Modeling and Simulation of Two-Stage Expansion Air-Powered Engine

2014 ◽  
Vol 694 ◽  
pp. 49-53
Author(s):  
Qing Ping Zheng ◽  
Jin Wang ◽  
Su Li ◽  
Jing Jing Zhou ◽  
Feng Bai

Thermodynamic simulation of a two-stage expansion air-powered engine was established and working process simulation was conducted based on this model. The effects of inlet pressure (0.8Mpa-3.0Mpa), intake duration angle of second-stage (100°CA-160°CA) and cylinder diameter of second-stage (120mm-200mm) on the gas temperature, pressure and output torque were analyzed. The results show that effective power and output torque both increase with the increase of above three parameters, meanwhile effective gas consumption rate decreases.

1996 ◽  
Vol 34 (5-6) ◽  
pp. 367-374 ◽  
Author(s):  
S. Deswaef ◽  
T. Salmon ◽  
S. Hiligsmann ◽  
X. Taillieu ◽  
N. Milande ◽  
...  

The reduction of high concentrations of gypsum (up to 110 kg/m3) is investigated in a two stage immobilised cell bioreactor. The first stage is mainly colonised by a consortium of acidogenic bacteria and sulphate reducing bacteria oxidising volatile fatty acids with more than 2 carbons (mainly, butyrate and propionate). The gypsum consumption rate is rather high (11 kg/m3.day). Most of acetate remains unconverted in this first stage. It is partially converted in the second stage (residence time : 12 days) which is predominantly colonised by acetate oxidising bacteria. The gypsum consumption rate is much lower than in the first stage: 3 kg/m3.day. With both stages, it is possible to reach an almost complete conversion of gypsum with an overall capacity of 6.1 kg gypsum/m3.day. We propose also a very simple model to describe the different transformation rates. It allows us to clearly identify the activity levels of the different types of sulphate reducing bacteria in both stages.


The two-stage ignition of acetaldehyde + oxygen-nitrogen (‘air’) mixtureis investigated under rapid compression. The conditions are parallel to those brought about during the compression stroke in spark ignition and diesel engines. An approximately tenfold compression of pre-mixed gases is brought about mechanically. The time for rapid motion of the piston is 22 ms, and gases are ultimately compressed into a cylindrical volume, length 2.10 cm and diameter 4.50 cm. When air is used the temperature of the gases at the end of compression but before reaction is about 580 K. Heat losses are important in the stages following rapid compression. In a novel development, a miniature turbine is fitted to the combustion chamber. It is used to en­hance cooling rates. Two-stage combustion occurs in the post-compression period. There is a delay before the first stage is perceptible. The first stage itself is only mildly exothermic ( ca . 100 W cm -3 ) and the temperature rises associated with it are not large (∆ T < 150 K). These properties are not greatly affected by initial composition or by the temperature reached during compression. The second stage, however, is very strongly affected in intensity by such changes. Increases of mixture strength and of compressed gas temperature enhance both the extent and the rate of temperature change. For example, the second stage of combustion is weak in very lean mixtures ( ϕ < 0.25) and the approach to the maximum tem­perature is sufficiently slow for reaction to be markedly non-adiabatic; however, with mixtures for which ϕ > 0.5, very vigorous reaction occurs, and flame temperatures achieve adiabatic values. The total delay time before second-stage ignition is least for a mixture with ϕ = 0.6. This minimum reflects differing responses to mixture strength of its two constituent times. The duration of the first phase (ז 1 ) in which there is no measurable heat output, does not vary much in very lean mixtures, but it lengthens in the range 0.5 < ϕ < 1.0. The effect may be attributed to a small diminution in compressed gas temperature due to a decrease in γ . The duration of the second phase (ז 2 ) in which two-stage heat release is observed falls very sharply as the mixture strength is increased, and for ϕ > 0.5 it is negligible. However, the second phase is lengthened substantially when heat-transfer rates are artificially enhanced; the second stage of two-stage ignition may even be quenched in consequence. This is a most important and novel conclusion of the present study.


