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NASA NTRSDate not supplied

A Machine-Learning Approach to Assess Aircraft Engine System Performance

Artificial intelligence (AI)/machine learning, and big data are transforming the global business environment. They have become the most disruptive technologies for organizations to improve workplace efficiency and productivity. This work explored the application of machine learning-based predictive analytics that would enable aircraft engine designers to estimate engine system performance quickly during the conceptual design stage. Supervised machine-learning algorithm was employed to study patterns in an existing database of production and research turbofan engines, and built predictive analytics for use in predicting system performance of new turbofan designs. Specifically, the author developed deep-learning analytics to predict turbofan system weight, using turbofan design parameters as the input. The predictive analytics were trained and deployed in Keras, an open-source neural networks API (application program interface) written in Python, with TensorFlow (an open-source artificial AI library developed by Google) serving as the backend engine. The current engine-weight prediction results, together with those for the TSFC (thrust specific fuel consumption) and core-size predictions that were studied previously by the author, show that machine learning-based predictive analytics can be an effective, time-saving tool for aircraft engine design-space exploration during the conceptual design stage. It would enable expeditious identification of the best engine design amongst several candidates.

Michael T Tong↗

DOE OSTI2026

Field and Model Data Associated with the Manuscript “Drivers of Streamflow Intermittency in Humid Regions: 1. Evaluating Above- and Below-ground Controls of Flow Persistence in a Forested Catchment”

This package contains field data, modeling files, and scripts supporting the investigation of the drivers of streamflow intermittency in a forested catchment. It includes the field data collected from electrical resistivity tomography (ERT) surveys, ground penetrating radar (GPR), continuous self-potential (SP) monitoring, electromagnetic (EM) imaging, groundwater and stilling well. In addition, it contains the data and results of the coupled water- and electrical-flow model developed using the COMSOL Multiphysics and Advanced Terrestrial Simulator (ATS), as well as software files and Jupyter notebooks used to process the data and generate figures in the manuscript submitted for peer review. The data archive is organized in the following directories: 1) Climate Includes hourly precipitation and daily evapotranspiration time series (2024 – 2025) provided as CSV files, alongside a text file detailing dataset units. 2) Coupled_model Contains two subfolders: Synthetic and Field_Application subfolder. Synthetic subfolder contains the ATS XML input script (can be opened using any code editor) for the four synthetic hydrological cases tested (Connected and gaining, Connected and losing, Disconnected and losing, and dry stream). It also includes other experimental cases to test the influence of precipitation and concentration gradient. For each synthetic case, the flow model simulation is executed using the ATS XML scripts and the included Python script (generate_data_set.py) to convert ATS output to COMSOL-ready input. COMSOL Multiphysics template (.mph can be opened with the commercial software COMSOL and requires a license) is executed using the ATS output data to simulate the potential field. It also includes the Synthetic_model_plot.ipynb (can be opened using any code editor) to visualize the SP result and generate manuscript figures. The data subfolder contains mesh files to run both the ATS (.exo and .stl files can be viewed using Paraview; .h5 files can be opened using HDFView software and h5py Python package) and COMSOL models. Field_Application subfolder contains two subfolders: ES_MDA_inversion and Final_Model. ES_MDA_inversion contains the Python script (.py can be opened using any code editor) and SP observation data used to run the Ensemble Smoother with Multiple Data Assimilation (ES-MDA) inversion sequence to get the optimal model parameters. The Final_model subfolder contains the ATS XML input scripts, data files, output data for the two SP sites. The same workflow steps outlined for the Synthetic subfolder apply here. It also contains the Jupyter notebook (Plot_final_calib.ipynb) to visualize the results of the modeled SP, stream-groundwater exchange and moisture content. 3) Discharge Includes the electrical conductivity (EC) time series (provided as CSV files) from salt slug injections. It also includes the Jupyter notebook (Discharge_process.ipynyb) used to estimate discharge. All discharge measurements collated into rating_curve_processed.csv 4) EM Contains the CSV file of the EM data from the DUALEM-42, including spatial coordinates (x, y, z), apparent conductivity, and in-phase measurements at 2 m coil separations for horizontal coplanar (HCP) and perpendicular (PRP) geometries. 5) ERT Contains raw resistivity data (provided as CSV files), spatial location of each of the electrodes (provided as CSV files), and files used for the resistivity inversion (.resipy can be opened with the open-source ResIPy software). 6) GPR Includes GPR field datasets collected at 100 MHz and 250 MHz antenna frequencies, along with the processing/interpretation project file (GPR_process.gpz can be viewed using EKKO_Project 6, a commercial software by Sensors & Software that requires a license). 7) Slug_test Includes the slug test data at all the groundwater wells provided as CSV files, as well as the Jupyter notebook (Slug_test.ipynb) for calculating hydraulic conductivity. 8) SP Contains the SP data collected in field at the two SP sites (one in the perennial reach and the other in the intermittent reach), provided as DAT files. 9) Well_data Contains two subfolders: 1) Raw, which provides unprocessed pressure, electrical conductivity and temperature timeseries downloaded from the loggers in all the groundwater and stilling wells, and 2) Processed, which contains sorted, QA/QC timeseries data for each well. The data archive also contains data_process.ipynb, a Jupyter notebook used for field data analysis and generating figures (plotting well, SP, climate, and discharge data, as well as calculating head gradient at sites with nested groundwater wells). It also includes DTW.ipynb, a Jupyter notebook containing the code for the dynamic time warping (DTW) with sliding window to evaluate SP signal synchronicity.

