Modem SRM ignition transient modeling. V - Prospective developments in CFD simulation

Computational fluid dynamic (CFD) simulations in support of the SHARP-B2 (Slender Hypervelocity Aerothermodynamic Research Probes) program are presented. Pre-flight simulations of the aerothermal flight environment have been made to aid in the design of the flight hardware and instrumentation. Simulations of a critical flight qualification ground test performed in the 20 MW Panel Test Facility (PTF) arc jet, which is equipped with a semi-elliptical nozzle, have also been performed. The arc jet simulations were performed with the same CFD code that is used in the flight calculations. For both the flight and the arc jet simulations, the CFD results are used to provide surface heat transfer coefficients that serve as boundary conditions for an in-depth conduction thermal response code. By using the same CFD tool to compute the aerothermal environments for both the ground test and the flight test environment, useful comparisons are made regarding the traceability of the ground test environment to flight. Introduction The NASA Ames Research Center has been developing new Ultra High Temperature Ceramics (UHTC's) with the goal that these materials will enable sharp leading edges to be used for future space vehicles. Because of their unique thermo-structural properties, these materials are capable of operation at temperatures near 5100 °F without ablation. The materials have been developed and tested in ground-based arc jet facilities, and a flight test program designated SHARP (Slender Hypervelocity Aerothermodynamic Research Probes) has been initiated. The first flight demonstration, SHARP-B1, incorporated a UHTC nosetip mounted on a U.S. Air Force Mkl2A entry vehicle in collaboration with Sandia National Laboratory (SNL), and was successfully flown in May, 1997. The second flight test, SHARP-B2, which incorporated four instrumented UHTC strakes mounted on the side of the entry vehicle, was flown in September 2000. The goal of these flight tests is to assess the performance of the materials under entry conditions. The use of Computational Fluid Dynamic (CFD) simulations for the design of Thermal Protection Systems (TPS) has evolved to an extent that they have been integrated into the preliminary design process of Reusable Launch Vehicle (RLV) programs such as the X-33. Real-gas CFD simulations with finite-rate chemistry and proper boundary conditions for coupling with material thermal response codes are now routinely performed on high-end laboratory workstations. Much of the ground based testing of advanced TPS components is done in arc-heated test facilities such as those located in the Arc Jet Complex at the NASA Ames Research Center. These facilities are capable of simulating the high enthalpy flow environment experienced by entry vehicles for a broad range of test conditions and configurations. Semi-elliptical nozzles are available for the testing of flat panels in a high enthalpy boundary layer environment. CFD is • Aerospace Engineer, Reacting Flow Environments Branch, Member AIAA. t Senior Research Scientist, ELORET, Member AIAA. Copyright © 2001 by the American Institute of Aeronautics and Astronautics, Inc. No copyright is asserted in the United States under Title 17, U.S. Code. The U.S. Government has a royalty-free license to exercise all rights under the copyright claimed herein for Governmental Purposes. All other rights are reserved by the copyright owner. (c)2001 American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s)' Sponsoring Organization.

Prediction of Nitrous oxides (NOx) emission and development in combustion burning devices utilizing numerical simulation is significant now days because of strict legislation about environment impact of contaminated gases. This work managed consolidates approach of Computational Fluid Dynamics (CFD) and Chemical Reactor Networking (CRN) method to analyze the pollutant discharged NOx from gas turbine Ignition chamber. A 2D model was created of the gas turbine combustor utilizing design tool and CFD simulations were done by using FLUENT software and determined the data about the flow field, temperature, velocity insight the combustor by varying the equivalence ratios, using methane gas (CH4) as burning fuel. On the base of CFD data 2 CRN models were made, the first one depended on 5 PSR's CRN reactors which divided the combustor into 5 flame sections that named as primary CRN model and the second one was increased number of CRN reactors with 12 flame sections that contained 12 PSR's reactors named complex CRN model by using ANSYS CHEMKIN Software. The NOx emission prediction was simulated using methane GRI3.0 mechanism. This paper examined the CFD and CRN simulations results comparing with determined temperature and NOx of both CRN models. The predicted NOx discharge at the combustion chamber outlet from CFD and CRN were compared the available experimental values for the results validation. At the Key end determined that complex CRN model predictions of NOx showed better, efficient and accurate results with experimental values than primary CRN model in very less Computation time, even though the primary CRN model consequences showed also reasonable NOx Prediction. The four NOx Formation mechanisms were also executed for NOx creation pathway analysis and for deep understanding about NOx behavior at combustion Chamber.

