The New Performance Calculation Method of Fouled Axial Flow Compressor

Fouling is the most important performance degradation factor, so it is necessary to accurately predict the effect of fouling on engine performance. In the previous research, it is very difficult to accurately model the fouled axial flow compressor. This paper develops a new performance calculation method of fouled multistage axial flow compressor based on experiment result and operating data. For multistage compressor, the whole compressor is decomposed into two sections. The first section includes the first 50% stages which reflect the fouling level, and the second section includes the last 50% stages which are viewed as the clean stage because of less deposits. In this model, the performance of the first section is obtained by combining scaling law method and linear progression model with traditional stage stacking method; simultaneously ambient conditions and engine configurations are considered. On the other hand, the performance of the second section is calculated by averaged infinitesimal stage method which is based on Reynolds’ law of similarity. Finally, the model is successfully applied to predict the 8-stage axial flow compressor and 16-stage LM2500-30 compressor. The change of thermodynamic parameters such as pressure ratio, efficiency with the operating time, and stage number is analyzed in detail

1-[3,5-Bis(trifluoro­meth­yl)phen­yl]-3-(2-pyrid­yl)thio­urea

The title compound, C14H9F6N3S, exhibits a nearly planar conformation in the solid state, with a dihedral angle between the planes of the benzene and pyridine rings of 14.86 (3)°. The pyridine N atom allows for the formation of a six-membered N—H⋯Npy hydrogen-bonded ring, thus forcing the two amide H atoms of the thio­urea group to point in opposite directions. The second N—H group forms an inter­molecular N—H⋯S hydrogen bond with the S atom of an adjacent mol­ecule. The F atoms of the two trifluoro­methyl groups display rotational disorder around the C—CF3 axis, with an occupancy ratio of 0.54 (1):0.46 (1)

Prediction of Agricultural Water Consumption in 2 Regions of China Based on Fractional-Order Cumulative Discrete GreyModel

In this paper, a new forecasting method of agricultural water demand, fractional-order cumulative discrete grey model, is proposed. Firstly, the best fitting of historical data is used to construct the optimization model. MATLAB programming is applied to solve the optimization model and obtain the optimal order. Secondly, the fractional-order cumulative discrete grey model in this paper is compared with GM (1, 1) model to verify the performance of the model. Finally, Handan region of Hebei Province and Jingzhou region of Hubei Province were selected as the study areas to predict their agricultural water consumptions. .e results show that the fractional-order cumulative discrete grey model has better prediction performance than the GM (1, 1) model. It can be used as an effective method for forecasting agricultural water consumption

AIF Downregulation and Its Interaction with STK3 in Renal Cell Carcinoma

Apoptosis-inducing factor (AIF) plays a crucial role in caspase-independent programmed cell death by triggering chromatin condensation and DNA fragmentation. Therefore, it might be involved in cell homeostasis and tumor development. In this study, we report significant AIF downregulation in the majority of renal cell carcinomas (RCC). In a group of RCC specimens, 84% (43 out of 51) had AIF downregulation by immunohistochemistry stain. Additional 10 kidney tumors, including an oxyphilic adenoma, also had significant AIF downregulation by Northern blot analysis. The mechanisms of the AIF downregulation included both AIF deletion and its promoter methylation. Forced expression of AIF in RCC cell lines induced massive apoptosis. Further analysis revealed that AIF interacted with STK3, a known regulator of apoptosis, and enhanced its phosphorylation at Thr180. These results suggest that AIF downregulation is a common event in kidney tumor development. AIF loss may lead to decreased STK3 activity, defective apoptosis and malignant transformation

Bayesian adversarial multi-node bandit for optimal smart grid protection against cyber attacks

The cyber security of smart grids has become one of key problems in developing reliable modern power and energy systems. This paper introduces a non-stationary adversarial cost with a variation constraint for smart grids and enables us to investigate the problem of optimal smart grid protection against cyber attacks in a relatively practical scenario. In particular, a Bayesian multi-node bandit (MNB) model with adversarial costs is constructed and a new regret function is defined for this model. An algorithm called Thompson–Hedge algorithm is presented to solve the problem and the superior performance of the proposed algorithm is proven in terms of the convergence rate of the regret function. The applicability of the algorithm to real smart grid scenarios is verified and the performance of the algorithm is also demonstrated by numerical examples

MacFormer: Map-Agent Coupled Transformer for Real-time and Robust Trajectory Prediction

Predicting the future behavior of agents is a fundamental task in autonomous vehicle domains. Accurate prediction relies on comprehending the surrounding map, which significantly regularizes agent behaviors. However, existing methods have limitations in exploiting the map and exhibit a strong dependence on historical trajectories, which yield unsatisfactory prediction performance and robustness. Additionally, their heavy network architectures impede real-time applications. To tackle these problems, we propose Map-Agent Coupled Transformer (MacFormer) for real-time and robust trajectory prediction. Our framework explicitly incorporates map constraints into the network via two carefully designed modules named coupled map and reference extractor. A novel multi-task optimization strategy (MTOS) is presented to enhance learning of topology and rule constraints. We also devise bilateral query scheme in context fusion for a more efficient and lightweight network. We evaluated our approach on Argoverse 1, Argoverse 2, and nuScenes real-world benchmarks, where it all achieved state-of-the-art performance with the lowest inference latency and smallest model size. Experiments also demonstrate that our framework is resilient to imperfect tracklet inputs. Furthermore, we show that by combining with our proposed strategies, classical models outperform their baselines, further validating the versatility of our framework.Comment: Accepted by IEEE Robotics and Automation Letters. 8 Pages, 9 Figures, 9 Tables. Video: https://www.youtube.com/watch?v=XY388iI6sP

Study on cutting performance of SiCp/Al composite using textured YG8 carbide tool

Precision machining of SiCp/Al composites is a challenge due to the existence of reinforcement phase in this material. This work focuses on the study of the textured tools’ cutting performance on SiCp/Al composite, as well as the comparison with non-textured tools. The results show that the micro-pit textured tool can reduce the cutting force by 5–13% and cutting length by 9–39%. Compared with non-textured tools, the cutting stability of the micro-pit textured tools is better. It is found that the surface roughness is the smallest (0.4 μm) when the texture spacing is 100 μm, and the residual stress can be minimized to around 15 MPa in the case of texture spacing 80 μm. In addition, the SiC particles with size of around 2–12 μm in the SiCp/Al composite may play a supporting role between the texture and the chips, which results in three-body friction, thereby reducing tool wear, sticking, and secondary cutting phenomenon. At the same time, some SiC particles enter into the micro-pit texture, so that the number of residual particles on the surface is reduced and the friction between the tool and the surface then decreases, which improves the surface roughness, and reduces the surface residual stress.TU Berlin, Open-Access-Mittel - 202