The Time-Variant Aerodynamic Response of a Stator Row Including the Effects of Airfoil Camber

1980 ◽  
Vol 102 (2) ◽  
pp. 334-342 ◽  
Author(s):  
S. Fleeter ◽  
W. A. Bennett ◽  
R. L. Jay
Keyword(s):  
Flat Plate ◽  
Pressure Difference ◽  
Large Scale ◽  
State Of The Art ◽  
Dynamic Pressure ◽  
Pressure Coefficient ◽  
Forced Response ◽  
Phase Lag ◽  
Forcing Function ◽  
Wide Range

An experimental investigation was conducted to quantitatively determine the validity and applicability of state-of-the-art transverse gust cascade analyses. This was accomplished by obtaining fundamental time-variant forced response data at realistic values of key parameters in a large-scale, low-speed, single-stage research compressor. The forcing function, the velocity defect created by the rotor blade wakes, was measured with a crossed hot-wire probe. The resulting time-variant aerodynamic response was measured by means of flush mounted high response pressure transducers on both flat plate and cambered airfoil stator vane rows over a wide range of incidence angles. These dynamic data were then analyzed to determine the chordwise variation of the unsteady pressure difference in terms of a dimensionless dynamic pressure coefficient and an aerodynamic phase lag referenced to a transverse gust at the leading edge of the vanes. These dimensionless pressure difference data were all correlated with predictions obtained from a state-of-the-art compressible transverse gust, flat plate cascade analysis. Correlation of the classical flat plate unsteady data with the predictions permitted the range of validity of the analysis to be assessed in terms of incidence angle. Correlation of the cambered vane unsteady data with those for the flat plate and with the predictions allowed the effects of airfoil camber as well as the applicability of the flat plate prediction to realistic cambered airfoils to be quantitatively determined.

Rotor Wake Generated Unsteady Aerodynamic Response of a Compressor Stator

1978 ◽  
Vol 100 (4) ◽  
pp. 664-675 ◽  
Author(s):  
S. Fleeter ◽  
R. L. Jay ◽  
W. A. Bennett
Keyword(s):  
Pressure Difference ◽  
Second Harmonic ◽  
Incidence Angle ◽  
Dynamic Pressure ◽  
Pressure Differential ◽  
Phase Lag ◽  
Trailing Edge ◽  
Unsteady Pressure ◽  
Forcing Function ◽  
Reduced Frequency

An experimental investigation was conducted to determine the fluctuating pressure distribution on a stationary vane row, with the primary source of excitation being the wakes from the upstream rotor blades. This was accomplished in a large scale, low speed, single stage research compressor. The forcing function, the velocity defect created by the rotor wakes, was measured with a crossed hot-wire probe. The aerodynamic response on the vanes was measured by means of flush mounted high response dynamic pressure transducers. The dynamic data were analyzed to determine the chordwise distribution of the dynamic pressure coefficient and aerodynamic phase lag as referenced to a transverse gust at the vane leading edge. Vane suction and pressure surface data as well as the pressure difference across the vane were obtained for reduced frequency values ranging from 3.65 to 16.80 and for an incidence angle range of 35.5 deg. The pressure difference data were correlated with a state-of-the-art aerodynamic cascade transverse gust analysis. The correlation was quite good for all reduced frequency values for small values of incidence. For the more negative incidence angle data points, it was shown that a convected wake phenomena not modeled in the analysis existed. Both the first and second harmonic unsteady pressure differential magnitude data decrease in the chordwise direction. The second harmonic magnitude data attains a value very nearly zero at the vane trailing edge transducer location, while the first harmonic data is still finite, albeit small, at this location. That the magnitude of the unsteady pressure differential data approaches zero near to the trailing edge, particularly the second harmonic data which has reduced frequency values to 16.8, is significant in that it reflects upon the validity of the Kutta condition for unsteady flows.

