Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Python software”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Scoping Study of Effects on the Advanced Test Reactor’s Center Flux Trap Due to Various Loadings in the H and Inner A Test Positions

This study determined the effects experienced by the In-Pile Tube, of the Advanced Test Reactor’s center flux trap, due to various loading configurations of the H and Inner A positions. The effects of concern included: the axial profile of the linear heat generation rate of each fuel rodlet, the axial flux shape, and spectrum, in the fuel, and cladding, of each rodlet, and the total fission heating rate of the entire In-Pile Tube. Additionally, the reactivity worth of each configuration, relative to an ATR configuration with water filling all concerning test positions, was determined. The test positions were filled with commonly used test specimens, backup test specimens, and homogeneous substances. The study was performed using the modeling software MCNP6.2.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

ppdx : Automated modeling of protein–protein interaction descriptors for use with machine learning

This paper describes ppdx, a python workflow tool that combines protein sequence alignment, homology modeling, and structural refinement, to compute a broad array of descriptors for characterizing protein–protein interactions. The descriptors can be used to predict various properties of interest, such as protein–protein binding affinities, or inhibitory concentrations (IC 50 ), using approaches that range from simple regression to more complex machine learning models. The software is highly modular. It supports different protocols for generating structures, and 95 descriptors can be currently computed. More protocols and descriptors can be easily added. The implementation is highly parallel and can fully exploit the available cores in a single workstation, or multiple nodes on a supercomputer, allowing many systems to be analyzed simultaneously. As an illustrative application, ppdx is used to parametrize a model that predicts the IC 50 of a set of antigens and a class of antibodies directed to the influenza hemagglutinin stalk.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

CHESS 2025: Leaf Area Index (LAI) for meadow, shrub, tree, and understory vegetation

This dataset contains Leaf Area Index (LAI) measurements made as part of the Colorado Headwaters Ecological Spectroscopy Study (CHESS) during June and July of 2025. Data were collected in the Upper Gunnison Basin, Colorado, across three study domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). Field observations of LAI were collected within 72 hours of airborne data collection by the National Ecological Observatory Network’s Aerial Observation Platform (NEON AOP). The NEON AOP collected waveform LiDAR (Light Detection and Ranging) and imaging spectrometer data in 426 spectral bands from the visible to shortwave infrared. LAI measurements were collected using the LICOR LAI-2200C Plant Canopy Analyzer following protocols outlined in the instrument manual (LI-COR 2019). Sampling targeted four distinct vegetation types: meadows, shrubs, trees, and aspen forest understory. We have archived data separately by site type because different field methods were used for each. At meadow sites, measurements were made at the four corners of 1m x 1m plots, with the instrument moving inward toward the center of the plot. At shrub sites, we measured the canopies of individual shrubs. At tree sites, we made measurements within a 10m x 10m subplot centered around a focal tree, with 30 observations taken on a regular grid. At aspen understory sites, we measured overstory trees following the tree protocol and understory herbaceous vegetation following the meadow protocol. All measurements included above-canopy (A) and below-canopy (B) readings, with specific protocols for scattering correction measurements in direct-sun conditions. Data were processed using the R package `rlai` (Worsham 2025). This package includes functions to calculate LAI, gap fraction, apparent clumping factor (Ω), scattering correction, and other canopy metrics. Package contents: Full file descriptions appear in ‘flmd.csv’. Files named according to the convention ‘lai_*_summary_data_cleaned.csv’ contain summary values of LAI, apparent clumping factor (Ωapp), and scattering correction factors for each site. These are the analysis-ready products that most data users will work with. Files named ‘lai_*_metadata_cleaned.csv’ contain additional site-level observations made during field collection. We have also archived intermediate and supplementary data for users who wish to check our processing approach or apply alternative methods. ‘raw_lai_2200C.zip’ contains the raw files as read from the LI-COR instrument, with no processing applied, in TXT format. The zip archive contains subdirectories by site type, which are further subdivided by sampling area. Filenames correspond to the sampling site number. ‘intermediate_results.zip’ contains detailed output from the processing routines, in JSON format. The zip archive contains subdirectories by site type; filenames correspond to the sampling site number. ‘scattering_correction_logs.zip’ contains logfiles from the implementation of Kobayashi et al.'s (2013) scattering correction algorithm. The logfiles report values of several parameters at each iteration of the algorithm, as the model converges toward a stable solution. They are intended for users who want to verify scattering correction performance. The zip archive contains subdirectories by site type; filenames correspond to the sampling site number. ‘spot_checks.csv’ reports LAI and other values for a small number of files processed with LI-COR FV2200 software (LI-COR 2013) using the same control parameters as in our R-based approach. Additional metadata are provided in a data dictionary describing column names and definitions (dd.csv), and in a file-level metadata file (flmd.csv). All zip files can be expanded with common archive utilities. TXT, CSV, and JSON files can be ingested into R or Python computing environments or read in common text editor utilities. Geospatial information: Geospatial data for mapping measurement site locations are in the files CHESS_polygons_lai_UTM.geojson, CHESS_polygons_shrub_UTM.geojson, and CHESS_polygons_meadow_UTM.geojson in the companion geospatial package for the 2025 CHESS campaign, ‘CHESS 2025: Location data for field observations and sampling’ (Henderson et al., 2026). CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. * Todorov and Worsham are co–first authors.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2022

