Mihai Diaconeasa

The main objective of the proposed work is to develop, demonstrate, and evaluate a probabilistic risk assessment (PRA) software platform needed to address the major challenges of the current legacy PRA tools, such as better quantification speed, integration of multi-hazard models into traditional PRAs, and model modification simplification and documentation automation. To achieve the main objective, we will first perform benchmarking and profiling of current PRA tools, such as SCRAM and SAPHIRE, to investigate the current bottlenecks in the quantification speed and memory requirements. Secondly, we will design, implement, and benchmark a PRA software platform based on a web-based stack using the latest technologies available to overcome the mentioned challenges. Finally, we will evaluate the performance gains of this framework by modeling and quantifying large PRA models that would have been too expensive to run using the legacy PRA tools.

Fission batteries are unique from a technological standpoint given their proposed autonomous operation and tamper-proof features. Also, the actual operation of fission batteries will be novel in that local users may only have simple on/off control capabilities, while the manufacturer will need to be able to remotely monitor a fleet of units spread across different geographical regions. Furthermore, it must be self-detecting/protecting from human adversaries. The burden of local monitoring and control is therefore shifted to the autonomous technology and limited remote-monitoring capability of the manufacturer. Reliability analysis for cyber-physical systems (CPS) such as fission batteries is challenging since modern CPS incorporate distributed and networked heterogeneous software, hardware, and physical components that operate and interact in tandem. Human actions, such as those of the adversary, can also play an important role and need to be considered in the design process. All these ingredients yield highly structural and behavioral complexity for CPS models, making them computationally expensive to predict, model, and test. Consequently, highly sophisticated failure scenarios emerge, revealing new challenges for state-of-the-art quantitative reliability metrics and evaluation methods. To execute our proposal, the risk modeling needed for autonomous operations will first require newly developed dynamic PRA methods, due to the self-diagnosis, self-adjustment, and duration-prediction capabilities needed for autonomous operations. Second, reliability modeling will require the ability to integrate autonomous control, associated error-detection algorithms, and human actions for both cyber and tamper-proof designs. Finally, to perform the reliability/resilience evaluations, we will use Dual-Graph Error Propagation Models (DEPM) based on discrete-time Markov chain (DTMC) models.

In this project we plan to develop a methodology to quantitatively characterize the drug shortages in terms of their frequency, persistence, and intensity by modeling their supply chain. Quantitative metrics, such as relative importance or criticality, will be developed to identify the most important contributors to shortages. With the estimation of the impact of shortages using available public information, we will be able to demonstrate the methodology by developing a risk of shortage profile in terms of frequency and consequences for a number of drug supply chains.