ATS↗

NASA NTRSDate not supplied

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of trained machine-learning models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a Windows app that has been created to deploy trained machine-learning models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of machine-learning application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). Current version of the app focuses on the performance prediction of conventional turbofans. The app gets user input for a turbofan design, preprocesses the input data, and deploys trained machine-learning models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The machine-learning predictive models were built by employing supervised deep-learning algorithm to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these machine-learning models using the app shows that Aero-Engines AI is an easy-to-use and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage.

machine learning↗

DOE OSTI2025

Improving Runtime Performance of Tensor Computations using Rust From Python

In this work, we investigate improving the runtime performance of key computational kernels in the Python Tensor Toolbox (pyttb), a package for analyzing tensor data across a wide variety of applications. Recent runtime performance improvements have been demonstrated using Rust, a compiled language, from Python via extension modules leveraging the Python C API—e.g., web applications, data parsing, data validation, etc. Using this same approach, we study the runtime performance of key tensor kernels of increasing complexity, from simple kernels involving sums of products over data accessed through single and nested loops to more advanced tensor multiplication kernels that are key in low-rank tensor decomposition and tensor regression algorithms. In numerical experiments involving synthetically generated tensor data of various sizes and these tensor kernels, we demonstrate consistent improvements in runtime performance when using Rust from Python over 1) using Python alone, 2) using Python and the Numba just-in-time Python compiler (for loop-based kernels), and 3) using the NumPy Python package for scientific computing (for pyttb kernels).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

PvaPy streaming framework for real-time data processing

User facility upgrades, new measurement techniques, advances in data analysis algorithms as well as advances in detector capabilities result in an increasing amount of data collected at X-ray beamlines. Some of these data must be analyzed and reconstructed on demand to help execute experiments dynamically and modify them in real time. In turn, this requires a computing framework for real-time processing capable of moving data quickly from the detector to local or remote computing resources, processing data, and returning results to users. In this paper, we discuss the streaming framework built on top of PvaPy, a Python API for the EPICS pvAccess protocol. We describe the framework architecture and capabilities, and discuss scientific use cases and applications that benefit from streaming workflows implemented on top of this framework. We also illustrate the framework's performance in terms of achievable data-processing rates for various detector image sizes.

EPICS pvAccess↗

NASA NTRS2015

A Web Service and Android Application for the Distribution of Rainfall Estimates and Earth Observation Data