GIS-Based Graphical User Interface Tools for Analyzing Solar Thermal Desalination Systems & High-Potential Implementation

This project developed a user-friendly, open-source, software that enables a comparative evaluation of solar thermal desalination technology options and employs geospatial data layers to identify regions of high-potential for solar thermal desalination. This was accomplished by integrating solar models with desalination models and enhancing their utility by providing GIS-based data inputs. The developed Solar Energy Desalination Analysis Tool (SEDAT) enables techno-economical evaluation of desalination technologies and selection of regions with the highest potential for using solar energy to power desalination plants. It simplifies the planning, design, and valuation of solar thermal and solar hybrid desalination systems in the U.S. and worldwide. SEDAT uses Dash for integrating various layers of large volumes of GIS data with Python-based models of solar energy generation and desalination technologies. It derives time-series of energy generation and water production, with details of plant performance and suggestions for improving the solar-desalination coupling. It is a one of-a-kind tool of analysis representing a definitive advancement in the state-of-the-art. Of solar desalination modeling This report summarizes the various phases of the tool’s development, and presents examples of the results.

14 SOLAR ENERGY↗
DOE OSTI2021

PyKrev: A Python Library for the Analysis of Complex Mixture FT-MS Data

In this study, we present PyKrev, a Python library for the analysis of complex mixture Fourier transform mass spectrometry (FT-MS) data. PyKrev is a comprehensive suite of tools for analysis and visualization of FT-MS data after formula assignment has been performed. These comprise formula manipulation and calculation of chemical properties, intersection analysis between multiple lists of formulas, calculation of chemical diversity, assignment of compound classes to formulas, multivariate analysis, and a variety of visualization tools producing van Krevelen diagrams, class histograms, PCA score, and loading plots, biplots, scree plots, and UpSet plots. The library is showcased through analysis of hot water green tea extracts and Scotch whisky FT-ion cyclotron resonance-MS data sets. PyKrev addresses the lack of a single, cohesive toolset for researchers to perform FT-MS analysis in the Python programming environment encompassing the most recent data analysis techniques used in the field.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

CEGANN: CRYSTAL EDGE GRAPH ATTENTION NEURAL NETWORK

SF-22-156 Machine learning (ML) models and applications in materials design and discovery typically involve the use of feature representations or descriptors followed by a learning algorithm that maps them to user desired properties of interest. Most popular mathematical formulation-based descriptors are not unique across atomic environments and suffer from transferability issues across different application domains and/or material classes. The CEGANN code provides a unified interface to facilitate material characterization across materials across multiple scales (from atomic to mesoscale) and diverse classes of materials ranging from metals oxides, non-metals, and even hierarchical materials such as zeolites and semi ordered materials such as mesophases. CEGANN implements a Graph Attention Network (GAT) type convolution architecture. The details of network architecture can be found in the paper https://doi.org/10.48550/arXiv.2207.10168. The software comes with pretrained examples and dataset for the classification of the following representative systems: (1) Structure-level representation such as space group (2) Structural dimensionality (e.g., bulk, 2D, clusters etc.) (3) Grain boundary identification (4) Nucleation and growth of a zeolite polymorph (5) Characterization of binary mesophases and their phase transitions (6) Growth of ice. The code is written in python programming language.

