Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “open source model”

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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Geometric Scale-up Experiments on Fluidization of Geldart B Glass Beads

The objective of this work is to provide a valuable database from controlled experiments for validating computational models. Recently, coarse-grained techniques such as particle-in-cell (PIC) or coarse-grained discrete element modeling (DEM) have gained popularity due to their computational efficiency while modeling large-scale systems; however, the influence of model parameters and their sensitivities at different geometric scales and flow conditions remain to be analyzed. These datasets are critical for the multiphase flow research community to assess predictive capability of modeling techniques as well as elucidate the hydrodynamic behavior in these systems. This study performed fluidization experiments using three different test sections with internal diameters of 2.5, 4, and 6 in. The operating conditions, bed material, and range of flow velocities at the inlet were constant in all the units, which were not hydrodynamically scaled. Glass beads having a Sauter Mean Diameter of 332 μm were used. Superficial velocity was varied from 2.97 to 5.35 times the minimum fluidization velocity and the initial static bed height was 0.1524 m. The order in which the experiments were performed was randomized and replicates were included to provide uncertainty in measurements. Statistics of differential pressure and bed height from these tests were reported. Future plans include validating PIC methodology in the open-source software, MFiX (Multiphase Flow with Interphase Exchanges) using results from this study. This could further be extended to determine optimal model parameters using inverse techniques such as deterministic calibration or Bayesian inference.

42 ENGINEERING↗
NASA NTRS1989

IR emission and UV extinction in two open clusters

Recent models of interstellar extinction have shown the importance of understanding both the UV and IR properties of interstellar dust grains. IRAS data have shown variations in 60 and 100 micron emissions presumably due to the presence of IR cirrus, while recent observations in the UV by Fitzpatrick and Massa have identified components in the UV extinction curve which vary in different star regions. A Draine and Anderson model connects these results by proposing that different size variations in interstellar grains would cause distinct changes in both the IR emission and the UV extinction. In order to test this model it is necessary to make observations in well defined locations away from peculiar extinction regions. In the infrared this means looking away from the galactic plane so as to limit non-local sources of IR radiation. Two open clusters that are out of the galactic plane and which contain a number of late B and early A stars suitable for UV extinction studies, and whose IRAS data show variations in the 60/100 micron ratio were studied. Based on the Drain and Anderson model, variations were expected in their UV extinction curves that correlate with the IR cirrus emission.

Hackwell, James A.↗
DOE OSTI2022

ArQTiC: A Full-stack Software Package for Simulating Materials on Quantum Computers

ArQTiC is an open-source, full-stack software package built for the simulations of materials on quantum computers. It currently can simulate materials that can be modeled by any Hamiltonian derived from a generic, one-dimensional, time-dependent Heisenberg Hamiltonian. ArQTiC includes modules for generating quantum programs for real- and imaginary-time evolution, quantum circuit optimization, connection to various quantum backends via the cloud, and post-processing of quantum results. By enabling users to seamlessly design, execute, and analyze materials simulations on quantum computers, ArQTiC opens this field to a broader community of scientists from a wider range of scientific domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

University of Hawai‘i, Shallow Geothermal Resources: Energy Technology Innovation Partnership Project (Final Report)

