Progresses in Particle-Laden Flows Simulations in Multistage Turbomachinery With OpenFOAM

2021 ◽  
Author(s):  
Stefano Oliani ◽  
Riccardo Friso ◽  
Nicola Casari ◽  
Michele Pinelli ◽  
Alessio Suman ◽  
...  
Keyword(s):  
Real Life ◽  
Industrial Applications ◽  
Numerical Code ◽  
Test Case ◽  
Multi Stage ◽  
Common Technique ◽  
Particle Laden Flows ◽  
Aerodynamic Surfaces ◽  
Multiple Reference ◽  
Individual Particles

Abstract Numerical simulations of particle-laden flows have received growing attention in the last decade, due to the broad spectrum of industrial applications in which discrete phases prediction is of interest. Among these, ingestion of particles by turbomachinery is an area that is seeing vivid research and studies. The most common technique to tackle this kind of problem is the Eulerian-Lagrangian method, in which individual particles are tracked inside the domain. At the same time, in multi-stage turbomachinery simulations interfaces are needed to couple the flow solution in adjacent domains in relative motion. In this work, an open-source extension for Lagrangian simulations in multistage rotating machines is presented in the foam-extend environment. Firstly, a thorough discussion of the implementation is presented, with particular emphasis on particle passage through General Grid Interfaces (GGI) and mixing planes. Moreover, a mass-conservative particle redistribution technique is described, as such a property is requested at interfaces between Multiple Reference Frame (MRF). The peculiarities of the algorithm are then shown on a relevant test-case. Eventually, three turbomachinery applications are presented, with growing complexity, to show the capabilities of the numerical code in real-life applications. Simulation results in terms of erosion and impacts on aerodynamic surfaces are also presented as examples of possible parameters of interest in particle-laden flow computations.

Design and Optimization of Multi-Stage Centrifugal Compressors With Uncertainty Quantification of Off Design Performance

2017 ◽  
Author(s):  
A. Romei ◽  
R. Maffulli ◽  
C. Garcia Sanchez ◽  
S. Lavagnoli
Keyword(s):  
Uncertainty Quantification ◽  
Oil And Gas ◽  
Low Cost ◽  
Design Procedure ◽  
Design Tool ◽  
Test Case ◽  
Centrifugal Compressors ◽  
Design Performance ◽  
Leading Role ◽  
Multi Stage

The use of multi-stage centrifugal compressors carries out a leading role in oil and gas process applications. Green operation and market competitiveness require the use of low-cost reliable compression units with high efficiencies and wide operating range. A methodology is presented for the design optimization of multi-stage centrifugal compressors with prediction of the compressor map and estimation of the uncertainty limits. A one-dimensional (1D) design tool has been developed that automatically generates a multi-stage radial compressor satisfying the target machine requirements based on a few input parameters. The compressor performance map is then assessed using the method proposed by Casey-Robinson [1], and the approach developed by Al-Busaidi-Pilidis [2]. The off-design performance method relies on empirical correlations calibrated on the performance maps of many single-stage centrifugal compressors. An uncertainty quantification study on the predicted performance maps was conducted using Monte Carlo method (MCM) and generalized Polynomial Chaos Expansion (gPCE). Finally, the design procedure has been coupled to an in-house optimizer based on evolutionary algorithms. The complete design procedure has been applied to a multi-stage industrial compressor test case. A multi-objective optimization of a multi-stage industrial compressor has been performed targeting maximum compressor efficiency and flow range. The results of the optimization show the existence of optimum compressor architectures and how the Pareto fronts evolve depending on the number of stages and shafts.

scholarly journals Fire safety case study of a railway tunnel: Smoke evacuation

2007 ◽  
Vol 11 (2) ◽  
pp. 207-222 ◽  
Author(s):  
Maele van ◽  
Bart Merci
Keyword(s):  
Fire Safety ◽  
Large Scale ◽  
Natural Ventilation ◽  
Real Life ◽  
Test Case ◽  
Fire Dynamics ◽  
Railway Tunnel ◽  
Tunnel Section ◽  
Safety Case ◽  
Smoke Evacuation

