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Parker, Robert

Publications and source records attributed to Parker, Robert.

16 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

MathOptAI.jl

Optimization and Machine Learning Toolbox-Embed trained machine learning predictors in JuMP

Dowson, Oscar↗
DOE OSTI2024

Model Diagnostics for Equation Oriented Models: Roadblocks and the Path Forward

This poster was presented at the Foundations of Computer Aided Process Design (FOCAPD) 2024 conference with a paper describing efforts to develop a unified workflow and toolbox for diagnosing issues in equation-oriented models as part of the IDAES modeling framework. The poster presents the work currently underway within the IDAES project to develop and integrate cutting edge model analysis techniques into a common toolbox and workflow for model developers to use.

Lee, Andrew↗
DOE OSTI2024

Model Diagnostics for Equation Oriented Models: Roadblocks and the Path Forward

Conference paper for FOCAPD24 describing efforts to develop a unified workflow and toolbox for diagnosing issues in equation-oriented models as part of the IDAES modeling framework. The paper presents the work currently underway within the IDAES project to develop and integrate cutting edge model analysis techniques into a common toolbox and workflow for model developers to use.

Lee, Andrew↗
DOE OSTI2024

A set of test problems for nonlinear optimization

The software is a set of test problems for nonlinear optimization algorithms, including subroutines such as linear algebra routines and automatic differentiation algorithms. The test problems come from chemical engineering open literature, and describe optimization tasks related to the design and operation of processes such as carbon capture, Hydrogen production, heat exchange, and distillation.

Parker, Robert↗
DOE OSTI2023

Tools for analysis of optimization models

The software is code for analyzing, debugging, and solving optimization models. The code implements several algorithms and provides convenient APIs to apply these algorithms to optimization models. The algorithms are primarily based on graph theory. They compute well-known partitions of graphs, and use these partitions to provide the user information about their optimization model, including diagnosing certain types of structural modeling errors. Some algorithms may be used as a subroutine to solve an optimization problem, and may call an optimization solver to facilitate this. Well-known linear algebra routines are called to provide further diagnostics. The software contains no data and no models other than toy models used for testing.

Parker, Robert↗
DOE OSTI2022

Application of an equation‐oriented framework to formulate and estimate parameters of chemical looping reaction models

Abstract Accurate, predictive reaction models are critical for the design and optimization of chemical looping combustion (CLC) reactors. The formulation and estimation of kinetic parameters for these reaction models using a first‐principles equation‐oriented (EO) approach is particularly beneficial as large amounts of experimental data spanning process‐relevant conditions can be used to estimate parameters in a computationally tractable way. This work demonstrates the application of a novel EO framework to develop reduction reaction kinetic models of an iron‐based CLC oxygen carrier (OC). An optimization problem is formulated to estimate kinetic parameters that provide the best fit to the experimental data. The model predicts the state of the OC with mean square error values of 2.5%–4.4% across the full range of validation data, including multiple reduction cycles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Kinetic model development and Bayesian uncertainty quantification for the complete reduction of Fe-based oxygen carriers with CH 4 , CO, and H 2 for chemical looping combustion

In this work, three kinetic models are developed and calibrated for the complete multi-step reduction of an Fe-based oxygen carrier (OC) particle with CH 4 , CO, and H 2 , using data from thermogravimetric analysis. The complete reduction rate profiles exhibit complex dynamics whose trajectory is significantly different depending on the reducing gas. A Bayesian model building and parameter estimation framework is applied for simultaneous parameter and model structure uncertainty quantification. The final models show excellent agreement between model predictions and calibration data, as well as new data not used for calibration (for the reduction of the OC with CH 4 ). Parameter uncertainty is quantified by determining joint posterior distribution, and model structure uncertainty is addressed by incorporating Gaussian process stochastic functions (represented by Bayesian smoothing splines) into the kinetic models. The final kinetic models with discrepancy functions are readily employable in equation-oriented simulation and optimization platforms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Numerical analysis and wave tank validation on the optimal design of a two-body wave energy converter

To improve the performance of a ‘point absorber’ type wave energy converter (WEC), an additional submerged body can be deployed. The submerged body can be used to increase the equivalent excitation force on the WEC, as well as provide resonance tuning. This paper presents numerical analysis and experimental validation of a two-body point absorber type WEC using a mechanical motion rectifier (MMR) based power takeoff. The two-body point absorber consists of a floating buoy connected to a neutrally buoyant submerged body via the power-takeoff. The mechanical motion rectifier and a ball screw translate the relative heave motion of the two bodies into unidirectional rotation, which in turn spins a generator. Frequency domain analysis suggests there is an optimal submerged body mass for maximum WEC power absorption. Regular wave simulations in the time domain are compared to the results obtained in the frequency domain. While the time domain and frequency domain results predict the same optimal mass ratio, time domain analysis provides a more complex and accurate power result. To validate the time domain model, experimental wave tank testing is conducted using a 1:30 scale model WEC. The experiment shows the two-body WEC can produce twice the amount of power as the single-body WEC with same floating buoy and can be further increased by PTO design and power electronics optimization. Furthermore, wave tank testing shows the two-body WEC has a capture width ratio up to 58% at 59 kW/m and 51% at 36 kW/m.

16 TIDAL AND WAVE POWER↗