Mohammad Abdo

Idaho National Laboratory | INL · Nuclear Engineering Modeling and Simulation

Over the past two decades, the nuclear engineering community, empowered by the exponential growth in computer power, has heavily invested in advancing modeling and simulation techniques to improve the predictability of complex nuclear systems such as nuclear reactors. Despite the noticeable increase in computer power, the execution of high fidelity...

ROM techniques have been recognized as an essential ingredient in predictive computing. The basic requirement for any ROM technique is the ability to execute the high fidelity physics model a number of times to explore its true and effective dimensionality. The best algorithms require a number of model executions that is equal to the effective dime...

Reduced Order Modeling (ROM) is essential to enable the identification of the so-called active parameter subspace for high-fidelity models that are employed in computationally intensive analyses such as uncertainty characterization and data assimilation. Given the complexity of models for real-world application, brute force methods to identify the...

This manuscript further develops a recent methodology, denoted by Physics-guided Coverage Mapping (PCM), to support model validation for neutronic depletion calculations. The overarching goal of model validation is to develop confidence in model predictions for the application of interest via fusion of both simulation results and measurements from...

Business analytics augmented by artificial intelligence and machine learning (AI/ML) have revolutionized the role of data in the modern world. In recent years, businesses have incorporated data into their decision-making process for better prediction, risk assessment, content creation, etc. While such businesses often seek to leverage the full use...

The present study aims to evaluate the effective thermal conduction behavior of crack-containing media through micromechanical modeling. Numerical analyses were performed using the finite element method, first using periodic arrays of elliptical pores and then reducing the elliptical minor axis to reach the crack limit. This parametric approach was...

The present investigation aims to evaluate the thermal conduction behavior of closed-cell porous media through micromechanical modeling. Numerical analyses were conducted using the finite element method. The overall thermal conductivity of the model structures, encompassing a range of porosity and pore geometry, was investigated. Attention is devot...

The purpose of this study was to develop a process to convert input signals from one facility into another by reflecting geometric and environmental settings. The Dynamic Energy Transport and Integration Laboratory (DETAIL) is a research facility in development. Its aim is to emulate the daily interactions among power production industry systems an...

An algorithm based on dynamic mode decomposition (DMD) for acceleration of the power method (PM) is presented. The PM is a simple technique for determining the dominant eigenmode of an operator A, and variants of the PM are widely used in reactor analysis. DMD is an algorithm for decomposing a time series of spatially dependent data and producing a...

Dynamic Mode Decomposition (DMD) was used to produce surrogates for the evolution of spatially-varying nuclide compositions in a TRIGA reactor. First, the method was explored by applying it to a single fuel element in which the data used was produced from the Polaris sequence in SCALE-6.2. Then the method was applied to a full core for which the da...

Presented is an algorithm based on dynamic mode decomposition (DMD) for acceleration of the power method (PM). The power method is a simple technique for determining the dominant eigenmode of an operator $\mathbf{A}$, and variants of the power method are widely used in reactor analysis. Dynamic mode decomposition is an algorithm for decomposing a t...

INTRODUCTION The next frontier of reactor modeling will require large-scale, full-core, transient models. However, the herculean efforts involved will not likely be feasible for applications in which a system model must be executed for multiple system perturbations, e.g., design optimization and uncertainty quan-tification. Hence, relatively inexpe...

Ongoing work at Kansas State University aims to develop a high fidelity computational model for the KSU TRIGA Mark II research reactor. One objective of this work is to estimate the current isotopic compositions of the fuel elements with uncertainties. The challenge is that most of the fuel in the reactor core came to KSU with the previous burnup w...

Reduced-Order Modeling (ROM) has become an indispensable tool for reducing the cost of repetitive executions common to Sensitivity Analysis (SA), Uncertainty Characterization (UC). Presented here is the application of Dynamic Mode Decomposition (DMD) to build a data-driven, reduced-complexity surrogate that predicts the fuel concentration of a sing...

We propose an estimation method of sensitivity coefficients of core neutronics parameters based on a multi-level reduced-order modeling approach. The idea is to use lower-level models to identify the dominant input parameter variations, constrained to the so-called active subspace, which are employed to determine the sensitivity coefficients of the...

This manuscript investigates the level of conservatism of the bounds developed in earlier work to capture the errors resulting from reduced order modeling. Reduced order modeling is premised on the fact that large areas of the input and/or output spaces can be safely discarded from the analysis without affecting the quality of predictions for the q...

Reduced order modeling ha.s proven to be an effective tool when repeated execution of reactor analysis codes is required. ROM operates on the assumption that the intrinsic dimensionality of the associated reactor physics models is sufficiently small when compared to the nominal dimensionality of the input and output data streams. By employing a tru...

This manuscript investigates the level of conservatism of the bounds developed in earlier work to capture the errors resulting from reduced order modeling. Reduced order modeling is premised on the fact that large areas of the input and/or output spaces can be safely discarded from the analysis without affecting the quality of predictions for the q...

INTRODUCTION Reduced order modeling (ROM) has been recognized to be an essential tool in support of modern predictive strategies relying on the use of high fidelity and often computationally expensive models [1]. ROM provides an efficient manner by which these models can be executed repeatedly for various engineering analyses, such as design optimi...

INTRODUCTION The role of reduced order modeling (ROM) has been emphasized in recent years in order to address the curse of dimensionality, a typical characteristic of the advanced modeling and simulation software tools currently being developed in various institutions around the country. This follows as the shift in design paradigm for nuclear syst...