Validation of the 4C Code on the AC Loss Tests of a Full-Scale ITER Coil

The AC loss tests on the first ITER Central Solenoid Module (CSM) have been modelled and compared to the test results. The model has been implemented in the 4C code, a thermal-hydraulic modelling tool which includes the CSM winding pack and the cryogenic circuit of the test facility. Two modes of operation of the circuit have been analyzed: the nominal and the “isolation” mode, i.e., when the cryogenic circuit valves are operated to isolate the coil during the current dumps. The computed mass flow rate, pressure and coil outlet temperature at different locations have been compared with the measurements, showing a very good agreement in both modes of operation of the circuit. The validated model helped in the interpretation of the experimental results, such as the backflow at the coil inlet -which cannot be measured- or the non-monotonic outlet temperature evolution following the current dump. Furthermore, the code was used to qualify the isochoric method for the quantification of the deposited energy due to AC losses, as it was the only method applicable in case of current dumps from high current

Analysis of the DC performance of the ITER CSI coil using the 4C code

The DC performance of the ITER Central Solenoid Insert (CSI) coil, a single layer solenoid wound using the same Nb3Sn conductor that will be adopted for the 3L module of ITER CS, was measured during the 2015 test campaign in different magnetic field and current operating conditions, before and after electromagnetic and thermal cycles, as well as before and after quench tests. The 4C thermal-hydraulic code is applied here to the analysis of the CSI performance: first, the free parameters of the model are calibrated; then, the model is validated against measurements not used for its calibration. The model is then used to compute the current sharing temperature, to be compared with the measured jacket temperature, and to assess the performance after quench tests

Effects of RANS-Type turbulence models on the convective heat loss computed by CFD in the solar two power tower

The effect of the choice of Reynolds-Averaged Navier-Stokes (RANS) type turbulence closure on the Computational Fluid Dynamics (CFD) prediction of convective heat losses from the Solar Two central receiver is considered in this paper for a simplified receiver geometry approximated by flat panels. Computed convective losses at steady state are ~ 2-3% (1%) of the total power absorbed by the receiver, at high (low) wind speed, depending on the turbulence model chosen. The simulation results are consistent with those of available correlations for rough cylinders, if the macroscopic roughness due to the panel edges is accounted for, as well as with the low speed experimental results, within the respective error bars

Artificial Neural Network (ANN) modeling of the pulsed heat load during ITER CS magnet operation

Artificial Neural Networks (ANNs) are applied to the development of a simplified transient model of the ITER Central Solenoid (CS), aiming at predicting the evolution of the pulsed heat load from the CS to the LHe bath during plasma operation. The ANNs are trained using the thermal–hydraulic evolution in the CS, computed with the 4C code, due to AC losses. The capability of the ANN model to predict the heat load to the LHe bath is successfully demonstrated in the case of different transients, among which a nominal plasma operating scenario. The gain in speed of the simplified model with respect to the 4C code results is by order of magnitudes, with a small loss of accuracy

Application of the Polynomial Chaos Expansion to the Uncertainty Propagation in Fault Transients in Nuclear Fusion Reactors: DTT TF Fast Current Discharge

Nuclear fusion reactors are composed of several complex components whose behavior may be not certain a priori. This uncertainty may have a significant impact on the evolution of fault transients in the machine, causing unexpected damage to its components. For this reason, a suitable method for the uncertainty propagation during those transients is required. The Monte Carlo method would be the reference option, but it is, in most of the cases, not applicable due to the large number of required, repeated simulations. In this context, the Polynomial Chaos Expansion has been considered as a valuable alternative. It allows us to create a surrogate model of the original one in terms of orthogonal polynomials. Then, the uncertainty quantification is performed repeatedly, relying on this much simpler and faster model. Using the fast current discharge in the Divertor Tokamak Test Toroidal Field (DTT TF) coils as a reference scenario, the following method has been applied: the uncertainty on the parameters of the Fast Discharge Unit (FDU) varistor disks is propagated to the simulated electrical and electromagnetic relevant effects. Eventually, two worst-case scenarios are analyzed from a thermal–hydraulic point of view with the 4C code, simulating a fast current discharge as a consequence of a coil quench. It has been demonstrated that the uncertainty on the inputs (varistor parameters) strongly propagates, leading to a wide range of possible scenarios in the case of accidental transients. This result underlines the necessity of taking into account and propagating all possible uncertainties in the design of a fusion reactor according to the Best Estimate Plus Uncertainty approach. The uncertainty propagation from input data to electrical, electromagnetic, and thermal hydraulic results, using surrogate models, is the first of its kind in the field of the modeling of superconducting magnets for nuclear fusion applications

Artificial Neural Networks: a viable tool to design heat load smoothing strategies for the ITER Toroidal Field coils

