Search

Supercritical CO2 (S-CO2) has the potential to be used as the working fluid in a power cycle since S-CO2 shows a density value high as its liquid phase while the viscosity value remains closer to its gaseous phase. Thus, it requires much less work to compress due to its low compressibility as well as relatively small flow resistance. However, the S-CO2 leakage flow from turbo-machinery via seal becomes one of the important issues since not only it influences the cycle efficiency due to parasitic loss but also it is important for evaluating the system safety under various operating conditions. In this paper, the gap effect, which is studied by changing the diameter of an orifice, cavity length effect, and the number of tooth effect in a simulated labyrinth seal geometry nozzle are presented. Experimental results under various conditions including not only single phase flow but also two phase flow conditions are presented.

Due to small footprint and high efficiency, a Supercritical CO2 (S-CO2) power cycle is considered to be one of the promising next-generation power cycles. Although S-CO2 is reported as a powerful cleaning agent, the performance of a power system operating with S-CO2 will also inevitably degrade over time. Previous researchers have shown that turbomachinery deterioration could be a major subject regarding the system performance degradation. Nevertheless, no quantitative evaluation has yet been made so far. In this study, the impact of the performance degradation of the S-CO2 turbomachinery on the overall performance of the system is analyzed quantitatively. The concept of Health Parameter is used to simulate turbomachinery degradation. To quantify the impact, an S-CO2 direct-cycle small modular reactor is selected as a target system. A transient analysis platform is built using a nuclear system safety analysis code and the deep neural network (DNN)-based S-CO2 turbomachinery off-design performance model. System dynamics are evaluated for primary frequency control ability and secondary load-following capability. Results show that the control problems during the transient state can occur when the output fluctuations are large and the performance degradation is severe. It has been confirmed that even control failures of the PID controllers can occur. Therefore, the performance degradation of turbomachinery must be monitored and considered, for an operation strategy for S-CO2 systems.

A solar-nuclear hybrid system that combines Concentrating Solar Power (CSP) and nuclear power was suggested previously to meet the electricity demands for remote microgrids. The hybrid system combines CSP, Thermal Energy Storage (TES) and a Micro Modular Reactor (MMR). A Supercritical Carbon dioxide (sCO2) power system is used for power generation system. CSP, which has relatively high efficiency, no refueling, low operation and installation costs, is suitable as an energy source for the microgrid. However, since it is challenging to meet 100% electricity demand with a standalone CSP system due to excessive energy storage system and large land area requirement, a hybrid system is proposed by adding a nuclear system to resolve this issue. The hybrid system showed a higher capacity factor than the standalone CSP system while reducing the required energy storage. However, the TES capacity was still excessive to meet 100% of the electricity demand in the previous study to overcome seasonal variation. Therefore, in this study, the hybrid system is re-designed by optimizing solar to nuclear ratio while tracing change in the Levelized Cost Of Electricity (LCOE) of the system. A sensitivity analysis is performed with respect to the five variables; nuclear to solar heat ratio, direct normal irradiation, peak-to-average electricity demand ratio, nuclear island cost, discount rate affecting LCOE. As a result of economic evaluation, it is confirmed that the hybrid system is cost competitive to the standalone CSP system. This study showed that the nuclear-CSP hybrid system can be utilized as a power source with a high potential for isolated microgrid.

After the Fukushima accident, the importance of an emergency power supply for a nuclear power plant has been emphasized more. In order to maximize the performance of the existing emergency power source in operating nuclear power plants, adding a waste heat recovery system for the emergency power source is suggested for the first time in this study. In order to explore the possibility of the idea, a comparison of six supercritical carbon dioxide (S-CO2) power cycle layouts recovering waste heat from a 7.2 MW alternate alternating current diesel generator (AAC DG) is first presented. The diesel engine can supply two heat sources to the waste heat recovery system: one from exhaust gas and the other from scavenged air. Moreover, a sensitivity study of the cycles for different design parameters is performed, and the thermodynamic performances of the various cycles were evaluated. The main components, including turbomachinery and heat exchangers, are designed with in-house codes which have been validated with experiment data. Based on the designed cycle and components, the bottoming S-CO2 cycle performance under part load operating condition of AAC DG is analyzed by using a quasi-steady state cycle analysis method. It was found that a partial heating cycle has relatively higher net produced work while enjoying the benefit of a simple layout and smaller number of components. This study also revealed that further waste heat can be recovered by adjusting the flow split merging point of the partial heating cycle.