Scientists at Lawrence Berkeley National Laboratory (Berkeley Lab) have teamed up with the University of Hawai‘i at Manoa (UH Manoa) through the U.S. Department of Energy’s Energy Technology Innovation Partnership Project to evaluate the technological and market feasibility of shallow geothermal heat exchanger (GHE) technology. UH requested this analysis to evaluate opportunities in building cooling, energy efficiency, and emissions reduction applications in Hawai‘i. UH has an abundance of geologic and geothermal data and is looking to the national labs’ expertise to execute this analysis. UH is also interested in investigating policy, regulatory, and business conditions advantageous for implementation of a pilot project and more broad deployment of this technology in Hawai‘i. In many locations around the world, the demands for heating and cooling are roughly balanced over the course of the year, so GHEs do not cause significant long-term changes in subsurface temperature. This is not the case in Hawai’i, where the demand for heating is very small, meaning that, over time, GHEs will add heat to the subsurface. If temperatures increase significantly, GHE systems will not work as designed. Regional groundwater flow has the potential to sweep heated water away from boreholes, thereby maintaining the functionality of the GHE system. Significant regional groundwater flow requires two things: a sufficiently large driving hydraulic head gradient (usually closely related to surface topography), and sufficient porosity and permeability to enable groundwater to flow in large enough quantities to enable near-borehole temperatures to be maintained at ambient values. Hawai‘i’s volcanic terrain offers ample surface topographic variation. The lava itself shows an extremely large range of porosity and permeability, so sites with large enough values of these properties must be selected. Numerical modeling of coupled groundwater and heat flow can be used to determine how large is large enough. Primarily, closed-loop systems have been investigated. Other options considered are open-loop systems and using cool seawater as the chilling source. Project work investigated the feasibility of GHE technology at two scales. At the island scale, GIS layers of various attributes relevant for GHE were combined to develop an overall favorability map for employing GHE in Hawai‘i. At the local scale, a hydrogeologic model for the subsurface component of a closed-loop system was developed for the Stan Sheriff Center at the UH Manoa campus. This site is considered promising because the rock below and immediately downgradient of the borefield is highly permeable, consisting of a subsurface karst system (limestone containing high-permeability open channels), which is underlain by a thick, high-permeability fractured basalt. Moreover, the site is near the base of the Ko‘olau Range, providing a large hydraulic head gradient. Thus, groundwater flow through the site is expected to be large, enabling efficient removal of heated groundwater. A full-GHE-system model of the site was also developed, with a simplified representation of the subsurface, in which groundwater flow is not considered and heat transfer is purely by conduction. Using the building cooling load data provided by UH, simulation results show that with groundwater flow present, a GHE can operate successfully for at least 10 years, but with no groundwater flow, the subsurface begins to heat up after only one year of operation, making the GHE unviable within 2-6 years. The team also developed a techno-economic model for this site to compare the cost of cooling using a GHE system with the costs of operating the current air-conditioning system. The GHE system is advantageous economically if favorable tax incentives and interest rates can be obtained.

15 GEOTHERMAL ENERGY↗
NASA NTRSDate not supplied

AMMPER: a user-friendly agent-based model that recapitulates simple metabolic responses of yeast to deep-space radiation

For humans venturing to deep space, radiation exposure poses a major health risk. Fundamental research into the biological effects of space radiation are essential for enabling exploration, and the first experimental organisms we send to deep space will be microbial. Yet there are many ways in which microorganisms are likely to experience the effects of high-energy particle radiation (such as Galactic Cosmic Rays) differently from multicellular animals, partly due to the simple fact that microbes are small and unicellular-- less likely to get hit in the first place, and less likely to communicate damage between cells. Computational modeling can aid in designing experiments and predicting the biological effects of radiation, but thus far particle radiation models have not focused on microbes. Here we present the latest developments in AMMPER, the Agent-based Model for Microbial Populations Exposed to Radiation. Originally written in 2021, AMMPER is a Python-based model that incorporates radiation track data from NASA's RITRACKS software and simulates the growth, damage, and death of yeast cells in 3D. It is now freely available as an open-source package on NASA's GitHub repository. Recent improvements include the ability to simulate the dynamics of alamarBlue, a color-changing redox dye commonly used to track metabolic activity in microbial spaceflight experiments. We demonstrate that a simple blue-pink-clear transition model is able to recapitulate key features observed in empirical data from ground studies. AMMPER also includes a new graphical user interface and introductory tutorial to facilitate ease of use by a wider audience. AMMPER can help us to understand how spatially heterogeneous particle radiation damage at the single-cell level can translate to growth differences at the population level, ultimately allowing us to better interpret experiments using microbes as model organisms and how well their results apply to humans.

yeast↗
NASA NTRS2004

Reduced-Order Modeling and Wavelet Analysis of Turbofan Engine Structural Response Due to Foreign Object Damage (FOD) Events

