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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.

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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Generalized approach for rapid entropy calculation of liquids and solids

We build a comprehensive methodology for the fast computation of entropy across both solid and liquid phases. The proposed method utilizes a single trajectory of molecular dynamics (MD) to facilitate the calculation of entropy, which is composed of three components. The electronic entropy is determined through the temporal average acquired from density functional theory MD simulations. The vibrational entropy, typically the predominant contributor to the total entropy, even within the liquid state, is evaluated by computing the phonon density of states via the velocity autocorrelation function. The most arduous component to quantify, the configurational entropy, is assessed by probability analysis of the local structural arrangement and atomic distribution. We illustrate, through a variety of examples, that this method is both a versatile and valid technique for characterizing the thermodynamic states of both solids and liquids. Furthermore, this method is employed to expedite the calculation of melting temperatures, demonstrating its practical utility in computational thermodynamics.

36 MATERIALS SCIENCE↗
DOE OSTI2019

A Two-Tier Convolutional Neural Network for Combined Detection and Segmentation in Biological Imagery

Deep learning techniques have been useful for modern microscopy imaging techniques to further study and analyze biological structures and organs. Convolutional neural networks (CNN) have improved 2D object detection, localization, and segmentation. For imagery containing biological structures with depth, it is especially desirable to perform these tasks in 3D. Traditionally, performing these tasks simultaneously in 3D has proven to be computationally expensive. Currently available methodologies thus largely work to segment 3D objects from 2D images (without context from captured 3D volumes). In this work, we present a novel approach to perform fast and accurate localization, detection, and segmentation of volumes containing cells. Specifically, in our method, we modify and tune two state-of-the-art CNNs, namely 2D YOLOv2 and 3D U-Net, and combine them with a new fusion and image processing algorithms. Annotated volumes in this space are limited, and we have created synthetic data that mimics actual structures for training and testing our proposed approach. Promising results on this test data demonstrate the value of the technique and offers a methodology for 3D cell analysis in real microscopy imagery.

Ziabari, Amir↗
DOE OSTI2017

Visualizing the Uncertainty of Graph-based 2D Segmentation with Min-path Stability

Here, this paper presents a novel approach to visualize the uncertainty in graph-based segmentations of scalar data. Segmentation of 2D scalar data has wide application in a variety of scientific and medical domains. Typically, a segmentation is presented as a single unambiguous boundary although the solution is often uncertain due to noise or blur in the underlying data as well as imprecision in user input. Our approach provides insight into this uncertainty by computing the “min-path stability”, a scalar measure analyzing the stability of the segmentation given a set of input constraints. Our approach is efficient, easy to compute, and can be generally applied to either graph cuts or live-wire (even partial) segmentations. In addition to its general applicability, our new approach to graph cuts uncertainty visualization improves on the time complexity of the current state-of-the-art with an additional fast approximate solution. We also introduce a novel query enabled by our approach which provides users with alternate segmentations by efficiently extracting local minima of the segmentation optimization. Finally, we evaluate our approach and demonstrate its utility on data from scientific and medical applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Choppy Lite

The existing methodologies for gathering and computing zonal statistics from the combination of a raster image and a shapefile either require advanced programming knowledge in at least one language or the use of proprietary software such as ArcGIS. The software “Choppy Lite” offers several solutions for acquiring zonal statistics that can be used without proprietary software or programming knowledge. This software provides a Docker-based ecosystem, allowing users of any experience level to simply run a single line command in a terminal prompt independent of the operating system. Intermediate to advanced users can import the class provided by the software package into their own Python scripts.