In superconducting tokamaks, cryoplants provide the helium needed to cool the superconducting magnet systems. The evaluation of the heat load from the magnets to the cryoplant is fundamental for the design of the latter and the assessment of suitable strategies to smooth the heat load pulses induced by the pulsed plasma scenarios is crucial for the operation. Here, a simplified thermal-hydraulic model of an ITER Toroidal Field (TF) magnet, based on Artificial Neural Networks (ANNs), is developed and inserted into a detailed model of the ITER TF winding and casing cooling circuits based on the state-of-the-art 4C code, which also includes active controls. The low computational effort requested by such a model allows performing a fast parametric study, to identify the best smoothing strategy during standard plasma operation. The ANNs are trained using 4C simulations, and the predictive capabilities of the simplified model are assessed against 4C simulations, both with and without active smoothing, in terms of accuracy and computational time

Design and optimization of Artificial Neural Networks for the modelling of superconducting magnets operation in tokamak fusion reactors

In superconducting tokamaks, the cryoplant provides the helium needed to cool different clients, among which by far the most important one is the superconducting magnet system. The evaluation of the transient heat load from the magnets to the cryoplant is fundamental for the design of the latter and the assessment of suitable strategies to smooth the heat load pulses, induced by the intrinsically pulsed plasma scenarios characteristic of today's tokamaks, is crucial for both suitable sizing and stable operation of the cryoplant. For that evaluation, accurate but expensive system-level models, as implemented in e.g. the validated state-of-the-art 4C code, were developed in the past, including both the magnets and the respective external cryogenic cooling circuits. Here we show how these models can be successfully substituted with cheaper ones, where the magnets are described by suitably trained Artificial Neural Networks (ANNs) for the evaluation of the heat load to the cryoplant. First, two simplified thermal-hydraulic models for an ITER Toroidal Field (TF) magnet and for the ITER Central Solenoid (CS) are developed, based on ANNs, and a detailed analysis of the chosen networks' topology and parameters is presented and discussed. The ANNs are then inserted into the 4C model of the ITER TF and CS cooling circuits, which also includes active controls to achieve a smoothing of the variation of the heat load to the cryoplant. The training of the ANNs is achieved using the results of full 4C simulations (including detailed models of the magnets) for conventional sigmoid-like waveforms of the drivers and the predictive capabilities of the ANN-based models in the case of actual ITER operating scenarios are demonstrated by comparison with the results of full 4C runs, both with and without active smoothing, in terms of both accuracy and computational time. Exploiting the low computational effort requested by the ANN-based models, a demonstrative optimization study has been finally carried out, with the aim of choosing among different smoothing strategies for the standard ITER plasma operation

Modeling the Transport of Activated Corrosion Products in the WCLL PbLi Loop for ITER and the EU DEMO With the GETTHEM Code

This work presents the results of the implementation in the GEneral Tokamak THErmal-hydraulic Model of available models for the generation and transport of any dispersed material in flowing PbLi eutectic mixture. In particular, the focus is on Activated Corrosion Products circulating as solid suspension in the PbLi loop of the Water-Cooled Lithium-Lead Breeding Blanket of the EU DEMO fusion reactor. A simple test case is used to show that the distribution of the concentration of activated corrosion products at any point of the PbLi loop, both in the water-cooled lithium-lead breeding blanket and in the related ITER Test Blanket System, can be determined by the model. Moreover, thanks to the model’s dynamic nature, operational transients can be simulated; for instance, starting from zero impurities in the PbLi alloy, the evolution of the concentration of corrosion products is shown, until the steady-state is reached. The results obtained with this tool can be useful not only for radiological safety purposes, but also because activated corrosion products may affect the PbLi flow itself and the efficiency of the tritium removal system, with consequences on the achievable Tritium Breeding Ratio. A rigorous verification of the model is also performed

Incorporating data into grazing management decisions: supporting farmer learning

Pasture consumption is an important contributor to farm business profitability in pasture based dairy systems around the world, including Tasmania. Research, development and extension prioritizes further increasing pasture consumption in the Tasmanian dairy industry, through progressing technical innovations and providing services to support increased farmer adoption of proven practices. Increasing farmer adoption of best practice grazing management recommendations relies on the continued development of extension delivery to meet farmer information and skill development needs. A social research study identified some of these needs by exploring pasture management approaches and associated learning processes of farmers whose practices were more versus less aligned to recommended practices. The aim was to improve understanding of the grazing management learning process and implications for extension in the context of data made available through new technology. Qualitative interview data revealed that pasture managers whose practices are more closely aligned to recommended practices have used pasture measurement tools and carried out associated calculations intensively for an extended period (≥ 1 year), before adapting best practices to suit their farm management approach. Less aligned pasture managers were aware of the importance of grazing management, but were less aware they lacked knowledge and skills required to implement recommended practices. The data suggest there is ‘unconscious incompetence’ at play, and that these farmers had not engaged in a supported learning process. These findings suggest that introducing innovative ways to acquire pasture growth data will not result in practice change unless dairy farmers have progressed through the grazing management learning process and come to understand how to use data effectively