When a fire occurs in a tunnel, it is of great importance to assure the safety of the occupants of the tunnel. This is achieved by creating smoke-free spaces in the tunnel through control of the smoke gases. In this paper, results are presented of a study concerning the fire safety in a real scale railway tunnel test case. Numerical simulations are performed in order to examine the possibility of natural ventilation of smoke in inclined tunnels. Several aspects are taken into account: the length of the simulated tunnel section, the slope of the tunnel and the possible effects of external wind at one portal of the tunnel. The Fire Dynamics Simulator of the National Institute of Standards and Technology, USA, is applied to perform the simulations. The simulations show that for the local behavior of the smoke during the early stages of the fire, the slope of the tunnel is of little importance. Secondly, the results show that external wind and/or pressure conditions have a large effect on the smoke gases inside the tunnel. Finally, some idea for the value of the critical ventilation velocity is given. The study also shows that computational fluid dynamics calculations are a valuable tool for large scale, real life complex fire cases. .

scholarly journals Towards Expert-Inspired Automatic Criterion to Cut a Dendrogram for Real-Industrial Applications

2021 ◽  
Author(s):  
Shikha Suman ◽  
Ashutosh Karna ◽  
Karina Gibert
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Algorithms ◽  
Computational Cost ◽  
Real Life ◽  
Ground Truth ◽  
Industrial Applications ◽  
Underlying Structure ◽  
Cluster Validity ◽  
Cluster Validity Index ◽  
Number Of Clusters

Hierarchical clustering is one of the most preferred choices to understand the underlying structure of a dataset and defining typologies, with multiple applications in real life. Among the existing clustering algorithms, the hierarchical family is one of the most popular, as it permits to understand the inner structure of the dataset and find the number of clusters as an output, unlike popular methods, like k-means. One can adjust the granularity of final clustering to the goals of the analysis themselves. The number of clusters in a hierarchical method relies on the analysis of the resulting dendrogram itself. Experts have criteria to visually inspect the dendrogram and determine the number of clusters. Finding automatic criteria to imitate experts in this task is still an open problem. But, dependence on the expert to cut the tree represents a limitation in real applications like the fields industry 4.0 and additive manufacturing. This paper analyses several cluster validity indexes in the context of determining the suitable number of clusters in hierarchical clustering. A new Cluster Validity Index (CVI) is proposed such that it properly catches the implicit criteria used by experts when analyzing dendrograms. The proposal has been applied on a range of datasets and validated against experts ground-truth overcoming the results obtained by the State of the Art and also significantly reduces the computational cost.

scholarly journals Modified Energy Statistic for Unsupervised Anomaly Detection

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Rupam Mukherjee
Keyword(s):  
Anomaly Detection ◽  
Real Life ◽  
Industrial Applications ◽  
Test Point ◽  
Distance Metrics ◽  
Data Set ◽  
Energy Distance ◽  
New Energy ◽  
Statistical Distance ◽  
Computational Simplicity

For prognostics in industrial applications, the degree of anomaly of a test point from a baseline cluster is estimated using a statistical distance metric. Among different statistical distance metrics, energy distance is an interesting concept based on Newton’s Law of Gravitation, promising simpler computation than classical distance metrics. In this paper, we review the state of the art formulations of energy distance and point out several reasons why they are not directly applicable to the anomaly-detection problem. Thereby, we propose a new energy-based metric called the P-statistic which addresses these issues, is applicable to anomaly detection and retains the computational simplicity of the energy distance. We also demonstrate its effectiveness on a real-life data-set.

