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Integrated Reflection Seismic Monitoring and Reservoir Modeling for Geologic CO 2 Sequestration
The US DOE/NETL CCS MVA program funded a project with Fusion Petroleum Technologies Inc. (now SIGMA) to model the proof of concept of using sparse seismic data in the monitoring of CO 2 injected into saline aquifers. The goal of the project was to develop and demonstrate an active source reflection seismic imaging strategy based on deployment of spatially sparse surface seismic arrays. The primary objective was to test the feasibility of sparse seismic array systems to monitor the CO 2 plume migration injected into deep saline aquifers. The USDOE/RMOTC Teapot Dome (Wyoming) 3D seismic and reservoir data targeting the Crow Mountain formation was used as a realistic proxy to evaluate the feasibility of the proposed methodology. Though the RMOTC field has been well studied, the Crow Mountain as a saline aquifer has not been studied previously as a CO 2 sequestration (storage) candidate reservoir. A full reprocessing of the seismic data from field tapes that included prestack time migration (PSTM) followed by prestack depth migration (PSDM) was performed. A baseline reservoir model was generated from the new imaging results that characterized the faults and horizon surfaces of the Crow Mountain reservoir. The 3D interpretation was integrated with the petrophysical data from available wells and incorporated into a geocellular model. The reservoir structure used in the geocellular model was developed using advanced inversion technologies including Fusion's ThinMAN™ broadband spectral inversion. Seal failure risk was assessed using Fusion's proprietary GEOPRESS™ pore pressure and fracture pressure prediction technology. CO 2 injection was simulated into the Crow Mountain with a commercial reservoir simulator. Approximately 1.2MM tons of CO 2 was simulated to be injected into the Crow Mountain reservoir over 30 years and subsequently let 'soak' in the reservoir for 970 years. The relatively small plume developed from this injection was observed migrating due to gravity to the apexes of the double anticline in the Crow Mountain reservoir of the Teapot dome. Four models were generated from the reservoir simulation task of the project which included three saturation models representing snapshots at different times during and after simulated CO 2 injection and a fully saturated CO 2 fluid substitution model. The saturation models were used along with a Gassmann fluid substitution model for CO 2 to perform fluid volumetric substitution in the Crow Mountain formation. The fluid substitution resulted in a velocity and density model for the 3D volume at each saturation condition that was used to generate a synthetic seismic survey. FPTI's (Fusion Petroleum Technologies Inc.) proprietary SeisModelPRO™ full acoustic wave equation software was used to simulate acquisition of a 3D seismic survey on the four models over a subset of the field area. The simulated acquisition area included the injection wells and the majority of the simulated plume area.
PLUSWIND Derived Data
This dataset consists of annual CSV files containing multiple sources of modeled, hourly wind speeds and generation. For complete information about this dataset, including validation of modeled generation versus recorded generation, please see the Scientific Data article: Millstein, D., Jeong, S., Ancell, A., & Wiser, R. (2023). A database of hourly wind speed and modeled generation for US wind plants based on three meteorological models. Scientific Data, 10(1), 883. https://doi.org/10.1038/s41597-023-02804-w
Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials
A major challenge in materials design is how to efficiently search the vast chemical design space to find the materials with desired properties. One effective strategy is to develop sampling algorithms that can exploit both explicit chemical knowledge and implicit composition rules embodied in the large materials database. Here, we propose a generative machine learning model (MatGAN) based on a generative adversarial network (GAN) for efficient generation of new hypothetical inorganic materials. Trained with materials from the ICSD database, our GAN model can generate hypothetical materials not existing in the training dataset, reaching a novelty of 92.53% when generating 2 million samples. The percentage of chemically valid (charge-neutral and electronegativity-balanced) samples out of all generated ones reaches 84.5% when generated by our GAN trained with such samples screened from ICSD, even though no such chemical rules are explicitly enforced in our GAN model, indicating its capability to learn implicit chemical composition rules to form compounds. Our algorithm is expected to be used to greatly expand the range of the design space for inverse design and large-scale computational screening of inorganic materials.
