Article
Construction & Building Technology
Ankur Bhogayata, Sneh Kakadiya, Rinkesh Makwana
Summary: The paper discusses the development and application of artificial neural network (ANN) model in predicting the compressive and splitting tensile strength of geopolymer-based concrete composites. The study shows that ANN modeling can optimize the mixture design and evaluate its accuracy by comparing it with experimental results.
ACI MATERIALS JOURNAL
(2021)
Article
Engineering, Multidisciplinary
B. R. Hosamani, Syed Abbas Ali, Vadiraj Katti
Summary: In this study, the thermal performance and emissions of a VCR diesel engine using a blend of two biodiesels in varying ratios were assessed using artificial neural network (ANN). By training the ANN model and selecting the optimal architecture, the estimated parameters closely matched the experimental values with high accuracy.
ALEXANDRIA ENGINEERING JOURNAL
(2021)
Review
Construction & Building Technology
Min Zhou, Zemei Wu, Xue Ouyang, Xiang Hu, Caijun Shi
Summary: A highly efficient mixture design for concrete should balance workability, strength, durability, economic efficiency, and sustainability. Ultra-high-performance concrete (UHPC) requires a thoughtful approach that integrates different design methods to meet specific requirements efficiently.
CEMENT & CONCRETE COMPOSITES
(2021)
Article
Computer Science, Artificial Intelligence
Hai-Bang Ly, May Huu Nguyen, Binh Thai Pham
Summary: The study utilized the PSO-LMA-ANN algorithm to optimize the structure and parameters of Levenberg-Marquardt-based Artificial Neural Network for accurate and quick prediction of the FCCS. The results showed that the algorithm is a highly efficient predictor, and Partial Dependence Plots were used to interpret the relationship between mixture components and FCCS.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Construction & Building Technology
Babatunde Abiodun Salami, Mudassir Iqbal, Abdulazeez Abdulraheem, Fazal E. Jalal, Wasiu Alimi, Arshad Jamal, T. Tafsirojjaman, Yue Liu, Abidhan Bardhan
Summary: This study proposed AI-based models to predict the compressive strength of foamed concrete and experimented with three different AI approaches. After training the models with experimental data, verification and analysis revealed that the GBT model had relatively better performance.
CEMENT & CONCRETE COMPOSITES
(2022)
Article
Construction & Building Technology
Amin Tanhadoust, Seyed Amir Ali Emadi, Sepideh Nasrollahpour, Farshad Dabbaghi, Moncef L. Nehdi
Summary: This research investigates the use of recycled crumb rubber as a partial replacement for fine aggregates in concrete. Through fracture analysis and Life Cycle Assessment, the study evaluates the damage and environmental impact of 10 different Recycled Rubber-Filled Concrete mixtures. Multi-objective optimization is used to identify the optimal mixture proportions, considering concrete characteristic objectives, environmental assessment, and cost constraints. The results show that increasing the crumb rubber content improves toughness and energy absorption but reduces compressive and tensile strengths. The study emphasizes the importance of carefully balancing water-to-cement ratio and crumb rubber content for achieving a balance of environmental impact, affordability, and performance.
CONSTRUCTION AND BUILDING MATERIALS
(2023)
Article
Green & Sustainable Science & Technology
Nidal H. Abu-Hamdeh, Mashhour A. Alazwari, Elias M. Salilih, S. Mohammad Sajadi, Randa I. Hatamleh
Summary: This study synthesized Graphene/Silica-Water nanofluid at different mass fractions and measured thermal conductivity and viscosity at various temperatures. Artificial Neural Network algorithms and Fuzzy model were used to predict trends in heat transfer and viscosity, showing better results for the Fuzzy model. The nanofluid was applied in a Flat-plate Solar collector to enhance efficiency in industrial thermal and energy systems.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2021)
Article
Construction & Building Technology
Rabab Allouzi, Hatem Almasaeid, Amer Alkloub, Osama Ayadi, Rawan Allouzi, Ramia Alajarmeh
Summary: This study investigates the current thermal insulation practices in Jordan, a country with limited natural resources and no enforced thermal insulation codes. It reviews the progress of modern architecture and construction in Jordan and examines the structural use of foamed concrete, verifying its applicability in structural members. Artificial Neural Network is trained to bridge the mixture components and mechanical properties, showing good agreement with experimental outcomes. Furthermore, three exterior walls constructed using foamed concrete exhibit significantly lower U-values compared to a wall made with conventional concrete, indicating potential energy savings for clients.
