Simultaneous Real-Time Flow Visualizations and Acoustic Measurements on a Tabbed Mach 1.3 Jet

2003 ◽  
Author(s):  
James Hileman ◽  
Mo Samimy
Keyword(s):  
Real Time ◽  
Acoustic Measurements ◽  
Time Flow ◽  
Flow Visualizations

scholarly journals Exploring Noise Sources Using Simultaneous Acoustic Measurements and Real-Time Flow Visualizations in Jets

AIAA Journal ◽  
2002 ◽  
Vol 40 (12) ◽  
pp. 2382-2392 ◽  
Author(s):  
James Hileman ◽  
Brian Thurow ◽  
Mo Samimy
Keyword(s):  
Real Time ◽  
Acoustic Measurements ◽  
Noise Sources ◽  
Time Flow ◽  
Flow Visualizations

Real-Time Flow Stability Advisor

2020 ◽  
Author(s):  
R. Andreas Burgos S. ◽  
Lars Wollebaek ◽  
Vegard Elverhaug ◽  
Zongchang Yang ◽  
Hatef Khaledi
Keyword(s):  
Real Time ◽  
Flow Stability ◽  
Time Flow

scholarly journals Experimental Verification of Real-Time Flow-Rate Estimations in a Tilting-Ladle-Type Automatic Pouring Machine

2021 ◽  
Vol 11 (15) ◽  
pp. 6701
Author(s):  
Yuta Sueki ◽  
Yoshiyuki Noda
Keyword(s):  
Real Time ◽  
Molten Metal ◽  
Flow Rate ◽  
Kalman Filters ◽  
Estimation Method ◽  
Estimation Methods ◽  
Liquid Volume ◽  
Model Parameters ◽  
Rate Estimation ◽  
Time Flow

This paper discusses a real-time flow-rate estimation method for a tilting-ladle-type automatic pouring machine used in the casting industry. In most pouring machines, molten metal is poured into a mold by tilting the ladle. Precise pouring is required to improve productivity and ensure a safe pouring process. To achieve precise pouring, it is important to control the flow rate of the liquid outflow from the ladle. However, due to the high temperature of molten metal, directly measuring the flow rate to devise flow-rate feedback control is difficult. To solve this problem, specific flow-rate estimation methods have been developed. In the previous study by present authors, a simplified flow-rate estimation method was proposed, in which Kalman filters were decentralized to motor systems and the pouring process for implementing into the industrial controller of an automatic pouring machine used a complicatedly shaped ladle. The effectiveness of this flow rate estimation was verified in the experiment with the ideal condition. In the present study, the appropriateness of the real-time flow-rate estimation by decentralization of Kalman filters is verified by comparing it with two other types of existing real-time flow-rate estimations, i.e., time derivatives of the weight of the outflow liquid measured by the load cell and the liquid volume in the ladle measured by a visible camera. We especially confirmed the estimation errors of the candidate real-time flow-rate estimations in the experiments with the uncertainty of the model parameters. These flow-rate estimation methods were applied to a laboratory-type automatic pouring machine to verify their performance.

A Back Allocation Methodology to Estimate the Real-Time Flow and Assist Production Monitoring

2021 ◽  
Author(s):  
Gabriela Chaves ◽  
Danielle Monteiro ◽  
Virgilio José Martins Ferreira
Keyword(s):  
Real Time ◽  
Input Data ◽  
Oil Field ◽  
Well Testing ◽  
Well Test ◽  
Time Data ◽  
Time Flow ◽  
Well Model ◽  
Operational Information ◽  
The Individual

Abstract Commingle production nodes are standard practice in the industry to combine multiple segments into one. This practice is adopted at the subsurface or surface to reduce costs, elements (e.g. pipes), and space. However, it leads to one problem: determine the rates of the single elements. This problem is recurrently solved in the platform scenario using the back allocation approach, where the total platform flowrate is used to obtain the individual wells’ flowrates. The wells’ flowrates are crucial to monitor, manage and make operational decisions in order to optimize field production. This work combined outflow (well and flowline) simulation, reservoir inflow, algorithms, and an optimization problem to calculate the wells’ flowrates and give a status about the current well state. Wells stated as unsuited indicates either the input data, the well model, or the well is behaving not as expected. The well status is valuable operational information that can be interpreted, for instance, to indicate the need for a new well testing, or as reliability rate for simulations run. The well flowrates are calculated considering three scenarios the probable, minimum and maximum. Real-time data is used as input data and production well test is used to tune and update well model and parameters routinely. The methodology was applied using a representative offshore oil field with 14 producing wells for two-years production time. The back allocation methodology showed robustness in all cases, labeling the wells properly, calculating the flowrates, and honoring the platform flowrate.

scholarly journals Real-time flow simulation of indoor environments using lattice Boltzmann method

2015 ◽  
Vol 8 (4) ◽  
pp. 405-414 ◽  
Author(s):  
M. Amirul Islam Khan ◽  
Nicolas Delbosc ◽  
Catherine J. Noakes ◽  
Jonathan Summers
Keyword(s):  
Lattice Boltzmann Method ◽  
Real Time ◽  
Lattice Boltzmann ◽  
Flow Simulation ◽  
Indoor Environments ◽  
Time Flow ◽  
Boltzmann Method

Development of a Fast Fluid Dynamics Model Based on PISO Algorithm for Simulating Indoor Airflow

2021 ◽  
Author(s):  
Sibo Li ◽  
Hongtao Qiao
Keyword(s):  
Fluid Dynamics ◽  
Real Time ◽  
Computational Cost ◽  
Flow Simulation ◽  
Building Design ◽  
Energy Performance ◽  
Computational Effort ◽  
Computational Method ◽  
Dimensional Case ◽  
Time Flow

Abstract Real-time or faster-than-real-time flow simulation is crucial for studying airflow and heat transfer in buildings, such as building design, building emergency management and building energy performance evaluation. Computational Fluid Dynamics (CFD) with Pressure Implicit with Splitting of Operator (PISO) or Semi-Implicit Method for Pressure Linked Equations (SIMPLE) algorithm is accurate but requires great computational resources. Fast Fluid Dynamics (FFD) can reduce the computational effort but generally lack prediction accuracy due to simplification. This study developed a fast computational method based on FFD in combination with the PISO algorithm. Boussinesq approximation is adopted for simulating buoyancy effect. The proposed solver is tested in a two-dimensional case and a three-dimensional case with experimental data. The predicted results have good agreement with the experimental results. In the two test cases, the proposed solver generates lower Root Mean Square Error (RMSE) compared to the FFD and at the same time, the proposed method reduces computational cost by a factor of 10 and 13 in the two cases compared to CFD.

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