Enhancing power grid resilience to winter storms via generator winterization with equity considerations
Here we develop two-stage stochastic programming models for generator winterization that enhance power grid resilience while incorporating social equity. The first stage in our models captures the investment decisions for generator winterization, and the second stage captures the operation of a degraded power grid, with the objective of minimizing load shed and social inequity. To incorporate equity into our models, we propose a concept called adverse effect probability that captures the disproportionate effects of power outages on communities with varying vulnerability levels. Grid operations are modeled using DC power flow, and equity is captured through mean or maximum adverse effects experienced by communities. We apply our models to a synthetic Texas power grid, using winter storm scenarios created from the generator outage data from the 2021 Texas winter storm. Our extensive numerical experiments show that more equitable outcomes, in the sense of reducing adverse effects experienced by vulnerable communities during power outages, are achievable with no impact on total load shed through investing in winterization of generators in different locations and capacities.
Predicting grid-size-dependent fracture strains of DP980 with a microstructure-based post-necking model
Ductile fracture is a local phenomenon, and it is well established that fracture strain levels depend on both stress triaxiality and the resolution (grid size) of strain measurements. Two-dimensional plane strain post-necking models with different representative volume element (RVE) sizes are used to predict the size-dependent fracture strain of a commercial dual-phase steel, DP980. The models are generated from the actual microstructures, and the individual phase flow properties and literature-based individual phase damage parameters for the Johnson-Cook model are used for ferrite and martensite. A monotonic relationship is predicted: the smaller the model size, the higher the fracture strain. Thus, a general framework is developed to quantify the size-dependent fracture strains for multiphase materials. In addition to the RVE sizes, the influences of intrinsic microstructure features, i.e., the flow curve and fracture strains of the two constituent phases, on the predicted fracture strains also are examined. Application of the derived fracture strain versus RVE size relationship is demonstrated with large clearance trimming simulations with different element sizes.
Genome –Scale Reconstruction of Metabolic Networks of Lactobacillus casei ATCC 334 and 12A
Lactobacillus casei strains are widely used in industry and the utility of this organism in these industrial applications is strain dependent. Hence, tools capable of predicting strain specific phenotypes would have utility in the selection of strains for specific industrial processes. Genome-scale metabolic models can be utilized to better understand genotype-phenotype relationships and to compare different organisms. To assist in the selection and development of strains with enhanced industrial utility, genome-scale models for L. casei ATCC 334, a well characterized strain, and strain 12A, a corn silage isolate, were constructed. Draft models were generated from RAST genome annotations using the Model SEED database and refined by evaluating ATP generating cycles, mass-and-charge-balances of reactions, and growth phenotypes. After the validation process was finished, we compared the metabolic networks of these two strains to identify metabolic, genetic and ortholog differences that may lead to different phenotypic behaviors. We conclude that the metabolic capabilities of the two networks are highly similar. The L. casei ATCC 334 model accounts for 1,040 reactions, 959 metabolites and 548 genes, while the L. casei 12A model accounts for 1,076 reactions, 979 metabolites and 640 genes. The developed L. casei ATCC 334 and 12A metabolic models will enable better understanding of the physiology of these organisms and be valuable tools in the development and selection of strains with enhanced utility in a variety of industrial applications.
Finite-element modeling for an explosively loaded ferroelectric generator
A preliminary finite-element model has been developed using the ALEGRA-FE code for explosive driven depoling of a PZT 95/5 ferroelectric generator. The ferroelectric material is characterized using hysteresis-loop and hydrostatic depoling tests. These characteristics are incorporated into ALEGRA-FE simulations that model the explosive drive mechanism and shock environment in the material leading to depoling, as well as the ferroelectric response and the behavior of a coupled circuit. The ferroelectric-to-antiferroelectric phase transition is captured, producing an output voltage pulse that matches experimental data to within 10% in rise time, and to within about 15% for the final voltage. Both experimental and modeled pulse magnitudes are less than the theoretical maximum output of the material. Observations from materials characterization suggest that unmodeled effects such as trapped charge in the stored FEG material may have influenced the experimentally observed output.