CASE STUDIES IN CONSTRUCTION MATERIALS
(2023)
Article
Construction & Building Technology
Ning Li, Panagiotis G. Asteris, Trung-Tin Tran, Biswajeet Pradhan, Hoang Nguyen
Summary: This study proposes a robust AI model, ICA-ANN, based on the social behaviour of the imperialist competitive algorithm and artificial neural network for modelling the deflection of reinforced concrete beams. Experimental results show that the ICA-ANN model outperforms other models in accurately approximating the deflection of reinforced concrete beams.
STEEL AND COMPOSITE STRUCTURES
(2022)
Article
Green & Sustainable Science & Technology
Emadaldin Mohammadi Golafshani, Alireza Kashani, Taehwan Kim, Mehrdad Arashpour
Summary: Chloride-induced steel reinforcement corrosion poses a threat to the durability of concrete structures, potentially leading to significant economic losses and environmental impacts. Accurate estimation of chloride diffusion in steel-reinforced concrete helps in reliably predicting its service life. This study combines marine creature-based metaheuristic optimization algorithms with artificial neural networks to model the apparent chloride diffusion of concrete.
JOURNAL OF CLEANER PRODUCTION
(2022)
Article
Construction & Building Technology
Ruinan Zhang, Deming Liu, Ligang Shi
Summary: This study aims to improve the thermal-comfort performance of the Tianjin Tuanbo tennis stadium through an accurate calculation model and a valid morpho-logical optimization method. By using energy simulations and computational fluid dynamics simulations, the PCave of the optimized stadium was improved by 8.96%.
BUILDING AND ENVIRONMENT
(2022)
Article
Construction & Building Technology
Muhammad Atasham ul Haq, Wencheng Xu, Muhammad Abid, Fuyuan Gong
Summary: This study analyzed the impact of freeze-thaw exposure on concrete structural behavior and proposed a model to predict the deterioration of compressive strength. The results demonstrated the accuracy of the proposed model and highlighted key factors influencing the freeze-thaw durability of concrete.
Review
Construction & Building Technology
Mohammad Mohtasham Moein, Ashkan Saradar, Komeil Rahmati, Seyed Hosein Ghasemzadeh Mousavinejad, James Bristow, Vartenie Aramali, Moses Karakouzian
Summary: Concrete is widely used in civil engineering and its mechanical properties are important for design and evaluation. Machine learning and deep learning have been applied to predict these properties, offering advantages such as accuracy and responsiveness. This paper reviews successful applications of ML and DL models and provides suggestions for future research.
JOURNAL OF BUILDING ENGINEERING
(2023)
Article
Engineering, Civil
Xianlin Wang, Yuqing Liu, Haohui Xin
Summary: A soft computing strategy incorporating artificial neural networks (ANN) with genetic algorithm (GA) or particle swarm optimization (PSO) was proposed to predict the bond strength in concrete-encased steel (CES) structures. The developed GA-ANN and PSO-ANN models outperformed the conventional ANN model and existing empirical equations in terms of performance. Sensitivity analysis revealed that the relative concrete cover has the most significant effect on bond strength, while the influence of relative bonded length is relatively minimal.
Article
Engineering, Multidisciplinary
Rishika Shah, R. K. Pandit, M. K. Gaur
Summary: This study focuses on the development and validation of an Artificial Neural Network (ANN) model for predicting hourly thermal comfort indices. The results show that the ANN model has excellent predictive ability and can accurately predict thermal comfort indices using a few input parameters such as air temperature.
ALEXANDRIA ENGINEERING JOURNAL
(2022)
Article
Chemistry, Multidisciplinary
Abdullah K. Alanazi, Seyed Mehdi Alizadeh, Karina Shamilyevna Nurgalieva, Slavko Nesic, John William Grimaldo Guerrero, Hala M. Abo-Dief, Ehsan Eftekhari-Zadeh, Ehsan Nazemi, Igor M. Narozhnyy
Summary: This innovative non-invasive system uses a dual-energy gamma source and two detectors, along with artificial intelligence, to determine the flow pattern and volume percentage of two-phase flow by considering the thickness of scales in the pipelines.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
Abdulilah Mohammad Mayet, Ahmed S. Salama, Seyed Mehdi Alizadeh, Slavko Nesic, John William Grimaldo Guerrero, Ehsan Eftekhari-Zadeh, Ehsan Nazemi, Abdullah M. Iliyasu
Summary: In this study, an artificial intelligence method is proposed to determine the flow regime and volume percentage of a two-phase flow in the presence of scale inside the test pipe. By using dual-energy source and neural networks, the proposed system achieves accurate measurement and classification, offering better precision and simpler structure compared to conventional systems.