Supercritical CO2 (S-CO2) power cycles have received much attention due to their desired advantages in various applications. Due to the substantial difference in specific heat of S-CO2 at different pressure, unique characteristics inside heat exchangers, especially in the recuperator, are observed and affect the cycle design. In this research, the problem of inner pinch occurring inside the S-CO2 recuperator is identified and resolved by suggesting a new framework for the definition of heat exchanger effectiveness using the point of zero inner pinch. The design methodology of S-CO2 cycles is improved using this definition by developing a module to calculate the zero inner pinch using artificial neural network (ANN) to undergo a learning process. As a result, the computation time required for cycle analysis is reduced by an order of 10 3 in the case of simple recuperated Brayton cycle, under the accuracy of 10 - 7 error bound. As the readers gain the access to the developed module for calculating the zero inner pinch, the procedure for S-CO2 cycle optimization will become far more manageable for researchers, and as a result, this result can allow the conceptualization of even further complex layouts in S-CO2 cycle research.

Adjoint sensitivity based optimization methodology for a supercritical CO2 (S-CO2) cycle is developed in this paper. The adjoint sensitivity analysis method (adjoint method) is a way to analyze sensitivity very quickly. By using the developed methodology, the optimal state variation due to the change of cycle parameters is analyzed. The S-CO2 recompression Brayton cycle is used for an example case for the demonstration of the proposed method. The independence of time consumption for the developed adjoint sensitivity analysis method to the number of optimized variables is demonstrated. For the test case, the developed algorithm shows the ability to make the design parameters converge with a precision of 10−6 by more than ten times faster for calculating the sensitivity than conventional optimization methods. Validation of the obtained optimal point is also included in the paper. For the validation, a response surface analysis is performed to visualize the pathway during the iteration for optimization. It is very challenging to carry out the optimization having the same precision using a brute force algorithm or a probability-based optimization algorithm since the number of variables to be optimized is substantial for this type of a problem. The smoothness of an optimal cycle efficiency variation is observed in every case.

Although the number of meshes and their distribution are important parameters that affect both computation resources and the accuracy of the numerical results, meshing of most of all computational problems still depends highly on the users’ experiences. In this paper, a deterministic adjoint-based method of optimizing mesh distribution is proposed. The developed method is applied to a nuclear power plant safety analysis. The mesh optimization was performed with 1D steady state cylindrical nuclear fuel problem first. Radial and axial mesh distributions are optimized respectively. With no surprise, the optimized mesh system performs superior than the same number of uniformly meshed system. However, it was unexpected that the optimized mesh retains generality and therefore, the optimized mesh system can be still the best mesh system for given number of meshes under different condition or even during transient analysis. The authors applied the optimized mesh distribution to nuclear system safety analysis. A large pressurized water reactor cold leg guillotine break (LBLOCA) scenario was analyzed and the consequence of different mesh systems is investigated and discussed. From this preliminary study the usefulness and implication of the adjoint based mesh optimization method for the nuclear safety analysis is uncovered.