Self-consistent modeling of radio-frequency plasma generation in stellarators
A self-consistent model of radio-frequency (RF) plasma generation in stellarators in the ion cyclotron frequency range is described. The model includes equations for the particle and energy balance and boundary conditions for Maxwell’s equations. The equation of charged particle balance takes into account the influx of particles due to ionization and their loss via diffusion and convection. The equation of electron energy balance takes into account the RF heating power source, as well as energy losses due to the excitation and electron-impact ionization of gas atoms, energy exchange via Coulomb collisions, and plasma heat conduction. The deposited RF power is calculated by solving the boundary problem for Maxwell’s equations. When describing the dissipation of the energy of the RF field, collisional absorption and Landau damping are taken into account. At each time step, Maxwell’s equations are solved for the current profiles of the plasma density and plasma temperature. The calculations are performed for a cylindrical plasma. The plasma is assumed to be axisymmetric and homogeneous along the plasma column. The system of balance equations is solved using the Crank-Nicholson scheme. Maxwell’s equations are solved in a one-dimensional approximation by using the Fourier transformation along the azimuthal and longitudinal coordinates. Results of simulations of RF plasma generation in the Uragan-2M stellarator by using a frame antenna operating at frequencies lower than the ion cyclotron frequency are presented. The calculations show that the slow wave generated by the antenna is efficiently absorbed at the periphery of the plasma column, due to which only a small fraction of the input power reaches the confinement region. As a result, the temperature on the axis of the plasma column remains low, whereas at the periphery it is substantially higher. This leads to strong absorption of the RF field at the periphery via the Landau mechanism.
RFQAmodel: Random Forest Quality Assessment to identify a predicted protein structure in the correct fold
While template-free protein structure prediction protocols now produce good quality models for many targets, modelling failure remains common. For these methods to be useful it is important that users can both choose the best model from the hundreds to thousands of models that are commonly generated for a target, and determine whether this model is likely to be correct. We have developed Random Forest Quality Assessment (RFQAmodel), which assesses whether models produced by a protein structure prediction pipeline have the correct fold. RFQAmodel uses a combination of existing quality assessment scores with two predicted contact map alignment scores. These alignment scores are able to identify correct models for targets that are not otherwise captured. Our classifier was trained on a large set of protein domains that are structurally diverse and evenly balanced in terms of protein features known to have an effect on modelling success, and then tested on a second set of 244 protein domains with a similar spread of properties. When models for each target in this second set were ranked according to the RFQAmodel score, the highest-ranking model had a high-confidence RFQAmodel score for 67 modelling targets, of which 52 had the correct fold. At the other end of the scale RFQAmodel correctly predicted that for 59 targets the highest-ranked model was incorrect. In comparisons to other methods we found that RFQAmodel is better able to identify correct models for targets where only a few of the models are correct. We found that RFQAmodel achieved a similar performance on the model sets for CASP12 and CASP13 free-modelling targets. Finally, by iteratively generating models and running RFQAmodel until a model is produced that is predicted to be correct with high confidence, we demonstrate how such a protocol can be used to focus computational efforts on difficult modelling targets. RFQAmodel and the accompanying data can be downloaded from http://opig.stats.ox.ac.uk/resources.
SU-F-R-44: Modeling Lung SBRT Tumor Response Using Bayesian Network Averaging
Purpose: The prediction of tumor control after a patient receives lung SBRT (stereotactic body radiation therapy) has proven to be challenging, due to the complex interactions between an individual’s biology and dose-volume metrics. Many of these variables have predictive power when combined, a feature that we exploit using a graph modeling approach based on Bayesian networks. This provides a probabilistic framework that allows for accurate and visually intuitive predictive modeling. The aim of this study is to uncover possible interactions between an individual patient’s characteristics and generate a robust model capable of predicting said patient’s treatment outcome. Methods: We investigated a cohort of 32 prospective patients from multiple institutions whom had received curative SBRT to the lung. The number of patients exhibiting tumor failure was observed to be 7 (event rate of 22%). The serum concentration of 5 biomarkers previously associated with NSCLC (non-small cell lung cancer) was measured pre-treatment. A total of 21 variables were analyzed including: dose-volume metrics with BED (biologically effective dose) correction and clinical variables. A Markov Chain Monte Carlo technique estimated the posterior probability distribution of the potential graphical structures. The probability of tumor failure was then estimated by averaging the top 100 graphs and applying Baye’s rule. Results: The optimal Bayesian model generated throughout this study incorporated the PTV volume, the serum concentration of the biomarker EGFR (epidermal growth factor receptor) and prescription BED. This predictive model recorded an area under the receiver operating characteristic curve of 0.94(1), providing better performance compared to competing methods in other literature. Conclusion: The use of biomarkers in conjunction with dose-volume metrics allows for the generation of a robust predictive model. The preliminary results of this report demonstrate that it is possible to accurately model the prognosis of an individual lung SBRT patient’s treatment.