The development of a wavelet-based feature extraction technique specifically targeting FOD-event induced vibration signal changes in gas turbine engines is described. The technique performs wavelet analysis of accelerometer signals from specified locations on the engine and is shown to be robust in the presence of significant process and sensor noise. It is envisioned that the technique will be combined with Kalman filter thermal/health parameter estimation for FOD-event detection via information fusion from these (and perhaps other) sources. Due to the lack of high-frequency FOD-event test data in the open literature, a reduced-order turbofan structural model (ROM) was synthesized from a finite element model modal analysis to support the investigation. In addition to providing test data for algorithm development, the ROM is used to determine the optimal sensor location for FOD-event detection. In the presence of significant noise, precise location of the FOD event in time was obtained using the developed wavelet-based feature.

Turso, James↗
NASA NTRS2007

Reduced-Order Modeling and Wavelet Analysis of Turbofan Engine Structural Response Due to Foreign Object Damage "FOD" Events

The development of a wavelet-based feature extraction technique specifically targeting FOD-event induced vibration signal changes in gas turbine engines is described. The technique performs wavelet analysis of accelerometer signals from specified locations on the engine and is shown to be robust in the presence of significant process and sensor noise. It is envisioned that the technique will be combined with Kalman filter thermal/ health parameter estimation for FOD-event detection via information fusion from these (and perhaps other) sources. Due to the lack of high-frequency FOD-event test data in the open literature, a reduced-order turbofan structural model (ROM) was synthesized from a finite-element model modal analysis to support the investigation. In addition to providing test data for algorithm development, the ROM is used to determine the optimal sensor location for FOD-event detection. In the presence of significant noise, precise location of the FOD event in time was obtained using the developed wavelet-based feature.

Turso, James A.↗
NASA NTRS1999

Effect of Directional Array Size on the Measurement of Airframe Noise Components

A study was conducted to examine the effects of overall size of directional (or phased) arrays on the measurement of aeroacoustic components. An airframe model was mounted in the potential core of an open-jet windtunnel, with the directional arrays located outside the flow in an anechoic environment. Two array systems were used; one with a solid measurement angle that encompasses 31.6 deg.of source directivity and a smaller one that encompasses 7.2 deg. The arrays, and sub-arrays of various sizes, measured noise from a calibrator source and flap edge model setups. In these cases, noise was emitted from relatively small, but finite size source regions, with intense levels compared to other sources. Although the larger arrays revealed much more source region detail, the measured source levels were substantially reduced due to finer resolution compared to that of the smaller arrays. To better understand the measurements quantitatively, an analytical model was used to define the basic relationships between array to source region sizes and measured output level. Also, the effect of noise scattering by shear layer turbulence was examined using the present data and those of previous studies. Taken together, the two effects were sufficient to explain spectral level differences between arrays of different sizes. An important result of this study is that total (integrated) noise source levels are retrievable and the levels are independent of the array size as long as certain experimental and processing criteria are met. The criteria for both open and closed tunnels are discussed. The success of special purpose diagonal-removal processing in obtaining integrated results is apparently dependent in part on source distribution. Also discussed is the fact that extended sources are subject to substantial measurement error, especially for large arrays.

Brooks, Thomas F.↗
DOE OSTI2024

Laboratory Upgrade Point Absorber (LUPA) CAD Files

The Laboratory Upgrade Point Absorber (LUPA) is an open-source wave energy converter designed and tested by Oregon State University. The computer-aided design (CAD) files are provided here in two forms: the original SOLIDWORKS (2021) model as "LUPA SOLIDWORKS.zip" and as a STEP file "LUPA-A1000.step". The bill of materials is provided as an Excel file with assemblies (LUPA-Axxx), part numbers (LUPA-Axxx-Pyyy), part descriptions, manufacturers, and manufacturer part numbers. This comprehensive CAD model represents LUPA as it was deployed in Fall 2022 testing at the O.H. Hinsdale Wave Research Laboratory. The mass properties including mass, center of gravity, and moments of inertia have been overridden for some parts and assemblies to match the physical device properties as determined from experiments. This appears as "overridden by user" when viewing mass properties in SOLIDWORKS. The LUPA-A1000.SLDASM file from the LUPA SOLIDWORKS.zip folder is the topmost assembly, open this file to see the entire model as one assembly. See "PMEC Page", "OpenEI Wiki Page", and the "Signature Project Page" resources below for more information on LUPA.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Landscaper v1

