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- Creator:
- Towne, Aaron and Dawson, Scott
- Description:
- This dataset contains data from direct numerical simulations of two-dimensional stationary and pitching flat-plate airfoils at Reynolds number 100. The dataset contains time-resolved snapshots of the velocity field, lift and drag coefficients, and airfoil kinematics spanning 40-100 convective time units. Cases include a stationary airfoil and eight different pitching frequencies. All data are stored within hdf5 files, and a Matlab script showing how the data can be read and manipulated is provided. Please see the ‘airfoilDNS_README.pdf’ file for more information. We recommend using the ‘airfoilDNS_example.zip’ file as an entry point to the dataset. and The dataset is part of “A database for reduced-complexity modeling of fluid flows” (see references below) and is intended to aid in the conception, training, demonstration, evaluation, and comparison of reduced-complexity models for fluid mechanics. The paper introduces the flow setup and computational methods, describes the available data, and provides an example of how these data can be used for reduced-complexity modeling. Users of these data should cite the papers listed below.
- Keyword:
- fluid mechanics and aerodynamics
- Citation to related publication:
- Towne, A., Dawson, S., Brès, G. A., Lozano-Durán, A., Saxton-Fox, T., Parthasarthy, A., Biler, H., Jones, A. R., Yeh, C.-A., Patel, H., Taira, K. (2022). A database for reduced-complexity modeling of fluid flows. AIAA Journal 61(7): 2867-2892. and Dawson, S. T. M., Floryan, D. C., Rowley, C. W., and Hemati, M. S. (2016) Lift enhancement of high angle of attack airfoils using periodic pitching. AIAA Paper 2016-2069.
- Discipline:
- Engineering and Science
-
- Creator:
- Towne, Aaron S. and Lozano-Durán, Adrián
- Description:
- This dataset contains data from two direct numerical simulations of a turbulent zero-pressure-gradient flat-plate boundary layer spanning friction Reynolds numbers from 292 to 728 (BL1) and 488 to 1024 (BL2). The dataset contains time-resolved snapshots of the three-dimensional velocity field for both cases: roughly 10,000 snapshots spanning 20 eddy-turnover times for BL1 and 7,500 snapshots spanning 7 eddy-turnover times for BL2 . Also included for both cases are pre-processed correlations at several wall-normal distances, mean and root-mean-squared velocity and vorticity profiles, several boundary-layer metrics, and time-resolved velocity data in the streamwise-wall-normal plane. All data are stored within hdf5 files, and a Matlab script showing how the data can be read and manipulated is provided. Please see the ‘BLdns_README.pdf’ file for more information. We recommend using the ‘BLdns_example.zip’ file as an entry point to the dataset. and The dataset is part of “A database for reduced-complexity modeling of fluid flows” (see references below) and is intended to aid in the conception, training, demonstration, evaluation, and comparison of reduced-complexity models for fluid mechanics. The paper introduces the flow setup and computational methods, describes the available data, and provides an example of how these data can be used for reduced-complexity modeling. Users of these data should cite the paper listed below.
- Keyword:
- fluid mechanics, boundary layer, and turbulence
- Citation to related publication:
- Towne, A., Dawson, S., Brès, G. A., Lozano-Durán, A., Saxton-Fox, T., Parthasarthy, A., Biler, H., Jones, A. R., Yeh, C.-A., Patel, H., Taira, K. (2022). A database for reduced-complexity modeling of fluid flows. AIAA Journal 61(7): 2867-2892.
- Discipline:
- Engineering and Science
-
- Creator:
- Towne, Aaron, Saxton-Fox, Theresa, and Parthasarthy, Aadhy
- Description:
- This dataset contains experimental measurements of a zero-pressure-gradient flat-plate boundary layer at five different Reynolds numbers collected using particle image velocimetry. For each Reynolds number, the dataset contains approximately 6000 snapshots of planar velocity fields as well as raw particle image pairs. All data are stored within hdf5 files, and a Matlab script showing how the data can be read and manipulated is provided. Please see the ‘BLexp_README.pdf’ file for more information. We recommend using the ‘BLexp_example.zip’ file as an entry point to the dataset. and The dataset is part of “A database for reduced-complexity modeling of fluid flows” (see references below) and is intended to aid in the conception, training, demonstration, evaluation, and comparison of reduced-complexity models for fluid mechanics. The paper introduces the flow setup and computational methods, describes the available data, and provides an example of how these data can be used for reduced-complexity modeling. Users of these data should cite the papers listed below.
