The Development of RELAP5 for Reactor System Analysis and Simulation Use of Visualized Modularization Software (RELAP-MV)

The integrated energy system connects various types of energy closely to form a complex multi-energy flow network to improve energy quality and support large-scale renewable energy access. To study the technologies of multi-energy flow modeling and simulation for the integrated energy system, the coupling characteristics and differences of mul-ti-energy flow are first summarized. Then three multi-energy flow unified modeling methods are introduced, i.e., en-ergy hub modeling method, unified energy path modeling method and energy network modeling method. In terms of model solving, the advantages and disadvantages of unified solution and decomposition solution are analyzed and compared. On this basis, the multi-period simulation process of a typical IES is expounded, and the difficulties and future of simulation technology are analyzed and prospected.

Establishing a mathematical model of a Francis turbine is the foundation for the simulation of hydropower station operation and is of great significance for the analysis of the hydropower station’s transient process. Currently, in engineering practice, the model is often established based on the comprehensive characteristic curves of the Francis turbine provided by the manufacturer, using the external characteristic method. Traditional modeling methods mostly adopt manual reading of points or the use of dedicated numerical software for curve tracing to discretely sample the comprehensive characteristic curves of the turbine. This method is labor-intensive, inefficient, and relies on manual experience, with a small sample size, which, to some extent, affects the accuracy and reliability of the numerical processing results and cannot meet the needs of transient process simulation analysis. To address these shortcomings, this paper proposes a refined modeling method based on image numerical processing and an RBF neural network. Taking the HLA685 Francis turbine as an example, the method first uses image processing to achieve large-scale automated discrete sampling of the turbine’s high-efficiency zone characteristic data, then reasonably extends the small-opening and low-speed regions, and finally uses the RBF neural network method for interpolation and extrapolation to obtain the full characteristic data. This method can effectively improve the efficiency and accuracy of comprehensive characteristic modeling of the turbine and has good reference significance for the comprehensive characteristic modeling of blade-type machinery.

To accurately reflect the switching characteristics of GaN High Electron Mobility Transistor (GaN HEMT), this article proposes a fast and accurate Spice circuit simulation model of GaN HEMT. The model mainly imports the saturation current modulation factor $\theta_{(V g s)}$) and focuses on the modeling accuracy of the nonlinear capacitance $C_{\text{g}\text{d}}$, which would affect the switching process. In addition, to address the present problems of relying on commercial fitting software with high copyright fees for parameter extraction and extracting model parameters very intricately by traditional modeling methods, a combined optimization method is proposed to extract the model parameters for modeling. The combined algorithm is based on the Genetic algorithm of global optimization and the Levenberg-Marquardt algorithm of the local optimum and it has the advantages of the global optimal solution and fast convergence. The proposed modeling method is verified to be fast and accurate by building a Double-Pulse Test experimental platform and comparing the simulated and experimental waveforms of the dynamic and static characteristics of GaN HEMT.

In this paper, the high-pressure common rail system of the marine diesel engine is taken as case study to establish a real-time simulation model of the high-pressure common rail system that can be used as the controlled object of the control system. On the premise of ensuring accuracy, the real-time simulation should also respond quickly to instructions issued by the control system. The development of the real-time simulation is based on the modular modeling method, and the high-pressure common rail system is divided into submodels, including the high-pressure oil pump, common rail tube, injector, and mass conversion. The submodels are built using the “surrogate model” method, which is mainly composed of MAP data and empirical formulas. The data used to establish the real-time simulation are not only from the empirical research into the high-pressure common rail system, but also from simulations of the high-pressure common rail system undertaken in AEMSim. The data obtained from this real-time simulation were compared with the experimental data to verify the model. The error in fuel injection quality is less than 5%, under different pressures and injection durations. In order to carry out dynamic verification, the PID control strategy, the model-based control strategy, and the established real-time simulation are all closed-loop tested. The results show that the developed real-time simulation can simulate the rail pressure wave caused by cyclic injection according to the control signal, and can feedback the control effect of different control strategies. Through verification, it is clear that the real-time simulation of the high-pressure common rail system can depict the rail pressure fluctuation caused by each cycle of fuel injection, while ensuring the accuracy and responsiveness of the simulation, which provides the ideal conditions for the study of a rail pressure control strategy.