A numerical technique is presented for optimizing a set of solid rocket motor (SRM) ignition control variables to achieve a specific requirement or set of requirements to be used in preliminary design of an SRM igniter. The mathematical model of the igniter transients uses a simplified ignition simulation routine to calculate igniter and SRM performance. The object function to be optimized (minimized) is typically the summation of The absolute values of the differences between the desired and computed thrust values of the SRM at specified times during ignition. Constraints are imposed in the form of limiting values of igniter characteristics, flame-spreading speeds and/or maximum rate of pressure rise in the SRM. Optimization is obtained using a direct pattern search technique to determine the required values of the controlling variables. Examples are presented which illustrate the ability of the technique to meet practical design requirements. Computational results are shown to be consistent with the sta...

Predicting the hydrodynamic properties of a bioreactor: Conditional density estimation as a surrogate model for CFD simulations

The integration of CFD modeling and simulation into plume measurement programs

Thermal management is very challenging for Solid State Drive (SSD) product especially for M.2 due to its small form factor and high energy density. A better understanding of thermal prediction of SSD can help optimize performance and avoid thermal runaway issue. Using traditional Computational Fluid Dynamics (CFD) simulation approach to predict temperature under user defined power input is very time consuming and tedious. For example, to predict a typical thermal throttling profile lasting three cycles, for CFD simulation it could take from days to a week to finish. CFD approach is not favorable to be incorporated into SSD performance simulator either. Therefore, a fast, robust, and easy to use thermal prediction model for SSD is becoming more and more critical to meet these challenges. This paper has used an earlier fast prediction model methodology with modified thermal impedance using partial fraction circuit. For a recent M.2 SSD product model, constant pairs can be extracted accordingly for NAND when powering one package only at a time. R-square values have been shown to be good in curve fitting. A detailed SSD in a laptop environment CFD model has been established and validated through experiment. Constant pairs were extracted from the CFD simulation results. Temperature profile using these parameters was perfectly aligned with CFD results with R-square values close to 1. Using linear superposition principle, the predicted temperature profile was compared with CFD results under arbitrary cycling power mode for verification. Result consistency is within expected range. This approach was then applied to predict M.2 SSD thermal throttling for 1000 seconds. Different throttling temperatures have been studied. Results have shown that increasing temperature threshold for both upper and lower limit may help improve SSD thermal performance.

This study develops a comprehensive framework that integrates computational fluid dynamics (CFD) and machine learning (ML) to predict milk flow behavior in lactating breasts. Utilizing CFD and other high-fidelity simulation techniques to tackle fluid flow challenges often entails significant computational resources and time investment. Artificial neural networks (ANNs) offer a promising avenue for grasping complex relationships among high-dimensional variables. This study leverages this potential to introduce an innovative data-driven approach to CFD. The initial step involved using CFD simulations to generate the necessary training and validation datasets. A machine learning pipeline was then crafted to train the ANN. Furthermore, various ANN architectures were explored, and their predictive performance was compared. The design of experiments method was also harnessed to identify the minimum number of simulations needed for precise predictions. This study underscores the synergy between CFD and ML methodologies, designated as ML-CFD. This novel integration enables a neural network to generate CFD-like results, resulting in significant savings in time and computational resources typically required for traditional CFD simulations. The models developed through this ML-CFD approach demonstrate remarkable efficiency and robustness, enabling faster exploration of milk flow behavior in individual lactating breasts compared to conventional CFD solvers.

