Education

Research Description

Dr. Avramova's research areas include reactor thermal-hydraulics; core design; transient and safety analysis; multi-physics and multi-scale simulations; verification and validation; and uncertainty and sensitivity analysis.

Grants

Membership in Consortium for Nuclear Power (CNP), Full Member

Duke Energy Carolinas, LLC (Duke Power)(7/01/22 - 6/30/25)

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Membership in Consortium for Nuclear Power (CNP), Full Member

Sponsor: Duke Energy Carolinas, LLC (Duke Power)

Start Date: 7/01/22

End Date: 6/30/25

Abstract

CNP Membership

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Uncertainty Analysis Support (ARDP 3.4.2.1.4 )

US Dept. of Energy (DOE)(11/07/22 - 12/23/23)

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Uncertainty Analysis Support (ARDP 3.4.2.1.4 )

Sponsor: US Dept. of Energy (DOE)

Start Date: 11/07/22

End Date: 12/23/23

Abstract

The scope of this work package to develop an uncertainty / sensitivity methodology for Xe-100 equilibrium core analysis and apply it to VSOP99 best estimate model. The methodology includes identifying the most important parameters in the VSOP99 model including the multi-group constants and propagating the uncertainties to the selected Figures of Merit (FOMs).

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BWXT Small Mobile Nuclear Reactor (SMNR) Program Phase 2 for North Carolina State University (NCSU)

US Dept. of Defense (DOD)(1/12/23 - 12/15/23)

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BWXT Small Mobile Nuclear Reactor (SMNR) Program Phase 2 for North Carolina State University (NCSU)

Sponsor: US Dept. of Defense (DOD)

Start Date: 1/12/23

End Date: 12/15/23

Abstract

Input to the Preliminary PIRT report: meeting participation, design familiarization, identification, description and ranking of candidate limiting design-basis events with respect to phenomena, assessment and ranking of state of knowledge of identified phenomena

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Machine Learning Model Development in 2022 (ARPA-E WP 2.3.3)

US Dept. of Energy (DOE) - Advanced Research Projects Agency - Energy (ARPA-E)(1/03/22 - 12/31/22)

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Machine Learning Model Development in 2022 (ARPA-E WP 2.3.3)

Sponsor: US Dept. of Energy (DOE) - Advanced Research Projects Agency - Energy (ARPA-E)

Start Date: 1/03/22

End Date: 12/31/22

Abstract

This project is aims for the development and application of machine learning (ML) models to the nuclear power plant diagnosis and prognosis, and predictive maintenance, to support the reduction of operation and maintenance (O&M) cost of advanced reactor concepts. The first phase is focused on the development of a ML model for pump degradation prediction and failure mode diagnosis, more specifically aiming to make remaining useful life (RUL) predictions and corresponding failure mode of pumps. A synthetic database for the pump operation (and degradation) over time will be generated using the physics model mentioned above. Next, a ML model will be trained, tested, and validated for the pump degradation prediction and failure mode diagnosis using the sensor readings synthesized based on the pump degradation database. In the second phase, we will integrate the physics-based pump degradation model developed in Phase 1 with the Xe-100 simulator and develop a ML degradation model that is capable of making predictions using system-wise sensor data.

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Probabilistic Risk Assessment - Phase 1 (2022) (ARPA-E WP 3.2.2)

US Dept. of Energy (DOE) - Advanced Research Projects Agency - Energy (ARPA-E)(11/16/20 - 12/30/23)

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Probabilistic Risk Assessment - Phase 1 (2022) (ARPA-E WP 3.2.2)

Sponsor: US Dept. of Energy (DOE) - Advanced Research Projects Agency - Energy (ARPA-E)

Start Date: 11/16/20

End Date: 12/30/23

Abstract

This project is part of X-energy's grant from the U.S. Department of Energy’s Advanced Research Projects Agency-Energy (ARPA-E) on the “Advanced Operation & Maintenance Techniques Implemented in the Xe-100 Plant Digital Twin to Reduce Fixed O&M Cost.” X-energy’s digital twin project aims to reduce the fixed operations and management (O&M) cost of its advanced nuclear reactor design to $2 per MWh. Under the NCSU‘s subaward for 2021, we are supporting X-energy’s efforts in the project by co-leading the Xe-100 PRA model development to inform the human factors engineering program and regulatory engagement on the control room staffing. We are supporting the appropriate control room staffing levels by evaluating the procedures and timing of events under normal and abnormal conditions obtained from computer simulations and physical control room simulator exercises.

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Membership in Consortium for Nuclear Power (CNP), Affiliate Member

Kernkraftwerk Leibstadt AG(7/01/22 - 6/30/25)

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Membership in Consortium for Nuclear Power (CNP), Affiliate Member

Sponsor: Kernkraftwerk Leibstadt AG

Start Date: 7/01/22

End Date: 6/30/25

Abstract

Development and Validation Support for Coupling of KKL TRACE-S3K Model Order for Membership period, from July 2022 - June 2023

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Membership in Consortium for Nuclear Power (CNP), Full Member

General Electric (GE) -Hitachi (7/01/22 - 6/30/25)

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Membership in Consortium for Nuclear Power (CNP), Full Member

Sponsor: General Electric (GE) -Hitachi

Start Date: 7/01/22

End Date: 6/30/25

Abstract

CNP Full Member

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Membership in Consortium for Nuclear Power (CNP), Observer/Adviser Member

Battelle Energy Alliance, LLC(7/01/22 - 6/30/25)

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Membership in Consortium for Nuclear Power (CNP), Observer/Adviser Member

Sponsor: Battelle Energy Alliance, LLC

Start Date: 7/01/22

End Date: 6/30/25

Abstract

Observer/Advisor Member

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Membership in Consortium for Nuclear Power (CNP), Full Member

Electric Power Research Institute, Inc.(7/01/21 - 6/30/24)

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Membership in Consortium for Nuclear Power (CNP), Full Member

Sponsor: Electric Power Research Institute, Inc.

Start Date: 7/01/21

End Date: 6/30/24

Abstract

Full Membership with CNP

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Membership in Consortium for Nuclear Power (CNP), Affiliate Member

Rolls-Royce SMR(7/01/21 - 6/30/24)

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Membership in Consortium for Nuclear Power (CNP), Affiliate Member

Sponsor: Rolls-Royce SMR

Start Date: 7/01/21

End Date: 6/30/24

Abstract

Affiliate Membership with CNP

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