Understanding the inner workings of machine learning models through their loss landscapes offers crucial insights into model properties, optimization dynamics, and generalizability. However, accessing these insights has traditionally required specialized mathematical expertise, limiting broader adoption. Landscaper is an open-source Python package designed to bridge this gap. Landscaper seamlessly integrates a suite of multi-dimensional loss landscape analyses with cutting-edge topological data analysis (TDA) methods. This powerful combination makes both fundamental loss landscape analysis and advanced TDA techniques accessible to the broader scientific ML community, without requiring deep pre-existing mathematical knowledge. Landscaper offers three key functionalities: * Construction: Builds detailed loss landscape representations through versatile low and high-dimensional sampling techniques. * Quantification: Applies advanced metrics, including a novel topological data analysis (TDA) based smoothness metric, enabling new perspectives on model behavior. * Visualization: Offers intuitive tools to visualize and interpret loss landscapes, providing actionable insights beyond traditional performance metrics.

Weber, Gunther [Lawrence Berkeley National Laborat↗
DOE OSTI2021

Using the CYBER-Champ Model to Determine Cyber Competencies and Role Alignment

Cybersecurity roles, tasks, and skills are seen by many organizations as nonessential, abstract, or complicated. Although standards are in place, such as the National Institute of Standards and Technology (NIST) 800-181 document titled “Workforce Framework for Cybersecurity (NICE Framework)” available since September 2012, companies are still showing security vulnerabilities in their cyber networks (Hatzes, 2020). Barriers towards creating a cyber-ready workforce are not always due to a lack of resources, but often caused by organizational structure. The need for cyber-cognizant job postings, improved communication between Operational Technology (OT) and Information Technology (IT) employees, increases in staffing and funding for cyber teams, and better communication of standards and training can all contribute to increasing an organization’s cyber resilience. Cybersecurity Competency Health and Maturity Progression model (CYBER-CHAMP) is a model aimed at evaluating an organization’s structure and individual employee’s cyber knowledge and helps develop a plan to reach competency. This model was used to engage with organizations to promote proper cyber protocols and policies. The aim of this paper is to answer if it is possible for an organization to build a cyber-ready workforce by providing education and training options for current employees and prepare the future workforce to be ready on day one of employment. Both open source and firsthand interviews with organizations were used to conduct the research contained in this document. Further research may include additional development to the CYBER-CHAMP model and its delivery platform.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

mystic : software for autonomous discovery and design under uncertainty

Throughout the diverse range of science and engineering applications, there is a growing desire to develop computational methods that can reliably predict the behavior of complex systems. Specifically, there is a strategic need for tools that can robustly forecast the behavior of complex physical systems, where data may be high-dimensional, noisy, or sparse, and models of the system may be time-dependent or include uncertainty. We use mystic to build tools that leverage statistical learning, physics-informed learning, and active learning in the efficient generation of reliably predictive surrogates for complex physical systems. mystic is a robust, proven, open-source optimization and uncertainty quantification toolkit with over a decade of use in the design and optimization of neutron instrumentation, solar-powered drones, and gasguns, and in iterative tuning of models for Raman spectroscopy and elastoplastic materials strength. Recent developments have focused on automated learning of statistically robust surrogates under uncertainty, with applications in materials in extreme environments, nanostructures, materials simulations and strength models, and the failure of shielding under particle radiation. In 2020, McKerns demonstrated active learning of optimally robust surrogates with respect to new simulated data for molecular dynamics simulations of materials mixing in warm dense matter, and is currently applying active learning to the automated steering of particle accelerator beams and the optimal design and control of quantum optical sensor instrumentation.