CFD simulation of the atmospheric boundary layer: wall function problems

In the absence of powerful rigorous models, in this research a simple but practical method for calculating the temperature and residence time and carbon black yield in oil-furnace reactors is proposed. For this purpose an empirical formula of the form CxHy is assumed for the carbon black feedstock based on typical feedstock used in this industry. Based on the prevailing reactor conditions, thermodynamic considerations and available outlet tail gas from the reactor, a few representative reactions are considered to describe the entire reaction network. These include complete combustion for the fuel and, incomplete combustion and pyrolysis for the feedstock. Carbon black yield can be estimated from a mass balance. From the combined mass and heat balance, the reactor temperature as well as the residence time is predicted. The reactions can also be used to obtain the composition of the tail gas. Despite the simplicity of the model, in all cases studied, relatively good agreements were observed between the calculated values and the available industrial data as well as those obtained from computational fluid dynamics (CFD) simulation. An exception was the effects of the air/oil ratio on the reactor mean residence time in the CFD approach. The influences of air/oil ratio and inlet air temperature on reactor mean-temperature were studied. In both cases one observes good agreement between our results and CFD simulation data. They both show an increase in reactor mean-temperature as expected. The effects of process air flow rate on reactor residence time also were studied and a good agreement was observed. However, comparison the effects of the air/oil ratio on reactor residence time showed quite different trends. While the results of CFD simulation predicts insignificant change in reactor residence time versus air/oil ratio, our results show a decline in the residence time in this case. To explain this, prediction of carbon black particle size was considered. Utilizing our predicted residence time results for prediction of particle size showed a good agreement with industrial data, while in contrast the CFD simulation data show opposite trend with the industrial data and our work. Therefore, it can be said that one of the most important reasons caused poor prediction in referred CFD simulation in some cases could be considerable error in prediction of the residence time.

In this paper, the proposed novel technique using computational fluid dynamics (CFD) approach to design control systems is validated experimentally for a chip cooling device. Both experimental approach and CFD simulations are employed to extract the dynamic characteristics from the non-linear chip cooling system around a specific operating point, which are then used to construct the linear dynamic model for the chip cooling system. The linear dynamic model has two inputs, the heat load generated by the chip and the cooling fan voltage. The output is the temperature of the case housing the chip. The results from the linear dynamic model obtained by the CFD approach are compared with those obtained experimentally under the same dynamic conditions to validate the feasibility of using the CFD approach to design a control system for a thermal-fluid system.

The heat transfer improvements by simultaneous usage of the nanofluids and metallic porous foams are still an attractive research area. The Computational fluid dynamics (CFD) methods are widely used for thermal and hydrodynamic investigations of the nanofluids flow inside the porous media. Almost all studies dedicated to the accurate prediction of the CFD approach. However, there are not sufficient investigations on the CFD approach optimization. The mesh increment in the CFD approach is one of the challenging concepts especially in turbulent flows and complex geometries. This study, for the first time, introduces a type of artificial intelligence algorithm (AIA) as a supplementary tool for helping the CFD. According to the idea of this study, the CFD simulation is done for a case with low mesh density. The artificial intelligence algorithm uses learns the CFD driven data. After the intelligence achievement, the AIA could predict the fluid parameters for the infinite number of nodes or dense mesh without any limitations. So, there is no need to solve the CFD models for further nodes. This study is specifically focused on the genetic algorithm-based fuzzy inference system (GAFIS) to predict the velocity profile of the water-based copper nanofluid turbulent flow in a porous tube. The most intelligent GAFIS could perform the most accurate prediction of the velocity. Hence, the intelligence of GAFIS is tested for different values of cluster influence range (CIR), squash factor(SF), accept ratio (AR) and reject ratio (RR), the population size (PS), and the percentage of crossover (PC). The maximum coefficient of determination (~ 0.97) was related to the PS of 30, the AR of 0.6, the PC of 0.4, CIR of 0.15, the SF 1.15, and the RR of 0.05. The GAFIS prediction of the fluid velocity was in great agreement with the CFD. In the most intelligent condition, the velocity profile predicted by GAFIS was similar to the CFD. The nodes increment from 537 to 7671 was made by the GAFIS. The new predictions of the GAFIS covered all CFD results.

