle method

Recent advances in coaxial rotor design have shown benefits of this configuration. Nevertheless, issues related to rotor-head drag, aerodynamic performance, wake interference, and vibration should also be considered. Simulating the unsteady aerodynamic loads for a coaxial rotor, including the aerodynamic interactions between rotors and rotor blades, is an essential part of analysing their vibration characteristics. In this article, an unsteady aerodynamic analysis based on a vortex particle method is presented. In this method, a reversed-flow model for the retreating side of the coaxial rotor is proposed based on an unsteady panel technique. To account for reversed flow, shedding a vortex from the leading edge is used rather than from the trailing edge. Moreover, vortex-blade aerodynamic interactions are accounted for. The model considers the unsteady pressure term induced on a blade by tip vortices of other blades, and thus accounts for the aerodynamic interaction between the rotors and its contribution to the unsteady airloads. Coupling the reversed-flow model and the vortex-blade aerodynamic interaction model with the viscous vortex-particle method is used to simulate the complex wake of the coaxial rotor. The unsteady aerodynamic loads on the X2 coaxial rotor are simulated in forward flight, and compared with the results of PRASADUM (Parallelized Rotorcraft Analysis for Simulation And Design, developed at the University of Maryland) and CFD/CSD computations with the OVERFLOW and the CREATE-AV Helios tools. The results of the present method agree with the results of the CFD/CSD method, and compare to it better than the PRASADUM solutions. Furthermore, the influence of the aerodynamic interaction between the coaxial rotors on the unsteady airloads, frequency, wake structure, induced flow, and force distributions are analysed. Additionally, the results are also compared against computations for a single-rotor case, simulated at similar conditions as the coaxial rotor. It is shown that the effect of tip vortex interaction plays a significant role in unsteady airloads of coaxial rotors at low speeds, while the rotor blade passing effect is obviously strengthened at high-speed

Simulation of Unsteady Aerdynamic Load for Rigid Coaxial Rotor in Forward Flight with Vortex Particle Method

Co-axial rotor systems are frequently used for high-speed helicopters. Nevertheless, issues related to rotor-head drag, aerodynamic performance and vibration should also be considered. Simulating the unsteady aerodynamic loads for a rigid coaxial rotor, including the aerodynamic interactions between rotors and rotor blades, is an essential part of analyzing their vibration characteristics. In this paper, an unsteady aerodynamic analysis based on the vortex-lattice method is presented. In this method, a reversed flow model on the retreating side of the coaxial rotor is proposed based on the unsteady panel method. To account for reversed flow, shedding a vortex from the leading-edge is used rather than from the trailing-edge. Moreover, vortex-blade aerodynamic interactions are modelled. The model considers the unsteady pressure term induced on a blade by tip vortices of other blades, and thus accounts for the aerodynamic interaction between the rotors and its contribution to the unsteady airloads. Coupling the reversed flow model and the vortex-blade aerodynamic interaction model with a viscous vortex particle method is used to simulate the complex wake of the coaxial rotor, closing the loop in modelling aerodynamic interactions of coaxial rotors. Following this, the unsteady aerodynamic loads on the X2 coaxial rotor are simulated in forward flight, and compared with the results of PRASADUM (Parallelized Rotorcraft Analysis for Simulation And Design, developed at the University of Maryland) and CFD/CSD computations with the OVERFLOW and the CREATE-AV Helios tools. The results of the present method agree with the results of the CFD/CSD method, and compare better than the PRASADUM solutions. Furthermore, the influence of the aerodynamic interaction between the coaxial rotors on the unsteady airloads, frequency, wake structure, induced flow and force distributions are analyzed. Additionally, the results are also compared against computation for a single rotor case, simulated at similar conditions as the coaxial rotor. It is shown that the effect of tip vortex interaction plays a significant role in unsteady airloads of coaxial rotors at low-speeds, while the rotor blade passing effect is obvious strengthened at high-speed

Simulation of the Aerodynamic Interaction between Rotor and Ground Obstacle Using Vortex Method

The mutual aerodynamic interaction between rotor wake and surrounding obstacles is complex, and generates high compensatory workload for pilots, degradation of the handling qualities, and performance, and unsteady force on the structure of the obstacles. The interaction also affects the minimum distance between rotorcrafts and obstacles to operate safely. A vortex-based approach is then employed to investigate the complex aerodynamic interaction between rotors and ground obstacle, and identify the distance where the interaction ends, and this is also the objective of the GARTEUR AG22 working group activities. In this approach, the aerodynamic loads of the rotor blades are described through a panel method, and the unsteady behaviour of the rotor wake is modelled using a vortex particle method. The effects of the ground plane and obstacle are accounted for via a viscous boundary model. The method is then applied to a “Large” and a “Wee” rotor near the ground and obstacle, and compared with the earlier experiments carried out at the University of Glasgow. The results show that predicted rotor induced inflow and flow field compare reasonably well with the experiments. Furthermore, at certain conditions, the tip vortices are pushed up and re-injected into the rotor wake due to the effect of the obstacle resulting in a recirculation. Moreover, contrary to without the obstacle case, peak and thickness of the radial outwash near the obstacle are smaller due to the barrier effect of the obstacle, and an upwash is observed. In addition, as the rotor closes to the obstacle, the rotor slipstreams impinge directly on the obstacle, and the upwash near the obstacle is faster, indicating a stronger interaction between the rotor wake and the obstacle. In addition, contrary to the case without the obstacle, the fluctuations of the rotor thrust, and rolling and pitching moments are obviously strengthened. When the distance between the rotor and the obstacle is larger than 3R, the effect of the obstacle is small

