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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Grey-Box System Identification of Grid-Forming Inverters

This paper demonstrates the use of grey-box system identification methods for simplifying and understanding the nonlinear power dynamics of grid-forming inverters (GFMs). The power and frequency outputs of complex high-order GFM models are fed into system identification software in order to fit them to a predetermined LTI system and learn system parameters such as (synthetic) inertia and droop constants. The same process is then run for a high-order synchronous generator model, and the outputs are fit to the same set of LTI equations. Simulation of a network of GFM inverters with diverse control architecture is also performed for the same process. The intent is threefold: first, to demonstrate the appropriateness of unified LTI models for describing the power and frequency dynamics of individual resources and connected networks, in order to facilitate analysis of larger heterogeneous networked systems; second, to discover the relationship between internal control parameters of GFMs and their externally observed values; and third, to validate that grey-box data-driven system identification techniques can be a valuable tool to discover the values of important parameters in the absence of explicit vendor models.

analytical models↗
DOE OSTI2023

Domain Aware Deep-learning Algorithms Integrated with Scientific-computing Technologies (DADAIST)

This technical report summarized the contribution of the DADAIST project funded by the Data Model Convergence Initiative via the Laboratory Directed Research and Development (LDRD) investments at Pacific Northwest National Laboratory (PNNL). Specifically, we report the development of the NeuroMANCER (Neural Modules with Adaptive Nonlinear Constraints and Efficient Regularizations), a new open-source Scientific Machine Learning library for formulating and solving parametric constrained optimization problems, physics-informed system identification, and parametric optimal control problems. NeuroMANCER is using differentiable programming to combine modern data-driven models and optimization modeling language into a coherent algorithmic and software framework. NeuroMANCER is a Pytorch-based framework and adopts much of its philosophy focused on research and development, rapid prototyping, and streamlined deployment. Strong emphasis is given to extensibility, interoperability with the PyTorch ecosystem, and quick adaptability to custom domain problems. Neuromancer repository contains a comprehensive library of differentiable modules, including custom activation functions, matrix factorizations, deep learning architectures, neural differential equations, differential equation solvers, implicit layers such as iterative solvers, high-level API for symbolic expressions, API for modeling and control of dynamical systems, and extensive set of tutorial code examples in the form of python scripts and jupyter notebooks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

PVDeg: Enhancing Usability and AI-Driven Multi-Mechanism Degradation Modeling

PVDeg version 0.7.0, released in December 2025, introduced major enhancements to improve usability and performance. This update reorganized tutorials and tool notebooks to create a more intuitive experience, enabling users to easily follow and adapt workflows for their specific analyses. In addition to structural improvements, both the notebooks and core logic underwent significant optimization for efficiency, robustness, and style. These refinements were supported by new testing frameworks built on nbval and pytest, adherence to PEP8 standards, and extensive code refactoring, which collectively simplify onboarding for new developers. Looking ahead, version 0.8.0 will deliver advanced AI-driven capabilities. The primary focus is to further develop and automate the degradation workflow, designed to analyze PV module degradation across diverse locations and system configurations. By integrating large language models (LLMs) to scan literature and compile a comprehensive database of materials and degradation rates, this feature will enable modeling of multiple materials and mechanisms within a single, streamlined workflow. Users will be able to evaluate degradation impacts on different system architectures under varying environmental conditions, facilitating informed decisions on bill-of-materials optimization for specific deployment scenarios. These advancements position PVDeg as a powerful, user-friendly tool for accelerating PV reliability research and system design.

14 SOLAR ENERGY↗
NASA NTRS1993

Acting to gain information

This report is concerned with agents that act to gain information. In previous work, we developed agent models combining qualitative modeling with real-time control. That work, however, focused primarily on actions that affect physical states of the environment. The current study extends that work by explicitly considering problems of active information-gathering and by exploring specialized aspects of information-gathering in computational perception, learning, and language. In our theoretical investigations, we analyzed agents into their perceptual and action components and identified these with elements of a state-machine model of control. The mathematical properties of each was developed in isolation and interactions were then studied. We considered the complexity dimension and the uncertainty dimension and related these to intelligent-agent design issues. We also explored active information gathering in visual processing. Working within the active vision paradigm, we developed a concept of 'minimal meaningful measurements' suitable for demand-driven vision. We then developed and tested an architecture for ongoing recognition and interpretation of visual information. In the area of information gathering through learning, we explored techniques for coping with combinatorial complexity. We also explored information gathering through explicit linguistic action by considering the nature of conversational rules, coordination, and situated communication behavior.

