CALIBRATION OF A CFD METHODOLOGY FOR THE SIMULATION OF ROUGHNESS EFFECTS ON FRICTION AND HEAT TRANSFER IN ADDITIVE MANUFACTURED COMPONENTS

2022 ◽  
pp. 1-18
Author(s):  
Lorenzo Mazzei ◽  
Riccardo Da Soghe ◽  
Cosimo Bianchini

Abstract It is well-known from the literature that surface roughness significantly affects friction and heat transfer. This is even more evident for additive manufactured (AM) components, which are taking an increasingly important role in the gas turbine field. However, the exploitation of numerical approaches to improve their design is hindered by the lack of dedicated correlations and CFD models developed for such high roughness conditions. Usually the additive manufactured components are simulated considering the surfaces as smooth or applying an equivalent sand-grain roughness (ks) that results in a velocity shift in the boundary layer. However, determining a priori the most appropriate value of ks is challenging, as dozens of correlations are available, returning scattered and uncertain results. A previous work proved how the CFD prediction of friction and heat transfer returns significant deviations, even exploiting the ks values obtained from experimental tests on the very same test case. That work also allowed identification of a promising CFD methodology based on friction and thermal corrections proposed by Aupoix from ONERA. The aim of this work is to further the assessment and calibration activity of the model, by analyzing additional experimental data of friction factor and Nusselt number from new test cases considering different geometries and flow conditions. The new coupons consisted of straight circular channels and wavy channels. This work represents a further step in the generation of a more validated and general methodology for the high-fidelity CFD analysis of additive-manufactured components.

2021 ◽  
Author(s):  
Lorenzo Mazzei ◽  
Riccardo Da Soghe ◽  
Cosimo Bianchini

Abstract It is well-known from the literature that surface roughness significantly affects friction and heat transfer. This is even more evident for additive manufactured (AM) components, which are taking an increasingly important role in the gas turbine field. However, the exploitation of numerical approaches to improve their design is hindered by the lack of dedicated correlations and CFD models developed for such high roughness conditions. Usually the additive manufactured components are simulated considering the surfaces as smooth or applying an equivalent sand-grain roughness (ks) that results in a velocity shift in the boundary layer. However, determining a priori the most appropriate value of ks is challenging, as dozens of correlations are available, returning scattered and uncertain results. A previous work proved how the CFD prediction of friction and heat transfer returns significant deviations, even exploiting the ks values obtained from experimental tests on the very same test case. That work also allowed identification of a promising CFD methodology based on friction and thermal corrections proposed by Aupoix from ONERA. The aim of this work is to further the assessment and calibration activity of the model, by analyzing additional experimental data of friction factor and Nusselt number from new test cases considering different geometries and flow conditions. The new coupons consisted of straight circular channels and wavy channels. This work represents a further step in the generation of a more validated and general methodology for the high-fidelity CFD analysis of additive-manufactured components.


2019 ◽  
Vol 3 (2) ◽  
pp. 59 ◽  
Author(s):  
Peter Renze ◽  
Kevin Akermann

A verification and validation study was performed using the open source computational fluid dynamics software package OpenFOAM version 6-dev for conjugate heat transfer problems. The test cases had a growing complexity starting from a simple steady state problem over unsteady heat transfer to more realistic engineering applications. First, a fin effectiveness study was performed. Then, the external convection at pipes and internal pipe heat transfer were investigated. The validity of the techniques was shown for each test case by comparing the simulation results with experimental and analytic data available in the literature. Finally, a simplified shell-and-tube heat exchanger was simulated to demonstrate how these methods can be applied to plant scale engineering problems.


2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zhanwei Liu ◽  
Xinyu Li ◽  
Tenglong Cong ◽  
Rui Zhang ◽  
Lingyun Zheng ◽  
...  

The prediction of flow and heat transfer characteristics of liquid sodium with CFD technology is of significant importance for the design and safety analysis of sodium-cooled fast reactor. The accuracies and uncertainties of the CFD models should be evaluated to improve the confidence of the numerical results. In this work, the uncertainties from the turbulent model, boundary conditions, and physical properties for the flow and heat transfer of liquid sodium were evaluated against the experimental data. The results of uncertainty quantization show that the maximum uncertainties of the Nusselt number and friction coefficient occurred in the transition zone from the inlet to the fully developed region in the circular tube, while they occurred near the reattachment point in the backward-facing step. Furthermore, in backward-facing step flow, the maximum uncertainty of temperature migrated from the heating wall to the geometric center of the channel, while the maximum uncertainty of velocity occurred near the vortex zone. The results of sensitivity analysis illustrate that the Nusselt number was negatively correlated with the thermal conductivity and turbulent Prandtl number, while the friction coefficient was positively correlated with the density and Von Karman constant. This work can be a reference to evaluate the accuracy of the standard k-ε model in predicting the flow and heat transfer characteristics of liquid sodium.