scholarly journals DeepMAsED: evaluating the quality of metagenomic assemblies

2020 ◽  
Vol 36 (10) ◽  
pp. 3011-3017 ◽  
Author(s):  
Olga Mineeva ◽  
Mateo Rojas-Carulla ◽  
Ruth E Ley ◽  
Bernhard Schölkopf ◽  
Nicholas D Youngblut
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Ground Truth ◽  
Supplementary Information ◽  
Learning Approach ◽  
Wide Range ◽  
Metagenome Assembly ◽  
Model Training ◽  
Reference Genomes

Abstract Motivation Methodological advances in metagenome assembly are rapidly increasing in the number of published metagenome assemblies. However, identifying misassemblies is challenging due to a lack of closely related reference genomes that can act as pseudo ground truth. Existing reference-free methods are no longer maintained, can make strong assumptions that may not hold across a diversity of research projects, and have not been validated on large-scale metagenome assemblies. Results We present DeepMAsED, a deep learning approach for identifying misassembled contigs without the need for reference genomes. Moreover, we provide an in silico pipeline for generating large-scale, realistic metagenome assemblies for comprehensive model training and testing. DeepMAsED accuracy substantially exceeds the state-of-the-art when applied to large and complex metagenome assemblies. Our model estimates a 1% contig misassembly rate in two recent large-scale metagenome assembly publications. Conclusions DeepMAsED accurately identifies misassemblies in metagenome-assembled contigs from a broad diversity of bacteria and archaea without the need for reference genomes or strong modeling assumptions. Running DeepMAsED is straight-forward, as well as is model re-training with our dataset generation pipeline. Therefore, DeepMAsED is a flexible misassembly classifier that can be applied to a wide range of metagenome assembly projects. Availability and implementation DeepMAsED is available from GitHub at https://github.com/leylabmpi/DeepMAsED. Supplementary information Supplementary data are available at Bioinformatics online.

On the Response of a Strongly Diffusing Flow to Propagating Wakes

2009 ◽  
Vol 131 (2) ◽  
Author(s):  
J. P. Gostelow ◽  
R. L. Thomas ◽  
D. S. Adebayo
Keyword(s):  
Flat Plate ◽  
Large Scale ◽  
Separation Bubble ◽  
Leading Edge ◽  
Adverse Pressure Gradient ◽  
Turbulent Spots ◽  
Stall Margin ◽  
Transverse Jet ◽  
Wide Range ◽  
Calming Effect

Further evidence on the similarities between transition and separation phenomena occurring in turbomachinery and wind tunnel flows is provided by measurements on a large scale flat plate under a strong adverse pressure gradient. The flat plate has a long laminar separation bubble and is subjected to a range of disturbances with triggering caused by injection of a transverse jet and subsequently by wakes generated by rods moving transversely upstream of the leading edge. Wakes were originally presented individually. Each individual wake provoked a vigorous turbulent patch, resulting in the instantaneous collapse of the separation bubble. This was followed by a very strong, and stable, calmed region. Following the lead given by the experiments of Gutmark and Blackwelder (1987, “On the Structure of Turbulent Spot in a Heated Laminar Boundary Layer,” Exp. Fluids, 5, pp. 207–229.) on triggered turbulent spots, wakes were then presented in pairs at different wake spacing intervals. In this way wake interaction effects could be investigated in more detail. As in the work on triggered turbulent spots the spacing between impinging wakes was systematically varied; it was found that for close wake spacings the calmed region acted to suppress the turbulence in the following turbulent patch. To investigate whether this phenomenon was a recurring one or whether the flow then reverted back to its unperturbed state, the experiments were repeated with three and four rods instead of two. This has the potential for making available a wide range of variables including direction and speed of rod rotation. It was found that the subsequent wakes were also suppressed by the calming effect. It may be anticipated that this repeating situation is present in a turbomachine, resulting in hidden benefits for blade count and efficiency. There may also conceivably be blade loading advantages while retaining favorable heat transfer conditions in high pressure turbines or stall margin in axial compressors. The inherent and prospective benefits of the calming effect therefore need to be understood thoroughly and new opportunities exploited where this is feasible.a