Grant, JoshuaN.↗
DOE OSTI2015

Advanced Supercritical Carbon Dioxide Brayton Cycle Development

Fluids operating in the supercritical state have promising characteristics for future high efficiency power cycles. In order to develop power cycles using supercritical fluids, it is necessary to understand the flow characteristics of fluids under both supercritical and two-phase conditions. In this study, a Computational Fluid Dynamic (CFD) methodology was developed for supercritical fluids flowing through complex geometries. A real fluid property module was implemented to provide properties for different supercritical fluids. However, in each simulation case, there is only one species of fluid. As a result, the fluid property module provides properties for either supercritical CO 2 (S-CO 2 ) or supercritical water (SCW). The Homogeneous Equilibrium Model (HEM) was employed to model the two-phase flow. HEM assumes two phases have same velocity, pressure, and temperature, making it only applicable for the dilute dispersed two-phase flow situation. Three example geometries, including orifices, labyrinth seals, and valves, were used to validate this methodology with experimental data. For the first geometry, S-CO 2 and SCW flowing through orifices were simulated and compared with experimental data. The maximum difference between the mass flow rate predictions and experimental measurements is less than 5%. This is a significant improvement as previous works can only guarantee 10% error. In this research, several efforts were made to help this improvement. First, an accurate real fluid module was used to provide properties. Second, the upstream condition was determined by pressure and density, which determines supercritical states more precise than using pressure and temperature. For the second geometry, the flow through labyrinth seals was studied. After a successful validation, parametric studies were performed to study geometric effects on the leakage rate. Based on these parametric studies, an optimum design strategy for the see-through labyrinth seals was proposed. A stepped labyrinth seal, which mimics the behavior of the labyrinth seal used in the Sandia National Laboratory (SNL) S-CO 2 Brayton cycle, was also tested in the experiment along with simulations performed. The rest of this study demonstrates the difference of valves' behavior under supercritical fluid and normal fluid conditions. A small-scale valve was tested in the experiment facility using S-CO 2 . Different percentages of opening valves were tested, and the measured mass flow rate agreed with simulation predictions. Two transients from a real S-CO 2 Brayton cycle design provided the data for valve selection. The selected valve was studied using numerical simulation, as experimental data is not available.

42 ENGINEERING↗
DOE OSTI2016

Modeling primary atomization of liquid fuels using a multiphase DNS/LES Approach

We report on the development of a model framework to simulate spray flames from direct injection of liquid fuel into an automotive cylinder engine. The approach to this challenging problem was twofold. On one hand, the interface-capturing multiphase computer code CLSVOF was used to resolve the rapidly evolving, topologically convoluted interfaces that separate the liquid fuel from the gas at injection: the main challenges to address were the treatment of the high-pressure flow inside the injector, which required the inclusion of compressibility effects; and the computational framework necessary to achieve a Direct Numerical Simulation (DNS) level of accuracy. On the other hand, the scales of turbulent fuel mixing and combustion in the cylinder engine were ad- dressed by the high-performance computer code RAPTOR within the Large Eddy Simulation (LES) framework. To couple the two computational methods, a novel methodology was developed to describe the dense spray dynamics in Raptor from the assigned spray size distribution and dispersion angle derived from CLSVOF. This new, independent Eulerian Multi-Fluid (EMF) spray module was developed based on the kinetic description of a system of droplets as a pressure-less gas; as we will show, it was demonstrated to efficiently render the near-nozzle coupling in mass, momentum, and energy with the carrier gas phase.

02 PETROLEUM↗
DOE OSTI2016

Modeling primary atomization of liquid fuels using a multiphase DNS/LES Approach

We report on the development of a model framework to simulate spray flames from direct injection of liquid fuel into an automotive cylinder engine. The approach to this challenging problem was twofold. On one hand, the interface-capturing multiphase computer code CLSVOF was used to resolve the rapidly evolving, topologically convoluted interfaces that separate the liquid fuel from the gas at injection: the main challenges to address were the treatment of the high-pressure flow inside the injector, which required the inclusion of compressibility effects; and the computational framework necessary to achieve a Direct Numerical Simulation (DNS) level of accuracy. On the other hand, the scales of turbulent fuel mixing and combustion in the cylinder engine were addressed by the high-performance computer code RAPTOR within the Large Eddy Simulation (LES) framework. To couple the two computational methods, a novel methodology was developed to de- scribe the dense spray dynamics in Raptor from the assigned spray size distribution and dispersion angle derived from CLSVOF. This new, independent Eulerian Multi-Fluid (EMF) spray module was developed based on the kinetic description of a system of droplets as a pressure-less gas; as we will show, it was demonstrated to efficiently render the near-nozzle coupling in mass, momentum, and energy with the carrier gas phase.

02 PETROLEUM↗
DOE OSTI2021

Aero-Thermal Characterization of Accelerating and Diffusing Passages Downstream of Rotating Detonation Combustors

Cycle benefits of rotating detonation engines show up to five percentage points of efficiency gain for low-pressure ratio engines. An optimal integration between the combustor and the turbine needs to be guaranteed to realize this potential gain. The rotating detonation combustor (RDC) exhausts transonic flow with shocks rotating at frequencies ranging from a few to tens of kilohertz depending on the number of present waves. Hence, the turbine design requires precise knowledge of the fluctuations and losses downstream of the combustor. This paper focuses on the quantification of fluctuations and losses for accelerating and diffusing passages. The analysis of the combustor is performed via reactive unsteady Reynolds Averaged Navier-Stokes (URANS) simulations. The unsteady RANS equations are solved via CFD++ from Metacomp with a one-step reaction mechanism for an H2-air mixture. The resolving of the boundary layer is achieved with a structured mesh of around 36 million cells. Inlet pressure of 10 bar and two different back pressures are applied to the combustor to model the interconnection with downstream turbines. Finally, we present and assess a methodology to reduce the computational time to model these passages ten times.