CHAN, HENRYT↗
DOE OSTI2021

Data processing methods and data acquisition for samples larger than the field of view in parallel-beam tomography

Parallel-beam tomography systems at synchrotron facilities have limited field of view (FOV) determined by the available beam size and detector system coverage. Scanning the full size of samples bigger than the FOV requires various data acquisition schemes such as grid scan, 360-degree scan with offset center-of-rotation (COR), helical scan, or combinations of these schemes. Though straightforward to implement, these scanning techniques have not often been used due to the lack of software and methods to process such types of data in an easy and automated fashion. The ease of use and automation is critical at synchrotron facilities where using visual inspection in data processing steps such as image stitching, COR determination, or helical data conversion is impractical due to the large size of datasets. Here, we provide methods and their implementations in a Python package, named Algotom, for not only processing such data types but also with the highest quality possible. The efficiency and ease of use of these tools can help to extend applications of parallel-beam tomography systems.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Hybrid-BPR (Bayesian Personalized Ranking with Feature Embeddings and Explicit Negative Sampling) [SWR-26-039]

Hybrid-BPR is a Python library for Bayesian Personalized Ranking (BPR) with two key capabilities that go beyond standard BPR implementations: 1. User and item feature embeddings - incorporate content-based signals (genres, tags, metadata) alongside collaborative filtering. 2. Implicit negative interactions - use observed non-interactions (e.g. viewed-but-not-clicked) as negative training signal instead of random sampling from the full item space. The software is built for recommender systems research with MLflow experiment tracking, parallel hyperparameter sweeps, and standard ranking metrics.

Sandhu, Rimple [National Laboratory of the Rockies↗
DOE OSTI2025

Accessible, uniform protein property prediction with a scikit-learn based toolset AIDE

Summary Protein property prediction via machine learning with and without labeled data is becoming increasingly powerful, yet methods are disparate and capabilities vary widely over applications. The software presented here, “Artificial Intelligence Driven protein Estimation (AIDE)”, enables instantiating, optimizing, and testing many zero-shot and supervised property prediction methods for variants and variable length homologs in a single, reproducible notebook or script by defining a modular, standardized application programming interface (API), i.e. drop-in compatible with scikit-learn transformers and pipelines. Availability and implementation AIDE is an installable, importable python package inheriting from scikit-learn classes and API and is installable on Windows, Mac, and Linux. Many of the wrapped models internal to AIDE will be effectively inaccessible without a GPU, and some assume CUDA. The newest stable, tested version can be found at https://github.com/beckham-lab/aide_predict and a full user guide and API reference can be found at https://beckham-lab.github.io/aide_predict/. Static versions of both at the time of writing can be found on Zenodo.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Mesh Computing Remote Automatic Workflow

The software suite uses a microservice architecture using Docker and `docker-compose`. The microservices are as follows: 1. User interface. This interface is written in JavaScript using the Svelte framework. It exposes form elements and a 3D visualizer to prompt the user through the definition of microstructure parameters, and setting parameters for mesh generation and refinement. 2. Mesh generator. This is a container running the Python package for DREAM3D to generate a voxelized mesh that represents a microstructure defined by the user in the interface. 3. Cubit runner. This is a secure shell protocol tool that makes the submitting the DREAM mesh to an HPC instance and starts to run Cubit shell commands to smooth the grain boundaries with its `sculpt` library, applies user-defined boundary node sets, and bundles and returns the simulation-ready meshes and input files as a zipped directory.