State of the art in processing of shape memory alloys with electrical discharge machining: A review

2020 ◽  
pp. 095440542095877
Author(s):  
Ranjit Singh ◽  
Ravi Pratap Singh ◽  
Rajeev Trehan
Keyword(s):  
Shape Memory Alloys ◽  
Shape Memory ◽  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Real Life ◽  
Orthopedic Implants ◽  
Industrial Applications ◽  
Future Research ◽  
Tool Electrode ◽  
Micro Tools

Shape memory alloys (SMAs) have been well known for their superior and excellent properties which makes them an eligible candidate of paramount importance in real-life industrial applications such as; orthopedic implants, actuators, micro tools, stents, coupling and sealing elements, aerospace components, defense instruments, manufacturing elements, bio-medical appliances, etc. In spite of their exceptional properties, the effective processing of these alloys is always seen as a challenge by researchers around the globe. The present article has been therefore attempted to explore the numerous studies conducted to process these alloys by employing the principles of electrical discharge machining (EDM) and its allied approaches. The NiTi-based SMAs have been revealed to be explored majorly among the several types SMAs. The several investigations carried out in the domain of EDM, Wire-EDM, and some conventional processing of various types of SMAs have also been critically reviewed and reported. It also highlights the numerous experimental, theoretical, modeling, and optimization-based researches attempted in EDM of SMAs. It was also reported that the proper selection of process variables, tool electrode, and the dielectrics can substantially improve the overall process effectiveness. Among the various accessible EDM variants used for the processing of SMAs, attempted by the umpteen investigators, the wire-cut EDM process has been revealed as the most explored one for cutting SMAs than the other allied processes such as: die-sinking EDM and powder-mixed EDM. The micro-machining applications of EDM have also been deliberated briefly. The last section of the article reports about the opportunities and the challenges for future research.

Range of Variability in the Dynamics of Semi-Cylindrical Friction Dampers for Turbine Blades

2008 ◽  
Author(s):  
Stefano Zucca ◽  
Daniele Botto ◽  
Muzio M. Gola
Keyword(s):  
Degrees Of Freedom ◽  
Turbine Blades ◽  
Numerical Test ◽  
Forced Response ◽  
Numerical Code ◽  
Test Case ◽  
Force Coefficient ◽  
System A ◽  
Flat Side ◽  
Fatigue Failures

Under-platform dampers are used to reduce resonant stresses in turbine blades to avoid high cycle fatigue failures. In this paper a model of semi-cylindrical under-platform damper (i.e. with one flat side and one curved side) for turbine blades is described. The damper kinematics is characterized by three degrees of freedom (DOFs): in-plane translations and rotation. Static normal loads acting on the damper sides are computed using the three static balance equations of the damper. Non-uniqueness of normal pre-loads acting on the damper sides is highlighted. Implementation of the model in a numerical code for the forced response calculation of turbine blades with under-platform dampers shows that non-uniqueness of normal pre-loads leads to non-uniqueness of the forced response of the system. A numerical test case is presented to show the capabilities of the model and to analyze the effect of the main system parameters (damper mass, excitation force, coefficient of friction and damper rotation) on the damper behavior and on the system dynamics.

scholarly journals CNN-Based Deep Architecture for Health Monitoring of Civil and Industrial Structures Using UAVs

Proceedings ◽  
2019 ◽  
Vol 42 (1) ◽  
pp. 69
Author(s):  
Thomas Harweg ◽  
Annika Peters ◽  
Daniel Bachmann ◽  
Frank Weichert
Keyword(s):  
Health Monitoring ◽  
Real Life ◽  
Image Data ◽  
Steel Structures ◽  
Point Of View ◽  
Object Localization ◽  
Data Annotation ◽  
Industrial Structures ◽  
Multi Stage ◽  
Novel Approach

Health monitoring of civil and industrial structures has been gaining importance since the collapse of the bridge in Genoa (Italy). It is vital for the creation and maintenance of reliable infrastructure. Traditional manual inspections for this task are crucial but time consuming. We present a novel approach for combining Unmanned Aerial Vehicles (UAVs) and artificial intelligence to tackle the above-mentioned challenges. Modern architectures in Convolutional Neural Networks (CNNs) were adapted to the special characteristics of data streams gathered from UAV visual sensors. The approach allows for automated detection and localization of various damages to steel structures, coatings, and fasteners, e.g., cracks or corrosion, under uncertain and real-life environments. The proposed model is based on a multi-stage cascaded classifier to account for the variety of detail level from the optical sensor captured during an UAV flight. This allows for reconciliation of the characteristics of gathered image data and crucial aspects from a steel engineer’s point of view. To improve performance of the system and minimize manual data annotation, we use transfer learning based on the well-known COCO dataset combined with field inspection images. This approach provides a solid data basis for object localization and classification with state-of-the-art CNN architectures.

