Nonlinear thermoacoustics of ducted premixed flames: The influence of perturbation convection speed

When a premixed flame is placed within a duct, acoustic waves induce velocity perturbations at the flame’s base. These travel down the flame, distorting its surface and modulating its heat release. This can induce self-sustained thermoacoustic oscillations. Although the phase speed of these perturbations is often assumed to equal the mean flow speed, experiments conducted in other studies and Direct Numerical Simulation (DNS) conducted in this study show that it varies with the acoustic frequency. In this paper, we examine how these variations affect the nonlinear thermoacoustic behaviour. We model the heat release with a nonlinear kinematic G-equation, in which the velocity perturbation is modelled on DNS results. The acoustics are governed by linearised momentum and energy equations. We calculate the flame describing function (FDF) using harmonic forcing at several frequencies and amplitudes. Then we calculate thermoacoustic limit cycles and explain their existence and stability by examining the amplitude-dependence of the gain and phase of the FDF. We find that, when the phase speed equals the mean flow speed, the system has only one stable state. When the phase speed does not equal the mean flow speed, however, the system supports multiple limit cycles because the phase of the FDF changes significantly with oscillation amplitude. This shows that the phase speed of velocity perturbations has a strong influence on the nonlinear thermoacoustic behaviour of ducted premixed flames.     

Tensile properties of modified 9Cr-1Mo steel by shear punch testing and correlation with microstructures

Modified 9Cr-1Mo ferritic steel (P91) is subjected to a series of heat treatments consisting of soaking for 5 min at the selected temperatures in the range 973 K–1623 K (below Ac₁ to above Ac₄) followed by oil quenching and tempering at 1033 K for 1 h to obtain different microstructural conditions. The tensile properties of the different microstructural conditions are evaluated from small volumes of material by shear punch test technique. A new methodology for evaluating yield strength, ultimate tensile strength and strain hardening exponent from shear punch test by using correlation equations without employing empirical constants is presented and validated. The changes in the tensile properties are related to the microstructural changes of the steel investigated by electron microscopic studies. The steel exhibits minimum strength and hardness when soaked between Ac₁ and Ac₃ (intercritical range) temperatures due to the replacement of original lath martensitic structure with subgrains. The finer martensitic microstructure produced in the steel after soaking at temperatures above Ac₃ leads to a monotonic increase in hardness and strength with decreasing strain hardening exponent. For soaking temperatures above Ac₄, the hardness and strength of the steel increases marginally due to the formation of soft δ ferrite.     

Effect of vacuum annealing on field emission from heavily phosphorus doped homoepitaxial (111) diamond

Vacuum anneal treatments effects on field emission properties of phosphorus doped diamond are discussed. The C1s core level of diamond is characterized by X-ray photoelectron spectroscopy (XPS). With increasing annealing temperature, firstly oxygen desorption takes place from surface, which induce surface reconstruction followed by graphitization of surface. Field emission properties, therefore, strongly depend on vacuum anneal temperature. The threshold voltage of diamond annealed at 900 °C is the lowest. Fowler–Nordheim plots indicate that diamond annealed at 900 °C has the lowest barrier height, which is in good agreement with electron affinities as measured on carbon reconstructed surface. Further increased annealing temperature induces surface graphitization, which causes a threshold voltage increase of field emission.     

Why do Firms Locate Across Multiple Clusters? Cluster Density,Capabilities and Ethnic Ties

It is well-accepted that firms locate in clusters to benefit from spillover effects from similar firms in the location. However, some firms choose to locate in multiple clusters. In this paper, we focus on the phenomenon of multi-cluster presence. Through an empirical investigation of 95 firms from the information technology enabled service industry within India, we analyze the drivers of membership across multiple clusters. Our findings indicate first that firms that are located in lower density clusters show a tendency to locate in a larger number of clusters. Second, firms that are looking for people-based creative capabilities also tend to locate in a larger number of clusters. Finally, the firms that are not founded at the location of ethnic origin of the founder CEO also tend to locate in a larger number of clusters.     