Rosenchein, Stanley J.↗
NASA NTRS1996

Use of the Collaborative Optimization Architecture for Launch Vehicle Design

Collaborative optimization is a new design architecture specifically created for large-scale distributed-analysis applications. In this approach, problem is decomposed into a user-defined number of subspace optimization problems that are driven towards interdisciplinary compatibility and the appropriate solution by a system-level coordination process. This decentralized design strategy allows domain-specific issues to be accommodated by disciplinary analysts, while requiring interdisciplinary decisions to be reached by consensus. The present investigation focuses on application of the collaborative optimization architecture to the multidisciplinary design of a single-stage-to-orbit launch vehicle. Vehicle design, trajectory, and cost issues are directly modeled. Posed to suit the collaborative architecture, the design problem is characterized by 5 design variables and 16 constraints. Numerous collaborative solutions are obtained. Comparison of these solutions demonstrates the influence which an priori ascent-abort criterion has on development cost. Similarly, objective-function selection is discussed, demonstrating the difference between minimum weight and minimum cost concepts. The operational advantages of the collaborative optimization

Braun, R. D.↗
NASA NTRSDate not supplied

Simulation of the Aerosol Size Distribution Using a Neural Network Surrogate for the Modal Aerosol Module (MAM7)

One objective of atmospheric simulations is to quantify the distribution of aerosols and their properties. Accurate parameterizations of the processes governing aerosol mass, particle number, and particle size distribution are important for predicting the Earth’s net radiative balance and aerosol-cloud interactions. The Modal Aerosol Module (MAM7) is a two-moment aerosol model that simulates mass, number, and size distribution of seven modes comprised of internally mixed aerosol species. The two-moment scheme adds significant computational expense but allows for the prediction of varying particle size distribution relative to the bulk method which predicts only total mass. In this work, we developed a neural network surrogate model for MAM7 (MAMnet) to predict the aerosol number concentration in NASA’s Global Earth Observing System (GEOS) without adding prohibitive computational expense. MAMnet, can be driven by output from a single moment, mass-based, aerosol scheme (Goddard Chemistry Aerosol and Radiation model (GOCART)) or from reanalysis products (Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2)). MAMnet was trained using number concentrations from a 5-year GEOS/MAM7 simulation at 1-degree horizontal resolution and using the total mass calculated across modes as inputs, as well as temperature and air density. The model architecture for MAMnet was based on AlexNet, the 2012 winner of the ImageNet Large Scale Visual Recognition Challenge. While some modifications were necessary to accommodate our problem, important aspects of the network were preserved. MAMnet was able to reproduce zonal dynamics and spatial distributions of the aerosol number concentration however predictability in the upper troposphere was poor.