The primary purpose of this research is to examine the stress corrosion cracking (SCC) resistance of Alloy 800NG in pressurized water reactor (PWR) primary water and pressurized heavy water reactor (PHWR) primary water. Rates of SCC growth of 20% cold-worked (CW) Alloy 800NG measured over the temperature range between 270°C and 360°C were compared with previously reported results for 20% CW Alloy TT690 and 20% CW Alloy 600 in order to consider which material is the most SCC resistant among materials presently being used for steam generator (SG) tubing worldwide. The secondary purpose is to examine the effect of chromium addition on SCC growth in PWR primary water of a series of alloys based on the Alloy 800 composition. SCC growth measurements were performed in PWR primary water over the chromium concentration range from 16% to 27% to obtain fundamental knowledge useful for considering a future alternative SCC-resistant material for SG tubing in extended life PWRs and PHWRs. The third objective is to examine the rate of cavity formation of 20% CW Alloy 800NG to obtain basic knowledge of one possible mechanism for SCC initiation after long-term operation. Measured rates of cavity formation in 20% CW Alloy 800NG were compared with previously reported results of 20% CW Alloy TT690 to compare the rate of SCC initiation caused by cavity formation. Four important patterns were observed. First, excellent SCC growth resistance was observed for 20% CW Alloy 800NG compared to 20% CW Alloy TT690 at 320°C, 340°C, and 360°C. Second, an inverse temperature dependence on SCC growth was observed in Alloy 800NG. The rate of SCC growth increased with decreasing temperature which was completely different from the trend for Alloy 600. Third, a significant beneficial effect by chromium addition in 800 series alloys on SCC growth resistance was observed in PWR primary water in the operating temperature range of PWRs and PHWRs. The rate of SCC growth decreased with increasing chromium concentration in the chromium concentration range between 16% and 27% chromium at 270°C, 290°C, and 320°C. However, no beneficial effect of chromium addition in these alloys was observed at 340°C and 360°C. Finally, a more than 10 times slower rate of cavity formation was observed in 20% CW Alloy 800NG than for 20% CW Alloy TT690. Results suggested that because of cavity formation, a more than 10-fold faster crack initiation occurred in Alloy TT690 than in Alloy 800NG. Further, carbide coverage and grain size significantly affected the rate of cavity formation. Detailed and comprehensive studies of long-term SCC initiation are necessary to ensure the future reliability of life-extended PWRs and PHWRs.

Introduction: Three-dimensional (3D)-printed custom pelvic implants have become a clinically viable option for patients undergoing pelvic cancer surgery with resection of the hip joint. However, increased clinical utilization has also necessitated improved implant durability, especially with regard to the compression screws used to secure the implant to remaining pelvic bone. This study evaluated six different finite element (FE) screw modeling methods for predicting compression screw pullout and fatigue failure in a custom pelvic implant secured to bone using nine compression screws. Methods: Three modeling methods (tied constraints (TIE), bolt load with constant force (BL-CF), and bolt load with constant length (BL-CL)) generated screw axial forces using functionality built into Abaqus FE software; while the remaining three modeling methods (isotropic pseudo-thermal field (ISO), orthotropic pseudo-thermal field (ORT), and equal-and-opposite force field (FOR)) generated screw axial forces using iterative physics-based relationships that can be implemented in any FE software. The ability of all six modeling methods to match specified screw pretension forces and predict screw pullout and fatigue failure was evaluated using an FE model of a custom pelvic implant with total hip replacement. The applied hip contact forces in the FE model were estimated at two locations in a gait cycle. For each of the nine screws in the custom implant FE model, likelihood of screw pullout failure was predicted using maximum screw axial force, while likelihood of screw fatigue failure was predicted using maximum von Mises stress. Results: The three iterative physics-based modeling methods and the non-iterative Abaqus BL-CL method produced nearly identical predictions for likelihood of screw pullout and fatigue failure, while the other two built-in Abaqus modeling methods yielded vastly different predictions. However, the Abaqus BL-CL method required the least computation time, largely because an iterative process was not needed to induce specified screw pretension forces. Of the three iterative methods, FOR required the fewest iterations and thus the least computation time. Discussion: These findings suggest that the BL-CL screw modeling method is the best option when Abaqus is used for predicting screw pullout and fatigue failure in custom pelvis prostheses, while the iterative physics-based FOR method is the best option if FE software other than Abaqus is used.