Accelerating Machine-Learning Algorithms on FPGAs using Pattern-Based Decomposition

Machine-learning algorithms are employed in a wide variety of applications to extract useful information from data sets, and many are known to suffer from super-linear increases in computational time with increasing data size and number of signals being processed (data dimension). Certain principal machine-learning algorithms are commonly found embedded in larger detection, estimation, or classification operations. Three such principal algorithms are the Parzen window-based, non-parametric estimation of Probability Density Functions (PDFs), K-means clustering and correlation. Because they form an integral part of numerous machine-learning applications, fast and efficient execution of these algorithms is extremely desirable. FPGA-based reconfigurable computing (RC) has been successfully used to accelerate computationally intensive problems in a wide variety of scientific domains to achieve speedup over traditional software implementations. However, this potential benefit is quite often not fully realized because creating efficient FPGA designs is generally carried out in a laborious, case-specific manner requiring a great amount of redundant time and effort. In this paper, an approach using pattern-based decomposition for algorithm acceleration on FPGAs is proposed that offers significant increases in productivity via design reusability. Using this approach, we design, analyze, and implement a multi-dimensional PDF estimation algorithm using Gaussian kernels on FPGAs. First, the algorithm’s amenability to a hardware paradigm and expected speedups are predicted. After implementation, actual speedup and performance metrics are compared to the predictions, showing speedup on the order of 20× over a 3.2 GHz processor. Multi-core architectures are developed to further improve performance by scaling the design. Portability of the hardware design across multiple FPGA platforms is also analyzed. After implementing the PDF algorithm, the value of pattern-based decomposition to support reuse is demonstrated by rapid development of the K-means and correlation algorithms.     

基于NPU和NSE系统的控制平面分析

典型的网络系统包括一个或多个用于监视数据平面分组处理的控制平面CPU。通过CPU进行的控制更新将导致存储于网络搜索引擎(NSE)和相联SRAM中的路由表和政策表的更新。在数据平面中，NSE和SRAM通过诸如网络处理机论坛的LookAside-1(LA-1)接口等高速接口连接至网络处理单元(NPU)。从外部CPU至NSE和SRAM的控制平面更新信息可以经由带内线路(即在NPU内部并通过NPU的LA-1接口)来传送。     

Modeling and Multiresponse Optimization of the Mechanical Properties of Roselle Fiber-Reinforced Vinyl Ester Composite

In this study, the roselle fiber-reinforced vinyl ester composites were prepared based on Taguchi’s L₂₇ experimental design using hand lay-up technique. A gray-based Taguchi technique was used to optimize the process parameters with mechanical properties (multiple performance characteristics). The results also show that the fiber content is the most significant process parameter which greatly affects the mechanical properties. It was proved that the multiple performance characteristics of the plant-based natural fiber-reinforced polymer composites can be effectively improved by this method. The proposed response surface mathematical models to predict mechanical properties of composite were found statistically valid.     

Dark fermentative biohydrogen production by Acinetobacter junii-AH4 utilizing various industry wastewaters

Hydrogen (H₂) is one of the most promising renewable energy sources, anaerobic bacterial H₂ fermentation is considered as one of the most environmentally sustainable alternatives to meet the potential fossil fuel demand. Bio-H₂ is the cleanest and most effective source of energy provided by the dark fermentation utilizing organic substrates and different wastewaters. In this study, the bio-H₂ production was achieved by using the bacteria Acinetobacter junii-AH4. Further, optimization was carried out at different pH (5.0–8.0) in the presence of wastewaters as substrates (Rice mill wastewater (RMWW), Food wastewater (FWW) and Sugar wastewater (SWW). In this way, the optimized experiments excelled with the maximum cumulative H₂ production of 566.44 ± 3.5 mL/L (100% FWW at pH 7.5) in the presence of Acinetobacter junii-AH4. To achieve this, a bioreactor (3 L) was employed for the effective production of H₂ and Acinetobacter junii-AH4 has shown the highest cumulative H₂ of 613.2 ± 3.0 mL/L, HPR of 8.5 ± 0.4 mL/L/h, HY of 1.8 ± 0.09 mol H₂/mol glucose. Altogether, the present study showed a COD removal efficiency of 79.9 ± 3.5% by utilizing 100% food wastewater at pH 7.5. The modeled data established a batch fermentation system for sustainable H₂ production. This study has aided to achieve an ecofriendly approach using specific wastewaters for the production of bio-H₂.     

Cloud vendor selection for the healthcare industry using a big data-driven decision model with probabilistic linguistic information

Applied Intelligence - The role of cloud services in the data-intensive industry is indispensable. Cision recently reported that the cloud market would grow to 55 billion USD, with an active...