- Keyword:
- fluid mechanics
- Citation to related publication:
- Towne, A., Dawson, S., Brès, G. A., Lozano-Durán, A., Saxton-Fox, T., Parthasarthy, A., Biler, H., Jones, A. R., Yeh, C.-A., Patel, H., Taira, K. (2022). A database for reduced-complexity modeling of fluid flows. AIAA Journal 61(7): 2867-2892.
- Discipline:
- Engineering and Science
-
- Creator:
- Towne, Aaron S. and Brès, Guillaume
- Description:
- This dataset contains data from a large eddy simulation of a turbulent jet at Mach number 0.9. The dataset contains 10000 time-resolved snapshots of three-dimensional velocity, density, and pressure fields spanning 2000 acoustic time units and also includes pre-processed azimuthal Fourier modes for each snapshot and the mean flow. All data are stored within hdf5 files, and a Matlab script showing how the data can be read and manipulated is provided. Please see the ‘jet_README.pdf’ file for more information. We recommend using the ‘jet_example.zip’ file as an entry point to the dataset. and The dataset is part of “A database for reduced-complexity modeling of fluid flows” (see references below) and is intended to aid in the conception, training, demonstration, evaluation, and comparison of reduced-complexity models for fluid mechanics. The paper introduces the flow setup and computational methods, describes the available data, and provides two examples of how these data can be used for reduced-complexity modeling. Users of these data should cite the two papers listed below.
- Keyword:
- fluid mechanics, jets, and turbulence
- Citation to related publication:
- Towne, A., Dawson, S., Brès, G. A., Lozano-Durán, A., Saxton-Fox, T., Parthasarthy, A., Biler, H., Jones, A. R., Yeh, C.-A., Patel, H., Taira, K. (2022). A database for reduced-complexity modeling of fluid flows. AIAA Journal 61(7): 2867-2892. and Brès, G. A., Jordan, P., Jaunet, V., Le Rallic, M., Cavalieri, A. V. G., Towne, A., Lele, S. K., Colonius, T., Schmidt, O. T. (2018) Importance of the nozzle-exit boundary-layer state in subsonic turbulent jets. J. Fluid Mech., 851:83–124.
- Discipline:
- Engineering and Science
-
- Creator:
- Ruas, Terry, Ferreira, Charles H. P., Grosky, William, França, Fabrício O., and Medeiros, Débora M. R,
- Description:
- The relationship between words in a sentence often tell us more about the underlying semantic content of a document than its actual words, individually. Recent publications in the natural language processing arena, more specifically using word embeddings, try to incorporate semantic aspects into their word vector representation by considering the context of words and how they are distributed in a document collection. In this work, we propose two novel algorithms, called Flexible Lexical Chain II and Fixed Lexical Chain II that combine the semantic relations derived from lexical chains, prior knowledge from lexical databases, and the robustness of the distributional hypothesis in word embeddings into a single decoupled system. In short, our approach has three main contributions: (i) unsupervised techniques that fully integrate word embeddings and lexical chains; (ii) a more solid semantic representation that considers the latent relation between words in a document; and (iii) lightweight word embeddings models that can be extended to any natural language task. Knowledge-based systems that use natural language text can benefit from our approach to mitigate ambiguous semantic representations provided by traditional statistical approaches. The proposed techniques are tested against seven word embeddings algorithms using five different machine learning classifiers over six scenarios in the document classification task. Our results show that the integration between lexical chains and word embeddings representations sustain state-of-the-art results, even against more complex systems. Github: https://github.com/truas/LexicalChain_Builder
- Keyword:
- document classification, lexical chains, word embeddings, synset embeddings, chain2vec, and natural language processing
- Citation to related publication:
- Terry Ruas, Charles Henrique Porto Ferreira, William Grosky, Fabrício Olivetti de França, Débora Maria Rossi de Medeiros, "Enhanced word embeddings using multi-semantic representation through lexical chains", Information Sciences, 2020, https://doi.org/10.1016/j.ins.2020.04.048
- Discipline:
- Other, Science, and Engineering
-
- Creator:
- Ponder, Brandon M., Ridley, Aaron J., Goel, Ankit, and Bernstein, Dennis S.
- Description:
- This research was completed to statistically validate that a data-model refinement technique could integrate real measurements to remove bias from physics-based models via changing the forcing parameters such as the thermal conductivity coefficients.