Katherine H Breen↗
NASA NTRS1997

Cooperative Data Sharing: Simple Support for Clusters of SMP Nodes

Libraries like PVM and MPI send typed messages to allow for heterogeneous cluster computing. Lower-level libraries, such as GAM, provide more efficient access to communication by removing the need to copy messages between the interface and user space in some cases. still lower-level interfaces, such as UNET, get right down to the hardware level to provide maximum performance. However, these are all still interfaces for passing messages from one process to another, and have limited utility in a shared-memory environment, due primarily to the fact that message passing is just another term for copying. This drawback is made more pertinent by today's hybrid architectures (e.g. clusters of SMPs), where it is difficult to know beforehand whether two communicating processes will share memory. As a result, even portable language tools (like HPF compilers) must either map all interprocess communication, into message passing with the accompanying performance degradation in shared memory environments, or they must check each communication at run-time and implement the shared-memory case separately for efficiency. Cooperative Data Sharing (CDS) is a single user-level API which abstracts all communication between processes into the sharing and access coordination of memory regions, in a model which might be described as "distributed shared messages" or "large-grain distributed shared memory". As a result, the user programs to a simple latency-tolerant abstract communication specification which can be mapped efficiently to either a shared-memory or message-passing based run-time system, depending upon the available architecture. Unlike some distributed shared memory interfaces, the user still has complete control over the assignment of data to processors, the forwarding of data to its next likely destination, and the queuing of data until it is needed, so even the relatively high latency present in clusters can be accomodated. CDS does not require special use of an MMU, which can add overhead to some DSM systems, and does not require an SPMD programming model. unlike some message-passing interfaces, CDS allows the user to implement efficient demand-driven applications where processes must "fight" over data, and does not perform copying if processes share memory and do not attempt concurrent writes. CDS also supports heterogeneous computing, dynamic process creation, handlers, and a very simple thread-arbitration mechanism. Additional support for array subsections is currently being considered. The CDS1 API, which forms the kernel of CDS, is built primarily upon only 2 communication primitives, one process initiation primitive, and some data translation (and marshalling) routines, memory allocation routines, and priority control routines. The entire current collection of 28 routines provides enough functionality to implement most (or all) of MPI 1 and 2, which has a much larger interface consisting of hundreds of routines. still, the API is small enough to consider integrating into standard os interfaces for handling inter-process communication in a network-independent way. This approach would also help to solve many of the problems plaguing other higher-level standards such as MPI and PVM which must, in some cases, "play OS" to adequately address progress and process control issues. The CDS2 API, a higher level of interface roughly equivalent in functionality to MPI and to be built entirely upon CDS1, is still being designed. It is intended to add support for the equivalent of communicators, reduction and other collective operations, process topologies, additional support for process creation, and some automatic memory management. CDS2 will not exactly match MPI, because the copy-free semantics of communication from CDS1 will be supported. CDS2 application programs will be free to carefully also use CDS1. CDS1 has been implemented on networks of workstations running unmodified Unix-based operating systems, using UDP/IP and vendor-supplied high- performance locks. Although its inter-node performance is currently unimpressive due to rudimentary implementation technique, it even now outperforms highly-optimized MPI implementation on intra-node communication due to its support for non-copy communication. The similarity of the CDS1 architecture to that of other projects such as UNET and TRAP suggests that the inter-node performance can be increased significantly to surpass MPI or PVM, and it may be possible to migrate some of its functionality to communication controllers.

DiNucci, David C.↗
DOE OSTI2013

Data Management Technology Survey and Recommendation

The Data Management Plan (DMP) document already released describes the architecture and plan for developing the data management framework needed by the Carbon Capture Simulation Initiative (CCSI) toolset. The CCSI toolset includes: experimental data, physical property methods and parameters, empirical correlations, validation data set, models, simulation inputs and outputs, reduced-order models, cost models, risk models, uncertainty estimation, etc. Each tool in the toolset requires and generates data, and the data management system organizes and stores the data. The data management framework will be driven by end-users and interact with CCSI workflow components such as Turbine. This document reports on the technology survey completed as the first step in the Data Management Plan. It also provides recommendations for a path forward based on the survey results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Automated bubble analysis of high-speed subcooled flow boiling images using U-net transfer learning and global optical flow

Capturing and analyzing the bubble dynamics is crucial to improving the understanding of boiling heat transfer mechanisms and predicting boiling heat transfer coefficient and boiling crisis. High speed video (HSV) imaging has been used for decades towards this end. Still, there is no universal approach to quantitatively analyze bubble dynamics from HSV images. In this study, we propose a data-driven post-processing approach to segment, track, and identify wall-attached vapor bubbles from HSV images of the boiling process in subcooled flow conditions. Firstly, we employ a transfer learning framework with a U-Net-based convolution neural network (CNN) architecture to detect and segment bubbles in HSV images of diverse contrast and surface texture using very little data (e.g., 10 images) for training. Then, we evaluate the trained CNN model with 100 ground-truth images, and the validation results show that the model accuracy and precision in detecting the optical footprint of bubbles are higher than 90%. Finally, we suggest a criterion to identify a condensing bubble based on the divergence of the bubble displacement, which is calculated from sequential segmented bubble images using a global optical flow code. Using this combination of machine learning and optical flow, we can identify nucleation sites and track the growth of bubbles nucleating at each site to quantify nucleation site density, nucleation frequency, and other fundamental boiling parameters. The proposed system is validated using results obtained on a special heater, which enables both infrared (IR) thermometry and HSV imaging on a metallic surface. We compare the fundamental boiling parameters obtained by the two different diagnostics. The results show good agreement. In conclusion, the difference between the measurements of nucleation site density, averaged nucleation frequency, and averaged growth time performed with the two techniques is always within ± 20% and mostly ± 10% of the values measured with IR thermometry.