Article
Mathematics
Abdulaziz S. Alkabaa, Osman Taylan, Mustafa Tahsin Yilmaz, Ehsan Nazemi, El Mostafa Kalmoun
Summary: The central nervous system (CNS) is the main required organ of the biological system, composed of neurons, synapses, and glias. This paper introduces the use of the Izhikevich neuronal model to achieve a high copy of the primary nervous block. The proposed approach is based on Look-Up Table (LUT) modeling of mathematical neuromorphic systems and shows high-speed performance through digital hardware synthesis and implementation.
Article
Energy & Fuels
Abdulilah Mohammad Mayet, Seyed Mehdi Alizadeh, Karina Shamilyevna Nurgalieva, Robert Hanus, Ehsan Nazemi, Igor M. Narozhnyy
Summary: In this paper, a novel technique is proposed to control the transmission of liquid petrochemical and petroleum products in a pipe. Through simulation setup and the application of neural networks, the volume ratios of different petroleum products in a mixture can be accurately predicted. The innovation of this study lies in the increased prediction accuracy, making it an efficient method in the oil industry.
Article
Mathematics
Abdulilah Mohammad Mayet, Seyed Mehdi Alizadeh, Zana Azeez Kakarash, Ali Awadh Al-Qahtani, Abdullah K. Alanazi, Hala H. Alhashimi, Ehsan Eftekhari-Zadeh, Ehsan Nazemi
Summary: This study presents a method for detecting the volume percentage of two-phase flow by considering the presence of scale inside the test pipe using artificial intelligence networks. The proposed system is able to accurately determine the volume percentages regardless of the type of flow regime and the amount of scale inside the pipe. By using feature extraction techniques, the system reduces costs and increases accuracy.
Article
Instruments & Instrumentation
T-C Chen, O. Taylan, S. M. Alizadeh, M. T. Yilmaz, E. Nazemi, M. Balubaid, G. H. Roshani, D. Karaboga
Summary: This study investigates the application of X-ray tubes combined with feature extraction and artificial intelligence techniques in determining volumetric percentages in two-phase flows. The results show that the use of the GMDH neural network model with extracted time-domain features effectively predicts volume percentages.
MAPAN-JOURNAL OF METROLOGY SOCIETY OF INDIA
(2023)
Article
Engineering, Chemical
Tzu-Chia Chen, Seyed Mehdi Alizadeh, Marwan Ali Albahar, Mohammed Thanoon, Abdullah Alammari, John William Grimaldo Guerrero, Ehsan Nazemi, Ehsan Eftekhari-Zadeh
Summary: This research presents an intelligent system for detecting the volume percentage of three-phase fluids passing through oil pipes. The system includes an X-ray tube, a Pyrex glass pipe, and two sodium iodide detectors. By simulating different flow regimes and volume percentages, the system extracts characteristics from the recorded signals and applies a particle swarm optimization algorithm to determine the best combination of features. The accuracy in determining volume percentages is significantly improved compared to previous works.
Article
Medicine, General & Internal
Yanjie Lu, Nan Zheng, Mingtao Ye, Yihao Zhu, Guodao Zhang, Ehsan Nazemi, Jie He
Summary: Air kerma is a crucial parameter in medical diagnostic radiology, used to evaluate organ doses and patient hazards. An intelligent technique based on radial basis function neural network (RBFNN) is presented to predict air kerma at any point within the X-ray beam's field of view in medical diagnostic imaging systems. The trained RBFNN model can estimate air kerma at random positions within the range of medical diagnostic radiology.