Hydro Francis Runner Stability and Forced Response Calculations

2019 ◽  
Author(s):  
Charles Seeley ◽  
Sunil Patil ◽  
Andy Madden ◽  
Stuart Connell ◽  
Gwenael Hauet ◽  
...  
Keyword(s):  
Large Scale ◽  
Forced Response ◽  
Operating Conditions ◽  
Mode Shapes ◽  
Symmetric Model ◽  
Validation Data ◽  
Fea Model ◽  
Forcing Function ◽  
Cyclic Symmetric ◽  
Work Done

Abstract Hydroelectric power generation accounts for 7% of the total world electric energy production. Francis turbines are often employed in large-scale hydro projects and represent 60% of the total installed base. Outputs up to 800 MW are available and efficiencies of 95% are common. Cost, performance, and design cycle time are factors that continue to drive new designs as well as retrofits. This motivates the development of more sophisticated analysis tools to better assess runner performance earlier in the design phase. The focus of this paper is to demonstrate high fidelity and time-efficient runner damping and forced response calculations based on one-way fluid-structure interaction (FSI) using loosely coupled commercial finite element analysis (FEA) and computational fluid dynamics (CFD) codes. The runner damping is evaluated based on the work done by the fluid on the runner. The calculation of the work first involves determining the runner mode shapes and natural frequencies using a cyclic symmetric FEA model with structural elements to represent the runner hardware, and acoustic fluid elements to represent the mass loading effect of the fluid. The mode shapes are then used in a transient CFD calculation to determine the damping which represents the work done by the fluid on the runner. Positive damping represents stability from flutter perspective while negative damping represents unstable operating conditions. A transient CFD calculation was performed on a runner to obtain engine order forcing function from upstream stationary vanes. This unsteady forcing function was mapped to the FEA model. Care is taken to account for the proper inter-blade phase angle on the cyclic symmetric model. The hydraulic damping from flutter calculations was also provided as input to the forced response. The forced response is then determined using this equivalent proportional damping and modal superposition of the FEA model that includes both the structural and acoustic elements. Results of the developed analysis procedure are presented based on the Tokke runner, that has been the basis of several studies through the Norwegian HydroPower Center. Unique features of the workflow and modeling approaches are discussed in detail. Benefits and challenges for both the FEA model and the CFD model are discussed. The importance of the hydraulic damping, that is traditionally ignored in previous analysis is discussed as well. No validation data is available for the forced response, so this paper is focused on the methodology for the calculations.

scholarly journals Deep Learning-Based Sentimental Analysis for Large-Scale Imbalanced Twitter Data

2019 ◽  
Vol 11 (9) ◽  
pp. 190 ◽  
Author(s):  
Jamal ◽  
Xianqiao ◽  
Aldabbas
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
State Of The Art ◽  
Hybrid Approach ◽  
Principal Component ◽  
Specific Topic ◽  
Weighting Method ◽  
Psychological Conditions ◽  
Twitter Data ◽  
Wide Range

Emotions detection in social media is very effective to measure the mood of people about a specific topic, news, or product. It has a wide range of applications, including identifying psychological conditions such as anxiety or depression in users. However, it is a challenging task to distinguish useful emotions’ features from a large corpus of text because emotions are subjective, with limited fuzzy boundaries that may be expressed in different terminologies and perceptions. To tackle this issue, this paper presents a hybrid approach of deep learning based on TensorFlow with Keras for emotions detection on a large scale of imbalanced tweets’ data. First, preprocessing steps are used to get useful features from raw tweets without noisy data. Second, the entropy weighting method is used to compute the importance of each feature. Third, class balancer is applied to balance each class. Fourth, Principal Component Analysis (PCA) is applied to transform high correlated features into normalized forms. Finally, the TensorFlow based deep learning with Keras algorithm is proposed to predict high-quality features for emotions classification. The proposed methodology is analyzed on a dataset of 1,600,000 tweets collected from the website ‘kaggle’. Comparison is made of the proposed approach with other state of the art techniques on different training ratios. It is proved that the proposed approach outperformed among other techniques.

