FIRED - Fire risk evaluation and Defence-in-Depth

The main objective of the FIRED –project is to develop the tools for fire risk evaluation and create a new methodology for assessing the defense-in-depth in the context of fire safety. In general, the results may be divided into three categories: the First one is the basic research that increases understanding and contributes to future work, second is the education of experts and developing the current methodology, and the third one is the direct applications to NPPs.

The results of FIRED work packages are illustrated in Figure 2.35.

Figure 2.35 Result categories in WPs of FIRED.

Specific goals in 2018

The active tasks during 2018 were:

• WP 1: Cable fire risks during plant life cycle o Task 1: New flame retardant polymers,

• WP 2: Fire-Barrier performance assessment

o Task 1: Barrier performance assessment with Fire-CFD.

• WP 3: Fire simulation development, maintenance and validation.

o Task 3: Pyroplot development, maintenance and validation.

• WP 4: Participation to PRISME3.

o Task 1: participation fee.

In WP1, we carried out reactive molecular dynamics simulations based on the ReaxFF reactive force field to study the effect of aluminium (tri)hydroxide on the thermal decomposition of polyethylene. The simulations reproduced the endothermic decomposition of aluminium (tri)hydroxide into alumina and water. Other known mechanisms of flame retardancy, such as heat absorption by the filler and its residue, were reproduced with reasonable accuracy. The simulations also revealed a chemical interaction between polyethylene and aluminium (tri)hydroxide, in which hydroxyl radicals released by the aluminium (tri)hydroxide abstracted hydrogen from the surrounding polyethylene, resulting in enhanced water production and enhanced charring of polyethylene.

Furthermore, we analysed the data in order to obtain kinetic parameters for the pyrolysis model of the Fire Dynamics Simulator. Specifically, a set of thermal decomposition simulations using linear heating rates in the range 0.3 - 5 K/ps were performed for model systems of pure aluminium trihydroxide (modelled as gibbsite), pure amorphous polyethylene, and a composite of PE+ATH. The simulations can be regarded as a set of atomistic scale thermogravimetric experiments. Kinetic parameters for the decomposition reaction of each system were extracted from the data using two model-free isoconversional methods, where ‘model-free’ means that no assumptions need to be made on the reaction model. It was enough to assume an Arrhenius-type behaviour for the reaction rate constant, and to derive the associated pre- exponential factor A and activation energy Ea from the isoconversional analysis. Examples of results are shown in Figure 2.36 for pure polyethylene.

Figure 2.36 Left: kinetic parameters for polyethylene decomposition from isoconversional analysis of RMD data. Right: FDS simulation of the cone calorimeter experiment under nitrogen atmosphere using the kinetic parameters form RMD.

In WP2, work aimed at extending the previous study carried out to develop a 1D numerical model capable of predicting the fire resistance of stone-wool based fire barrier. Using the previously developed 1D model, we simulated the fire resistance test for three different types of stone-wools. We also presented separate 1D and 3D models where the standard fire was alternatively modeled using Neumann boundary condition. For model validation, small-scale fire resistance test data was collected on samples measuring 60 x 60 cm and consisting of a 1 mm thick steel sheet and three different types of wool layers (thickness between 60 and 76 mm). Figure 2.37 shows the predicted and measured cold-side temperatures for the three wools and the three different models used.

Figure 2.37. Measured and predicted cold side temperature. Left: 1D model. Middle:

Alternative 1D model. Right: 3D model.

For the 1D model, the left plot shows that the predicted values are close to the measured one for wool I and II but not for wool III. When the stone-wool is heated, the binder and oil undergo exothermic reactions and provide additional heat to the stone-wool fibers. In the case of wool I, the binder content is relatively low and the heat released due to the exothermic behavior is not sufficient to influence the cold-side temperature. For wool II and III, the binder content is sufficiently high and the additional heat release due to the exothermic reaction affects the cold- side temperature, i.e., the asymmetric bell shape observed at 20 min and 25 min for wool II and III respectively. The density of wool II, however, is much lower than that of wool III.

Predicted values being correct for wool II and incorrect for III, suggests that the heating of the test sample having both the high density and high oil or binder content cannot be correctly simulated using the current model.

The middle and right plots show that the alternative 1D model and 3D model prediction are matching to each other but not to the measured values. The rise in the predicted cold side temperature at the beginning of the test is much quicker than the measured one. The possible reason could be the incorrect heat flux representing the standard fire. The heat flux specified on the face of the test sample depends only upon the standard fire curve. While in reality it also depends upon the hot side surface temperature of the test sample. As the hot side surface temperature of the test sample begins to rise the heat flux growth rate should decrease. This effect is not properly modeled in the current setup.

In WP3, a new tool called PyroPython was developed as a successor to the Pyroplot tool devloped in earlier SAFIR projects. The motivation for writing a new tool was to make it faster and more accessible. Python was chosen as the programming lanquage of the project due to the rich open source scientific computing ecosystem available for Python. Unlike Pyroplot, which used genetic algorithms for optimizing the model parameters, PyroPython aims to be agnostic to the choice of the optimizer. Currently, the software supports Bayesian Optimization (BO), optimization by multistart method using a derivative-free optimization algorithm (Nelder- Mead), differential evolution, and random sampling. Usage of BO for parameter identification is a novel aspect of the PyroPython tool. It involves fitting a response surface model to model evalua-tions and using the response surface to explore promising solution candidates.

Several optimization methods were tested on a very challenging 16 parameter py-rolysis model fitting problem. It was found that, at least for the present optimization problem, the traditional optimization methods, Nelder-Mead simplex and differential evolution have better performance than the Bayesian Optimization methodology. This conclusion may, however, change if the optimization problem at hand would be more costly to evaluate, say a long cone calorimeter experiment or a bench scale experiment.

The participation to PRISME 3 was continued. The project will provide high quality, large-scale experimental data on the topics that are relevant to fire safety of nuclear power plants. These

results can be utilized directly in the safety assessments, or for simulating and validating the simulation tools.

Deliverables in 2018

• In Task 1.1 it was demonstrated that kinetic parameters for continuum-scale pyrolysis model can be derived from atomistic simulations. The work was documented as a VTT research report.

• In task 2.1, three different heat transfer models were evaluated to predict cold-side temperatures of a fire barrier element.An Aalto research report was written.

• In task 3.2, a new tool (‘PyroPython’) was developed as a successor for the Pyroplot tool for pyrolysis model parameter identification. Development of the tool is described in a VTT research report.

• The PyroPython software is available online at https://github.com/PyroId/PyroPython.

The PyroPython documentation is available athttp://pyroid.github.io/

2.3.3 FOUND - Analysis of fatigue and other cumulative ageing to extend lifetime