Inverse design of photonic surfaces via multi fidelity ensemble framework and femtosecond laser processing
We demonstrate a multi-fidelity (MF) machine learning ensemble framework for the inverse design of photonic surfaces, trained on a dataset of 11,759 samples that we fabricate using high throughput femtosecond laser processing. The MF ensemble combines an initial low fidelity model for generating design solutions, with a high fidelity model that refines these solutions through local optimization. The combined MF ensemble can generate multiple disparate sets of laser-processing parameters that can each produce the same target input spectral emissivity with high accuracy (root mean squared errors < 2%). SHapley Additive exPlanations analysis shows transparent model interpretability of the complex relationship between laser parameters and spectral emissivity. Finally, the MF ensemble is experimentally validated by fabricating and evaluating photonic surface designs that it generates for improved efficiency energy harvesting devices. Our approach provides a powerful tool for advancing the inverse design of photonic surfaces in energy harvesting applications.
Data and Code for Understanding Generative AI Content with Embedding Models
This repository contains code for the experiments in the paper "Understanding Generative AI Content with Embedding Models". Constructing high-quality features is critical to any quantitative data analysis. While feature engineering was historically addressed by carefully hand-crafting data representations based on domain expertise, deep neural networks (DNNs) now offer a radically different approach. DNNs implicitly engineer features by transforming their input data into hidden feature vectors called embeddings. For embedding vectors produced by foundation models -- which are trained to be useful across many contexts -- we demonstrate that simple and well-studied dimensionality-reduction techniques such as Principal Component Analysis uncover inherent heterogeneity in input data concordant with human-understandable explanations. Of the many applications for this framework, we find empirical evidence that there is intrinsic separability between real samples and those generated by artificial intelligence (AI).
Unit Commitment With Continuous-Time Generation and Ramping Trajectory Models
There is increasing evidence of shortage of ramping resources in the real-time operation of power systems. To explain and remedy this problem systematically, we take a novel look at the way the day-ahead unit commitment (UC) problem represents the information about load, generation and ramping constraints. We specifically investigate the approximation error made in mapping of the original problem, that would decide the continuous-time generation and ramping trajectories of the committed generating units, onto the discrete-time problem that is solved in practice. We first show that current practice amounts to approximating the trajectories with linear splines. We then offer a different representation through cubic splines that provides physically feasible schedules and increases the accuracy of the continuous-time generation and ramping trajectories by capturing sub-hourly variations and ramping of load in the day-ahead power system operation. The corresponding day-ahead UC model is formulated as an instance of mixed-integer linear programming (MILP), with the same number of binary variables as the traditional UC formulation. Numerical simulation over real load data from the California ISO demonstrate that the proposed UC model reduces the total day-ahead and real-time operation cost, and the number of events of ramping scarcity in the real-time operations.
Hydropower Energy Storage Capacity (HESC) Dataset
The Hydropower Energy Storage Capacity (HESC) Dataset catalogs characteristics that are relevant to evaluating reservoir storage and estimates of energy storage capacity based on varying levels of detail. Hydropower dams and reservoirs were included based on information from the National Inventory of Dams (NID; USACE, 2021) and Global Reservoir and Dam (GRanD v1.3) and Existing Hydropower Assets datasets. These data provide a foundation for understanding available resources at existing hydropower facilities and their potential to provide storage of energy and more flexible generation. Estimates of energy storage capacity include: • Level 1 – nominal energy storage capacity based on maximum storage capacities and hydraulic head • Level 2 – nominal energy storage capacity based on historical models or observations of reservoir volume and hydraulic head. These estimates are provided based on capacity from the entire historical period as well as monthly values. • Level 3 – modeled energy generation based on volume-elevation relationships, historical storage, observed/modeled inflows, and hydraulic capacity of turbines and calculated both as overall and on a monthly basis. • Level 4 – modeled energy generation incorporating information from Level 3 and operational constraints. For facilities where installed capacity is known, there are also estimates for discharge duration (the length of time when a facility could provide generation at a given capacity).