Harris, BrennanKay↗
DOE OSTI2025

MCOR User Guide

User guide for the MCOR software package which is currently publicly hosted on Github (https://github.com/pnnl/MCOR). The Microgrid Component Optimization for Resilience (MCOR) tool simulates the operation of a renewable energy, battery, and back-up generator microgrid under a large range of outage conditions to understand how a potential system can meet the resilience goals of a particular site. It is an open-source, command line, Python-based tool that produces an output Excel spreadsheet as well as several types of plots to enable a user to compare different microgrid system sizes and costs. It is intended for high-level system planning and opportunity identification, and not for detailed electric system modeling and design. The tool includes a range of input parameters that can be adjusted or tuned to provide a more custom analysis as needed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Performance Debugging and Tuning of Flash-X with Data Analysis Tools

State-of-the-art multiphysics simulations running on large scale leadership computing platforms have many variables contributing to their performance and scaling behavior. We recently encountered an interesting performance anomaly in Flash-X, a multiphysics multicomponent simulation software, when characterizing its performance behavior on several large-scale HPC platforms. The anomaly was tracked down to the interaction between the use of dynamic allocation of scratch data and data locality in the cache hierarchy. In this paper we present the details of unexpected performance variability of Flash-X, its extensive analysis using the performance measurement tool TAU to collect the data and Python data analysis libraries to explore the data, and our insights from this experience. In this process, we discovered and removed or mitigated two additional performance limiting bottlenecks for performance tuning.

Huck, Kevin↗
DOE OSTI2022

BLAST aka: BLAST-Py see also BLAST-Lite (Battery Lifetime Analysis and Simulation Tool Suite - Python) [SWR-22-69]

Battery Lifetime Analysis and Simulation Tool Suite (BLAST or BLAST-Py) developed in the Python programming language. BLAST-Py predicts the evolution of lithium-ion battery performance metrics over their lifetime, using models trained on lab-based accelerated aging data to predict battery performance in dynamic, real-world use. BLAST-Py contains existing models for a variety of lithium-ion battery chemistries (NMC/Gr and LFP/Gr). See also the open-source version of this software tool known as "BLAST-Lite" at: https://github.com/NREL/BLAST-Lite

Smith, Kandler↗
DOE OSTI2024

AMReX and pyAMReX: Looking beyond the exascale computing project

AMReX is a software framework for the development of block-structured mesh applications with adaptive mesh refinement (AMR). AMReX was initially developed and supported by the AMReX Co-Design Center as part of the U.S. DOE Exascale Computing Project (ECP), and is continuing to grow post-ECP. In addition to adding new functionality and performance improvements to the core AMReX framework, we have also developed a Python binding, pyAMReX, that provides a bridge between AMReX-based application codes and the data science ecosystem. pyAMReX provides zero-copy application GPU data access for AI/ML, in situ analysis and application coupling, and enables rapid, massively parallel prototyping. In this paper we review the overall functionality of AMReX and pyAMReX, focusing on new developments, new functionality, and optimizations of key operations. We also summarize capabilities of ECP projects that used AMReX and provide an overview of new, non-ECP applications.

Myers, Andrew↗
DOE OSTI2023

Editorial: Neuroscience, computing, performance, and benchmarks: Why it matters to neuroscience how fast we can compute