2021 ◽  
Vol 26 (4) ◽  
Author(s):  
Man Zhang ◽  
Bogdan Marculescu ◽  
Andrea Arcuri

AbstractNowadays, RESTful web services are widely used for building enterprise applications. REST is not a protocol, but rather it defines a set of guidelines on how to design APIs to access and manipulate resources using HTTP over a network. In this paper, we propose an enhanced search-based method for automated system test generation for RESTful web services, by exploiting domain knowledge on the handling of HTTP resources. The proposed techniques use domain knowledge specific to RESTful web services and a set of effective templates to structure test actions (i.e., ordered sequences of HTTP calls) within an individual in the evolutionary search. The action templates are developed based on the semantics of HTTP methods and are used to manipulate the web services’ resources. In addition, we propose five novel sampling strategies with four sampling methods (i.e., resource-based sampling) for the test cases that can use one or more of these templates. The strategies are further supported with a set of new, specialized mutation operators (i.e., resource-based mutation) in the evolutionary search that take into account the use of these resources in the generated test cases. Moreover, we propose a novel dependency handling to detect possible dependencies among the resources in the tested applications. The resource-based sampling and mutations are then enhanced by exploiting the information of these detected dependencies. To evaluate our approach, we implemented it as an extension to the EvoMaster tool, and conducted an empirical study with two selected baselines on 7 open-source and 12 synthetic RESTful web services. Results show that our novel resource-based approach with dependency handling obtains a significant improvement in performance over the baselines, e.g., up to + 130.7% relative improvement (growing from + 27.9% to + 64.3%) on line coverage.


Author(s):  
M. Zugic ◽  
J. R. Culham ◽  
P. Teertstra ◽  
Y. Muzychka ◽  
K. Horne ◽  
...  

Compact, liquid cooled heat sinks are used in applications where high heat fluxes and boundary resistance preclude the use of more traditional air cooling techniques. Four different liquid cooled heat sink designs, whose core geometry is formed by overlapped ribbed plates, are examined. The objective of this analysis is to develop models that can be used as design tools for the prediction of overall heat transfer and pressure drop of heat sinks. Models are validated for Reynolds numbers between 300 and 5000 using experimental tests. The agreement between the experiments and the models ranges from 2.35% to 15.3% RMS.


Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1779
Author(s):  
Wanida Khamprapai ◽  
Cheng-Fa Tsai ◽  
Paohsi Wang ◽  
Chi-En Tsai

Test case generation is an important process in software testing. However, manual generation of test cases is a time-consuming process. Automation can considerably reduce the time required to create adequate test cases for software testing. Genetic algorithms (GAs) are considered to be effective in this regard. The multiple-searching genetic algorithm (MSGA) uses a modified version of the GA to solve the multicast routing problem in network systems. MSGA can be improved to make it suitable for generating test cases. In this paper, a new algorithm called the enhanced multiple-searching genetic algorithm (EMSGA), which involves a few additional processes for selecting the best chromosomes in the GA process, is proposed. The performance of EMSGA was evaluated through comparison with seven different search-based techniques, including random search. All algorithms were implemented in EvoSuite, which is a tool for automatic generation of test cases. The experimental results showed that EMSGA increased the efficiency of testing when compared with conventional algorithms and could detect more faults. Because of its superior performance compared with that of existing algorithms, EMSGA can enable seamless automation of software testing, thereby facilitating the development of different software packages.


2022 ◽  
Vol 171 ◽  
pp. 107248
Author(s):  
L.Y. Zhang ◽  
R.J. Duan ◽  
Y. Che ◽  
Z. Lu ◽  
X. Cui ◽  
...  

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Kevin M. Betts ◽  
Mikel D. Petty

Autonomous systems must successfully operate in complex time-varying spatial environments even when dealing with system faults that may occur during a mission. Consequently, evaluating the robustness, or ability to operate correctly under unexpected conditions, of autonomous vehicle control software is an increasingly important issue in software testing. New methods to automatically generate test cases for robustness testing of autonomous vehicle control software in closed-loop simulation are needed. Search-based testing techniques were used to automatically generate test cases, consisting of initial conditions and fault sequences, intended to challenge the control software more than test cases generated using current methods. Two different search-based testing methods, genetic algorithms and surrogate-based optimization, were used to generate test cases for a simulated unmanned aerial vehicle attempting to fly through an entryway. The effectiveness of the search-based methods in generating challenging test cases was compared to both a truth reference (full combinatorial testing) and the method most commonly used today (Monte Carlo testing). The search-based testing techniques demonstrated better performance than Monte Carlo testing for both of the test case generation performance metrics: (1) finding the single most challenging test case and (2) finding the set of fifty test cases with the highest mean degree of challenge.


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