Engineering topics

Ciesielski, Danielle K.

Publications and source records attributed to Ciesielski, Danielle K..

7 records

DOE OSTI2024

Deep operator network surrogate for phase-field modeling of metal grain growth during solidification

A deep operator network (DeepONet) has been constructed that generates accurate representations of phase-field model simulations for evolving two dimensional metal grain morphology growing from melt. These representations serve as lower resolution, computationally efficient stand-ins for quick parameter space exploration of solutions to the the Allen-Cahn equations that dictate the phase-field model simulations. The experimental target for the phase-field model is a uranium casting system cooling a 434 g uranium charge from a maximum temperature of 1400° C at an average rate of 30° C / min , traversing the crystallographic phases of the pure metal. Experimental parameters inform the phase-field model, whose higher resolution computational model solutions are used to train the DeepONet in a given parameter space with the aim of developing a faster, more efficient method for predicting the solidifying metal's microstructure at different potential experimental values. The final DeepONet generates high accuracy, lower resolution predictions with cumulative relative approximation error over all timesteps of less than 0.5%, while ensuring solutions remain within physically feasible ranges. Further, these relative error values are comparable with other state-of-the-art DeepONet models for microstructure evolution, while significantly reducing the amount of training data required. Training a convolutional neural network simultaneously with the DeepONet, enforcing realistic values at the complex metal grain boundaries, and mathematically encoding boundary conditions into the structure of the DeepONet improved prediction accuracy and computational efficiency over a standard DeepONet model.

36 MATERIALS SCIENCE↗

DOE OSTI2024

QC-GN 2 oMS 2 : a Graph Neural Net for High Resolution Mass Spectra Prediction

Predicting the mass spectrum of a molecular ion is often accomplished via three generalized approaches: rules-based methods for bond breaking, deep learning, or quantum chemical (QC) modeling. Rules-based approaches are often limited by the conditions for different chemical subspaces and perform poorly under chemical regimes with few defined rules. QC modeling is theoretically robust but requires significant amounts of computational time to produce a spectrum for a given target. Among deep learning techniques, graph neural networks (GNNs) have performed better than previous work with fingerprint-based neural networks in mass spectra prediction. To explore this technique further, we investigate the effects of including quantum chemically derived information as edge features in the GNN to increase predictive accuracy. The models we investigated include categorical bond order, bond force constants derived from extended tight-binding (xTB) quantum chemistry, and acyclic bond dissociation energies. Throughout this work, we evaluated these models against a control GNN with no edge features in the input graphs. Bond dissociation enthalpies yielded the best improvement with a cosine similarity score of 0.462 relative to the baseline model (0.437). In this work we also apply dynamic graph attention which improves performance on benchmark problems and supports the inclusion of edge features. Between implementations, we investigate the nature of the molecular embedding for spectra prediction and discuss the recognition of fragment topographies in distinct chemistries for further development in tandem mass spectrometry prediction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Visualizing Uranium Crystallization from Melt: Experiment-Informed Phase Field Modeling and Machine Learning

The focus of this project was to observe and simulate the solidification of uranium metal at the crystallographic level from its molten state. Melting experiments were conducted at two different scales to observe microstructural evolution using either a laboratory-scale induction furnace (hundreds of grams of metal) or a microscope heating stage (hundreds of milligrams of metal), respectively. Experimental parameters and characterization data were then used to inform a phase field model of gamma-U crystal growth as dendrites with or without secondary phase impurities in the form of uranium carbide particles. Finally, training datasets were generated by the phase field model as inputs to a neural network, developed with the aim of providing a faster, cheaper surrogate model for microstructural simulations within a given parameter space. Progress is reported herein for each of these task areas. Ultimately, 1) an optical microscope heating stage capability has been stood-up for uranium metal solidification studies, 2) a phase field model was advanced to simulate multiple uranium grains growing in the presence of carbide impurity particles and 3) a neural network was constructed and optimized to predict the microstructure features of individually growing uranium crystals.

36 MATERIALS SCIENCE↗

DOE OSTI2022

A Real-Time Operations Manual for the IEEE 118 Bus Transmission Model

This document presents a sample operations manual for the IEEE 118 Bus Model, which is a synthetic test case developed in 1962 from a section of the transmission grid operated by American Electric Power (AEP). The model is one of the most used synthetic test cases for development of power system applications and is referenced by over 9000 papers. However, the model lacks any context for use with real-time energy management system (EMS) applications, including common considerations, such as generator ramp rates, reactive capabilities, operating limits, and other information typically used by power system operators for real-time decision making. This manual divides the IEEE 118 Bus Model into three operating areas, defines various operating limits, and sets recommended operating procedures for responding to a few types of emergency operating conditions. The manual can be used in support of a wide variety of human-in-the-loop evaluation methodologies for new advanced power applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Augmentation of MS/MS Libraries with Spectral Interpolation for Improved Identification

Tandem mass spectrometry (MS/MS) is a primary tool for identification of small molecules and metabolites where resultant spectra are most commonly identified by matching them with spectra in MS/MS reference libraries. The high degree of variability in MS/MS spectrum acquisition techniques and parameters creates a significant challenge for building standardized reference libraries. Here we present a method to improve the usefulness of existing MS/MS libraries by augmenting available experimental spectra datasets with statistically interpolated spectra at unreported collision energies. We find that highly accurate spectral approximations can be interpolated from as few as three experimental spectra and that the interpolated spectra will be consistent with true spectra gathered from the same instrument as the experimental spectra. Supplementing existing spectral databases with interpolated spectra yields consistent improvements to identification accuracy on a range of instruments and precursor types. Applying this method yields significant improvements (~10% more spectra correctly identified) on large datasets (2,000 - 10,000 spectra), indicating this is a quick yet adept tool for improving spectral matching in situations where available reference libraries are not yet sufficient. We also find improvements of matching spectra across instrument types (between an Agilent Q-TOF and an Orbitrap Elite), at high collision energies (50 - 90 eV), and with smaller datasets available through MassBank.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Operator Insights and Usability Evaluation of Machine Learning Assistance for Power Grid Contingency Analysis

Introducing machine learning (ML) assistance into any established process comes with adoption barriers, including entrenched procedures, technological and human readiness levels, human-machine trust, and work culture resistance to change. These barriers are even greater in critical operations such as operating a national or regional power grid, in which both regulatory frameworks and the importance of maintaining reliability levels causes additional resistance to the adoption of new computational support. Developers of future systems and job aides must consider not only technical aspects, but also whether new systems are usable by power system operators. This work presents the methodology and results of a study to evaluate the usability and readiness of a prototype recommender system for power grid contingency analysis. We explore operator cognitive load and evaluate operator performance when solving a collection of scenarios both with and without recommender assistance. We also examine operator trust in the system. We report insights gained on the readiness of the system using a collection of evaluation techniques.

Human-Machine Teaming, Power Systems, usability ev↗

DOE OSTI2022

A Methodology for Evaluating Operator Usage of Machine Learning Recommendations for Power Grid Contingency Analysis

This work presents the application of a methodology to measure domain expert trust and workload, elicit feedback, and understand the technological usability and impact when a machine learning assistant is introduced into contingency analysis for real-time power grid simulation. The goal of this framework is to rapidly collect and analyze a broad variety of human factors data in order to accelerate the development and evaluation loop for deploying machine learning applications. We describe our methodology and analysis, and we discuss insights gained from a pilot participant about the current usability state of an early technology readiness level (TRL) artificial neural network (ANN) recommender.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