Dynamic PRA: an Overview of New Algorithms to Generate, Analyze and Visualize Data

State of the art PRA methods, i.e. Dynamic PRA (DPRA) methodologies, largely employ system simulator codes to accurately model system dynamics. Typically, these system simulator codes (e.g., RELAP5 ) are coupled with other codes (e.g., ADAPT, RAVEN that monitor and control the simulation. The latter codes, in particular, introduce both deterministic (e.g., system control logic, operating procedures) and stochastic (e.g., component failures, variable uncertainties) elements into the simulation. A typical DPRA analysis is performed by: 1. Sampling values of a set of parameters from the uncertainty space of interest 2. Simulating the system behavior for that specific set of parameter values 3. Analyzing the set of simulation runs 4. Visualizing the correlations between parameter values and simulation outcome Step 1 is typically performed by randomly sampling from a given distribution (i.e., Monte-Carlo) or selecting such parameter values as inputs from the user (i.e., Dynamic Event Tre

A dynamic approach to rebalancing bike-sharing systems

Bike-sharing services are flourishing in Smart Cities worldwide. They provide a low-cost and environment-friendly transportation alternative and help reduce traffic congestion. However, these new services are still under development, and several challenges need to be solved. A major problem is the management of rebalancing trucks in order to ensure that bikes and stalls in the docking stations are always available when needed, despite the fluctuations in the service demand. In this work, we propose a dynamic rebalancing strategy that exploits historical data to predict the network conditions and promptly act in case of necessity. We use Birth-Death Processes to model the stations' occupancy and decide when to redistribute bikes, and graph theory to select the rebalancing path and the stations involved. We validate the proposed framework on the data provided by New York City's bike-sharing system. The numerical simulations show that a dynamic strategy able to adapt to the fluctuating nature of the network outperforms rebalancing schemes based on a static schedule

Application of Subset Simulation to Seismic Risk Analysis

This paper presents the application of a new reliability method called Subset Simulation to seismic risk analysis of a structure, where the exceedance of some performance quantity, such as the peak interstory drift, above a specified threshold level is considered for the case of uncertain seismic excitation. This involves analyzing the well-known but difficult first-passage failure problem. Failure analysis is also carried out using results from Subset Simulation which yields information about the probable scenarios that may occur in case of failure. The results show that for given magnitude and epicentral distance (which are related to the ‘intensity’ of shaking), the probable mode of failure is due to a ‘resonance effect.’ On the other hand, when the magnitude and epicentral distance are considered to be uncertain, the probable failure mode correspondsto the occurrence of ‘large-magnitude, small epicentral distance’ earthquakes

The safety case and the lessons learned for the reliability and maintainability case

This paper examine the safety case and the lessons learned for the reliability and maintainability case

Crashworthiness assessment considering the dynamic damage and failure of a dual phase automotive steel

Analyzing crash worthiness of the automotive parts has been posing a great challenge in the sheet metal and automotive industry since several decades. The present contribution will focus on one of the most urging challenges of the crash worthiness simulations, namely, an enhanced constitutive formulation to predict the failure and cracking of structural parts made from high strength steel sheets under impact. A hybrid extended Modified Bai Wierzbicki damage plasticity model is devised to this end. The material model calibrated using the experimental data covering high strain rate deformation, damage and failure successfully predicted the instability and subsequent response of the crash box under impact. Simulation results provide the deformation shape and deformation energy in order to predict and evaluate the vehicle crashworthiness. The simulations further helped in discovering the irrefutable impact of strain rate and stress state on the impact response of the auto-body structure. The strain rate is found to adequately affect the energy absorption capacity of the crash box structure both in terms of impact load and fold formation whereas the complex stress state has a direct association to the development of instability within the structure and early damage appearance within the folds

Statistical Classification of Cascading Failures in Power Grids

We introduce a new microscopic model of the outages in transmission power grids. This model accounts for the automatic response of the grid to load fluctuations that take place on the scale of minutes, when the optimum power flow adjustments and load shedding controls are unavailable. We describe extreme events, initiated by load fluctuations, which cause cascading failures of loads, generators and lines. Our model is quasi-static in the causal, discrete time and sequential resolution of individual failures. The model, in its simplest realization based on the Directed Current description of the power flow problem, is tested on three standard IEEE systems consisting of 30, 39 and 118 buses. Our statistical analysis suggests a straightforward classification of cascading and islanding phases in terms of the ratios between average number of removed loads, generators and links. The analysis also demonstrates sensitivity to variations in line capacities. Future research challenges in modeling and control of cascading outages over real-world power networks are discussed.Comment: 8 pages, 8 figure