Author(s):  
Mohammad Rizk Assaf ◽  
Abdel-Nasser Assimi

In this article, the authors investigate the enhanced two stage MMSE (TS-MMSE) equalizer in bit-interleaved coded FBMC/OQAM system which gives a tradeoff between complexity and performance, since error correcting codes limits error propagation, so this allows the equalizer to remove not only ICI but also ISI in the second stage. The proposed equalizer has shown less design complexity compared to the other MMSE equalizers. The obtained results show that the probability of error is improved where SNR gain reaches 2 dB measured at BER compared with ICI cancellation for different types of modulation schemes and ITU Vehicular B channel model. Some simulation results are provided to illustrate the effectiveness of the proposed equalizer.


2021 ◽  
pp. 016555152199980
Author(s):  
Yuanyuan Lin ◽  
Chao Huang ◽  
Wei Yao ◽  
Yifei Shao

Attraction recommendation plays an important role in tourism, such as solving information overload problems and recommending proper attractions to users. Currently, most recommendation methods are dedicated to improving the accuracy of recommendations. However, recommendation methods only focusing on accuracy tend to recommend popular items that are often purchased by users, which results in a lack of diversity and low visibility of non-popular items. Hence, many studies have suggested the importance of recommendation diversity and proposed improved methods, but there is room for improvement. First, the definition of diversity for different items requires consideration for domain characteristics. Second, the existing algorithms for improving diversity sacrifice the accuracy of recommendations. Therefore, the article utilises the topic ‘features of attractions’ to define the calculation method of recommendation diversity. We developed a two-stage optimisation model to enhance recommendation diversity while maintaining the accuracy of recommendations. In the first stage, an optimisation model considering topic diversity is proposed to increase recommendation diversity and generate candidate attractions. In the second stage, we propose a minimisation misclassification cost optimisation model to balance recommendation diversity and accuracy. To assess the performance of the proposed method, experiments are conducted with real-world travel data. The results indicate that the proposed two-stage optimisation model can significantly improve the diversity and accuracy of recommendations.


Author(s):  
Lu Chen ◽  
Handing Wang ◽  
Wenping Ma

AbstractReal-world optimization applications in complex systems always contain multiple factors to be optimized, which can be formulated as multi-objective optimization problems. These problems have been solved by many evolutionary algorithms like MOEA/D, NSGA-III, and KnEA. However, when the numbers of decision variables and objectives increase, the computation costs of those mentioned algorithms will be unaffordable. To reduce such high computation cost on large-scale many-objective optimization problems, we proposed a two-stage framework. The first stage of the proposed algorithm combines with a multi-tasking optimization strategy and a bi-directional search strategy, where the original problem is reformulated as a multi-tasking optimization problem in the decision space to enhance the convergence. To improve the diversity, in the second stage, the proposed algorithm applies multi-tasking optimization to a number of sub-problems based on reference points in the objective space. In this paper, to show the effectiveness of the proposed algorithm, we test the algorithm on the DTLZ and LSMOP problems and compare it with existing algorithms, and it outperforms other compared algorithms in most cases and shows disadvantage on both convergence and diversity.


2020 ◽  
Vol 12 (12) ◽  
pp. 168781402098437
Author(s):  
Liu Jiang ◽  
Guo Zhiping ◽  
Miao Shujing ◽  
He Xiangxin ◽  
Zhu Xinyu

In order to meet the requirements of output torque, efficiency and compact shape of micro-spindles for small parts machining, a two-stage axial micro air turbine spindle with an axial inlet and outlet is proposed. Based on the k-ω turbulence model of SST, the flow field and operation characteristics of the two-stage axial micro air turbine spindle were studied using computational fluid dynamics (CFD) combined with an experimental study. We obtained the air turbine spindle under different working conditions of the loss and torque characteristics. When the inlet pressure was 300 KPa, the output speed of the two-stage turbine was 100,000 rpm, 9% higher than that of a single-stage turbine output torque. The total torque reached 6.39 N·mm, and the maximum efficiency of the turbine and the spindle were 42.2% and 32.3%, respectively. Through the research on the innovative structure of the two-stage axial micro air turbine spindle, the overall performance of the principle prototype has been significantly improved and the problems of insufficient output torque and low working efficiency in high-speed micro-machining can be solved practically, which laid a solid foundation for improving the machining efficiency of small parts and reducing the size of micro machine tool.


Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 543
Author(s):  
Alejandra Ríos ◽  
Eusebio E. Hernández ◽  
S. Ivvan Valdez

This paper introduces a two-stage method based on bio-inspired algorithms for the design optimization of a class of general Stewart platforms. The first stage performs a mono-objective optimization in order to reach, with sufficient dexterity, a regular target workspace while minimizing the elements’ lengths. For this optimization problem, we compare three bio-inspired algorithms: the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO), and the Boltzman Univariate Marginal Distribution Algorithm (BUMDA). The second stage looks for the most suitable gains of a Proportional Integral Derivative (PID) control via the minimization of two conflicting objectives: one based on energy consumption and the tracking error of a target trajectory. To this effect, we compare two multi-objective algorithms: the Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) and Non-dominated Sorting Genetic Algorithm-III (NSGA-III). The main contributions lie in the optimization model, the proposal of a two-stage optimization method, and the findings of the performance of different bio-inspired algorithms for each stage. Furthermore, we show optimized designs delivered by the proposed method and provide directions for the best-performing algorithms through performance metrics and statistical hypothesis tests.


Mathematics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 52
Author(s):  
José Niño-Mora

We consider the multi-armed bandit problem with penalties for switching that include setup delays and costs, extending the former results of the author for the special case with no switching delays. A priority index for projects with setup delays that characterizes, in part, optimal policies was introduced by Asawa and Teneketzis in 1996, yet without giving a means of computing it. We present a fast two-stage index computing method, which computes the continuation index (which applies when the project has been set up) in a first stage and certain extra quantities with cubic (arithmetic-operation) complexity in the number of project states and then computes the switching index (which applies when the project is not set up), in a second stage, with quadratic complexity. The approach is based on new methodological advances on restless bandit indexation, which are introduced and deployed herein, being motivated by the limitations of previous results, exploiting the fact that the aforementioned index is the Whittle index of the project in its restless reformulation. A numerical study demonstrates substantial runtime speed-ups of the new two-stage index algorithm versus a general one-stage Whittle index algorithm. The study further gives evidence that, in a multi-project setting, the index policy is consistently nearly optimal.


Author(s):  
D.W. Paty

ABSTRACT:MS could well be a two stage disease. The first stage involves the sequential development of multiple small lesions, mostly inflammatory, that accumulate at a given rate. The second stage could be that of consolidation and confluence of lesions that involves not only demyelination but gliosis. MRI now gives us an opportunity to watch the evolution of these processes and also to monitor treatment effects. It is only after the evolution of this process is understood that we can design rational therapies directed toward the prevention of spasticity in MS.


2020 ◽  
Vol 10 (11) ◽  
pp. 3833 ◽  
Author(s):  
Haidar Almubarak ◽  
Yakoub Bazi ◽  
Naif Alajlan

In this paper, we propose a method for localizing the optic nerve head and segmenting the optic disc/cup in retinal fundus images. The approach is based on a simple two-stage Mask-RCNN compared to sophisticated methods that represent the state-of-the-art in the literature. In the first stage, we detect and crop around the optic nerve head then feed the cropped image as input for the second stage. The second stage network is trained using a weighted loss to produce the final segmentation. To further improve the detection in the first stage, we propose a new fine-tuning strategy by combining the cropping output of the first stage with the original training image to train a new detection network using different scales for the region proposal network anchors. We evaluate the method on Retinal Fundus Images for Glaucoma Analysis (REFUGE), Magrabi, and MESSIDOR datasets. We used the REFUGE training subset to train the models in the proposed method. Our method achieved 0.0430 mean absolute error in the vertical cup-to-disc ratio (MAE vCDR) on the REFUGE test set compared to 0.0414 obtained using complex and multiple ensemble networks methods. The models trained with the proposed method transfer well to datasets outside REFUGE, achieving a MAE vCDR of 0.0785 and 0.077 on MESSIDOR and Magrabi datasets, respectively, without being retrained. In terms of detection accuracy, the proposed new fine-tuning strategy improved the detection rate from 96.7% to 98.04% on MESSIDOR and from 93.6% to 100% on Magrabi datasets compared to the reported detection rates in the literature.


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