Most of the power plants operating nowadays mainly have adopted a steam Rankine cycle or a gas Brayton cycle. To devise a better power conversion cycle, various approaches were taken by researchers and one of the examples is an S-CO2 (supercritical CO2) power cycle. Over the past decades, the S-CO2 power cycle was invented and studied. Eventually the cycle was successful for attracting attentions from a wide range of applications. Basically, an S-CO2 power cycle is a variation of a gas Brayton cycle. In contrast to the fact that an ordinary Brayton cycle operates with a gas phase fluid, the S-CO2 power cycle operates with a supercritical phase fluid, where temperatures and pressures of working fluid are above the critical point. Many advantages of S-CO2 power cycle are rooted from its novel characteristics. Particularly, a compressor in an S-CO2 power cycle operates near the critical point, where the compressibility is greatly reduced. Since the S-CO2 power cycle greatly benefits from the reduced compression work, an S-CO2 compressor prediction under off-design condition has a huge impact on overall cycle performance. When off-design operations of a power cycle are considered, the compressor performance needs to be specified. One of the approaches for a compressor off-design performance evaluation is to use the correction methods based on similitude analysis. However, there are several approaches for deriving the equivalent conditions but none of the approaches has been thoroughly examined for S-CO2 conditions based on data. The purpose of this paper is comparing these correction models to identify the best fitted approach, in order to predict a compressor off-design operation performance more accurately from limited amount of information. Each correction method was applied to two sets of data, SCEIL experiment data and 1D turbomachinery code off-design prediction code generated data, and evaluated in this paper.

Due to the lack of test data of a supercritical CO2 compressor operating under supercritical CO2 conditions, the work of loss model validation for supercritical CO2 compressor could not be conducted successfully before. Therefore, as the first step, this study gathers the existing accessible experiment data as much as possible while conducting additional supercritical CO2 compressor performance experiments to validate the loss models for the supercritical CO2 compressor. To evaluate the reliability of test results, the measurement uncertainty is estimated from the experiment data. Then, three different combinations of loss models based on the mean streamline method are compared with six available experimental cases to find the most suitable combination of loss models for the supercritical CO2 compressor design and analysis. The combination of loss models proposed by Lee was found to predict pressure ratio and isentropic efficiency of supercritical CO2 compressors operating both on-design and off-design conditions successfully. It is expected that the set of loss models presented herein can be utilized as a conceptual design tool in the preliminary design stage of supercritical CO2 compressors.

As more supercritical CO2 (S-CO2) power systems are operating around the world, long term operability and reliability of the system will become salient issues in the near future. In this research, the performance degradation of an S-CO2 compressor is investigated experimentally, and degradation models are developed to explain the performance degradation of the S-CO2 compressor at first. The investigated degradation is damaged on the impeller caused by foreign object approximately 3 mm size. The damage caused the shape change of the impeller. From the comparison of the performances of the damaged impeller to the intact impeller, it is confirmed that the performance degradation does not affect the surge margin. The correction model for the degradation of gas compressor performances has a limitation when the pressure ratio was used for measuring the performance. However, it is also shown that the correction model works well if the enthalpy rise is utilized instead of the pressure ratio. This study experimentally shows the possibility that the performance degradation of the S-CO2 compressor can be modeled through a linear model based on enthalpy. Further research is needed to validate the model for wider operating conditions and more severe degraded conditions.

A supercritical CO2 (S–CO2) Brayton cycle has a compact and simple layout, which suggests the possibility to serve as a small scale distributed power conversion system. Off-design behaviors of each component determine the system off-design performance in the S–CO2 power cycle. Among components, compressor performances have the largest impact on the system off-design analysis since it operates nearest to the critical point of CO2. In the system analysis, the off-design performance of a compressor is usually pre-calculated for the fixed inlet conditions. The performance map is then converted for the off-design performances prediction using corrected mass flow rate and corrected rpm. Similitude models are used for the conversion. Several similitude models have been developed mostly for air conditions previously, but the applicability of these models to S–CO2 still needs to be tested. In this paper, to evaluate the existing models for the S–CO2 conditions, experimentally validated 1D mean-line code is used to generate wide range of S–CO2 compressor data set. As a result of the evaluation, Pham model showed the most accurate enthalpy rise prediction resulting in the best pressure rise prediction, and the efficiency prediction could be modified with density correction to improve the off-design performance prediction.

