• Journal of Mechanical Science and Technology
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• v.20 no.12
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• pp.2218-2229
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• 2006

A conservative finite-volume numerical method for unstructured grids with the cell-centered method has been developed for computing flow and heat transfer by combining the attractive features of the existing pressure-based procedures with the advances made in unstructured grid techniques. This method uses an integral form of governing equations for arbitrary convex polyhedra. Care is taken in the discretization and solution procedure to avoid formulations that are cell-shape-specific. A collocated variable arrangement formulation is developed, i.e. all dependent variables such as pressure and velocity are stored at cell centers. For both convective and diffusive fluxes the forms superior to both accuracy and stability are particularly adopted and formulated through a systematic study on the existing approximation ones. Gradients required for the evaluation of diffusion fluxes and for second-order-accurate convective operators are computed by using a linear reconstruction based on the divergence theorem. Momentum interpolation is used to prevent the pressure checkerboarding and a segregated solution strategy is adopted to minimize the storage requirements with the pressure-velocity coupling by the SIMPLE algorithm. An algebraic solver using iterative preconditioned conjugate gradient method is used for the solution of linearized equations. The flow analysis code (PowerCFD) developed by the present method is evaluated for its application to several 2-D structured-mesh benchmark problems using a variety of unstructured quadrilateral and triangular meshes. The present flow analysis code by using unstructured grids with the cell-centered method clearly demonstrate the same accuracy and robustness as that for a typical structured mesh.

• Nuclear Engineering and Technology
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• v.45 no.5
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• pp.625-636
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• 2013

Pressing demands of economic competitiveness, the need for large-scale deployment, minimizing the need of human intervention, and experience from the past events and incidents at operating reactors have guided the evolution and innovations in reactor technologies. Indian innovative reactor 'AHWR' is a pressure-tube type natural circulation based boiling water reactor that is designed to meet such requirements, which essentially reflect the needs of next generation reactors. The reactor employs various passive features to prevent and mitigate accidental conditions, like a slightly negative void reactivity coefficient, passive poison injection to scram the reactor in event of failure of the wired shutdown systems, a large elevated pool of water as a heat sink inside the containment, passive decay heat removal based on natural circulation and passive valves, passive ECC injection, etc. It is designed to meet the fundamental safety requirements of safe shutdown, safe decay heat removal and confinement of activity with no impact in public domain, and hence, no need for emergency planning under all conceivable scenarios. This paper examines the role of the various passive safety systems in prevention and mitigation of severe plant conditions that may arise in event of multiple failures. For the purpose of demonstration of the effectiveness of its passive features, postulated scenarios on the lines of three major severe accidents in the history of nuclear power reactors are considered, namely; the Three Mile Island (TMI), Chernobyl and Fukushima accidents. Severe plant conditions along the lines of these scenarios are postulated to the extent conceivable in the reactor under consideration and analyzed using best estimate system thermal-hydraulics code RELAP5/Mod3.2. It is found that the various passive systems incorporated enable the reactor to tolerate the postulated accident conditions without causing severe plant conditions and core degradation.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.75-93
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• 2022

Machine learning (ML) data-driven meta-model is proposed as a surrogate model to reduce the excessive computational cost of the physics-based model and facilitate the real-time prediction of a nuclear power plant's transient response. To forecast the transient response three machine learning (ML) meta-models based on recurrent neural networks (RNNs); specifically, Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and a sequence combination of Convolutional Neural Network (CNN) and LSTM are developed. The chosen accident scenario is a control element assembly withdrawal at power concurrent with the Loss Of Offsite Power (LOOP). The transient response was obtained using the best estimate thermal hydraulics code, MARS-KS, and cross-validated against the Design and control document (DCD). DAKOTA software is loosely coupled with MARS-KS code via a python interface to perform the Best Estimate Plus Uncertainty Quantification (BEPU) analysis and generate a time series database of the system response to train, test and validate the ML meta-models. Key uncertain parameters identified as required by the CASU methodology were propagated using the non-parametric Monte-Carlo (MC) random propagation and Latin Hypercube Sampling technique until a statistically significant database (181 samples) as required by Wilk's fifth order is achieved with 95% probability and 95% confidence level. The three ML RNN models were built and optimized with the help of the Talos tool and demonstrated excellent performance in forecasting the most probable NPP transient response. This research was guided by the Systems Engineering (SE) approach for the systematic and efficient planning and execution of the research.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3423-3440
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• 2023

In this work, a multivariate time-series machine learning meta-model is developed to predict the transient response of a typical nuclear power plant (NPP) undergoing a steam generator tube rupture (SGTR). The model employs Recurrent Neural Networks (RNNs), including the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid CNN-LSTM model. To address the uncertainty inherent in such predictions, a Bayesian Neural Network (BNN) was implemented. The models were trained using a database generated by the Best Estimate Plus Uncertainty (BEPU) methodology; coupling the thermal hydraulics code, RELAP5/SCDAP/MOD3.4 to the statistical tool, DAKOTA, to predict the variation in system response under various operational and phenomenological uncertainties. The RNN models successfully captures the underlying characteristics of the data with reasonable accuracy, and the BNN-LSTM approach offers an additional layer of insight into the level of uncertainty associated with the predictions. The results demonstrate that LSTM outperforms GRU, while the hybrid CNN-LSTM model is computationally the most efficient. This study aims to gain a better understanding of the capabilities and limitations of machine learning models in the context of nuclear safety. By expanding the application of ML models to more severe accident scenarios, where operators are under extreme stress and prone to errors, ML models can provide valuable support and act as expert systems to assist in decision-making while minimizing the chances of human error.

• Nuclear Engineering and Technology
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• v.18 no.3
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• pp.200-208
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• 1986

The loss of coolant accident based on a double-ended cold leg break is analyzed with the discharge coefficient (Ca) of 0.4. This analysis covers the whole transient period from the start of depressurization to the complete refilling of the core by using RELAP4/MOD6-EM and RELAP4/ MOD6-HOT CHANNEL for the system thermal-hydraulics and the fuel performance during the blowdown phase respectively, and RELAP4/MOD6-FLOOD and TOODEE2 during the reflood phase. A simple analytical method has been developed to account for the lower plenum filling by approximating steam-water countercurrent flows and superheated wall effects at the downcomer during the refill period. Based on the informations. at the time of EOB (end-of-bypass), the refill duration time and the initial reflooding temperature were estimated and compared with the results from the RELAP4/MOD6, resulting in a good agreement. In addition, some parametric studies on the EOB were performed. The form loss coefficient between upper head and upper downcomer was found to be sensitive to the occurrence of the spurious EOB. Appropriate form loss coefficients should be taken into account to avoid the flow oscillations at the downcomer. The analyses with the six and three volume core nodalizations, respectively, show much similar trends in the system thermal-hydraulic performance, but the former case is recommended to obtain good results.