Today, many wells in heterogeneous carbonate reservoirs throughout the Middle East and Caspian Sea area are being completed with long intervals and complex completion equipment, such as limited entry liners and inflow control devices (ICDs). Accurate modeling and understanding of fluid placement during a stimulation operation through such long complex completions can be quite challenging. In this paper, an integrated approach of modeling, experiments and computational fluid dynamics (CFD) simulations will be presented. A computational tool based on a transient one-dimensional (1D) approach has been developed in-house to model stimulation through such completions. In order to validate the 1D approach for reliable fluid displacement predictions, annulus fluid displacement experiments in perforated liners supported by CFD simulations have been conducted. The experimental setup consists of an outer Lucite pipe representing the well-bore geometry and an inner aluminum pipe with multiple perforations of variable diameter. The setup mimics the real field application in a 1:1 scale with respect to the pipe and perforation diameters while the test section is limited to 27 ft. The experimental procedure is as follows: fluid is pumped into the annulus through the perforations until the annulus is completely filled,the fluid tank is switched "on-the-fly" to the tank with the displacing fluid resulting in the displacement of the annulus fluid by the displacing fluid through the perforations. The experiments and the CFD simulations indicate that even with a single open perforation inside the 27-ft test section, the three-dimensional (3D) flow field around the perforation rapidly transitions to uniform annular flow within a few feet uniformly displacing the annulus fluid. The model also provides insight into various well injection processes in sandstone and carbonate formation, such as scale squeezes, solvent treatments, and HF acid treatments. Introduction The length of production and injection intervals in a single well continues to increase in order to contact more reservoir rock with a single wellbore penetration and improve project economics. Multiple, thick stacked reservoirs are accessed by vertical and deviated wells while thinner reservoirs are being exploited with longer and longer horizontal wells1–3. To keep up with drilling capability, completion technology has advanced considerably in the past few years. Horizontal wells are "compartmentalized" using mechanical and/or swellable packers. Within a given compartment, injection and production can be controlled by use of inflow control devices (ICDs), limited entry (LE) liners, and/or sliding sleeves. The liners also make it easier to enter the completion interval with logging tools, wire line, and coiled tubing. While various tools are available to evaluate long-term, steady-state production and injection into such wells, modeling of short-term, transient injection into such completions is very challenging due to the long lateral lengths, limited entry points from the liner to each compartment, and the presence of an open annulus with occasional packer isolations. This is especially important for long wells in carbonate formations as all wells require acid stimulation upon initial completion, and occasionally throughout the well life.

CFD assisted modeling for control system design: A case study

CFD simulation of gas and particle flow and a carbon capture process using a circulating fluidized bed (CFB) reacting loop

A statistical approach for computational fluid dynamics (CFD) state-of-the-art (SoA) assessment is presented for specified benchmark test cases and validation variables, based on the combination of solution and N-version verification and validation (V&V). Solution V&V estimates the systematic numerical and modeling errors/uncertainties. N-version verification estimates the random simulation uncertainty. N-version validation estimates the random absolute error uncertainty, which is combined with the experimental and systematic numerical uncertainties into the SoA uncertainties and then used to determine whether or not the individual codes/simulations and the mean code are N-version validated at the USoAi and USoA intervals, respectively. The scatter is due to differences in models and numerical methods, grid types, domains, boundary conditions, and other setup parameters. Differences between codes/simulations and implementations are due to myriad possibilities for modeling and numerical methods and their implementation as CFD codes and simulation applications. Industrial CFD codes are complex software with many user options such that even in solving the same application, different results may be obtained by different users, not necessarily due to user error but rather the variability arising from the selection of various models, numerical methods, and setup options. Examples are shown for ship hydrodynamics applications using results from the Seventh CFD Ship Hydrodynamics and Second Ship Maneuvering Prediction Workshops. The role and relationship of individual code solution V&V versus N-version V&V and SoA assessment are discussed.