Using a Windbreak to Improve the Thermal Performance of a Flat Plate Solar Collector: A Feasibility Study

2011 ◽  
Author(s):  
Saeed Moaveni ◽  
Michael C. Watts
Keyword(s):  
Flat Plate ◽  
Heat Loss ◽  
Forced Convection ◽  
Thermal Performance ◽  
Solar Collector ◽  
Large Scale ◽  
Ambient Air ◽  
Absorber Plate ◽  
Wide Range ◽  
Flat Plate Solar Collector

During the past few decades, a wide range of studies have been performed to improve the performance of flat plate solar collectors by either reducing the heat loss from a collector or by increasing the amount of solar radiation absorbed by the absorber plate. Examples of these studies include adding transparent honeycomb to fill the air gap between the glazing and absorber plate to reduce convective heat loss, replacing the air in the gap by other gases such as Argon, Krypton, Xenon and Carbon Dioxide, or adding a chemical coating such as Copper Oxide to increase absorbtance and reduce the emittance of the absorber plate. While these methods improve the collector’s efficiency, they focus primarily on limiting the natural convection that occurs in the collector cavity, or on improving the optical properties of the absorber or glazing. None of these studies have addressed the problem of heat loss due to forced convection to the surrounding ambient air in any detail. Yet, research has shown that forced convection will contribute significantly to the heat loss from a collector. Windbreaks have traditionally been used to direct wind to protect farmland, and to direct wind drifts and sand dunes. Windbreaks also have been shown to provide protection for homes from winter winds which result in reduced heating costs for buildings. While windbreaks have been traditionally used for large scale applications, there is reason to believe that similar benefits can be expected for scaled down applications such as adding a windbreak along side of a flat-plate solar collector. In this paper, we examine the feasibility of using a windbreak to provide a flat plate solar collector protection from the wind in order to improve its performance. A series of experiments were performed wherein the thermal performance of two flat-plate collectors — one without a windbreaker and one with a windbreaker — were measured. The results of these experiments are reported in this paper and the need for further studies to explore different windbreak configurations is discussed.

scholarly journals A large-scale comparative study on peptide encodings for biomedical classification

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Sebastian Spänig ◽  
Siba Mohsen ◽  
Georges Hattab ◽  
Anne-Christin Hauschild ◽  
Dominik Heider
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
State Of The Art ◽  
Multiple Datasets ◽  
Wide Range ◽  
Fixed Parameter ◽  
Additional Sequence ◽  
Automated Machine Learning ◽  
Distinct Peptide ◽  
Comprehensive Study

Abstract Owing to the great variety of distinct peptide encodings, working on a biomedical classification task at hand is challenging. Researchers have to determine encodings capable to represent underlying patterns as numerical input for the subsequent machine learning. A general guideline is lacking in the literature, thus, we present here the first large-scale comprehensive study to investigate the performance of a wide range of encodings on multiple datasets from different biomedical domains. For the sake of completeness, we added additional sequence- and structure-based encodings. In particular, we collected 50 biomedical datasets and defined a fixed parameter space for 48 encoding groups, leading to a total of 397 700 encoded datasets. Our results demonstrate that none of the encodings are superior for all biomedical domains. Nevertheless, some encodings often outperform others, thus reducing the initial encoding selection substantially. Our work offers researchers to objectively compare novel encodings to the state of the art. Our findings pave the way for a more sophisticated encoding optimization, for example, as part of automated machine learning pipelines. The work presented here is implemented as a large-scale, end-to-end workflow designed for easy reproducibility and extensibility. All standardized datasets and results are available for download to comply with FAIR standards.

scholarly journals DeepMAsED: Evaluating the quality of metagenomic assemblies

2019 ◽  
Author(s):  
Mateo Rojas-Carulla ◽  
Ruth E. Ley ◽  
Bernhard Schölkopf ◽  
Nicholas D. Youngblut
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Ground Truth ◽  
Learning Approach ◽  
Wide Range ◽  
Metagenome Assembly ◽  
Model Training ◽  
Modelling Assumptions ◽  
Reference Genomes