As an application of probabilistic fracture mechanics (PFM), a risk–benefit analysis was performed for the purpose of optimizing maintenance activities of steam generator (SG) tubes used in pressurized water reactors (PWRs). The probabilities of the SG tube leakage and rupture are defined as risks in this study. A model was made modifying pc-PRAISE (Piping Reliability Analysis Including Seismic Events) to evaluate the risks during 60 year operations due to stress corrosion cracking (SCC) of the tubes under various maintenance strategies for SG tubes. In the risk analysis, parameters such as inspection accuracy, inspection interval, sampling inspection and crack propagation law were selected for sensitivity analysis. Based on the risk analysis, a risk–benefit analysis was conducted when implementing two maintenance strategies taking both costs and revenues for 60 year operations into account. In the risk–benefit analysis, the expected cost of leakage or rupture was calculated by multiplying ‘probability of leakage or rupture’ by ‘expected loss of leakage or rupture accident’. To justify whether it is worthwhile implementing the maintenance strategies or not, the net present value (NPV) was calculated as an index, which is one of the most fundamental financial indices for decision-making based on the discounted cash flow (DCF) method. The results demonstrated that in the risk analysis, the risks are influenced significantly by the crack propagation law, accuracy of inspection and sampling inspection. In the risk–benefit analysis, it was suggested that investment to improve inspection accuracy would reduce the total costs of 60 year operations significantly and increase the NPV. Although the analysis was mainly conducted for SG tubes made of Inconel 600 mill anneal (MA) material, the analysis was also carried out for Inconel 690 thermal treatment (TT) material, making assumptions on its crack initiation and crack propagation law. In addition, the effect of introducing maintenance criteria, namely, operation with a crack justified by certain criteria, on NPV was evaluated.

This paper proposes a simulation modeling method for the electrical switching system of hydropower station, which is a sub-topic for Hydropower Station Simulation Training System. The graphics model of commonly used electrical switch equipment is developed with a certain software. As vast and different types of Hydropower station circuit breakers and associated grounding switches, and each specific action of the switch process is not same, so the modular modeling method is described to solve the problem. According to the role and status in power plant, electrical switch system is divided into several sub modules, among which a number of small modules are sorted in. In each sub module, a common model is developed. Besides, the application method that the network topology analysis algorithm used in electrical switching system simulation logic judgment is introduced. With the ‘connecting line fusion’ technique, logic function expression member information table is automatically generated, thereby enhancing the suitability for the electrical switch simulation model. The methods mentioned above assure the real-time, typicality and flexibility in simulation, and have been successfully used in the development of a large hydropower station simulator. DOI: http://dx.doi.org/10.11591/telkomnika.v11i8.3135

In this study, the improved knowledge-leverage based TSK fuzzy system modeling method is proposed in order to overcome the weaknesses of the knowledge-leverage based TSK fuzzy system (TSK-FS) modeling method. In particular, two improved knowledge-leverage strategies have been introduced for the parameter learning of the antecedents and consequents of the TSK-FS constructed in the current scene by transfer learning from the reference scene, respectively. With the improved knowledge-leverage learning abilities, the proposed method has shown the more adaptive modeling effect compared with traditional TSK fuzzy modeling methods and some related methods on the synthetic and real world datasets.

Transient simulation of a differential piston warm gas self-pressurization system for liquid attitude and divert propulsion system

