A comprehensive risk-informed methodology for passive safety system design and performance assessment is presented and demonstrated on the Flexible Conversion Ratio Reactor (FCRR). First, the methodology provides a framework for risk-informed design decisions and as an example two design options for a decay heat removal system are assessed and quantitatively compared. Next, the reliability of the system is assessed by quantifying the uncertainties related to system performance and propagating these uncertainties through a response surface using Monte Carlo simulation. Finally, a sensitivity study is performed to measure the relative effects of each parameter and to identify ways to maintain, improve, and monitor system performance. A common characteristic of passive safety systems is that their driving force tends to be weak and therefore adverse or off-normal conditions may substantially degrade system performance [2]. Under certain conditions, system performance may be degraded to a level that results in unacceptable consequences. These consequences are typically identified by the system designer and are referred to as failure criteria. Failure criteria can be defined at the system level (e.g., flow rate, fluid temperature) or at a higher level (e.g., peak cladding temperature, containment pressure). Therefore, the conditional failure probability of a passive system can be defined as the probability that, given an initiating event, a set of thermal-hydraulic conditions will exist that cause the system to exceed one or more failure criteria. System conditions leading to failure are the result of adverse combinations of system parameter values such as pressure, temperature, and void fraction. Prediction of the exact values of these parameters is made difficult by several sources of uncertainty and typically, we can only assume a range of expected values and a corresponding probability distribution. We will refer to this type of uncertainty as parametric uncertainty. Second, there are uncertainties associated with the models used to predict system behavior. These can involve equations or empirical correlations used to model various phenomena or may stem from the numerical methods employed by computer codes. We will refer to this type of uncertainty as model uncertainty. Both parametric and model uncertainties are classified as epistemic since they are related to a lack of knowledge as opposed to aleatory uncertainty, which is related to randomness [3]. An estimate of system reliability can be obtained by quantifying parametric and model uncertainty and observing their effect on system performance. Further insights can be gained by evaluating the sensitivity of system performance to each parameter, and we will demonstrate several ways in which this can be done.The reliability of passive safety systems has been the subject of a great deal of research this decade both in the United States and internationally. System failure is assumed to occur when a physical quantity such as temperature exceeds a value th...

This paper presents a method for extracting system nonlinearities and time-localized transient response to impulsive loading by processing stationary/transient responses using the Hilbert—Huang transform (HHT) and a sliding-window fitting (SWF) technique. Time-dependent dynamic characteristics of nonlinear systems are derived using perturbation analysis. The SWF is introduced mainly to show the mathematical implications of HHT and the differences between HHT and the discrete Fourier transform. Similar to the wavelet transform the SWF uses windowed predetermined regular harmonics and function orthogonality to extract local harmonic components. It simultaneously decomposes a signal into just a few regular/distorted harmonics, and the obtained time-varying amplitudes and frequencies of the harmonics can reveal system nonlinearities. On the other hand the HHT uses the apparent time scales revealed by the signal's local maxima and minima and cubic splines of the extrema to sequentially sift components of different time scales, starting from high-frequency to low-frequency ones. Because HHT does not use predetermined basis functions and function orthogonality for component extraction, components are extracted without distortion and hence their time-varying amplitudes and frequencies can be accurately computed using the Hilbert transform to reveal system characteristics and nonlinearities. Moreover, because the first component extracted from HHT contains all discontinuities of the original signal, its time-varying frequency and amplitude are excellent indicators for pinpointing the time instants of impulsive loads. However, the discontinuity-induced Gibbs' phenomenon makes HHT analysis inaccurate around the two data ends. On the other hand, the SWF analysis suffers less from Gibbs' phenomenon at the two data ends, but it cannot extract accurate time-varying frequencies and amplitudes because the use of predetermined basis functions and function orthogonality in the sliding-window fitting process distorts the extracted components. Numerical and experimental results show that the proposed method with the use of HHT can provide accurate extraction of intrawave amplitude and phase modulations, distorted harmonic response under a single-frequency harmonic excitation, softening and hardening effects, different orders of nonlinearity, interwave amplitude and phase modulations, multiple-mode vibrations caused by internal/ external resonances, and time instants of impact loading on a structure. These are key phenomena for performing dynamics-based system identification and damage detection.

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Evaporation of the liquid microlayer developing underneath a bubble in the initial (inertia controlled) phase of its growth can be a significant vapor source in the later (heat-diffusion controlled) phase of bubble growth. In the literature, representation of this microlayer is typically limited to a very short (order of microns) region near the apparent Triple Phase Line (TPL) between the bubble and the wall. However, experimental observations show that the microlayer may actually extend hundreds of microns beyond the TPL region. Guided by this observation, we develop a simple model to predict the time evolution of the extended microlayer, and the associated corresponding evaporation rate and heat flux underneath a bubble. The model is derived as a special case of the complete governing equations, which account for the complicated effects of disjoining pressure, capillarity, vapor recoil and interfacial resistance. The predictions of the model are in reasonable quantitative agreement with the experimentally observed behavior of the microlayer.