Short-term management of hydropower reservoirs under meteorological uncertainty by means of multi-stage optimization

2015 ◽  
Vol 63 (7) ◽  
Author(s):  
Steffi Naumann ◽  
Dirk Schwanenberg ◽  
Divas Karimanzira ◽  
Fernando Fan ◽  
Christopher Allen
Keyword(s):  
Management System ◽  
Large Scale ◽  
Mathematical Formulation ◽  
Stochastic Approach ◽  
Army Corps ◽  
Test Case ◽  
Optimization Approach ◽  
Renewable Energy Resources ◽  
Short Term ◽  
Multi Stage

AbstractUncertainty in meteorology, market volatility and balancing requirements for introducing renewable energy resources into the power grid, environmental obligations require robust management of non-intermittent energy sources such as hydropower. In this paper, a probalistic management system is shown and its performance is discussed in relation to the deterministic one. In the system, scenario trees enable to setup a multi-stage stochastic optimization approach as the mathematical formulation of the short-term system management. The Federal Columbia River Power System (FCRPS), managed by the Bonneville Power Administration, the US Army Corps of Engineers and the Bureau of Reclamation, serves as a large-scale test case for the application of the management system and proves that the stochastic approach is feasible and verify the operational applicability within a real-time environment.

Numerical Computations of MHD Flow on Hypersonic and Re-Entry Vehicles

2016 ◽  
Author(s):  
Filipe Dias ◽  
José Páscoa ◽  
Carlos Xisto
Keyword(s):  
Numerical Model ◽  
Mhd Flow ◽  
Magnetic Reynolds Number ◽  
Numerical Code ◽  
Test Case ◽  
Flow Equations ◽  
Good Comparison ◽  
Spatial Discretisation ◽  
Radio Blackout ◽  
Mission Operation

In hypersonic flight of reentry vehicles the radio blackout is a typical problem, in particular because it arises during a critical mission operation point. To mitigate this radio blackout the magnetic window concept is proposed. In this work a numerical model is presented to accurately simulate the effect of a magnetic field interacting with ionized plasma surrounding the vehicle. The numerical model is based on the MHD flow equations. Initially, the code is validated for pure hypersonic gas dynamics. Diverse high resolution spatial discretisation schemes, within a Finite Volume framework, are analyzed for robustness. Afterwards, the numerical code is further validated for MHD flows using the well-known Hartmann case. A very good comparison between numerical and analytical results is verified. This allows a proper validation of the method in terms of Lorentz force, in particular under low-magnetic Reynolds number conditions. A very tough test-case is finally computed, being typical of a reentry capsule geometry. The accuracy of the model is then verified for different applied magnetic fields.

HIERARCHICAL DECISION MAKING IN STRATEGIC INVESTMENT BY A BOLTZMANN MACHINE

1999 ◽  
Vol 07 (04) ◽  
pp. 429-437 ◽  
Author(s):  
TERUYUKI WATANABE ◽  
JUNZO WATADA ◽  
KENJI ODA
Keyword(s):  
Mathematical Programming ◽  
Portfolio Selection ◽  
Real Life ◽  
Selection Problem ◽  
Boltzmann Machine ◽  
Strategic Investment ◽  
Portfolio Selection Problem ◽  
Multi Stage ◽  
Hierarchical Decision ◽  
Almost All

A conventional portfolio selection problem, which is based on a mean-variance model, is difficult to solve by using mathematical programming techniques. This difficulty is caused by the fact that the corresponding mathematical programming problems are large-dimensional one, since almost all variance-covariances of return rates are, typically, not zeros. In this paper, we propose an efficient method for solving a portfolio selection problem, a method which uses a Boltzmann machine. In a real-life problem, it is also important to find the optimal combination of a small number of invested securities out of many securities in a market, because of a limited amount of funds to invest into securities. So we also propose a portfolio selection method to obtain the invest ratio of limited number of securities out of huge number of securities using a multi-stage application of the Boltzmann machine.

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