An Innovative Hybrid Loop-Pool Design for Sodium Cooled Fast Reactor

The existing sodium cooled fast reactors (SFR) have two types of designs – loop type and pool type. In the loop type design, such as JOYO (Japan) [1] and MONJU (Japan), the primary coolant is circulated through intermediate heat exchangers (IHX) external to the reactor tank. The major advantages of loop design include compactness and easy maintenance. The disadvantage is higher possibility of sodium leakage. In the pool type design such as EBR-II (USA), BN-600M(Russia), Superphénix (France) and European Fast Reactor [2], the reactor core, primary pumps, IHXs and direct reactor auxiliary cooling system (DRACS) heat exchangers (DHX) all are immersed in a pool of sodium coolant within the reactor vessel, making a loss of primary coolant extremely unlikely. However, the pool type design makes primary system large. In the latest ANL’s Advanced Burner Test Reactor (ABTR) design [3], the primary system is configured in a pool-type arrangement. The hot sodium at core outlet temperature in hot pool is separated from the cold sodium at core inlet temperature in cold pool by a single integrated structure called Redan. Redan provides the exchange of the hot sodium from hot pool to cold pool through IHXs. The IHXs were chosen as the traditional tube-shell design. This type of IHXs is large in size and hence large reactor vessel is needed

Modulation of Type III Secretion System in Pseudomonas aeruginosa: Involvement of the PA4857 Gene Product

Pseudomonas aeruginosais an opportunistic pathogen that causes serious acute or chronic infections in humans.Acute infections typically involve the type Ш secretion systems (T3SS) and bacterial motility,whereas chronic infectionsare often associated with biofilm formation and the type VI secretion system (T6SS). To identifynew genes required for pathogenesis, a transposon mutagenesis library was constructed and the gene PA4857, named tspR, was found to modulateT3SS gene expression. Deletion of P. aeruginosa tspRreduced the virulence in a mouse acute lung infection model and diminished cytotoxicity. Suppression of T3SS gene expression in the tspR mutant resulted from compromised translation of the T3SS master regulator ExsA. TspR negatively regulated two small RNAs, RsmYand RsmZ, which control RsmA. Our data demonstrated that defects inT3SS expression and biofilm formation in retS mutant could be partially restored by overexpression of tspR. Taken together, our results demonstrated thatthe newly identifiedretS-tspRpathway is coordinated with the retS-gacSsystem, which regulates the genes associated with acute and chronic infections andcontrols the lifestyle choice of P. aeruginosa

Extended Forward Sensitivity Analysis for Uncertainty Quantification

This report presents the forward sensitivity analysis method as a means for quantification of uncertainty in system analysis. The traditional approach to uncertainty quantification is based on a “black box” approach. The simulation tool is treated as an unknown signal generator, a distribution of inputs according to assumed probability density functions is sent in and the distribution of the outputs is measured and correlated back to the original input distribution. This approach requires large number of simulation runs and therefore has high computational cost. Contrary to the “black box” method, a more efficient sensitivity approach can take advantage of intimate knowledge of the simulation code. In this approach equations for the propagation of uncertainty are constructed and the sensitivity is solved for as variables in the same simulation. This “glass box” method can generate similar sensitivity information as the above “black box” approach with couples of runs to cover a large uncertainty region. Because only small numbers of runs are required, those runs can be done with a high accuracy in space and time ensuring that the uncertainty of the physical model is being measured and not simply the numerical error caused by the coarse discretization. In the forward sensitivity method, the model is differentiated with respect to each parameter to yield an additional system of the same size as the original one, the result of which is the solution sensitivity. The sensitivity of any output variable can then be directly obtained from these sensitivities by applying the chain rule of differentiation. We extend the forward sensitivity method to include time and spatial steps as special parameters so that the numerical errors can be quantified against other physical parameters. This extension makes the forward sensitivity method a much more powerful tool to help uncertainty analysis. By knowing the relative sensitivity of time and space steps with other interested physical parameters, the simulation can be run at appropriate time and space steps that ensure numerical sensitivities will not affect the confidence of the physical parameter sensitivity results. Two well defined benchmark problems, thermal wave and nonlinear diffusion, are utilized to demonstrate the extended forward sensitivity analysis method. All the physical solutions, parameter sensitivity solutions, even time step sensitivity in one case, have analytical forms, which allows the verification to be done in the strictest sense. A pilot code, Extended Forward sensitivity Analysis pilot code (EFA), has been developed to implement the above work