A Mathematical Analysis of the R-MAT Random Graph Generator
The R-MAT graph generator introduced by Chakrabarti, Faloutsos, and Zhan offers a simple, fast method for generating very large directed graphs. These properties have made it a popular choice as a method of generating graphs for objects of study in a variety of disciplines, from social network analysis to high performance computing. We analyze the graphs generated by R-MAT and model the generator in terms of occupancy problems in order to prove results about the degree distributions of these graphs. We prove that the limiting degree distributions can be expressed as a mixture of normal distributions, contradicting the widely held belief that R-MAT degree distributions exhibit the power law or scale free distribution observed in many real world graphs. Additionally, this paper offers an efficient computational technique for computing the exact degree distribution, as well as concise expressions for a number of properties of R-MAT graphs.
An experimental and modeling study on autoignition of 2-phenylethanol and its blends with n-heptane
2-Phenylethanol (2-PE) is an aromatic alcohol with high research octane number, high octane sensitivity, and a potential to be produced using biomass. Considering that 2-PE can be used as a fuel additive for boosting the anti-knocking quality of gasoline in spark-ignition engines and as the low reactivity fuel or fuel component in dual-fuel reactivity controlled compression ignition (RCCI) engines, it is of fundamental and practical interest to understand the autoignition chemistry of 2-PE, especially at low-to-intermediate temperatures (<1000 K). Based upon the experimental ignition delay time (IDT) results of neat 2-PE obtained from our previous rapid compression machine (RCM) investigation and the literature shock tube study, a detailed chemical kinetic model of 2-PE is developed herein, covering low-to-high temperature regimes. Besides, RCM experiments using binary fuel blends of 2-PE and n-heptane (nC7) are conducted in this work to investigate the nC7/2-PE blending effects, as they represent a dual-fuel system for RCCI operations. Furthermore, the newly developed 2-PE model is merged with a well-validated nC7 kinetic model to generate the current nC7/2-PE binary blend model. Overall, the consolidated model reasonably predicts the experimental IDT data of neat 2-PE and nC7/2-PE blends, as well as captures the experimental effects of pressure, equivalence ratio, and blending ratio on autoignition. Finally, model-based chemical kinetic analyses are carried out to understand and identify the controlling chemistry accounting for the observed blending effects in RCM experiments. In conclusion, the analyses reveal that nC7 enhances 2-PE autoignition via providing extra $\dot{O}$H radicals to the shared radical pool, while the diminished nC7 promoting effect on 2-PE autoignition with increasing temperature is due to the negative temperature coefficient characteristics of nC7.
Large Ensemble Exploration of Global Energy Transitions Under National Emissions Pledges
Global climate goals require a transition to a deeply decarbonized energy system. Meeting the objectives of the Paris Agreement through countries' nationally determined contributions and long-term strategies represents a complex problem with consequences across multiple systems shrouded by deep uncertainty. Robust, large-ensemble methods and analyses mapping a wide range of possible future states of the world are needed to help policymakers design effective strategies to meet emissions reduction goals. This study contributes a scenario discovery analysis applied to a large ensemble of 5,760 model realizations generated using the Global Change Analysis Model. Eleven energy-related uncertainties are systematically varied, representing national mitigation pledges, institutional factors, and techno-economic parameters, among others. The resulting ensemble maps how uncertainties impact common energy system metrics used to characterize national and global pathways toward deep decarbonization. Results show globally consistent but regionally variable energy transitions as measured by multiple metrics, including electricity costs and stranded assets. Larger economies and developing regions experience more severe economic outcomes across a broad sampling of uncertainty. The scale of CO 2 removal globally determines how much the energy system can continue to emit, but the relative role of different CO 2 removal options in meeting decarbonization goals varies across regions. Previous studies characterizing uncertainty have typically focused on a few scenarios, and other large-ensemble work has not (to our knowledge) combined this framework with national emissions pledges or institutional factors. Our results underscore the value of large-ensemble scenario discovery for decision support as countries begin to design strategies to meet their goals.