Correction
Biology
Yule Wang, Osman Taylan, Abdulaziz S. Alkabaa, Ijaz Ahmad, Elsayed Tag-Eldin, Ehsan Nazemi, Mohammed Balubaid, Hanan Saud Alqabbaa
Article
Mathematics, Applied
Aryan Veisi, Mohammad Hossein Shahsavari, Gholam Hossein Roshani, Ehsan Eftekhari-Zadeh, Ehsan Nazemi
Summary: In this study, experimental validation was conducted to accurately determine phase fractions in two-phase flows in power plants, petroleum, and petrochemical industries. The simulations for water-air, two-phase flow in an annular pattern were verified through finite element simulations and experimental investigations. The results indicated a relatively low relative error between the simulation and experiment, validating the accuracy of the simulations. An Artificial Neural Network (ANN) model was developed to predict void fractions in different liquid-gas two-phase flows, and the void fraction was accurately measured using the proposed metering system.
Article
Engineering, Electrical & Electronic
Guodao Zhang, Yisu Ge, Haojie Xu, Abdulilah Mohammad Mayet, Yanjie Lu, Mingtao Ye, Ehsan Nazemi
Summary: This paper presents a high-speed, low-cost, and efficient digital circuit for emulating the plausible calcium-dynamic-based model of astrocyte which has spontaneous oscillations. The proposed model is able to accurately simulate biological cell behaviors and consumes only 2% of the resources of a Virtex 4 board. Timing analysis shows that the proposed model works at a high frequency of 371.56 MHz.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2023)
Article
Green & Sustainable Science & Technology
Xiaohua Hou, Bo Cheng, Zhiliang Xia, Haijun Zhou, Qi Shen, Yanjie Lu, Ehsan Nazemi, Guodao Zhang
Summary: In order to promote ecological sustainability, researchers are increasingly interested in the issue of sulphur dioxide emissions. However, current research mainly focuses on the relationship between sulphur dioxide (SO2) emissions, foreign direct investment (FDI), and trade, and the effects of trade on SO2 emissions, without giving sufficient consideration to the impact of the institutional environment and economic growth on SO2 emissions. This paper empirically tests the institutional environment and economic growth of sulphur dioxide (SO2) emissions using provincial panel data from 2008 to 2017, employing a fixed effects model. The results indicate an inverted U-shaped relationship between GDP growth and SO2 emissions. The institutional environment and higher government intervention in the region lead to a significant decrease in SO2 emissions, and the institutional environment and level of government intervention have a negative regulatory role on economic growth and SO2 emissions. The paper provides empirical evidence based on environmental governance research, particularly in the context of regional environmental governance and government environmental performance audit policy, thus promoting sustainable ecological and environmental development.
Article
Computer Science, Information Systems
Mohammad Hossein Shahsavari, Aryan Veisi, Gholam Hossein Roshani, Ehsan Eftekhari-Zadeh, Ehsan Nazemi
Summary: This paper investigates the measurement of volume fractions in multi-phase flows in petrochemical industries. Experiments and simulations were conducted to evaluate the performance of vertical concave, horizontal concave, and double-ring sensors in stratified two-phase flow. The simulation results were validated with experimental data. To extract more data, the COMSOL Multiphysics software was used due to the limited availability of experimental data, and the sensitivity of different directions of concave and double-ring sensors was compared. The results show that the overall sensitivity of the concave sensor is higher than the double-ring sensor, and the horizontal concave sensor has higher momentary sensitivity at higher void fractions, while the vertical concave sensor has higher sensitivity at lower void fractions.
Article
Medicine, General & Internal
Licheng Zhang, Fengzhe Xu, Lubing Wang, Yunkui Chen, Ehsan Nazemi, Guohua Zhang, Xicai Zhang
Summary: The amount of energy given off by a radioactive substance, known as the air kerma, is crucial for medical specialists using radiation for cancer diagnosis. This study presents a model-based approach using GMDH neural networks to predict air kerma at different positions within the radiation field of medical imaging instruments. The trained neural network accurately determines air kerma at any location in the X-ray field of view with a Mean Relative Error (MRE) of less than 0.25%.
Article
Engineering, Biomedical
Guodao Zhang, Ruyu Liu, Yisu Ge, Abdulilah Mohammad Mayet, Sixian Chan, Guoqing Li, Ehsan Nazemi
Summary: Neuromorphic engineering is an important science field that integrates issues in physics, mathematics, and electronics. This paper proposes a modified version of the Wilson Neuron model that uses power-2 based functions, Look-Up Table (LUT) approach, and shifters to achieve a multiplierless digital realization, reducing costs and increasing system frequency. Hardware results and numerical results demonstrate improvements over the original model, showing an increase in system frequency and a higher saving in FPGA resources for the proposed model. Additionally, the proposed model is tested in a network to investigate its effects on neuronal diseases like Epilepsy.
IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS
(2022)
Article
Nuclear Science & Technology
Orestes Castillo-Hernandez, P. E. Manuel Perdomo-Ojeda, C. R. Grantom, Pamela F. Nelson
Summary: Incorporating specified safety and production targets during the design phase can reduce costs and enhance the competitiveness of nuclear power plants. This paper presents two methods for proposing unavailability targets for nuclear reactor systems to optimize the design features. The methods are applied to a hypothetical facility, providing a basis for future work on estimating design alternatives affecting unavailabilities.
NUCLEAR ENGINEERING AND DESIGN
(2024)
Article
Nuclear Science & Technology
Qinjun Fu, Andre Bergeron, Philippe Fillion, Yohan Davit, Michel Quintard
Summary: In normal operating conditions, the flow within a pressurized water reactor (PWR) core primarily moves in the axial direction along the fuel rods. However, in accidents situations, transverse flows can have a significant impact on the thermal-hydraulic properties of the core. This study develops macroscopic pressure drop models for different flow directions and Reynolds numbers and validates them by comparing with existing system code results.
NUCLEAR ENGINEERING AND DESIGN
(2024)
Article
Nuclear Science & Technology
Xiang Chai, Xinyue Liu, Chaoran Guan, Tengfei Zhang, Xiaojing Liu
Summary: Micro nuclear reactors have gained attention for their high efficiency and long lifetime, making them suitable for remote and off-grid locations. This study examines the effects of burnable poisons on the performance of a micro nuclear reactor and improves the design for reactivity control. The computational results show that burnable poisons reduce excess reactivity and power peaking factor without significant impact on core lifetime.
NUCLEAR ENGINEERING AND DESIGN
(2024)
Article
Nuclear Science & Technology
Liwei Chen, Cong Zhou, Yu Wang, Yiran Zong, Tingting Lu, Chunhua Chen
Summary: This paper proposes an autonomous search method for leakage sources in nuclear emergency rescue based on the updated Infotaxis method. By considering factors such as radioactive decay and wet deposition, the method improves search efficiency and accuracy. Experimental results show that the method is particularly effective in searching for leakage sources under high emission rates and provides scientific information for early emergency response and consequence assessment.
NUCLEAR ENGINEERING AND DESIGN
(2024)
Article
Nuclear Science & Technology
Kirill S. Dolganov
Summary: This paper is the first part of a summary overview of IBRAE's work on the severe accident at Unit 1 of Fukushima Daiichi Nuclear Power Station. It focuses on the integral model of Unit 1 and its qualification with available data, including the comparison of simulation results with measurements for the initial phase of the accident. Important issues discussed include the direct modeling of isolation condenser performance and verification of the possibility to use an integral approach to estimate the nuclide inventory in the core.
NUCLEAR ENGINEERING AND DESIGN
(2024)
Article
Nuclear Science & Technology
F. Feria, C. Aguado, J. Benavides, J. Benavides, R. Canencia-Hernanz, M. Cristobal-Beneyto, J. Fernandez Garcia, H. Galan, C. Gonzalez, A. Hernandez-Avellaneda, L. E. Herranz, G. Jimenez, L. Martinez, J. C. Martinez-Murillo, A. Milena-Perez, A. Palacio Alonso, J. Penalva, R. Plaza, D. Perez-Gallego, L. Rey, N. Rodriguez-Villagra, J. Ruiz-Hervias, J. Saiz de Omenaca Tijero, P. Vinas-Pena
Summary: The Spanish R&D efforts in dry interim storage of spent nuclear fuel mainly focus on supporting safety under storage and transportation. Experimental and modelling activities are carried out to understand and predict the fuel response, with major outcomes being improvements in the characterization of dry stored fuel, essential for safety assessment of the back-end fuel cycle.
NUCLEAR ENGINEERING AND DESIGN
(2024)
Article
Nuclear Science & Technology
Ang Li, Yuqing Chen, Yuxian Rao, Qi Cai, Cong Wang
Summary: This paper focuses on the boiling heat transfer model of mini-channels casing-pipes once-through steam generator (MCOTSG). By calibrating and analyzing the different nucleate boiling heat transfer models based on steady state experimental results, the secondary loop heat transfer characteristics and overall operating characteristics of OTSG under feedwater flow rate reduction are simulated. The results can provide a basis for the safety analysis and optimal design of MCOTSG and small modular reactor under low flow rate conditions.