Habitat models are now broadly used in conservation planning on public lands. If implemented correctly, habitat modelling is a transparent and repeatable technique for describing and mapping biodiversity values, and its application in peri‐urban and agricultural landscape planning is likely to expand rapidly. Conservation planning in such landscapes must be robust to the scrutiny that arises when biodiversity constraints are placed on developers and private landholders. A standardized modelling and model evaluation method based on widely accepted techniques will improve the robustness of conservation plans. We review current habitat modelling and model evaluation methods and provide a habitat modelling case study in the New South Wales central coast region that we hope will serve as a methodological template for conservation planners. We make recommendations on modelling methods that are appropriate when presence‐absence and presence‐only survey data are available and provide methodological details and a website with data and training material for modellers. Our aim is to provide practical guidelines that preserve methodological rigour and result in defendable habitat models and maps. The case study was undertaken in a rapidly developing area with substantial biodiversity values under urbanization pressure. Habitat maps for seven priority fauna species were developed using logistic regression models of species‐habitat relationships and a bootstrapping methodology was used to evaluate model predictions. The modelled species were the koala, tiger quoll, squirrel glider, yellow‐bellied glider, masked owl, powerful owl and sooty owl. Models ranked sites adequately in terms of habitat suitability and provided predictions of sufficient reliability for the purpose of identifying preliminary conservation priority areas. However, they are subject to multiple uncertainties and should not be viewed as a completely accurate representation of the distribution of species habitat. We recommend the use of model prediction in an adaptive framework whereby models are iteratively updated and refined as new data become available.

SUMMARY Regional geomagnetic field models are used to delineate intricate details of the Earth’s magnetic field and have significant application value in precision navigation and geomagnetic exploration. However, traditional modelling methods often encounter challenges when applied to sparse data, leading to issues like low model resolution and accuracy, as well as limited generalizability. The recently developed physics-informed neural networks (PINNs), a powerful modelling tool, presents a viable alternative for regional geomagnetic field modelling. This study employed the PINNs method to construct a geomagnetic field model for satellite altitudes over the Chinese region, based on the Swarm satellite data set provided by the European Space Agency. An adaptive weight training method was used for the two-stage training process, involving an initial pre-training and subsequent fine-tuning of the model. Experimental verification shows that the proposed algorithm enhanced the model’s fidelity to physical laws, improved its resolution and prediction accuracy (reducing the root mean square errors for geomagnetic components to as low as 4 nT), and enhanced its generalizability, with the total field intensity F and the prediction accuracy of both the X- and Y-components demonstrating superiority over that of other traditional methods. Collectively, these advancements enable efficient regional geomagnetic field modelling while providing a foundation for more reliable and precise predictions.

In this paper, a new anti-dumping early-warning system for the export of China's textile products is presented. The early-warning system based on Rough sets and neuro-fuzzy decision tree modelling method, which is different from traditional modelling methods. We can get reduced information table by rough set, which implies that the number of index and qualitative variables is reduced with no information loss through rough set approach. And then, neural networks-fuzzy decision tree (a fuzzy decision tree structure with neural like parameter adaptation strategy) improves FDT's classification accuracy and carry through anti- dumping early-warning more accuracy. The other new attempt is the setting of early-warning intervals. The result of the positive research indicated that this system is very valid for anti-dumping prediction and it will have a good application prospect in this area.

The geometric bone model of patients is an important basis for individualized biomechanical modeling and analysis, formulation of surgical planning, design of surgical guide plate, and customization of artificial joint. In this study, a rapid three-dimensional (3D) reconstruction method based on statistical shape model was proposed for femur. Combined with the patient plain X-ray film data, rapid 3D modeling of individualized patient femur geometry was realized. The average error of 3D reconstruction was 1.597-1.842 mm, and the root mean square error was 1.453-2.341 mm. The average errors of femoral head diameter, cervical shaft angle, offset distance and anteversion angle of the reconstructed model were 0.597 mm, 1.163°, 1.389 mm and 1.354°, respectively. Compared with traditional modeling methods, the new method could achieve rapid 3D reconstruction of femur more accurately in a shorter time. This paper provides a new technology for rapid 3D modeling of bone geometry, which is helpful to promote rapid biomechanical analysis for patients, and provides a new idea for the selection of orthopedic implants and the rapid research and development of customized implants.

In this paper, two modeling methods, the forming method and the generating method, for hypoid gears with two kinds of transmission ratio are discussed by simulating the actual machining process. In the generating modeling method, the tooth profile of the gear is generated by boolean algorithm step by step after creating the models of the gear blank and the cutter and then rotating around their own axis by certain degrees until the cutter is outside the gear blank completely. In contrast with the generating modeling method, the tooth profile is formed by carrying out the boolean algorithm for one time after creating the models of the gear blank and cutter seperately in the forming modeling method. Then using feature instance, all the teeth are created both in the generating modeling method and in the forming modeling method. Using the two modeling methods given in this paper, the modeling process can be shortened and the modeling precision can be improved.