At the turn of the millennium the computational neuroscience community realized that neuroscience was in a software crisis: software development was no longer progressing as expected and reproducibility declined. The International Neuroinformatics Coordinating Facility (INCF) was inaugurated in 2007 as an initiative to improve this situation. The INCF has since pursued its mission to help the development of standards and best practices. In a community paper published this very same year, Brette et al. tried to assess the state of the field and to establish a scientific approach to simulation technology, addressing foundational topics, such as which simulation schemes are best suited for the types of models we see in neuroscience. In 2015, a Frontiers Research Topic “Python in neuroscience” by Muller et al. triggered and documented a revolution in the neuroscience community, namely in the usage of the scripting language Python as a common language for interfacing with simulation codes and connecting between applications. The review by Einevoll et al. documented that simulation tools have since further matured and become reliable research instruments used by many scientific groups for their respective questions. Open source and community standard simulators today allow research groups to focus on their scientific questions and leave the details of the computational work to the community of simulator developers. A parallel development has occurred, which has been barely visible in neuroscientific circles beyond the community of simulator developers: Supercomputers used for large and complex scientific calculations have increased their performance from ~10 TeraFLOPS (10 13 floating point operations per second) in the early 2000s to above 1 ExaFLOPS (10 18 floating point operations per second) in the year 2022. This represents a 100,000-fold increase in our computational capabilities, or almost 17 doublings of computational capability in 22 years. Moore's law (the observation that it is economically viable to double the number of transistors in an integrated circuit every other 18–24 months) explains a part of this; our ability and willingness to build and operate physically larger computers, explains another part. It should be clear, however, that such a technological advancement requires software adaptations and under the hood, simulators had to reinvent themselves and change substantially to embrace this technological opportunity. It actually is quite remarkable that—apart from the change in semantics for the parallelization—this has mostly happened without the users knowing. The current Research Topic was motivated by the wish to assemble an update on the state of neuroscientific software (mostly simulators) in 2022, to assess whether we can see more clearly which scientific questions can (or cannot) be asked due to our increased capability of simulation, and also to anticipate whether and for how long we can expect this increase of computational capabilities to continue.

biophysically detailed models↗
DOE OSTI2022

Real-Time Drilling Optimization System for Improved Overall Rate of Penetration and Reduced Cost Per Foot in Geothermal Drilling

The key to success in geothermal drilling is economic feasibility, and a major cost in the development of geothermal resources is the actual drilling of the wells. In this project, a real-time drilling optimization system for geothermal drilling was developed. The system couples three individual components while drilling. The first component is a drill stem vibration analysis model, the second is Mechanical Specific Energy (MSE) analyses, and the third is a detailed PDC Rate of Penetration (ROP) drill bit model for optimum RPM and WOB combinations. The benefit of the coupled system is that the range of WOB and RPM could be selected to avoid drill stem vibrations. Secondly, MSE is used as an efficiency measure and the detailed PDC drill bit model ensures the drill bit does not endure temperatures that exceed the temperature at which the PDC cutters experience accelerated wear. The new detailed PDC bit model is based on rock/bit interaction that physically tracks the PDC cutter wear flats as the bit drills ahead giving the capability to calculate the temperature being generated underneath the worn cutters to better advise on operational parameters to avoid accelerated cutter wear and failure and to ensure that operational parameters are applied so that overall ROP is maximized. By combining the drill stem vibrations and the detailed PDC bit cutter wear and “safe” non-accelerated cutter wear temperature and optimum ranges of operating parameters, it results in higher ROP and lower cost drilling. Single cutter PDC testing performed in different lithologies at Sandia was utilized to verify the PDC cutter forces and depth of cut for new and worn cutters. Based on single cutter PDC temperature modeling, verification using single cutter data from the testing done by National Oilwell Varco (NOV) was performed. Sandia’s Hard-Rock Drilling Facility (HRDF) was utilized to test different drill bit configurations with different cutter designs and wear status with different induced modes of vibration to obtain the critical bit RPM/WOB ranges resulting in ineffective drilling and low ROP. The collected test data were further used to verify and calibrate the full hole PDC ROP model that was developed based on single cutter interaction data. A full coupled drill stem vibration model was formulated and verified with geothermal field data from the Chocolate Mountain Aerial Gunnery Range (CMAGR). A graphical user interface (GUI) was developed using Tkinter library in the computer programming language Python, which integrates all the developed models in one system. The developed system consists mainly of the PDC ROP model, PDC bit wear model, PDC cutter temperature model, Mechanical Specific Energy (MSE) model, and drillstring vibration model integrated into one system. The developed system can be used for both, post well analysis and real-time optimization using different criteria such as ROP maximization or MSE minimization. The software uses Differential Evolution Algorithm (DEA) to find optimum values for operational parameters based on last foot drilled while avoiding the drillstring vibration and cutter temperature critical operating parameters.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Exploring MDSplus data-acquisition software and custom devices

