Education

Research Description

Dr. Turinsky's research interests include computational reactor physics and uncertainty quantification for simulations of physical phenomena.

Publications

Non-linear, time dependent target accuracy assessment algorithm for multi-physics, high dimensional nuclear reactor calculations

Khuwaileh, B. A., & Turinsky, P. J. (2019), PROGRESS IN NUCLEAR ENERGY, 114, 227–233. https://doi.org/10.1016/j.pnucene.2019.01.023

Verification of Reduced Order Modeling based Uncertainty/Sensitivity Estimator (ROMUSE)

Khuwaileh, B., Williams, B., Turinsky, P., & Hartanto, D. (2019), NUCLEAR ENGINEERING AND TECHNOLOGY, 51(4), 968–976. https://doi.org/10.1016/j.net.2019.01.020

Preface to Shippingport Atomic Power Station thematic issue

Turinsky, P. J. (2018, January), PROGRESS IN NUCLEAR ENERGY, Vol. 102, pp. 1–8. https://doi.org/10.1016/j.pnucene.2017.03.030

Special issue on the "Consortium for Advanced Simulation of Light Water Reactors Research and Development Progress"

Turinsky, P. J., & Martin, W. R. (2017, April 1), JOURNAL OF COMPUTATIONAL PHYSICS, Vol. 334, pp. 687–688. https://doi.org/10.1016/j.jcp.2017.01.028

Modeling and simulation challenges pursued by the Consortium for Advanced Simulation of Light Water Reactors (CASL)

Turinsky, P. J., & Kothe, D. B. (2016), JOURNAL OF COMPUTATIONAL PHYSICS, 313, 367–376. https://doi.org/10.1016/j.jcp.2016.02.043

STOCHASTIC OPTIMIZATION FOR NUCLEAR FACILITY DEPLOYMENT SCENARIOS USING VISION

Hays, R., & Turinsky, P. (2014), NUCLEAR TECHNOLOGY, 186(1), 76–89. https://doi.org/10.13182/nt13-68

Optimization of Thermal-Hydraulic Reactor System for SMRs via Data Assimilation and Uncertainty Quantification

Heo, J., Turinsky, P. J., & Doster, J. M. (2013), NUCLEAR SCIENCE AND ENGINEERING, 173(3), 293–311. https://doi.org/10.13182/nse11-113

ADVANCES IN MULTI-PHYSICS AND HIGH PERFORMANCE COMPUTING IN SUPPORT OF NUCLEAR REACTOR POWER SYSTEMS MODELING AND SIMULATION

Turinsky, P. J. (2012), NUCLEAR ENGINEERING AND TECHNOLOGY, 44(2), 103–112. https://doi.org/10.5516/net.01.2012.500

EXPERIMENT OPTIMIZATION TO REDUCE NUCLEAR DATA UNCERTAINTIES IN SUPPORT OF REACTOR DESIGN

Stover, T. E., & Turinsky, P. J. (2012), NUCLEAR TECHNOLOGY, 180(2), 216–230. https://doi.org/10.13182/nt12-a14635

Many-Group Cross-Section Adjustment Techniques for Boiling Water Reactor Adaptive Simulation

Jessee, M. A., Turinsky, P. J., & Abdel-Khalik, H. S. (2011), NUCLEAR SCIENCE AND ENGINEERING, 169(1), 40–55. https://doi.org/10.13182/nse09-67

View all publications via NC State Libraries

Grants

GAANN Interdisciplinary Doctoral Program in Scientific Computation

US Dept. of Education (DED)(8/16/12 - 8/15/17)

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GAANN Interdisciplinary Doctoral Program in Scientific Computation

Sponsor: US Dept. of Education (DED)

Start Date: 8/16/12

End Date: 8/15/17

Abstract

North Carolina State University (NC State) proposes to increase its already considerable commitment to graduate training in scientific computation (created in 1994), an inter-disciplinary field that integrates high performance computation with several areas of national need: chemistry, computer science, engineering, mathematics, and physics. Our objective is to enlarge the pool of U.S. citizens and permanent residents who will pursue teaching and research careers in scientific computation, thereby providing the scientific and academic infrastructure necessary for continued U.S. competitiveness in basic and applied science research. The program objectives are as follows: (1) recruit six doctoral students as GAANN fellows, including women and students who are minorities or have disabilities, to NC State?s program in scientific computation, an area of great national need; (2) ensure that the six GAANN fellows benefit from the varied resources in scientific computation available at NC State, graduate within five years, and find suitable employment. Anticipated program outcomes are the following: (1) six GAANN fellows will become Ph.D. scientists or engineers with scientific computation expertise, interdisciplinary research experience, undergraduate teaching experience, enhanced skills in instructional techniques, and professional development training; (2) GAANN fellows, including minority students, will be encouraged to find suitable post-doctoral employment in research or teaching positions, contributing to greater representation of underrepresented minorities, females and persons with disabilities among the nation?s scientific research and education workforce; and (3) the proposed program will result in an increase of overall doctoral enrollment in scientific computation, by more than the number of fellowships awarded, through enhancements to NC State?s Scientific Computation Program.