The full potential of Satellite Rainfall Estimates (SRE) can only be realized if timely access to the datasets is possible. Existing data distribution web portals are often focused on global products and offer limited customization options, especially for the purpose of routine regional monitoring. Furthermore, most online systems are designed to meet the needs of desktop users, limiting the compatibility with mobile devices. In response to the growing demand for SRE and to address the current limitations of available web portals a project was devised to create a set of freely available applications and services, available at a common portal that can: (1) simplify cross-platform access to Tropical Rainfall Measuring Mission Online Visualization and Analysis System (TOVAS) data (including from Android mobile devices), (2) provide customized and continuous monitoring of SRE in response to user demands and (3) combine data from different online data distribution services, including rainfall estimates, river gauge measurements or imagery from Earth Observation missions at a single portal, known as the Tropical Rainfall Measuring Mission (TRMM) Explorer. The TRMM Explorer project suite includes a Python-based web service and Android applications capable of providing SRE and ancillary data in different intuitive formats with the focus on regional and continuous analysis. The outputs include dynamic plots, tables and data files that can also be used to feed downstream applications and services. A case study in Southern Angola is used to describe the potential of the TRMM Explorer for SRE distribution and analysis in the context of ungauged watersheds. The development of a collection of data distribution instances helped to validate the concept and identify the limitations of the program, in a real context and based on user feedback. The TRMM Explorer can successfully supplement existing web portals distributing SRE and provide a cost-efficient resource to small and medium-sized organizations with specific SRE monitoring needs, namely in developing and transition countries.

precipitation↗

DOE OSTI2025

Galaxy cluster profiles: a Gaussian mixture model approach to halo miscentering

Measurements of the galaxy density and weak-lensing profiles of galaxy clusters typically rely on an assumed cluster center, which is taken to be the brightest cluster galaxy or other proxies for the true halo center defined as the minimum in the potential well. Departure of the assumed cluster center from the true halo center bias the resultant profile measurements, an effect known as miscentering bias. Currently, miscentering is typically modeled in stacked profiles of clusters with a two parameter model. We use an alternate approach in which the profiles of individual clusters are used with the corresponding likelihood computed using a Gaussian mixture model. We test the approach using halos and the corresponding subhalo profiles from the IllustrisTNG hydrodynamic simulations. We obtain significantly improved estimates of the miscentering parameters for both 3D and projected 2D profiles relevant for imaging surveys. We discuss applications to upcoming cosmological surveys. Our Python package for the Gaussian mixture model is publicly available at https://github.com/KyleMiller1/Halo-Miscentering-Mixture-Model.

Bayesian reasoning↗

DOE OSTI2025

Efficient Xml Interchange (exi) For Python (expy)

EXPy provides a native Python interface into the LF Energy EVerest V2G protocol stack. The protocol stack is implemented in C/C++ and compiled into shared object libraries. EXPy provides the Python Ctypes translation of the C/C++ libraries for use with pure Python software. This project eliminates the need for integrating Python with third-party communications applications and greatly reduces the code base and improves performance. The other major benefit is the ability for EXPy to support new EXI based protocols as additional V2G standards are produced (e.g. upgrade from ISO 15118-2 to ISO 15118-20).

Rohde, Kenneth [Idaho National Laboratory (INL), I↗

DOE OSTI2025

Litter Production and Foliar Nutrient Resorption in Pioneer and Non-Pioneer Species in a Selective Logging Experiment in the Central Amazon, BIONTE, ZF-2, Manaus, 2022-23

This dataset was collected near the city of Manaus, Brazil, at the Experimental Station of Tropical Forestry (EEST, aka “ZF2”), inside the BIONTE (BIOmass and NuTrient Experiment). The experiment included three levels of increasing selective logging intensity, along with control, with 1-hectare permanent plots (12 total) located at the center of 4-hectare treatment plots. The vegetation has a high floristic diversity, the soils of the region are poor in nutrients, and the topography is characterized by plateaus (where BIONTE is located), and also valley bottoms and slopes. Three treatments of differing logging intensities were applied in the BIONTE experiment (T1, T2 and T3). The study was conducted in Treatment 3 (Block I – permanent plot), which represents the most intensive logging treatment, with 69% of the basal area (m²∙ha⁻¹) removed in 1988. The present dataset spans the period from May 1, 2022, to May 1, 2023. The data package includes leaf_nutrient_data, litterfall_total_data, leaf_litterfall_species_specific_data, and species_info, all provided in .csv format. These formats allow users to process and analyze the data in various software applications and programming languages, such as Python and R. This dataset was collected to advance knowledge on nutrient cycling in Amazonian forests, specifically distinguishing between species with two distinct functional traits: fast-growing and slow-growing. It also aims to improve Earth System Models, such as the E3SM Functionally Assembled Terrestrial Ecosystem Simulator (FATES). Additionally, it was used in a paper currently in preparation (Carvalho et al., in prep.), which aims to quantify seasonal litter production and foliar nutrient resorption in pioneer (fast-growing) and non-pioneer (slow-growing) tree species in the central Amazon. Specifically, it seeks to answer two key questions: 1) Is there a difference in leaf litter production, leaf nutrient flux and leaf nutrient concentration between pioneers and non-pioneers species? Is there a difference in the efficiency of foliar nutrient resorption between pioneers and non-pioneers species?