AbstractMotivation/backgroundMethodological advances in metagenome assembly are rapidly increasing in the number of published metagenome assemblies. However, identifying misassemblies is challenging due to a lack of closely related reference genomes that can act as pseudo ground truth. Existing reference-free methods are no longer maintained, can make strong assumptions that may not hold across a diversity of research projects, and have not been validated on large scale metagenome assemblies.ResultsWe present DeepMAsED, a deep learning approach for identifying misassembled contigs without the need for reference genomes. Moreover, we provide an in silico pipeline for generating large-scale, realistic metagenome assemblies for comprehensive model training and testing. DeepMAsED accuracy substantially exceeds the state-of-the-art when applied to large and complex metagenome assemblies. Our model estimates close to a 5% contig misassembly rate in two recent large-scale metagenome assembly publications.ConclusionsDeepMAsED accurately identifies misassemblies in metagenome-assembled contigs from a broad diversity of bacteria and archaea without the need for reference genomes or strong modelling assumptions. Running DeepMAsED is straight-forward, as well as is model re-training with our dataset generation pipeline. Therefore, DeepMAsED is a flexible misassembly classifier that can be applied to a wide range of metagenome assembly projects.AvailabilityDeepMAsED is available from GitHub at https://github.com/leylabmpi/DeepMAsED.

Wake Induced Time-Variant Aerodynamics Including Rotor-Stator Axial Spacing Effects

1981 ◽  
Vol 103 (1) ◽  
pp. 59-66 ◽  
Author(s):  
S. Fleeter ◽  
R. L. Jay ◽  
W. A. Bennett
Keyword(s):  
Large Scale ◽  
Primary Source ◽  
Forced Response ◽  
Operating Conditions ◽  
Rotor Blades ◽  
Experimental Program ◽  
Axial Distance ◽  
Spacing Effects ◽  
Spacing Ratio ◽  
Wide Range

The overall objective of this experimental program was to quantify the effects of rotor-stator axial spacing on the fundamental time-variant aerodynamics relevant to forced response in turbomachinery. This was accomplished in a large-scale, low-speed, single-stage research compressor which permitted two rotor-stator axial spacing ratios representative of those found in advanced design compressors to be investigated. At each value of the axial spacing ratio, the aerodynamically induced fluctuating surface pressure distributions on the downstream vane row, with the primary source of excitation being the upstream rotor wakes, were measured over a wide range of compressor operating conditions. The velocity fluctuations created by the passage of the rotor blades were measured in the nonrotating coordinate system. Data obtained described the variation of the rotor wake with both loading and axial distance from the rotor as parameters. These data also served as a reference in the analysis of the resulting time-variant pressure signals on the vane surfaces.

scholarly journals CellFishing.jl: an ultrafast and scalable cell search method for single-cell RNA-sequencing

2018 ◽  
Author(s):  
Kenta Sato ◽  
Koki Tsuyuzaki ◽  
Kentaro Shimizu ◽  
Itoshi Nikaido
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
State Of The Art ◽  
Cell Types ◽  
Cell Search ◽  
Wide Range ◽  
Single Cell Rna Sequencing ◽  
Comparable Accuracy ◽  
Multicellular Organisms

AbstractRecent technical improvements in single-cell RNA sequencing (scRNA-seq) have enabled massively parallel profiling of transcriptomes, thereby promoting large-scale studies encompassing a wide range of cell types of multicellular organisms. With this background, we propose CellFishing.jl, a new method for searching atlas-scale datasets for similar cells and detecting noteworthy genes of query cells with high accuracy and throughput. Using multiple scRNA-seq datasets, we validate that our method demonstrates comparable accuracy to and is markedly faster than the state-of-the-art software. Moreover, CellFishing.jl is scalable to more than one million cells, and the throughput of the search is approximately 1,600 cells per second.

Sign in / Sign up

Export Citation Format

Share Document