MDSplus is a software tool designed for data acquisition, storage, and analysis of complex scientific experiments. Over the years, MDSplus has primarily been used for data management for fusion experiments. This paper demonstrates that MDSplus can be used for a much wider variety of systems and experiments. We present a step-by-step tutorial describing how to create a simple experiment, manage the data, and analyze it using MDSplus and Python. To this end, a custom example device was developed to be used as the data source. This device was built on an opensource electronic hardware platform, and it consists of a microcontroller and two sensors. We read data from these sensors, store it in MDSplus, and use JupyterLab to visualize and process it. This project and code demo are available on the GitHub site at this URL: https://github.com/santorofer/MDSplusAndCustomeDevices

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

INTEGRATE - Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements

The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project is developing a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. This AI-based design technology can capture complex non-linear aerodynamic effects while being 100 times faster than design approaches based on computational fluid dynamics. This project enables innovation in wind turbine design by accelerating time to market through higher-accuracy early design iterations to reduce the levelized cost of energy. INVERTIBLE NEURAL NETWORKS Researchers are leveraging a specialized invertible neural network (INN) architecture along with the novel dimension-reduction methods and airfoil/blade shape representations developed by collaborators at the National Institute of Standards and Technology (NIST) learns complex relationships between airfoil or blade shapes and their associated aerodynamic and structural properties. This INN architecture will accelerate designs by providing a cost-effective alternative to current industrial aerodynamic design processes, including: - Blade element momentum (BEM) theory models: limited effectiveness for design of offshore rotors with large, flexible blades where nonlinear aerodynamic effects dominate - Direct design using computational fluid dynamics (CFD): cost-prohibitive - Inverse-design models based on deep neural networks (DNNs): attractive alternative to CFD for 2D design problems, but quickly overwhelmed by the increased number of design variables in 3D problems AUTOMATED COMPUTATIONAL FLUID DYNAMICS FOR TRAINING DATA GENERATION - MERCURY FRAMEWORK The INN is trained on data obtained using the University of Marylands (UMD) Mercury Framework, which has with robust automated mesh generation capabilities and advanced turbulence and transition models validated for wind energy applications. Mercury is a multi-mesh paradigm, heterogeneous CPU-GPU framework. The framework incorporates three flow solvers at UMD, 1) OverTURNS, a structured solver on CPUs, 2) HAMSTR, a line based unstructured solver on CPUs, and 3) GARFIELD, a structured solver on GPUs. The framework is based on Python, that is often used to wrap C or Fortran codes for interoperability with other solvers. Communication between multiple solvers is accomplished with a Topology Independent Overset Grid Assembler (TIOGA). NOVEL AIRFOIL SHAPE REPRESENTATIONS USING GRASSMAN SPACES We developed a novel representation of shapes which decouples affine-style deformations from a rich set of data-driven deformations over a submanifold of the Grassmannian. The Grassmannian representation as an analytic generative model, informed by a database of physically relevant airfoils, offers (i) a rich set of novel 2D airfoil deformations not previously captured in the data , (ii) improved low-dimensional parameter domain for inferential statistics informing design/manufacturing, and (iii) consistent 3D blade representation and perturbation over a sequence of nominal shapes. TECHNOLOGY TRANSFER DEMONSTRATION - COUPLING WITH NREL WISDEM Researchers have integrated the inverse-design tool for 2D airfoils (INN-Airfoil) into WISDEM (Wind Plant Integrated Systems Design and Engineering Model), a multidisciplinary design and optimization framework for assessing the cost of energy, as part of tech-transfer demonstration. The integration of INN-Airfoil into WISDEM allows for the design of airfoils along with the blades that meet the dynamic design constraints on cost of energy, annual energy production, and the capital costs. Through preliminary studies, researchers have shown that the coupled INN-Airfoil + WISDEM approach reduces the cost of energy by around 1% compared to the conventional design approach. This page will serve as a place to easily access all the publications from this work and the repositories for the software developed and released through this pr...

aerodynamics↗