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2015

Meteor Shower Identification and Characterization with Python

The short development time associated with Python and the number of astronomical packages available have led to increased usage within NASA. The Meteoroid Environment Office in particular uses the Python language for a number of applications, including daily meteor shower activity reporting, searches for potential parent bodies of meteor showers, and short dynamical simulations. We present our development of a meteor shower identification code that identifies statistically significant groups of meteors on similar orbits. This code overcomes several challenging characteristics of meteor showers such as drastic differences in uncertainties between meteors and between the orbital elements of a single meteor, and the variation of shower characteristics such as duration with age or planetary perturbations. This code has been proven to successfully and quickly identify unusual meteor activity such as the 2014 kappa Cygnid outburst. We present our algorithm along with these successes and discuss our plans for further code development.

Moorhead, Althea↗

NASA NTRS2016

Damage Based Analysis (DBA): Theory, Derivation and Practical Application - Using Both an Acceleration and Pseudo-Velocity Approach

The objective of this presentation is to give a brief overview of the theory behind the (DBA) method, an overview of the derivation and a practical application of the theory using the Python computer language. The Theory and Derivation will use both Acceleration and Pseudo Velocity methods to derive a series of equations for processing by Python. We will take the results and compare both Acceleration and Pseudo Velocity methods and discuss implementation of the Python functions. Also, we will discuss the efficiency of the methods and the amount of computer time required for the solution. In conclusion, (DBA) offers a powerful method to evaluate the amount of energy imparted into a system in the form of both Amplitude and Duration during qualification testing and flight environments. Many forms of steady state and transient vibratory motion can be characterized using this technique. (DBA) provides a more robust alternative to traditional methods such Power Spectral Density (PSD) using a Maximax approach.

Pseudo Velocity↗

NASA NTRS2017

Damage Based Analysis (DBA) - Theory, Derivation and Practical Application Using Both an Acceleration and Pseudo Velocity Approach

Structural Dynamics↗

NASA NTRS2018

Seven Keys for Practical Understanding and Use of CGNS

We present key features of the CGNS standard, focusing on its two main elements, the data model (CGNS/SIDS) and its implementations (CGNS/HDF5 and CGNS/Python). The data model is detailed to emphasize how the topological user oriented information, such as families, are separated from the actual meshing that could be split or modified during the CFD work flow, and how this topological information is traced during the meshing process. We also explain why the same information can be described in multiple ways and how to handle such alternatives in an application. Two implementations, using HDF5 and Python, are illustrated in several use examples, both for archival and interoperability purposes. The CPEX extension formalized process is explained to show how to add new features to the standard in a consensual way; we present some of the next extensions to come. Finally we conclude by showing how powerful a consensual public approach like CGNS can be, as opposed to a stand-alone private one. All throughout the paper, we demonstrate how the use of CGNS could be of great benefit for both the meshing and CFD solver communities.

Poinot, Marc↗

NASA NTRSDate not supplied

Radiation Data Portal: Connection of Radiation Measurements on Airplane Flights with Observations of Solar-Terrestrial Environment

The impact of solar radiation dramatically increases at high altitudes in the Earth’s atmosphere and in space. Therefore, continuous monitoring of the radiation environment is critical for the safety of aircraft and spacecraft crews and passengers. Addressing the problem requires a complex approach of integration of different data sources and enhancement of the visualization and search capabilities. The Radiation Portal Database represents an interactive web-based application for convenient search and visualization of in-flight radiation measurements and exploration of various properties related to the radiation environment. The primary element of the Radiation Portal back-end is a MySQL relational database that currently contains the radiation measurements obtained from the Automated Radiation Measurements for Aerospace Safety (ARMAS)device, and soft X-ray and proton fluxes from Geostationary Orbiting Environmental Satellite (GOES). The developed Application Programming Interface (API) and related Python routines allow a user to retrieve the database records directly and efficiently, without interaction with the web interface. As a use case of the Radiation Portal, we examine the properties of the ARMAS flights taken during the enhanced Solar Proton (SP) fluxes and compare them to the flights of similar time and location taken during SP-quiet periods.

