@inproceedings{earthperson_aras_farag_diaconeasa_2023, title={Introducing OpenPRA: A Web-Based Framework for Collaborative Probabilistic Risk Assessment}, volume={13}, url={http://dx.doi.org/10.1115/IMECE2023-111708}, DOI={10.1115/IMECE2023-111708}, abstractNote={Abstract
               Probabilistic Risk Assessment (PRA) tools are crucial in the nuclear, aerospace, maritime, and chemical industries. This paper presents OpenPRA, an innovative, open-source, web-based platform designed to address the limitations of current Probabilistic Risk Assessment (PRA) tools. OpenPRA offers a collaborative and flexible environment that supports a wide array of risk models and quantification engines, thereby enhancing the adaptability and efficiency of risk assessment processes. The platform offers unique features including support for various risk models such as event trees, fault trees, Markov chains, Bayesian networks, and error propagation models. It also allows users to choose from a variety of existing quantification engines, enabling them to tailor their risk assessment process to their specific needs. The paper discusses the design, development, and potential of OpenPRA to transform the field of PRA. It also delves into the platform’s technical architecture, technology stack, and the OpenPRA Model Exchange Format. The paper concludes by outlining the current development status of OpenPRA and laying out the foundation for future work.}, booktitle={Volume 13: Research Posters; Safety Engineering, Risk and Reliability Analysis}, publisher={American Society of Mechanical Engineers}, author={Earthperson, Arjun and Aras, Egemen M. and Farag, Asmaa S. and Diaconeasa, Mihai A.}, year={2023}, month={Oct} }
 @inproceedings{aras_farag_earthperson_diaconeasa_2023, title={Method of Developing a SCRAM Parallel Engine for Efficient Quantification of Probabilistic Risk Assessment Models}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85184349317&partnerID=MN8TOARS}, DOI={10.13182/PSA23-41054}, booktitle={Proceedings of 18th International Probabilistic Safety Assessment and Analysis, PSA 2023}, author={Aras, Egemen and Farag, Asmaa and Earthperson, Arjun and Diaconeasa, Mihai}, year={2023}, pages={134–140} }
 @inproceedings{aras_farag_earthperson_diaconeasa_2023, title={Methodology and Demonstration for Performance Analysis of a Probabilistic Risk Assessment Quantification Engine: SCRAM}, url={http://dx.doi.org/10.13182/PSA23-41053}, DOI={10.13182/PSA23-41053}, booktitle={18th International Probabilistic Safety Assessment and Analysis (PSA 2023)}, publisher={American Nuclear Society}, author={Aras, Egemen and Farag, Asmaa and Earthperson, Arjun and Diaconeasa, Mihai}, year={2023}, pages={452–459} }
 @inproceedings{hamza_tezbasaran_aras_farag_diaconeasa_2023, title={Model Exchange Methodology Between Probabilistic Risk Assessment Tools: SAPHIRE and CAFTA Case Study}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85184349014&partnerID=MN8TOARS}, DOI={10.13182/PSA23-41025}, booktitle={Proceedings of 18th International Probabilistic Safety Assessment and Analysis, PSA 2023}, author={Hamza, M. and Tezbasaran, A. and Aras, E. and Farag, A.S. and Diaconeasa, M.A.}, year={2023}, pages={150–158} }
 @inproceedings{farag_aras_earthperson_wood_boyce_diaconeasa_2023, title={Preliminary Benchmarking of SAPHSOLVE, XFTA, and SCRAM Using Synthetically Generated Fault Trees with Common Cause Failures}, url={http://dx.doi.org/10.13182/PSA23-41031}, DOI={10.13182/PSA23-41031}, booktitle={18th International Probabilistic Safety Assessment and Analysis (PSA 2023)}, publisher={American Nuclear Society}, author={Farag, Asmaa and Aras, Egemen and Earthperson, Arjun and Wood, S. and Boyce, Jordan and Diaconeasa, Mihai}, year={2023}, pages={40–49} }
 @article{aras_hayes_2022, title={A Novel Approach for Detection of Illicit Nuclear Activities Using Optically Stimulated Dosimetry}, volume={64}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85133713677&partnerID=MN8TOARS}, DOI={10.3011/ESARDA.IJNSNP.2022.6}, number={1}, journal={ESARDA Bulletin}, author={Aras, E.M. and Hayes, R.B.}, year={2022}, pages={64–74} }
 @inproceedings{aras_farag_earthperson_diaconeasa_2022, title={Benchmark Study of XFTA and SCRAM Fault Tree Solvers Using Synthetically Generated Fault Trees Models}, volume={9}, url={http://dx.doi.org/10.1115/IMECE2022-95783}, DOI={10.1115/IMECE2022-95783}, abstractNote={Abstract
               The development of the nuclear industry’s Probabilistic Risk Assessment (PRA) has significantly contributed to the design, reliability, and safety of nuclear power plants (NPP). Today, PRAs form integration in all nuclear power plant development stages, including design, licensing, operation, maintenance, and decommissioning. Many legacy PRA tools have been used in the nuclear field; however, more powerful tools are needed to cope with the rapid development of the nuclear industry and NPP designs. These tools are now required to analyze new aspects of the plants that were never envisioned before, and more computational resources are needed.