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National University Consortium (NUC) Involvement and University Academic Center of Excellence(ACE) Activities at North Carolina State University

US Dept. of Energy (DOE)(2/01/05 - 9/30/14)

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National University Consortium (NUC) Involvement and University Academic Center of Excellence(ACE) Activities at North Carolina State University

Sponsor: US Dept. of Energy (DOE)

Start Date: 2/01/05

End Date: 9/30/14

Abstract

This proposl is in response to the "Statement of Work for Establishment, Planning and Development Activities for Academic Center of Excellence in Advanced Modeling and Simulation at North Carolina State University" issued by Idaho National Laboratory (INL). North Carolina State University is a member of the Battelle Energy Alliance (BEA) via the National University Consortium (NUC). BEA assumed the responsibility of operating INL on February 1, 2005 via a ten year contract awarded by the US Department of Energy. As a member of the BEA, the NUC is playing a role in improving INL's performance via providing critical reviews of laboratory mission and execution, and supporting the laboratories' research and educational objectives. The later task is performed via the NUC universities directly interacting with INL and by providing a linkage to other non-NUC universities on a regional basis for educational objectives and on a national basis in focused research areas for the research objectives. NC State is in the process of creating the Academic Center of Excellence in Advanced Modeling and Simulation (ACE-AMS) to support the focused research activity. This proposal is to support the planning activities associated with creating ACE-AMS and to provide support for faculty, staff and students to travel to INL to explore opportunities for joint educational and research activities.

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GAANN Interdisciplinary Doctoral Program in Scientific Computation

US Dept. of Education (DED)(8/15/09 - 8/14/14)

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GAANN Interdisciplinary Doctoral Program in Scientific Computation

Sponsor: US Dept. of Education (DED)

Start Date: 8/15/09

End Date: 8/14/14

Abstract

North Carolina State University (NC State) proposes to increase its already considerable commitment to graduate training in scientific computation, an interdisciplinary field that integrates high performance computation with several areas of national need: chemistry, computer science, engineering, mathematics, and physics. Our objective is to enlarge the pool of U.S. citizens and permanent residents who will pursue teaching and research careers in scientific computation, thereby providing the scientific and academic infrastruc??A?ture necessary for continued U.S. competitiveness in basic and applied science research. The program objectives are as follows: (1) recruit eight doctoral students as GAANN fellows, including women and students who are minorities or have disabilities, to NC State?s program in scientific computation, an area of great national need; (2) ensure that the eight GAANN fellows benefit from the varied resources in scientific computation available at NC State, graduate within five years, and find suitable employment. Anticipated program outcomes are the following: (1) eight GAANN fellows will become Ph.D. scientists or engineers in areas of national need with scientific computation expertise, interdisciplinary research team experience, undergraduate teaching experience, enhanced skills in instructional techniques, and professional development training; (2) GAANN fellows, including minority students, will be encouraged to find suitable post-doctoral employment in research or teaching positions, contributing to greater representation of underrepresented minorities, females and persons with disabilities among the nation?s scientific research and education workforce; and (3) The proposed program will result in an increase of overall doctoral enrollment in scientific computation, by more than the number of fellowships awarded, through enhancements to NC State?s Scientific Computation Program.

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Development of Adaptive Model Refinement Capability for Multiphysics and Multifidelity Problems (TASK AFM 1)

US Dept. of Energy (DOE)(10/01/09 - 3/31/14)

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Development of Adaptive Model Refinement Capability for Multiphysics and Multifidelity Problems (TASK AFM 1)

Sponsor: US Dept. of Energy (DOE)

