About

April Novak is an Assistant Professor in the Department of Nuclear, Plasma, and Radiological Engineering at the University of Illinois at Urbana-Champaign, a position she has held since 2023. She also holds a role at the National Center for Supercomputing Applications at Illinois. Her research interests include multiphysics methods and applications, multiscale thermal-hydraulics, Monte Carlo transport methods, reactor design and analysis, and high-performance computing. Novak earned her PhD in Nuclear Engineering from the University of California, Berkeley in 2020 and her BS in Nuclear, Plasma, and Radiological Engineering from the University of Illinois at Urbana-Champaign in 2015. Prior to her faculty appointment, she was a Maria Goeppert-Mayer Fellow at Argonne National Laboratory from 2020 to 2023. Her work involves the development and validation of advanced computational frameworks for nuclear energy applications, including multiphysics modeling, reactor physics, and thermal-hydraulics, contributing to the advancement of nuclear reactor design and safety analysis.

Research topics

• Computer Science
• Programming language
• Engineering
• Physics
• Computational science
• Mechanics
• Nuclear physics
• Ecology
• Parallel computing
• Nuclear engineering
• Structural engineering
• World Wide Web
• Biology
• Systems engineering
• Mechanical engineering
• Operating system

Selected publications

• Reproducible Benchmark for the SNAP 8 Experimental Reactor at Operating Conditions

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• A Self-Adjoint Scalar Flux Equation for Calculating Point-Source Uncollided Flux Moments Without Ray Effects

  Nuclear Science and Engineering · 2025-12-10

  article
  PublisherDOI

• MOOSE Web Server Interface: A Message-Based External Interface for Multiphysics Simulations

  2025-01-01

  article
  PublisherDOI

• Heuristic-Based Adaptive Mesh Refinement Applied to Ray Effect Mitigation Methods

  2025-01-01

  articleSenior author
  PublisherDOI

• Software Quality Assurance for the MOOSE-Based Open-Source Multiphysics Code Cardinal - An Expanded CI Testing Suite

  2025-01-31

  reportOpen access

  Cardinal is a wrapping of the GPU-oriented spectral element Computational Fluid Dynamics (CFD) code NekRS and the Monte Carlo particle transport code OpenMC within the Multiphysics Object-Oriented Simulation Environment (MOOSE).Cardinal provides high-resolution thermal-hydraulics and/or radiation transport feedback to MOOSE multiphysics simulations.Multiphysics feedback is implemented in a geometry-agnostic manner which eliminates the need for rigid one-to-one mappings.A generic data transfer implementation also allows NekRS and OpenMC to couple to any MOOSE application, enabling a broad set of multiphysics capabilities.Cardinal simulations can also leverage combinations of MPI, OpenMP, and GPU resources.Cardinal continuous development and improvement efforts have led to the software being considered as a high-fidelity design and licensing tool for key areas of nuclear reactor relevant physics, including neutron transport, fluid flow, heat transfer, and mechanical processes.The fast development and expansion of the software from a pure R&D framework towards its application in the nuclear industry and regulation require a focus on developing, enhancing and, maintaining Cardinal's software quality through strict adherence to a Software Quality Assurance (SQA) framework and SQA program.To facilitate compliance with SQA standards, the Cardinal SQA Program has been initiated during Fiscal Year 2023 (FY23).During the development of the Cardinal SQA Program, multiple gaps have been identified.These gaps are primarily related to model verification and code pedigree as they relate to the use of Cardinal as a safety analysis tool.These gaps have been captured in a report published in 2023.A second report highlighted the progress made during Fiscal Year 2024 (FY24) and described Argonne's effort to document and integrate software verification within Cardinal's software development process.This report documents a snapshot of the verification test cases currently available for Cardinal and NekRS in their assimilation into a Continuous Integration (CI) platform.Following the CI practice permits the integrating of source code changes frequently and ensuring that the integrated codebase clears the verification testing for the software.It should be noted that the SQA program itself, including the program plans, procedures, configuration management, and testing strategies, need to be developed in a future step of this task.