One-Dimensional Analysis of Thermal Stratification in AHTR and SFR Coolant Pools

Thermal stratification phenomena are very common in pool type reactor systems, such as the liquid-salt cooled Advanced High Temperature Reactor (AHTR) and liquid-metal cooled fast reactor systems such as the Sodium Fast Reactor (SFR). It is important to accurately predict the temperature and density distributions both for design optimation and accident analysis. Current major reactor system analysis codes such as RELAP5 (for LWR’s, and recently extended to analyze high temperature reactors), TRAC (for LWR’s), and SASSYS (for liquid metal fast reactors) only provide lumped-volume based models which can only give very approximate results and can only handle simple cases with one mixing source. While 2-D or 3-D CFD methods can be used to analyze simple configurations, these methods require very fine grid resolution to resolve thin substructures such as jets and wall boundaries, yet such fine grid resolution is difficult or impossible to provide for studying the reactor response to transients due to computational expense. Therefore, new methods are needed to support design optimization and safety analysis of Generation IV pool type reactor systems. Previous scaling has shown that stratified mixing processes in large stably stratified enclosures can be described using one-dimensional differential equations, with the vertical transport by free and wall jets modeled using standard integral techniques. This allows very large reductions in computational effort compared to three-dimensional numerical modeling of turbulent mixing in large enclosures. The BMIX++ (Berkeley mechanistic MIXing code in C++) code was originally developed at UC Berkeley to implement such ideas. This code solves mixing and heat transfer problems in stably stratified enclosures. The code uses a Lagrangian approach to solve 1-D transient governing equations for the ambient fluid and uses analytical or 1-D integral models to compute substructures. By including liquid salt properties, BMIX++ code is extended to analyze liquid salt pool systems in the current AHTR design, to provide an example of its application. Similar analysis is possible for liquid-metal cooled reactors. The current AHTR baseline design uses a large buffer salt tank to provide more thermal inertial and safety margin. Reactor vessel, intermediate heat exchangers, pool reactor auxiliary cooling system heat exchangers (PHX), and direct reactor auxiliary cooling system heat exchangers (DHX) are all immerged in the buffer salt pool. These structures provide major driving sources for vertical mixing and thermal stratification. Predication of the temperature distribution within the buffer salt tank directly affects the major safety systems design, such as the PHX and DHX, safety analysis results, and structure thermal stresses analysis. The BMIX++ code is used to predict mixing and thermal stratification in this pool system. This example shows the potential of 1-D analysis methods and BMIX++ to be included in system analysis codes for pool type of Gen-IV reactor systems

Molecular Simulation of Hyperbranched Polyester

A new types of hyperbranched polyester was synthesized by the 2,2-bis(hydroxymethyl) propionic acid as an AB2-type monomer and glycerol as the core moiety. Molecular weights were confirmed by Gel Permeation Chromatography. Acid values were titrated by KOH. The hydroxy value was obtained by titration. Furthermore, we calculate logarithmic value of acid value, hydroxy value, and molecular weight, respectively, and the simulation model curves were obtained. Based on the simulation model curves, we establish the empirical equation of the relationship of molecular weight, acid value and hydroxy value

Extended Forward Sensitivity Analysis for Uncertainty Quantification

Verification and validation (V&V) are playing more important roles to quantify uncertainties and realize high fidelity simulations in engineering system analyses, such as transients happened in a complex nuclear reactor system. Traditional V&V in the reactor system analysis focused more on the validation part or did not differentiate verification and validation. The traditional approach to uncertainty quantification is based on a 'black box' approach. The simulation tool is treated as an unknown signal generator, a distribution of inputs according to assumed probability density functions is sent in and the distribution of the outputs is measured and correlated back to the original input distribution. The 'black box' method mixes numerical errors with all other uncertainties. It is also not efficient to perform sensitivity analysis. Contrary to the 'black box' method, a more efficient sensitivity approach can take advantage of intimate knowledge of the simulation code. In these types of approaches equations for the propagation of uncertainty are constructed and the sensitivities are directly solved for as variables in the simulation. This paper presents the forward sensitivity analysis as a method to help uncertainty qualification. By including time step and potentially spatial step as special sensitivity parameters, the forward sensitivity method is extended as one method to quantify numerical errors. Note that by integrating local truncation errors over the whole system through the forward sensitivity analysis process, the generated time step and spatial step sensitivity information reflect global numerical errors. The discretization errors can be systematically compared against uncertainties due to other physical parameters. This extension makes the forward sensitivity method a much more powerful tool to help uncertainty qualification. By knowing the relative sensitivity of time and space steps with other interested physical parameters, the simulation is allowed to run at optimized time and space steps without affecting the confidence of the physical parameter sensitivity results. The time and space steps forward sensitivity analysis method can also replace the traditional time step and grid convergence study with much less computational cost. Several well defined benchmark problems with manufactured solutions are utilized to demonstrate the extended forward sensitivity analysis method. All the physical solutions, parameter sensitivity solutions, even the time step sensitivity in one case, have analytical forms, which allows the verification to be performed in the strictest sense

An overview of modeling methods for thermal mixing and stratification in large enclosures for reactor safety analysis

Thermal mixing and stratification phenomena play major roles in the safety of reactor systems with large enclosures, such as containment safety in current fleet of LWRs, long-term passive containment cooling in Gen III+ plants including AP-1000 and ESBWR, the cold and hot pool mixing in pool type sodium cooled fast reactor systems (SFR), and reactor cavity cooling system behavior in high temperature gas cooled reactors (HTGR), etc. Depending on the fidelity requirement and computational resources, 0-D steady state models (heat transfer correlations), 0-D lumped parameter based transient models, 1-D physical-based coarse grain models, and 3-D CFD models are available. Current major system analysis codes either have no models or only 0-D models for thermal stratification and mixing, which can only give highly approximate results for simple cases. While 3-D CFD methods can be used to analyze simple configurations, these methods require very fine grid resolution to resolve thin substructures such as jets and wall boundaries. Due to prohibitive computational expenses for long transients in very large volumes, 3-D CFD simulations remain impractical for system analyses. For mixing in stably stratified large enclosures, UC Berkeley developed 1-D models basing on Zuber’s hierarchical two-tiered scaling analysis (HTTSA) method where the ambient fluid volume is represented by 1-D transient partial differential equations and substructures such as free or wall jets are modeled with 1-D integral models. This allows very large reductions in computational effort compared to 3-D CFD modeling. This paper will present an overview on important thermal mixing and stratification phenomena in large enclosures for different reactors, major modeling methods and their advantages and limits, potential paths to improve simulation capability and reduce analysis uncertainty in this area for advanced reactor system analysis tools