Braun, James↗
DOE OSTI2026

SENTRA: A Modular Computational Graph Framework for Critical Mineral and Materials Supply Chains: Part I: Network Construction Latent-Quantity Estimation, and Temporal Graph Forecasting

Global supply chains for critical minerals and materials are complex, evolving networks of countries, products, production stages, and trade relationships. Existing analytical approaches are limited by fragmented data and static network representations that do not capture the dynamic production dependencies linking raw materials, intermediate products, and final goods across multiple countries. Trade and production statistics provide only a partial view of domestic production, inventories, and material flows, making it difficult to identify indirect sourcing pathways, hidden dependencies, and embedded foreign exposures. This paper introduces the Supply Chain Exposure Network Tracking and Risk Assessment (SENTRA) framework, a modular graph-based computational framework for constructing, analyzing, and forecasting dynamic supply chain networks. As the first paper in a three-part methodological series, it establishes the computational foundation of SENTRA by constructing a temporal attributed multi-relational graph whose nodes represent product–country pairs and whose edges encode observed trade and within-country value-chain relationships. Statistical estimation and constrained optimization recover latent production, final demand, and product input dependency coefficients while enforcing economic accounting constraints. Graph-derived exposure measures quantify direct, transshipment, value-chain, and multi-hop supply chain dependencies independently of the forecasting model. A temporal graph forecasting architecture based on a relational graph neural network then forecasts the evolution of the graph under mass-balance constraints with distribution-free conformal uncertainty quantification. Validation on the global aluminum supply chain shows that the learned graph representations recover economically meaningful supply chain structure, accurately forecast out-of-sample trade relationships, and produce well-calibrated prediction intervals. Subsequent papers apply this computational foundation to exposure assessment, disruption analysis, and scenario-based policy analysis, and extend the framework to multimaterial supply chain modeling and decision support.

36 MATERIALS SCIENCE↗
DOE OSTI2017

User-Centered Evaluation of Visual Analytics

Visual analytics systems are becoming very popular. More domains now use interactive visualizations to analyze the ever-increasing amount and heterogeneity of data. More novel visualizations are being developed for more tasks and users. We need to ensure that these systems can be evaluated to determine that they are both useful and usable. A user-centered evaluation for visual analytics needs to be developed for these systems. While many of the typical human-computer interaction (HCI) evaluation methodologies can be applied as is, others will need modification. Additionally, new functionality in visual analytics systems needs new evaluation methodologies. There is a difference between usability evaluations and user-centered evaluations. Usability looks at the efficiency, effectiveness, and user satisfaction of users carrying out tasks with software applications. User-centered evaluation looks more specifically at the utility provided to the users by the software. This is reflected in the evaluations done and in the metrics used. In the visual analytics domain this is very challenging as users are most likely experts in a particular domain, the tasks they do are often not well defined, the software they use needs to support large amounts of different kinds of data, and often the tasks last for months. These difficulties are discussed more in the section on User-centered Evaluation. Our goal is to provide a discussion of user-centered evaluation practices for visual analytics, including existing practices that can be carried out and new methodologies and metrics that need to be developed and agreed upon by the visual analytics community. The material provided here should be of use for both researchers and practitioners in the field of visual analytics. Researchers and practitioners in HCI and interested in visual analytics will find this information useful as well as a discussion on changes that need to be made to current HCI practices to make them more suitable to visual analytics. A history of analysis and analysis techniques and problems is provided as well as an introduction to user-centered evaluation and various evaluation techniques for readers from different disciplines. The understanding of these techniques is imperative if we wish to support analysis in the visual analytics software we develop. Currently the evaluations that are conducted and published for visual analytics software are very informal and consist mainly of comments from users or potential users. Our goal is to help researchers in visual analytics to conduct more formal user-centered evaluations. While these are time-consuming and expensive to carryout, the outcomes of these studies will have a defining impact on the field of visual analytics and help point the direction for future features and visualizations to incorporate. While many researchers view work in user-centered evaluation as a less-than-exciting area to work, the opposite is true. First of all, the goal is user-centered evaluation is to help visual analytics software developers, researchers, and designers improve their solutions and discover creative ways to better accommodate their users. Working with the users is extremely rewarding as well. While we use the term “users” in almost all situations there are a wide variety of users that all need to be accommodated. Moreover, the domains that use visual analytics are varied and expanding. Just understanding the complexities of a number of these domains is exciting. Researchers are trying out different visualizations and interactions as well. And of course, the size and variety of data are expanding rapidly. User-centered evaluation in this context is rapidly changing. There are no standard processes and metrics and thus those of us working on user-centered evaluation must be creative in our work with both the users and with the researchers and developers.