Active Example Selection for In-Context Learning

With a handful of demonstration examples, large-scale language models show strong capability to perform various tasks by in-context learning from these examples, without any fine-tuning. We demonstrate that in-context learning performance can be highly unstable across samples of examples, indicating the idiosyncrasies of how language models acquire information. We formulate example selection for in-context learning as a sequential decision problem, and propose a reinforcement learning algorithm for identifying generalizable policies to select demonstration examples. For GPT-2, our learned policies demonstrate strong abilities of generalizing to unseen tasks in training, with a $5.8\%$ improvement on average. Examples selected from our learned policies can even achieve a small improvement on GPT-3 Ada. However, the improvement diminishes on larger GPT-3 models, suggesting emerging capabilities of large language models.Comment: EMNLP 2022, code is available at https://github.com/ChicagoHAI/active-example-selectio

KCNK9 mediates the inhibitory effects of genistein on hepatic metastasis from colon cancer

Objective: The tyrosine-protein kinase inhibitor, genistein, can inhibit cell malignant transformation and has an antitumor effect on various types of cancer. It has been shown that both genistein and KNCK9 can inhibit colon cancer. This research aimed to investigate the suppressive effects of genistein on colon cancer cells and the association between the application of genistein and KCNK9 expression level. Methods: The Cancer Genome Atlas (TCGA) database was used to study the correlation between the KCNK9 expression level and the prognosis of colon cancer patients. HT29 and SW480 colon cancer cell lines were cultured to examine the inhibitory effects of KCNK9 and genistein on colon cancer in vitro, and a mouse model of colon cancer with liver metastasis was established to verify the inhibitory effect of genistein in vivo. Results: KCNK9 was overexpressed in colon cancer cells and was associated with a shorter Overall Survival (OS), a shorter Disease-Specific Survival (DFS), and a shorter Progression-Free Interval (PFI) of colon cancer patients. In vitro experiments showed that downregulation of KCNK9 or genistein application could suppress cell proliferation, migration, and invasion abilities, induce cell cycle quiescence, promote cell apoptosis, and reduce epithelial-mesenchymal transition of the colon cancer cell line. In vivo experiments revealed that silencing of KCNK9 or application of genistein could inhibit hepatic metastasis from colon cancer. Additionally, genistein could inhibit KCNK9 expression, thereby attenuating Wnt/β-catenin signaling pathway. Conclusion: Genistein inhibited the occurrence and progression of colon cancer through Wnt/β-catenin signaling pathway that could be mediated by KCNK9

DNN Filter Bank Cepstral Coefficients for Spoofing Detection

With the development of speech synthesis techniques, automatic speaker verification systems face the serious challenge of spoofing attack. In order to improve the reliability of speaker verification systems, we develop a new filter bank based cepstral feature, deep neural network filter bank cepstral coefficients (DNN-FBCC), to distinguish between natural and spoofed speech. The deep neural network filter bank is automatically generated by training a filter bank neural network (FBNN) using natural and synthetic speech. By adding restrictions on the training rules, the learned weight matrix of FBNN is band-limited and sorted by frequency, similar to the normal filter bank. Unlike the manually designed filter bank, the learned filter bank has different filter shapes in different channels, which can capture the differences between natural and synthetic speech more effectively. The experimental results on the ASVspoof {2015} database show that the Gaussian mixture model maximum-likelihood (GMM-ML) classifier trained by the new feature performs better than the state-of-the-art linear frequency cepstral coefficients (LFCC) based classifier, especially on detecting unknown attacks

Evaluation of ChatGPT as a Question Answering System for Answering Complex Questions

ChatGPT is a powerful large language model (LLM) that has made remarkable progress in natural language understanding. Nevertheless, the performance and limitations of the model still need to be extensively evaluated. As ChatGPT covers resources such as Wikipedia and supports natural language question answering, it has garnered attention as a potential replacement for traditional knowledge based question answering (KBQA) models. Complex question answering is a challenge task of KBQA, which comprehensively tests the ability of models in semantic parsing and reasoning. To assess the performance of ChatGPT as a question answering system (QAS) using its own knowledge, we present a framework that evaluates its ability to answer complex questions. Our approach involves categorizing the potential features of complex questions and describing each test question with multiple labels to identify combinatorial reasoning. Following the black-box testing specifications of CheckList proposed by Ribeiro et.al, we develop an evaluation method to measure the functionality and reliability of ChatGPT in reasoning for answering complex questions. We use the proposed framework to evaluate the performance of ChatGPT in question answering on 8 real-world KB-based CQA datasets, including 6 English and 2 multilingual datasets, with a total of approximately 190,000 test cases. We compare the evaluation results of ChatGPT, GPT-3.5, GPT-3, and FLAN-T5 to identify common long-term problems in LLMs. The dataset and code are available at https://github.com/tan92hl/Complex-Question-Answering-Evaluation-of-ChatGPT

A gut-to-brain signal of fluid osmolarity controls thirst satiation.

Satiation is the process by which eating and drinking reduce appetite. For thirst, oropharyngeal cues have a critical role in driving satiation by reporting to the brain the volume of fluid that has been ingested1-12. By contrast, the mechanisms that relay the osmolarity of ingested fluids remain poorly understood. Here we show that the water and salt content of the gastrointestinal tract are precisely measured and then rapidly communicated to the brain to control drinking behaviour in mice. We demonstrate that this osmosensory signal is necessary and sufficient for satiation during normal drinking, involves the vagus nerve and is transmitted to key forebrain neurons that control thirst and vasopressin secretion. Using microendoscopic imaging, we show that individual neurons compute homeostatic need by integrating this gastrointestinal osmosensory information with oropharyngeal and blood-borne signals. These findings reveal how the fluid homeostasis system monitors the osmolarity of ingested fluids to dynamically control drinking behaviour