Recently, supercritical CO2 (S-CO2) power system have received much attention due to their high efficiency and small size. S-CO2 exhibits a dramatic nonlinear property change near the critical point. Owing to this sensitivity, a bottleneck of S-CO2 system off-design analysis can be the turbomachinery off-design prediction. Conventionally, to reflect off-design performances of turbomachines within the system analysis, performance map with the correction method has been exploited. It is because computationally expensive to solve the Euler turbine equation for every iteration. However, due to the aforementioned behavior near the critical point, it is questionable if the method developed under air condition can still be valid for the S-CO2 system. The authors are proposing a method using Deep Neural Network (DNN) to build an S-CO2 turbomachinery off-design model. A statistical analysis revealed that the method showed 101 to 104 times better Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) indices than those of the existing correction methods. Analysis from the system point of view was also carried out. The results of the pre-trained DNN S-CO2 turbomachinery off-design model and the correction methods were compared by analyzing the off-design steady-state performance of an S-CO2 simple recuperated cycle. The results showed that system off-design performance predictions can be significantly distorted with the conventional correction methodologies, and that it can be avoided through the developed DNN based S-CO2 turbomachinery off-design model.

This paper first presents a review of compact heat exchanger technology for a supercritical carbon dioxide (S-CO2) power cycle application to provide holistic insights for designing a power system and selecting appropriate heat exchangers. This is because a compact heat exchanger plays a key role in the S-CO2 power system to have small footprint while maintaining structural integrity. The developments and characteristics of various compact heat exchangers are briefly described, and their heat transfer mechanisms and correlations obtained from open literatures are summarized, enabling the researchers to obtain information quickly for their needs. From the selected correlations, thermal hydraulic performances of compact heat exchangers are compared for the S-CO2 recuperator operating region.

Nuclear thermal-hydraulic system analysis codes with the Best Estimate plus Uncertainty approach are unavoidably uncertain and naturally, an uncertainty analysis has been an important issue. In general, the system is discretized depending on the user while satisfying some criteria. Previously, uncertainties related to spatial discretization in nodalization have been less considered due to their difficulty in quantification. However, there are some results that they are comparable to other uncertainties. Previous studies have been conducted by evaluating and optimizing the uncertainty related to user effect by performing iterative calculations. Since these methods consume a large amount of resources for computation, this study suggests a method of consuming less resources. For this purpose, the adjoint method is applied to the code for node optimization in this study. The proposed methodology is applied to two cases and confirmed its applicability for the node optimization and its utility in the node uncertainty quantification.

Recent climate initiatives had initiated a wave of reducing CO2 emissions for power generation. Many countries have been striving to develop and deploy low carbon emission technologies for power generation through efficiency improvement and use of renewable energy. For the regions that gain sufficient amount of solar to make solar power attractive overall, the challenge of intermittency still poses an issue for the electricity grid. The complementary role of natural gas power for the incoming renewables will be reinforced in the near future, but emission of CO2 needs to be avoided through means such as carbon capture and storage. A novel solution of integrating the s-CO2 oxy-combustion system with the concentrated solar power (CSP) is devised. The suggested concept will provide direction to satisfy the growing electricity demand, yet with environmental sustainability. The evaluation of this system in several regions possible for CSP installation shows reduction of fuel consumption by 17–38% compared to conventionally separated systems.