SMD↗

NASA NTRS2016

Computational Approaches to Simulation and Optimization of Global Aircraft Trajectories

This study examines three possible approaches to improving the speed in generating wind-optimal routes for air traffic at the national or global level. They are: (a) using the resources of a supercomputer, (b) running the computations on multiple commercially available computers and (c) implementing those same algorithms into NASAs Future ATM Concepts Evaluation Tool (FACET) and compares those to a standard implementation run on a single CPU. Wind-optimal aircraft trajectories are computed using global air traffic schedules. The run time and wait time on the supercomputer for trajectory optimization using various numbers of CPUs ranging from 80 to 10,240 units are compared with the total computational time for running the same computation on a single desktop computer and on multiple commercially available computers for potential computational enhancement through parallel processing on the computer clusters. This study also re-implements the trajectory optimization algorithm for further reduction of computational time through algorithm modifications and integrates that with FACET to facilitate the use of the new features which calculate time-optimal routes between worldwide airport pairs in a wind field for use with existing FACET applications. The implementations of trajectory optimization algorithms use MATLAB, Python, and Java programming languages. The performance evaluations are done by comparing their computational efficiencies and based on the potential application of optimized trajectories. The paper shows that in the absence of special privileges on a supercomputer, a cluster of commercially available computers provides a feasible approach for national and global air traffic system studies.

global air traffic optimization↗

NASA NTRS2015

Computational Approaches to Simulation and Optimization of Global Aircraft Trajectories

This study examines three possible approaches to improving the speed in generating wind-optimal routes for air traffic at the national or global level. They are: (a) using the resources of a supercomputer, (b) running the computations on multiple commercially available computers and (c) implementing those same algorithms into NASA’s Future ATM Concepts Evaluation Tool (FACET) and compares those to a standard implementation run on a single CPU. Wind-optimal aircraft trajectories are computed using global air traffic schedules. The run time and wait time on the supercomputer for trajectory optimization using various numbers of CPUs ranging from 80 to 10,240 units are compared with the total computational time for running the same computation on a single desktop computer and on multiple commercially available computers for potential computational enhancement through parallel processing on the computer clusters. This study also re-implements the trajectory optimization algorithm for further reduction of computational time through algorithm modifications and integrates that with FACET to facilitate the use of the new features which calculate time-optimal routes between worldwide airport pairs in a wind field for use with existing FACET applications. The implementations of trajectory optimization algorithms use MATLAB, Python, and Java programming languages. The performance evaluations are done by comparing their computational efficiencies and based on the potential application of optimized trajectories. The paper shows that in the absence of special privileges on a supercomputer, a cluster of commercially available computers provides a good option for computing wind-optimal trajectories for national and global air traffic system studies.

Ng, Hok K.↗

DOE OSTI2026

cwru-sdle/CEMENTO

CEMENTO is a component python package of the larger SDLE FAIR application suite of tools for creating scientific ontologies more efficiently. This package provides functional interfaces for converting draw.io diagrams of ontologies into RDF triple file formats and vice versa. This package is able to provide term matching between reference ontology files and terms used in draw.io diagrams allowing for faster ontology deployment while maintaining robust cross-references.

Ponon, GabrielObsequio [Case Western Reserve Univ.↗

DOE OSTI2025

Computerized Assignment of Reviewers to Applicants for the Truman Fellowship

This report presents a user-friendly tool for assigning reviewers to the President Harry S. Truman Postdoctoral Fellowship applicants, replacing a previous program that required technical skills. Built with Python libraries, it organizes reviewer preferences and balances workloads, outputting five solutions as Excel files. Validation confirms its effectiveness, making it suitable for broader applications while ensuring efficient assignments without technical expertise.

97 MATHEMATICS AND COMPUTING↗