intelligence analysis↗
DOE OSTI2023

Wave Detection and Tracking Within a Rotating Detonation Engine Through Object Detection

As the operational time window of experimental rotating detonation engines (RDEs) is expanded and the technology matures toward integration within gas turbines, monitoring techniques must evolve to offer computationally efficient and highly time-resolved diagnostics. In this study, computer vision object detection methodology that seeks to reduce data processing time and calculate wave velocity within drastically reduced time intervals as compared to traditional high-frame-rate RDE images analysis techniques is proposed. The adapted you-only-look-once object detection network is trained to detect individual detonation waves within single down-axis RDE images. The wave location and rotational direction detected within a frame are tracked through a series of high-speed images to calculate the frame-to-frame wave velocity with the time-step resolution of $\mathrm{20 μs}$ across a series of frames. The analysis of the annotation box size and image linearization effects is presented, demonstrating the lowest frame-to-frame velocity total uncertainty of $\mathrm{±3.8\%}$ and the highest classification speed of 9.5 frames per second using linearized images. Linearized images “unwrap” the RDE annulus pixel region to a reduced image size. Here, this new method offers great reductions in data processing times and unsteady detonation behavior insight at intervals more comparable to the timescales of detonation wave interactions via the application of machine learning to experimental RDE data.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Fast and Flexible Inference Framework for Continuum Reverberation Mapping Using Simulation-based Inference with Deep Learning

Continuum reverberation mapping (CRM) of active galactic nuclei (AGN) monitors multiwavelength variability signatures to constrain accretion disk structure and supermassive black hole (SMBH) properties. The upcoming Vera Rubin Observatory’s Legacy Survey of Space and Time will survey tens of millions of AGN over the next decade, with thousands of AGN monitored with almost daily cadence in the deep drilling fields. However, existing CRM methodologies often require long computation time and are not designed to handle such large amounts of data. In this paper, we present a fast and flexible inference framework for CRM using simulation-based inference (SBI) with deep learning to estimate SMBH properties from AGN light curves. We use a long short-term memory summary network to reduce the high dimensionality of the light curve data and then use a neural density estimator to estimate the posterior of SMBH parameters. Using simulated light curves, we find SBI can produce more accurate SMBH parameter estimation with 10 3 –10 5 times speed up in inference efficiency compared to traditional methods. The SBI framework is particularly suitable for wide-field CRM surveys as the light curves will have identical observing patterns, which can be incorporated into the SBI simulation. We explore the performance of our SBI model on light curves with irregular-sampled, realistic observing cadence and alternative variability characteristics to demonstrate the flexibility and limitation of the SBI framework.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Numerical Methodology for Optimization of Compression-Ignited Engines Considering Combustion Noise Control

It is challenging to develop highly efficient and clean engines while meeting user expectations in terms of performance, comfort, and drivability. One of the critical aspects in this regard is combustion noise control. Combustion noise accounts for about 40 percent of the overall engine noise in typical turbocharged diesel engines. The experimental investigation of noise generation is difficult due to its inherent complexity and measurement limitations. Therefore, it is important to develop efficient numerical strategies in order to gain a better understanding of the combustion noise mechanisms. In this work, a novel methodology was developed, combining computational fluid dynamics (CFD) modeling and genetic algorithm (GA) technique to optimize the combustion system hardware design of a high-speed direct injection (HSDI) diesel engine, with respect to various emissions and performance targets including combustion noise. The CFD model was specifically set up to reproduce the unsteady pressure field inside the combustion chamber, thereby allowing an accurate prediction of the acoustic response of the combustion phenomena. The model was validated by simulating several steady operating conditions and comparing the numerical results against experimental data, in both temporal and frequency domains. Thereafter, a GA optimization was performed with the goal of minimizing indicated specific fuel consumption (ISFC) and combustion noise, while restricting pollutant (soot and NOx) emissions to their respective baseline values. Eight design variables were selected pertaining to piston bowl geometry, nozzle inclusion angle, number of injector nozzle holes, and in-cylinder swirl. An objective merit function (MF) based on the emissions, ISFC, and combustion noise was constructed to quantify the strength of the engine designs and was determined using the CFD model as the function evaluator. The in-cylinder noise level was characterized by the total resonance energy of local pressure oscillations. The optimum engine configuration thus obtained showed a significant improvement in terms of efficiency and combustion noise compared to the baseline system, along with both soot and NOx emissions within their respective constraints. This optimum configuration included a deeper and tighter bowl geometry with higher swirl and larger number of nozzle holes. Subsequently, a more detailed acoustics analysis based on proper orthogonal decomposition (POD) technique was carried out to further explore the combustion noise benefits achieved by the GA optimum. In conclusion, this computational study is a first of its kind (to the best of the authors’ knowledge), which demonstrates a comprehensive framework to incorporate combustion noise into a numerical optimization strategy for engine design.