Start Date: 10/01/09

End Date: 3/31/14

Abstract

To further enhance simulation capabilities, whether it is of a nuclear power system or nuclear fuel assembly, the inclusion of high fidelity models of different physics phenomena coupled together in a tight fashion will be required. One of the challenges of accomplishing this is to do it in such a way that a given level of computational fidelity can be obtained without over burdening the computer resources that are available to end users. If all of the modeling of the physics is accomplished by using highly detailed models, it is doubtful that available computer resources would be able to complete typical design and safety analysis tasks. To overcome this limitation, multifidelity approaches have been proposed and to a limited extent pursued. [Note, within the context of the work proposed, multiscale models can be thought of as one class of multifidelity models.] If a multifidelity model approach is to be utilized, there must be the capability to determine the accuracy of the collection of fidelities for different physics models that are being employed so that higher-fidelity physics models can be called upon when necessary to satisfy stated accuracy requirements. For transient applications, one would expect the fidelity for each physics model to change with time. The proposed work will develop the capability to complete uncertainty quantification (UQ) for model introduced errors for the collection of fidelity-level models currently being employed, and automatically change this collection of fidelity-level models as necessary to just meet stated accuracy requirements. In this manner the minimum multiphysics and multifidelity (M&M) modeling computational resources would be expended to meet a stated set of accuracy requirements as controlled by UQ of modeling introduced errors. More concisely, an Adaptive Model Refinement (AMoR) capability will be developed for M&M modeling. When scoping calculations are called for, accuracy requirements would be relaxed resulting in automatically increasing the utilization of lower-fidelity models, significantly reducing computation resources required and thereby making possible extensive sensitivity studies.

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GAANN: Interdisciplinary Doctoral Program in Scientific Computation

US Dept. of Education (DED)(8/15/07 - 8/14/12)

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GAANN: Interdisciplinary Doctoral Program in Scientific Computation

Sponsor: US Dept. of Education (DED)

Start Date: 8/15/07

End Date: 8/14/12

Abstract

North Carolina State University (NC State) proposes to increase its already considerable commitment to graduate training in scientific computation, an interdisciplinary field that integrates high performance computation with several areas of national need: chemistry, computer science, engineering, mathematics, and physics. Our objective is to enlarge the pool of U.S. citizens and permanent residents who will pursue teaching and research careers in scientific computation, thereby providing the scientific and academic infrastructure necessary for continued U.S. competitiveness in basic and applied science research. The program objectives are as follows: (1) recruit eight doctoral students as GAANN fellows, including women and students who are minorities or have disabilities, to NC State?s program in scientific computation, an area of great national need; (2) ensure that the eight GAANN fellows benefit from the varied resources in scientific computation available at NC State, graduate within five years, and find suitable employment.

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Further Development of Adaptive Core Simulation Capability for Boiling Water Reactors

General Electric (GE) -Hitachi(1/31/08 - 1/30/12)

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Further Development of Adaptive Core Simulation Capability for Boiling Water Reactors

Sponsor: General Electric (GE) -Hitachi

Start Date: 1/31/08

End Date: 1/30/12

Abstract

Nuclear power plants routinely take measurements of core observables, such as in-core detector readings and reactivity measurements, as part of normal operations, startup physics tests, and special test programs. These measurements are utilized to confirm the core is operating within established limits and to contrast predicted and measured core attributes as part of the design methods certification process. More accurate design methods allow reduced design margins to be utilized. Since design margins reduce the reload core designer's freedom, the introduction of design margin comes at an economic expense. By contrasting measured and predicted core observables, improvements in the fidelity of design methods have been obtained. However, to date there has been no capability to do this on a large scale consistent basis. The proposed research will develop this capability by building upon the ongoing research on adaptive core simulation. The specific application will be to boiling water reactors, completed utilizing first virtual core observables followed by physical core observables, i.e. real plant measurement data.

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Chair of the National University Consortium

US Dept. of Energy (DOE)(10/01/09 - 9/30/11)

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Chair of the National University Consortium

Sponsor: US Dept. of Energy (DOE)

Start Date: 10/01/09

End Date: 9/30/11

Abstract

This proposal is in response to a Statement of Work (SOW) issued by Idaho National Laboratory. NC State University was elected by the National University Consortium (NUC) members of Battelle Energy Alliance (BEA), the management contractor for Idaho National Laboratory (INL), to chair the NUC. The NUC members have partnered with BEA to advance the work of INL. This proposal is in response to the SOW, defining the duties of the NUC Chair position and providing the financial support to execute this position for the time period FY10.

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Applications of Sensitivity/Uncertainty Analysis to Optimum Instrumentation and Control of Next Generation Nuclear Power Systems

US Dept. of Energy (DOE)(10/01/07 - 2/28/11)

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Applications of Sensitivity/Uncertainty Analysis to Optimum Instrumentation and Control of Next Generation Nuclear Power Systems

Sponsor: US Dept. of Energy (DOE)