42 ENGINEERING↗
NASA NTRSDate not supplied

Evaluation of VSPAERO Analysis Capabilities for Conceptual Design of Aircraft with Propeller-Blown Wings

Advancements in electric propulsion and the emergence of Advanced Air Mobility are driving the evolution of new aircraft designs. Since electric propulsion enables flexibility in propeller location, there is an increasing need for reliable, quick analyses of propeller-airframe interactions during the conceptual design phase. Many existing analysis tools capable of accurately modeling propeller-airframe interactions are computationally expensive and require a high level of expertise and significant time investment for setup. VSPAERO is a NASA-developed, open source, computational analysis tool that runs a Vortex-Lattice Method (VLM) solver and is targeted at conceptual design. This paper assesses the applicability of the VSPAERO VLM in the conceptual design phase by comparing VSPAERO predictions to predictions by OVERFLOW, a Reynolds-Averaged Navier-Stokes Computational Fluid Dynamics solver, and RoBIN, another VLM tool. The paper details the modeling, meshing, and analysis techniques used within VSPAERO. Analyses were performed for a wing in isolation, a propeller in isolation, and then for two propeller-blown wing configurations: one with a propeller located at the midspan and another with a propeller located at the wingtip. The propeller was modeled both as an actuator disk and as rotating blades.

VSPAERO↗
DOE OSTI2025

Advances in Modeling Capabilities for Critical Mineral Separation Technologies: A PrOMMiS Overview

This is an oral presentation at the TechConnect conference on the work developed by PrOMMiS. PrOMMiS builds on and extends capabilities developed within the Department of Energy’s (DOE) Institute for the Design of Advanced Energy Systems (IDAES), Integrated Platform, and Water Treatment Technoeconomic Assessment Platform (WaterTAP), which have been successfully leveraged by other Department of Energy research areas. The open-source toolkit facilitates validation, reproducibility, and accountability, allowing for easy extension of the framework to other systems. This talk presents an overview of the PrOMMiS capabilities, including unit model library, advances in thermophysical properties models, and capital cost libraries for simulation and optimization of mineral processing technologies. The PrOMMiS applications include (1) conceptual design and superstructure optimization for screening different process configurations and identifying promising technologies; (2) dynamic modeling and optimization to enable the creation of digital twins; (3) surrogate modeling tools to leverage data when predictive thermodynamic models are not currently available; (4) technical risk reduction via uncertainty quantification and robust optimization to identify process designs that are robust to process variability and uncertainties; and (5) deployment of uncertainty quantification tools to maximize knowledge gained from experimental campaigns, while reducing the number of experiments required

critical minerals and materials↗
DOE OSTI2022

DeePKS + ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials

Recently, the development of machine learning (ML) potentials has made it possible to perform large-scale and long-time molecular simulations with the accuracy of quantum mechanical (QM) models. However, for different levels of QM methods, such as density functional theory (DFT) at the meta-GGA level and/or with exact exchange, quantum Monte Carlo, etc., generating a sufficient amount of data for training an ML potential has remained computationally challenging due to their high cost. In this work, we demonstrate that this issue can be largely alleviated with Deep Kohn–Sham (DeePKS), an ML-based DFT model. DeePKS employs a computationally efficient neural network-based functional model to construct a correction term added upon a cheap DFT model. Upon training, DeePKS offers closely matched energies and forces compared with high-level QM method, but the number of training data required is orders of magnitude less than that required for training a reliable ML potential. As such, DeePKS can serve as a bridge between expensive QM models and ML potentials: one can generate a decent amount of high-accuracy QM data to train a DeePKS model and then use the DeePKS model to label a much larger amount of configurations to train an ML potential. Further, this scheme for periodic systems is implemented in a DFT package ABACUS, which is open source and ready for use in various applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Multibody for Everybody (M4E): A Symbolic Dynamics Modeling Tool with Applications in Simulation, Control, and Optimization