The Supercritical CO2 power cycle (S-CO2 cycle) is the power cycle that adopts CO2 as a working fluid and is designed to have a compression process near the critical point of CO2. Due to the non-linearity of CO2 pyhsical properties near the critical point, the S-CO2 cycle needs relatively less compression work. Therefore, the efficiency of the S-CO2 cycle is higher than traditional gas cycles. Furthermore, because of the relatively high system minimum pressure (near the critical point, ∼7.39 MPa), an S-CO2 cycle can be composed of smaller turbomachines. Considering these advantages, nowadays, there are many attempts to apply S-CO2 cycles to various fields, such as waste heat recovery, nuclear, coal, concentrated solar power plant and so on. These non-linear pyhsical properties become the cause of some unique issues. One of the most significant issues is the internal pinch point problem in a recuperator. Unlike the traditional gas-to-gas heat exchanger, each hot and cold side of the S-CO2 recuperator goes through the severe change of specific heat. This dramatic change of specific heat may cause the internal pinch point of the recuperator. When the internal pinch point phenomenon occurs, the performance of the recuperator may not able to be evaluated from the pre-fixed effectiveness. This can be an issue when the compressor inlet temperature decreases to transcritical or subcritical region. This may alter the optimal point of the S-CO2 power cycle. In this paper, optimal design points and optimal performance of the S-CO2 power cycle are tracked with the consideration of the internal pinch point phenomenon. While changing the system boundary conditions, the optimal point variation due to internal pinch point phenomenon is evaluated and compared with a traditional methodology. This research is progressed with an in-house integrated S-CO2 power cycle analysis code, which is named KAIST – ESCA (Evaluator for Supercritical CO2 Cycle based on Adjoint method). The target cycle layouts are Simple Recuperated, Intercooling, Recompression and Recompression with intercooling layouts. Both of the S-CO2 Rankine and Brayton cycles conditions are considered.

An advanced numerical method for printed circuit heat exchanger (PCHE) analysis and design is developed. It takes into account realistic flow path configurations for cross and parallel flow regions near header locations. Consideration of such header effects is found to be important in evaluating PCHE effectiveness particularly when PCHEs operate with relatively low effectiveness with a considerable heat transfer contribution from the cross and the parallel flow regions. The importance of modeling the header effects manifests even further when pressure drop is considered. Flow distributions subject to flow path configurations leads to a significant pressure drop increase, which is neglected in the widely used sole-counter flow approximation. The developed code can be served to advance the current modeling and design capabilities of PCHEs.

A worm and worm wheel gearing is widely used in a geared motor unit for the convenience and safety of an automobile. For mass production of a high quality worm, the current rolling process is substituted with the milling process. The milling process offers comparatively accurate machining quality and high production capacity for worm manufacturing. Moreover, since the milling process enables the integration of all operations of worm manufacturing on a CNC lathe, production efficiency can be remarkably improved. However, there are several important factors to be considered for producing high quality worms such as cutting force, tool-workpiece interference, and others. Planetary milling and side milling are generally applied to machine worms. In this study, the cutting characteristics of worm machining on an automatic lathe are investigated for two types of milling processes and those processes are compared with each other. A tool-tip trajectory model based on tool-workpiece interaction is proposed, and then tool-workpiece interference and cutting force are simulated with the model. The simulation results are verified through numerous experiments. The experimental results show the cutting characteristics of each milling process and the efficiency for mass production of a high quality worm.

In the ultra-precision diamond cutting process, the rake angle of the tool becomes negative because the edge radius of a tool is considerably larger compared to the sub-micrometer depth of the cut. The effects of plowing due to the large negative rake angle result in an unstable cutting process without continuous chip. For this reason, it is important to determine minimum cutting thickness in order to enable greater machining accuracy to be obtained by fine and stable machining. It was previously reported that the critical depth of cut with a continuous chip was determined by the tool sharpness and the friction coefficient between a workpiece and a tool [S.M. Son, et al., Effects of the friction coefficient on the minimum cutting thickness in micro cutting, International Journal of Machine Tools and Manufacture 45 (2005) 529–535]. For the same edge radius of a tool, the higher the friction coefficient of the tool–workpiece, the thinner the minimum cutting thickness becomes. Therefore, it is believed that increasing the friction coefficient by a physical method would be effective to achieve thinner stable cutting. In this study, the possibility of reducing the minimum cutting thickness was investigated through changing the friction coefficient of a tool–workpiece. The vibration cutting method is applied to increase the friction coefficient. Experimental results show that the cutting technology is efficient for increasing the friction coefficient and decreasing the minimum cutting thickness. The minimum cutting thickness was reduced by about 0.02–0.04 μm depending on materials and vibration conditions.