- Keyword:
- Thermosphere, GITM, CHAMP, GRACE, MSIS, Upper Atmosphere Modeling, and Data Assimilation
- Citation to related publication:
- Ponder, B. M., Ridley, A. J., Goel, A., & Bernstein, D. S. (2023). Improving forecasting ability of GITM using data-driven model refinement. Space Weather, 21, e2022SW003290. https://doi.org/10.1029/2022SW003290
- Discipline:
- Engineering and Science
-
- Creator:
- Thompson, Ellen P. and Ellis, Brian R.
- Description:
- Accurate prediction of physical alterations in carbonate reservoirs under dissolution is critical for development of subsurface energy technologies. The impact of mineral dissolution on flow characteristics depends on the connectivity and tortuosity of the pore network. Persistent homology is a tool from algebraic topology that describes the size and connectivity of topological features. When applied to 3D X-ray computed tomography (XCT) imagery of rock cores, it provides a novel metric of pore network heterogeneity. Prior works have demonstrated the efficacy of persistent homology in predicting flow properties in numerical simulations of flow through porous media. Its ability to combine size, spatial distribution, and connectivity information make it a promising tool for understanding reactive transport in complex pore networks, yet limited work has been done to apply persistence analysis to experimental studies on natural rocks. In this study, three limestone cores were imaged by XCT before and after acid-driven dissolution flow through experiments. Each XCT scan was analyzed using persistent homology. In all three rocks, permeability increase was driven by the growth of large, connected pore bodies. The two most homogenous samples saw an increased effect nearer to the flow inlet, suggesting emerging preferential flow paths as the reaction front progresses. The most heterogeneous sample showed an increase in along-core homogeneity during reaction. Variability of persistence showed moderate positive correlation with pore body size increase. Persistence heterogeneity analysis could be used to anticipate where greatest pore size evolution may occur in a reservoir targeted for subsurface development, improving confidence in project viability.
- Keyword:
- Carbonate dissolution, X-ray computed tomography, Porous media, Topology, and Persistent homology
- Citation to related publication:
- Thompson, E.P.; Ellis, B.R. (2023) Persistent Homology as a Heterogeneity Metric for Predicting Pore Size Change in Dissolving Carbonates. In Review.
- Discipline:
- Science and Engineering
-
- Creator:
- Limon, Garrett C.
- Description:
- The work guides the processing of CAM6 data for use in machine learning applications. We also provide workflow scripts for training both random forests and neural networks to emulate physic s schemes from the data, as well as analysis scripts written in both Python and NCL in order to process our results.
- Keyword:
- Machine Learning, Climate Modeling, and Physics Emulation
- Citation to related publication:
- Limon, G. C., Jablonowski, C. (2022) Probing the Skill of Random Forest Emulators for Physical Parameterizations via a Hierarchy of Simple CAM6 Configurations [Pre Print]. ESSOAr. https://10.1002/essoar.10512353.1
- Discipline:
- Engineering and Science
-
- Creator:
- Limon, Garrett C.
- Description:
- The data represents weekly output from three 60-year CAM6 model runs. The output includes state (.h0. files) and tendency (.h1. files) fields for three difference model configurations of increasing complexity. State fields include temperature, surface pressure, specific humidity, among others; while tendencies include temperature tendencies, specific humidity tendencies, as well as precipitation rates. Using the state variables at a given time step, machine learning techniques can be trained to predict the following tendency field, which can then be applied to the state variables to provide the state at the next physics time step of the model.
- Keyword:
- Machine Learning, Climate Modeling, and Physics Emulation
- Citation to related publication:
- Limon, G. C., Jablonowski, C. (2022) Probing the Skill of Random Forest Emulators for Physical Parameterizations via a Hierarchy of Simple CAM6 Configurations [Preprint]. ESSOAr. https://10.1002/essoar.10512353.1
- Discipline:
- Engineering and Science
-
- Creator:
- Szuromi, Matthew P. and Stacey, William C.
- Description:
- The data and scripts are meant to show how burster dynamics determine response to a single biphasic stimulus. The files include data which show trends in the propensity of termination for different burster types and the MATLAB scripts used to generate this data. The MATLAB scripts also allow the user to generate their own data sets for alternative bursting paths and stimulus parameter combinations. Furthermore, they allow the user to visually examine the effects of single stimuli in the voltage timeseries and in state space. How the user can access these features of the script is described in the file "ReadMe.pdf."
- Keyword:
- Epilepsy, Stimulation, Modelling, Dynamics, Seizure, and Dynamotype
- Citation to related publication:
- (PROVISIONAL) Optimization of Ictal Aborting Stimulation Using the Dynamotype Taxonomy
- Discipline:
- Health Sciences, Engineering, and Science