NUCLEAR ENGINEERING AND DESIGN
(2024)
Article
Nuclear Science & Technology
Shu Soma, Masahiro Ishigaki, Satoshi Abe, Yasuteru Sibamoto
Summary: In this paper, the analytical wall function approach was applied to analyze the condensation flow of steam/air mixtures, and good predictions were obtained through CFD analysis.
NUCLEAR ENGINEERING AND DESIGN
(2024)
Article
Nuclear Science & Technology
Jinho Song, Sungjoong Kim
Summary: In this study, a machine learning platform is proposed to assist operators in diagnosing the progression of severe accidents and predicting key parameters using long short term memory networks and MELCOR simulation data. The platform shows reasonable accuracy in predicting both similar and unseen test data, as well as lost signals and key parameters for accident management.
NUCLEAR ENGINEERING AND DESIGN
(2024)
Article
Nuclear Science & Technology
Liang Zhao, Zhengbai Chang, Chulin Mai, Hong Ran, Jin Jiang
Summary: This study investigates the dynamic characteristics, valve disc motion, and flow field of a nozzle check valve under different spring stiffness and fluid deceleration rates. Experimental tests and numerical simulations were performed to analyze the effects on various parameters during the dynamic closing process of the valve. The results provide insights for the optimization design of check valves and more accurate calculation of their dynamic characteristic curves.
NUCLEAR ENGINEERING AND DESIGN
(2024)
Article
Nuclear Science & Technology
Xun Lan, Yanbo Jiang, Dan Sun, Wenbo Liu, Wenjie Li
Summary: In this study, a three-dimensional phase-field model was developed to investigate the evolution of intergranular bubbles during irradiation. The study examined the dependency of bubble percolation on bubble shape, fission rate, and average grain size. The simulation results revealed the significant effects of these factors on the shape of GB bubbles, the percolation rate, and the connectivity threshold of GB bubbles.
NUCLEAR ENGINEERING AND DESIGN
(2024)
Article
Nuclear Science & Technology
J. M. S. Mendes, A. Heimlich, A. M. M. De Lima, F. C. Silva
Summary: An algorithm for solving the constitutive equations of the fourth-order Nodal Expansion Method (NEM) in parallel using GPU with quadratic transverse leakage has been proposed. The algorithm, implemented in CUDA language, showed comparable accuracy and reduced execution time compared to the CNFR code. This algorithm and the developed program have potential use in the optimization process of nuclear fuel reload patterns.
NUCLEAR ENGINEERING AND DESIGN
(2024)
Article
Nuclear Science & Technology
M. Jafari, H. Jafari, M. H. Choopan Dastjerdi, J. Mokhtari
Summary: This study investigates the ability of the PGNAA facility of the Isfahan MNSR reactor to measure boron concentration in solution samples. A measurement system model is developed using Monte Carlo calculation, and a cadmium sheet is used to reduce the effect of boron in the shield. The accuracy of the model is verified through experimental tests, resulting in a calibration curve.
NUCLEAR ENGINEERING AND DESIGN
(2024)
Article
Nuclear Science & Technology
Ibrahim Gad-el-Hak, Njuki Mureithi, Kostas Karazis, Brian Painter
Summary: This study investigates the risk of fluidelastic instability induced by degraded baffle-former bolts in a nuclear fuel assembly. Experimental results show that the stability threshold of the mock-up array strongly depends on the relative position of the jet flow with respect to the array centerline, and axial flow velocity also has a significant effect on the jet-induced instability.
NUCLEAR ENGINEERING AND DESIGN
(2024)
Article
Nuclear Science & Technology
Yuhang Niu, Scolaro Alessandro, Carlo Fiorina, Hao Qin, Gauthier Lazare, Yingwei Wu, Wenxi Tian, G. H. Su
Summary: This paper presents the incorporation of DNB prediction into the GeN-Foam code, which is based on OpenFOAM. The accuracy of GeN-Foam in modeling DNB conditions in PWR was assessed through validation against experimental data and other codes. The results show that GeN-Foam exhibits good performance in simulating two-phase flow boiling conditions and accurately predicts the occurrence of DNB.
NUCLEAR ENGINEERING AND DESIGN
(2024)