  PublisherOA PDFDOI

• CFD simulation of interassembly bypass flow in Sodium Fast Reactors

  Nuclear Engineering and Design · 2025-05-06

  articleOpen access1st authorCorresponding

  Interassembly flow in Sodium Fast Reactors (SFRs) represents a bypass flow path exterior to the fuel assembly ducts. Heat transferred across this thin gap is an important component of core radial expansion, where the coupling between thermal-fluids, neutronics, and solid mechanics results in time-dependent duct bowing. These geometry changes can constitute a significant portion of the total reactivity response in transients, but are difficult to model in high-fidelity. Interassembly flow is also an important heat transfer mode during natural convection cooling. To improve our understanding of interassembly flow, this paper provides NekRS Reynolds Averaged Navier–Stokes (RANS) and Large Eddy Simulations (LES) of the interassembly flow in a 19-bundle fast reactor core. Time-averaged LES compares reasonably well with a k - τ RANS model, though RANS is not able to capture a crossflow which occurs at a large change in flow area between the duct–duct gaps and the open peripheral region. We predict velocity distributions and illustrate a multiscale postprocessing system that can be used to generate coarse-mesh closures for subchannel and porous media tools, and provide a dataset with average velocity for comparison with coarse-mesh tools. • LES and RANS simulations are performed of the interassembly flow in a small-size sodium fast reactor. • The flow is laminar in the duct–duct gaps and turbulent in the peripheral region. • Spatial averaging of the CFD simulations provides data for code-to-code comparison.

  PublisherDOI

• High fidelity multiphysics tightly coupled model for a lead cooled fast reactor concept and application to statistical calculation of hot channel factors

  Nuclear Engineering and Design · 2025-02-24 · 5 citations

  articleOpen access

  • Demonstrate a tightly coupled high fidelity multi-physics code system for Lead Fast Reactor. • The hot channel factors are calculated with the MOOSE Stochastic Tools Module (STM) statistically. • The statistically-computed HCFs show improvement over both legacy techniques and use of the deterministic method. A tightly coupled multiphysics code system is established using the MOOSE framework for hot channel factor (HCF) evaluation on a Lead Fast Reactor (LFR) concept. The coupled system is driven by the Griffin multiphysics coupling capability under which the MOOSE Heat Transfer module and NekRS computational fluid dynamics solver are coupled for conjugate heat transfer using the Cardinal application. The coupled capability is demonstrated on an LFR assembly model based on materials and geometry of a prototypical lead-cooled fast reactor design by Westinghouse Electric Company, LLC. Moreover, the work integrates the Multiphysics Object Oriented Simulation Environment (MOOSE) Stochastic Tools Module (STM) to perform calculations for statistical analysis of HCF. The coupling strategy and workflow demonstrated in this paper is not only useful for predicting accurate hot channel factors for different kinds of advanced reactors but also for other engineering applications such as control rod worth assessment, generation of high-fidelity database for Artificial intelligence (AI)/machine learning (ML) training, design optimization and multi-resolution modeling.

  PublisherDOI

• Advanced Nuclear Reactor Driven Direct Air Capture for Achieving Net-Negative Emissions

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Application of a Physics-Informed Convolutional Neural Network for Monitoring the Temperature Fields in High-Temperature Gas Reactors

  Nuclear Science and Engineering · 2025-01-31 · 5 citations

  article
  PublisherDOI

• Thermomechanics Coupling to Monte Carlo Particle Transport on Unstructured Mesh Geometries

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

Frequent coauthors

• Elia Merzari

  Argonne National Laboratory

  14 shared

• Derek Gaston

  Idaho National Laboratory

  12 shared

• Richard Martineau

  8 shared

• David Andrš

  Idaho National Laboratory

  7 shared

• Patrick Shriwise

  7 shared

• Rachel Slaybaugh

  6 shared

• Sebastian Schunert

  Idaho National Laboratory

  6 shared

• Paul Romano

  Argonne National Laboratory

  6 shared

Labs

• Nuclear, Plasma & Radiological EngineeringPI

Education

• PhD, Nuclear Engineering

  University of California

  2020

• BS, Nuclear, Plasma, and Radiological Engineering

  University of Illinois Urbana-Champaign

  2015

Awards & honors

• Maria Goeppert-Mayer Fellow, Argonne National Laboratory (20…