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...