               This study uses synthetically generated fault trees of various sizes and specifications to benchmark fault tree solvers; XFTA version 1.3.1 and SCRAM version 0.16.2. The analysis is performed in two steps. The first step is to compare the probabilities, minimal cut sets, and importance factors. The second step is measuring CPU time, wall time, and the memory usage of each engine for benchmarking. The result for the first step for the same configuration is the same for each calculation engine. Overall, XFTA can complete most of the given runs in a shorter time with less memory usage than SCRAM. All computations and measurements are done on a specified computer.}, booktitle={Volume 9: Mechanics of Solids, Structures, and Fluids; Micro- and Nano-Systems Engineering and Packaging; Safety Engineering, Risk, and Reliability Analysis; Research Posters}, publisher={American Society of Mechanical Engineers}, author={Aras, Egemen M. and Farag, Asmaa S. and Earthperson, Arjun and Diaconeasa, Mihai A.}, year={2022}, month={Oct} }
 @article{aras_diaconeasa_2021, title={A Critical Look at the Need for Performing Multi-Hazard Probabilistic Risk Assessment for Nuclear Power Plants}, volume={2}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85127208749&partnerID=MN8TOARS}, DOI={10.3390/eng2040028}, abstractNote={Probabilistic Risk Assessment (PRA) is one of the technologies that is used to inform the design, licensing, operation, and maintenance activities of nuclear power plants (NPPs). A PRA can be performed by considering the single hazard (e.g., earthquake, flood, high wind, landslide) or by considering multi-hazards (e.g., earthquake and tsunami, high wind and internal fire). Single hazard PRA was thought sufficient to cover the analysis of a severe accident until the Fukushima Daiichi NPP accident in 2011. Since then, efforts were made to consider multi-hazards as well; thus, multi-hazard PRAs are starting to be seen as being indispensable for NPPs. In addition to the changing frequency of global and local natural hazards, other reasons to be highlighted are that the number and diversity of NPPs will probably increase. Moreover, advanced reactors are close to becoming a reality by designing them with passive safety systems, smaller, standardized, and even transportable to make them cheaper across the design, licensing construction, and operation stages. Thus, multi-hazards should be addressed in any future full-scope PRA. Although we found a few studies discussing multi-hazards, a general framework for multi-hazard PRA is still missing. In this paper, we argue that the starting point for any multi-hazard PRA general framework should be the Advanced Non-LWR Licensing Basis Event Selection (LBE) Approach and Probabilistic Risk Assessment Standard for Non-Light Water Reactor (non-LWR) Nuclear Power Plants. For Probabilistic Risk Assessment (PRA), history has shown us the path forward before, with Three Mile Accident being seen as one milestone to understand the necessity of PRA. The Fukushima Daiichi NPP Accident is another milestone in the development of PRA, showing the need for performing multi-hazard PRA for the current and future NPPs.}, number={4}, journal={Eng}, publisher={MDPI AG}, author={Aras, Egemen M. and Diaconeasa, Mihai A.}, year={2021}, pages={454–467} }
 @inproceedings{aras_hayes_2021, title={Low dose assessment uncertainty analysis for landaurer® nanodottm OSLDS}, volume={4}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85117684537&partnerID=MN8TOARS}, DOI={10.1115/ICONE28-65591}, abstractNote={Abstract This study aims to low-level background range measurements of commercial OSLDs. This work is the initial step to put a framework to detect any illicit nuclear activities in any nuclear facility at any time. The idea in this framework is to utilize already placed OSLDs in the facility which are normally read-out periodically. The results of these measurements could be distinguishable from the background radiation since this paper shows how the background dose with its statistical fluctuation provide detection limits in these applications. To do this, we measured dosimeters in two ways; without removal or replacement and full removal for each measurement. As a result of measurements, the initial dose, bleaching constant, and background dose was evaluated for different measurements. ANOVA was applied to all measurements and all measurements considered a measurement data set to analyze results. Consequently, we observed no statistically significant difference in these different kinds of measurement approaches relative to the total propagated uncertainty in any given dose estimate. This shows a passive detection can be verified with iterative measurements to improve statistics without compromising data quality when coupled with dose levels of potential interest serves to advance this potential nonproliferation application.}, booktitle={International Conference on Nuclear Engineering, Proceedings, ICONE}, author={Aras, E. and Hayes, R.}, year={2021} }