Probing aluminum reaction in high explosives via benchtop dynamic x-ray diffraction
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Brown, Cameron↗
Engineering topics
Brown, Cameron
Publications and source records attributed to Brown, Cameron.
Optimizing Batch Crystallization with Model-based Design of Experiments
Poster for Foundations of Computer Aided Process Design (FOCAPD) 2024 describing the formality of model-based design of experiments for batch crystallization. This paper focuses on the implementation of model-based design of experiments for parameter precision, enabled by Pyomo.DoE, a package in Python. Specifically, this work outlines an identifiability analysis for the estimability of the model parameters in a batch crystallization system.
Lynch, Hailey↗
Optimizing Batch Crystallization with Model-based Design of Experiments
Adaptive and self-optimizing intelligent systems such as digital twins are increasingly important in science and engineering. Digital twins utilize mathematical models to provide added precision to decision-making. However, physics-informed models are challenging to build, calibrate, and validate with existing data science methods. Model-based design of experiments (MBDoE) is a popular framework for optimizing data collection to maximize parameter precision in mathematical models and digital twins. In this work, we apply MBDoE, facilitated by the open-source package Pyomo.DoE, to train and validate mathematical models for batch crystallization. We quantitatively examined the estimability of the model parameters for experiments with different cooling rates. This analysis provides a quantitative explanation for the heuristic of using multiple experiments at different cooling rates.
Lynch, Hailey↗
A digital twin solution for floating offshore wind turbines validated using a full-scale prototype
Abstract. In this work, we implement, verify, and validate a physics-based digital twin solution applied to a floating offshore wind turbine. The digital twin is validated using measurement data from the full-scale TetraSpar prototype. We focus on the estimation of the aerodynamic loads, wind speed, and section loads along the tower, with the aim of estimating the fatigue lifetime of the tower. Our digital twin solution integrates (1) a Kalman filter to estimate the structural states based on a linear model of the structure and measurements from the turbine, (2) an aerodynamic estimator, and (3) a physics-based virtual sensing procedure to obtain the loads along the tower. The digital twin relies on a set of measurements that are expected to be available on any existing wind turbine (power, pitch, rotor speed, and tower acceleration) and motion sensors that are likely to be standard measurements for a floating platform (inclinometers and GPS sensors). We explore two different pathways to obtain physics-based models: a suite of dedicated Python tools implemented as part of this work and the OpenFAST linearization feature. In our final version of the digital twin, we use components from both approaches. We perform different numerical experiments to verify the individual models of the digital twin. In this simulation realm, we obtain estimated damage equivalent loads of the tower fore–aft bending moment with an accuracy of approximately 5 % to 10 %. When comparing the digital twin estimations with the measurements from the TetraSpar prototype, the errors increased to 10 %–15 % on average. Overall, the accuracy of the results is promising and demonstrates the possibility of using digital twin solutions to estimate fatigue loads on floating offshore wind turbines. A natural continuation of this work would be to implement the monitoring and diagnostics aspect of the digital twin to inform operation and maintenance decisions. The digital twin solution is provided with examples as part of an open-source repository.
17 WIND ENERGY↗
Modeling the TetraSpar Floating Offshore Wind Turbine Foundation as a Flexible Structure in OrcaFlex and OpenFAST
Floating offshore wind turbine technology has seen an increasing and continuous development in recent years. When designing the floating platforms, both experimental and numerical tools are applied, with the latter often using time-domain solvers based on hydro-load estimation from a Morison approach or a boundary element method. Commercial software packages such as OrcaFlex, or open-source software such as OpenFAST, are often used where the floater is modeled as a rigid six degree-of-freedom body with loads applied at the center of gravity. However, for final structural design, it is necessary to have information on the distribution of loads over the entire body and to know local internal loads in each component. This paper uses the TetraSpar floating offshore wind turbine design as a case study to examine new modeling approaches in OrcaFlex and OpenFAST that provide this information. The study proves the possibility of applying the approach and the extraction of internal loads, while also presenting an initial code-to-code verification between OrcaFlex and OpenFAST. As can be expected, comparing the flexible model to a rigid-body model proves how motion and loads are affected by the flexibility of the structure. OrcaFlex and OpenFAST generally agree, but there are some differences in results due to different modeling approaches. Since no experimental data are available in the study, this paper only forms a baseline for future studies but still proves and describes the possibilities of the approach and codes.
17 WIND ENERGY↗
VUSAT - A VERA-CS Uncertainty and Sensitivity Analysis Toolkit
VERA-CS (the Virtual Environment for Reactor Applications, Core Simulator) is a high-fidelity multiphysics core simulator under development by the Consortium for Advanced Simulation of Light Water Reactors (CASL). VERA-CS includes three main components: MPACT for reactor physics and neutron transport, COBRA-TF for thermal-hydraulics, and ORIGEN for isotopic depletion. VERA-CS has been applied to an array of problems including core physics analysis, and the modeling for all of the fuel cycles of Watts Bar Nuclear Unit 1 (WBN1). MPACT is a 3D pin-resolved reactor transport code developed by Oak Ridge National Laboratory (ORNL) and the University of Michigan. COBRA-TF (CTF) is a three-field two-phase flow subchannel analysis code developed by ORNL and North Carolina State University. CTF solves the mass, momentum, and energy equations for the liquid, vapor, and droplet phases for each subchannel in the core and captures the axial flow within each channel as well as cross-flow between channels. Direct coupling between neutronics (MPACT) and thermal-hydraulics (CTF) in VERA-CS provides temperature and density feedback. CTF includes a number of models important for LWR safety and design analysis including flow regime dependent two-phase wall heat transfer, inter-phase heat transfer and drag, droplet breakup, and quench-front tracking. ORIGEN is developed by ORNL and released through the SCALE package. ORIGEN is directly integrated in MPACT through its API to calculate the changes in nuclides for every depletable cross-section region. Uncertainties exist in safety calculations with computer codes for nuclear reactor modeling and simulation, and they come from code limitations, scaling inaccuracies embedded in the experimental data, and uncertainties associated with the state of the reactor at the initiation of the transient. Consequently, for applications of high fidelity calculations using VERA-CS, proper uncertainty quantification (UQ) and sensitivity analysis (SA) of the computing models of reactor neutronics and thermal-hydraulics are important to comprehensively understand the results. New methods in UQ and SA have to be developed in multi-physics calculations using VERA-CS. Toward this end, a software package called VUSAT which stands for VERA-CS Uncertainty and Sensitivity Analysis Toolkit has been developed to facilitate the applications of VERA-CS within the context of best estimate plus uncertainty (BEPU) analysis framework. In this work, the VUSAT application to analyze departure from nucleate boiling (DNB) is presented.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