42 ENGINEERING↗
DOE OSTI2016

Eastern Renewable Generation Integration Study: Redefining What’s Possible for Renewable Energy

NREL project manager Aaron Bloom introduces NREL’s Eastern Renewable Generation Integration Study (ERGIS) and high-performance computing capabilities and new methodologies that allowed NREL to model operations of the Eastern Interconnection at unprecedented fidelity. ERGIS shows that the Eastern Interconnection can balance the variability and uncertainty of wind and solar photovoltaics at a 5-minute level, for one simulated year.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

The Eastern Renewable Generation Integration Study: Insights on System Stress: Preprint

The Eastern Renewable Generation Integration Study (ERGIS) explores the operational impacts of the wide spread adoption of wind and solar photovoltaics (PV) resources in North America's Eastern and Quebec Interconnections. We explore the impact of large scale adoption of wind and solar generation on the unit commitment and economic dispatch of the largest coordinated power system in the world by simulating hourly and five-minute operations. Using NREL's high-performance computing capabilities and new methodologies to model operations, we found that the modeled system, as simulated with evolutionary change in 2026, could balance the variability and uncertainty of wind and solar PV at a five-minute level under a variety of conditions. Our simulations achieve instantaneous penetrations that exceed 50% of load while meeting an annual penetration of 30% on an energy basis. The system meets balanced load and supply in all intervals, with modest curtailment, using technologies and practices that are widely available today. However, a variety of the conditions present in these simulations deviate substantially from historical practice. In this work, we analyze potentially stressful system conditions that occur in the simulations and identify opportunities for innovation, regulatory reform, and changes in operating practices that require further analysis to enable the transition to a system with more wind and solar PV.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

A Nonlocal-Gradient Descent Method for Inverse Design in Nanophotonics

Local-gradient-based optimization approaches lack nonlocal exploration abilityrequired for escaping from local minima when searching non-convex landscapes.A directional Gaussian smoothing (DGS) approach was recently proposed in [29]and used to define a truly nonlocal gradient, referred to as the DGS gradient, inorder to enable nonlocal exploration in high-dimensional black-box optimization.Promising results show that replacing the traditional local gradient with the nonlocalDGS gradient can significantly improve the performance of gradient-based methodsin optimizing highly multi-modal loss functions. However, the current DGS methodis designed for unbounded and uncontrained optimization problems, making itinapplicable to real-world engineering optimization problems where the tuningparameters are often bounded and the loss function is usually constrained byphysical processes. In this work, we propose to extend to the DGS approachto the constrained inverse design framework in order to find better optima ofmulti-modal loss functions. A series of adaptive strategies for smoothing radiusand learning rate updating are developed to improve the computational efficiencyand robustness. Our methodology is demonstrated by an example of designing ananoscale wavelength demultiplexer, and shows superior performance compared tothe state-of-the-art approaches. By incorporating volume constraints, the optimizeddesign achieves an equivalently high performance but significantly reduces theamount of material usage.

Bi, Sirui↗
DOE OSTI2022

Optimal Transport for Super Resolution Applied to Astronomy Imaging

Super resolution is an essential tool in optics, especially on interstellar scales, due to physical laws restricting possible imaging resolution. We propose using optimal transport and entropy for super resolution applications. We prove that the reconstruction is accurate when sparsity is known and noise or distortion is small enough. We prove that the optimizer is stable and robust to noise and perturbations. We compare this method to a state of the art convolutional neural network and get similar results for much less computational cost and greater methodological flexibility.

Rawson, Michael G.↗