Geoffrey T. Parks
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View article: Synergistic Utilization of LLMs for Program Synthesis
Synergistic Utilization of LLMs for Program Synthesis Open
Advances in Large Language Models (LLMs) have led them to be used as black boxes in several evolutionary algorithms for program synthesis. While these methods tend to be agnostic about which model is used, they only allow for using one. Th…
View article: Recovering From Structural Instability in Nuclear Power Design Optimization
Recovering From Structural Instability in Nuclear Power Design Optimization Open
The Scientific Method—parsing a problem into isolated subproblems—is often necessarily employed in optimization efforts to reduce large and complex problems into more tractable parcels. However, adopting an ersatz action space relies on th…
View article: Prior-informed Uncertainty Modelling with Bayesian Polynomial Approximations
Prior-informed Uncertainty Modelling with Bayesian Polynomial Approximations Open
Orthogonal polynomial approximations form the foundation to a set of well-established methods for uncertainty quantification known as polynomial chaos. These approximations deliver models for emulating physical systems in a variety of comp…
View article: Bayesian assessments of aeroengine performance with transfer learning
Bayesian assessments of aeroengine performance with transfer learning Open
Aeroengine performance is determined by temperature and pressure profiles along various axial stations within an engine. Given limited sensor measurements, we require a statistically principled approach for inferring these profiles. In thi…
View article: Blade Envelopes Part I: Concept and Methodology
Blade Envelopes Part I: Concept and Methodology Open
Blades manufactured through flank and point milling will likely exhibit geometric variability. Gauging the aerodynamic repercussions of such variability, prior to manufacturing a component, is challenging enough, let alone trying to predic…
View article: Optimization by moving ridge functions: derivative-free optimization for computationally intensive functions
Optimization by moving ridge functions: derivative-free optimization for computationally intensive functions Open
A novel derivative-free algorithm, called optimization by moving ridge functions (OMoRF), for unconstrained and bound-constrained optimization is presented. This algorithm couples trust region methodologies with output-based dimension redu…
View article: Automatic Borescope Damage Assessments for Gas Turbine Blades via Deep Learning
Automatic Borescope Damage Assessments for Gas Turbine Blades via Deep Learning Open
To maximise fuel economy, bladed components in aero-engines operate close to\nmaterial limits. The severe operating environment leads to in-service damage on\ncompressor and turbine blades, having a profound and immediate impact on the\npe…
View article: Blade Envelopes Part II: Multiple Objectives and Inverse Design
Blade Envelopes Part II: Multiple Objectives and Inverse Design Open
Blade envelopes offer a set of data-driven tolerance guidelines for manufactured components based on aerodynamic analysis. In Part I of this two-part paper, a workflow for the formulation of blade envelopes is described and demonstrated. I…
View article: Bayesian Assessments of Aeroengine Performance.
Bayesian Assessments of Aeroengine Performance. Open
Aeroengine performance is determined by temperature and pressure profiles along various axial stations within an engine. Given limited sensor measurements along an axial station, we require a statistically principled approach to inferring …
View article: Bayesian Assessments of Aeroengine Performance with Transfer Learning
Bayesian Assessments of Aeroengine Performance with Transfer Learning Open
Aeroengine performance is determined by temperature and pressure profiles along various axial stations within an engine. Given limited sensor measurements both along and between axial stations, we require a statistically principled approac…
View article: Supporting multi-point fan design with dimension reduction
Supporting multi-point fan design with dimension reduction Open
Motivated by the idea of turbomachinery active subspace performance maps, this paper studies dimension reduction in turbomachinery 3D CFD simulations. First, we show that these subspaces exist across different blades—under the same paramet…
View article: Optimization by moving ridge functions: Derivative-free optimization for\n computationally intensive functions
Optimization by moving ridge functions: Derivative-free optimization for\n computationally intensive functions Open
A novel derivative-free algorithm, optimization by moving ridge functions\n(OMoRF), for unconstrained and bound-constrained optimization is presented.\nThis algorithm couples trust region methodologies with output-based dimension\nreductio…
View article: Neutronic investigation of alternative & composite burnable poisons for the soluble-boron-free and long life civil marine small modular reactor cores.
Neutronic investigation of alternative & composite burnable poisons for the soluble-boron-free and long life civil marine small modular reactor cores. Open
Concerns about the effects of global warming provide a strong case to consider how best nuclear power could be applied to marine propulsion. Currently, there are persistent efforts worldwide to combat global warming, and that also includes…
View article: Spatial Flow-Field Approximation Using Few Thermodynamic Measurements—Part I: Formulation and Area Averaging
Spatial Flow-Field Approximation Using Few Thermodynamic Measurements—Part I: Formulation and Area Averaging Open
Our investigation raises an important question that is of relevance to the wider turbomachinery community: how do we estimate the spatial average of a flow quantity given finite (and sparse) measurements? This paper seeks to advance effort…
View article: Spatial Flow-Field Approximation Using Few Thermodynamic Measurements—Part II: Uncertainty Assessments
Spatial Flow-Field Approximation Using Few Thermodynamic Measurements—Part II: Uncertainty Assessments Open
In this second part of our two-part paper, we provide a detailed, frequentist framework for propagating uncertainties within our multivariate linear least squares model. This permits us to quantify the impact of uncertainties in thermodyna…
View article: Spatial Flow-Field Approximation Using Few Thermodynamic Measurements\n Part I: Formulation and Area Averaging
Spatial Flow-Field Approximation Using Few Thermodynamic Measurements\n Part I: Formulation and Area Averaging Open
Our investigation raises an important question that is of relevance to the\nwider turbomachinery community: how do we estimate the spatial average of a\nflow quantity given finite (and sparse) measurements? This paper seeks to\nadvance eff…
View article: Spatial Flow-Field Approximation Using Few Thermodynamic Measurements\n Part II: Uncertainty Assessments
Spatial Flow-Field Approximation Using Few Thermodynamic Measurements\n Part II: Uncertainty Assessments Open
In this second part of our two-part paper, we provide a detailed, frequentist\nframework for propagating uncertainties within our multivariate linear least\nsquares model. This permits us to quantify the impact of uncertainties in\nthermod…