Developing the analytical model of a multibody system is often the initial step in control and optimization. The analytical model (equations of motion) describes a system’s time evolution under specified forcing conditions. Although developing these equations is easy for simple systems, this process becomes more complex for systems composed of multiple bodies. Deriving equations of motion for complex multibody systems requires specialized expertise in multibody dynamics, is time-consuming, and is susceptible to error. To address this issue, this paper presents an open-source, easy-to-use, systematic framework to derive symbolic equations of motion in both Python and MATLAB using the joint coordinate formulation. This formulation results in a set of ordinary differential equations that use the minimum set of coordinates needed to model a system. The symbolic representation provides better insight into the influence of design parameters on system performance, facilitates sensitivity analysis and parameter studies, and supports direct implementation of control and optimization routines. The tool enables numerical simulation for specified parameter sets, is modular for straightforward integration with other tools and libraries, and allows incorporation of hydrodynamics, mooring, and other external forces. The result is a reproducible, extensible pipeline for modeling, simulation, and design of complex multibody systems. The proposed tool is versatile and can be applied to domains such as robotics, control, and design. In addition, we integrated external libraries that provide capabilities for modeling offshore systems such as underwater robots and marine energy converters.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Open-Source and FAIR Research Software for Proteomics

Scientific discovery relies on innovative software as much as experimental methods, especially in proteomics, where computational tools are essential for mass spectrometer setup, data analysis, and interpretation. Since the introduction of SEQUEST, proteomics software has grown into a complex ecosystem of algorithms, predictive models, and workflows, but the field faces challenges, including the increasing complexity of mass spectrometry data, limited reproducibility due to proprietary software, and difficulties integrating with other omics disciplines. Closed-source, platform-specific tools exacerbate these issues by restricting innovation, creating inefficiencies, and imposing hidden costs on the community. Open-source software (OSS), aligned with the FAIR Principles (Findable, Accessible, Interoperable, Reusable), offers a solution by promoting transparency, reproducibility, and community-driven development, which fosters collaboration and continuous improvement. In this manuscript, we explore the role of OSS in computational proteomics, its alignment with FAIR principles, and its potential to address challenges related to licensing, distribution, and standardization. Drawing on lessons from other omics fields, we present a vision for a future where OSS and FAIR principles underpin a transparent, accessible, and innovative proteomics community.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Development of a Comprehensive Physics-Based Model for Study of NASA Gateway Lunar Dust Contamination

NASA is committed to landing the first woman and first person of color on the Moon by 2025 to begin a sustaining presence. A major component of this mission is NASA’s orbiting lunar outpost, Gateway, that will be subjected to the harsh environment of space during its 7-day orbit around the Moon. One aspect of this environment that is not well quantified is microscopic lunar regolith particles, or simply, lunar dust. As Apollo 17 Mission Commander Eugene Cernan said, “I think dust is probably one of our greatest inhibitors to a nominal operation on the moon. I think we can overcome other physiological or physical or mechanical problems except dust” [1]. These dust particles, most of which are smaller than the width of two human hairs and of highly irregular shape, are composed mainly of dielectric materials. Such characteristics can allow the dust particles to collect electric charge from the surrounding environment, resulting in electrostatic interactions between the dust, electromagnetic fields, and electrically charged surfaces [2-3]. Due to the nature of these fields, dust can collect on and contaminate charged surfaces. Lunar dust introduced into the Gateway environment by a lunar ascent vehicle element returning from the surface of the Moon presents risks to Gateway hardware such as radiators, solar arrays, antennae, and docking mechanisms. To quantify this risk and inform NASA, International Partner, and Commercial Provider stakeholders, Booz Allen, in partnership with NASA, is developing a toolset to model the interaction of charged lunar dust particles with the cis-lunar and deep-space environments. This includes a comprehensive physics model of the space plasma and solar radiation environment and electromagnetic interaction of spacecraft and lunar dust. Spacecraft charging and plasma environment are solved using the open-source code from The French Aerospace Lab (ONERA) and the European Space Agency (ESA), known as Spacecraft Plasma Interaction Software (SPIS) [4]. The physics of particle charging and motion is handled by a time-dependent implementation of Orbital-Motion-Limited (OML) theory which accounts for plasma flows and positive potential grains [5-7], using Siemens STAR-CCM+ as the framework. Model validation includes lab experiments by NASA experts and academia, as well as future on-orbit dust detecting payloads on the exterior of Gateway and Commercial Lunar Payload Services (CLPS) missions to the lunar surface [7-9]. The results and analyses will inform Gateway Program system owners at risk for lunar dust contamination.

Complex Plasma↗