Start Date: 10/01/07

End Date: 2/28/11

Abstract

Abstract Applications of Sensitivity/Uncertainty Analysis to Optimum Instrumentation and Control of Next Generation Nuclear Power Systems Instrumentation serves several functions, such as (1) providing sensors? inputs to control and safety algorithms that result in actions which move the plant towards a desired state, (2) diagnosing conditions of plant components and systems, and (3) improving predictive capability of system simulation models. What is desired is to select instrumentation type and location so as to address each of these functions in an optimum manner. The objectives associated with the three instrumentation functions have many things in common with regard to analysis capabilities required. They all require a system simulation capability. They all require a priori uncertainty information about input data and instrumentation. They all require the ability to propagate input data or instrumentation uncertainties through the simulation to obtain a prior values of system attributes? uncertainties. They all require a minimization of an objective function. And Functions (2) and (3) further require the ability to complete adaptive simulation and determine posterior input data and system attributes? uncertainties. NC State University has been working for a number of years in the areas of sensitivity/uncertainty analysis, adaptive simulation and the applications of high fidelity reactor systems simulations to the development of advanced control algorithms. In this project, we apply these techniques to the development of optimum instrumentation and control of next generation nuclear power systems.

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Managing Model Data Introduced Uncertainties in Simulator Predictions for Generation IV Systems Via Optimum Experimental Design

US Dept. of Energy (DOE)(3/22/06 - 12/31/10)

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Managing Model Data Introduced Uncertainties in Simulator Predictions for Generation IV Systems Via Optimum Experimental Design

Sponsor: US Dept. of Energy (DOE)

Start Date: 3/22/06

End Date: 12/31/10

Abstract

If uncertainties of complex engineering systems were understood, e.g. quantified, then appropriate design margins could be utilized. In addition, rational decisions could be made about the cost-benefit of reducing uncertainties via experimentation and improvements in modeling accuracy. Furthermore, whatever uncertainty understanding capabilities would have been developed for complex engineering systems could now also be utilized in understanding and designing experiments if determined to be cost-benefit justified. The proposed research addresses the understanding and management of uncertainties that originate due to the uncertainties of physical data that are utilized in modeling and simulation software, i.e. model data. The research will address the quantification of uncertainties and the optimum design of experiments that reduce these uncertainties. It will incorporate aspects of sensitivity analysis, uncertainty analysis, inverse theory and mathematical optimization. To validate the capabilities being developed, they will be applied to a Generation IV nuclear energy system concept. Specifically, the uncertainties of the key design attributes originating due to nuclear data uncertainties for the nuclear core of a proposed Generation IV nuclear energy system will be determined, and an optimum experimental design utilizing the INL ZIPPR facility as a test basis will be completed so as to reduce model data uncertainties where determined appropriate. Finally, a pseudo ZIPPR experiment for the optimum design will be completed via simulation to obtain pseudo observable values, which will be subsequently utilized to obtain values of the adapted nuclear data. This project will produce a number of results, each having merit as a stand alone item and collectively as a methodology for optimum experimental design. 1. Methodology to determine covariance matrices for responses of complex engineering system and experimental system. 2. Methodology to interpret experimental system results via adaptive simulation. 3. Methodology to optimize the experimental configuration most economically appropriate for reducing the uncertainties of the complex engineering system. 4. Determination of the covariance matrix originating from nuclear data uncertainties for the nuclear core of a specific Generation IV nuclear power system. 5. Determination of optimum experimental system properties applicable for a ZIPPR type experimental facility, most appropriate for reducing the uncertainties for the nuclear core of a specific Generation IV nuclear power system.

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Development and Utilization of Mathematical Optimization in Advanced Fuel Cycle Systems Analysis

US Dept. of Energy (DOE)(3/13/06 - 9/30/10)

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Development and Utilization of Mathematical Optimization in Advanced Fuel Cycle Systems Analysis

Sponsor: US Dept. of Energy (DOE)

Start Date: 3/13/06

End Date: 9/30/10

Abstract

This project proposes to develop the capability to determine the optimum deployment strategies for reactor/fuel/fuel cycle facilities within constraints utilizing mathematical optimization. A stochastic optimization approach will be employed, which will allow the tradeoff surface of this multi-objective optimization problem to be determined. Economic, energy, environmental and nonproliferation resistance metrics of the fuel cycle will be considered in the optimization. The DANESS code will be employed to model the fuel cycle. Optimization will be completed over a user specified planning horizon, which will allow the proper treatment of the impact of current decisions on future decisions. The developed capability will be exercised for fuel cycle scenarios of current interest. The developed capabilities will assure that optimum deployment strategies are determined with reduced scientist/engineering effort, providing higher confidence in utilizing the results in policy decision making. The automated capability should also make it possible for less technically sophisticated individuals to utilize fuel cycle simulation capability. Finally, determination of the tradeoff surface using multi-objective mathematical optimization will provide quantitative data to decision makers on such items as the cost of margin, e.g. relationship of repository thermal margin to energy costs. Such data can be employed by the government to establish policies concerning incentives and requirements to encourage the preferred evolution of the commercial nuclear power enterprise.

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