42 ENGINEERING↗
DOE OSTI2017

grim: A Flexible, Conservative Scheme for Relativistic Fluid Theories

Hot, diffuse, relativistic plasmas such as sub-Eddington black-hole accretion flows are expected to be collisionless, yet are commonly modeled as a fluid using ideal general relativistic magnetohydrodynamics (GRMHD). Dissipative effects such as heat conduction and viscosity can be important in a collisionless plasma and will potentially alter the dynamics and radiative properties of the flow from that in ideal fluid models; we refer to models that include these processes as Extended GRMHD. Here we describe a new conservative code, grim, that enables all of the above and additional physics to be efficiently incorporated. grim combines time evolution and primitive variable inversion needed for conservative schemes into a single step using an algorithm that only requires the residuals of the governing equations as inputs. This algorithm enables the code to be physics agnostic as well as flexibility regarding time-stepping schemes. grim runs on CPUs, as well as on GPUs, using the same code. We formulate a performance model and use it to show that our implementation runs optimally on both architectures. grim correctly captures classical GRMHD test problems as well as a new suite of linear and nonlinear test problems with anisotropic conduction and viscosity in special and general relativity. As tests and example applications, we resolve the shock substructure due to the presence of dissipation, and report on relativistic versions of the magneto-thermal instability and heat flux driven buoyancy instability, which arise due to anisotropic heat conduction, and of the firehose instability, which occurs due to anisotropic pressure (i.e., viscosity). Finally, we show an example integration of an accretion flow around a Kerr black hole, using Extended GRMHD.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Transcriptional networks underlying a primary ovarian insufficiency disorder in alligators naturally exposed to EDCs

Interactions between the endocrine system and environmental contaminants are responsible for impairing reproductive development and function. Despite the taxonomic diversity of affected species and attendant complexity inherent to natural systems, the underlying signaling pathways and cellular consequences are mostly studied in lab models. To resolve the genetic and endocrine pathways that mediate affected ovarian function in organisms exposed to endocrine disrupting contaminants in their natural environments, we assessed broad-scale transcriptional and steroidogenic responses to exogenous gonadotropin stimulation in juvenile alligators (Alligator missippiensis) originating from a lake with well-documented pollution (Lake Apopka, FL) and a nearby reference site (Lake Woodruff, FL). Here, we found that individuals from Lake Apopka are characterized by hyperandrogenism and display hyper-sensitive transcriptional responses to gonadotropin stimulation when compared to individuals from Lake Woodruff. Site-specific transcriptomic divergence appears to be driven by wholly distinct subsets of transcriptional regulators, indicating alterations to fundamental genetic pathways governing ovarian function. Consistent with broad-scale transcriptional differences, ovaries of Lake Apopka alligators displayed impediments to folliculogenesis, with larger germinal beds and decreased numbers of late-stage follicles. After resolving the ovarian transcriptome into clusters of co-expressed genes, most site-associated modules were correlated to ovarian follicule phenotypes across individuals. However, expression of two site-specific clusters were independent of ovarian cellular architecture and are hypothesized to represent alterations to cell-autonomous transcriptional programs. Collectively, our findings provide high resolution mapping of transcriptional patterns to specific reproductive function and advance our mechanistic understanding regarding impaired reproductive health in an established model of environmental endocrine disruption.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1995

Real-time value-driven diagnosis

Diagnosis is often thought of as an isolated task in theoretical reasoning (reasoning with the goal of updating our beliefs about the world). We present a decision-theoretic interpretation of diagnosis as a task in practical reasoning (reasoning with the goal of acting in the world), and sketch components of our approach to this task. These components include an abstract problem description, a decision-theoretic model of the basic task, a set of inference methods suitable for evaluating the decision representation in real-time, and a control architecture to provide the needed continuing coordination between the agent and its environment. A principal contribution of this work is the representation and inference methods we have developed, which extend previously available probabilistic inference methods and narrow, somewhat, the gap between probabilistic and logical models of diagnosis.

Dambrosio, Bruce↗
DOE OSTI2020

Information and Collaboration Levels in Vehicular Strings: A Comparative Study

The potential safety, productivity, and energy benefits of automated vehicles have driven a surge of research interest in their algorithms. Even within single-lane driving, control engineers now have a profusion of approaches available to them. Algorithm classes include classical controllers, receding horizon controllers, and constrained eco-driving formulae based on Pontryagin’s Minimum Principle. Differing connectivity architectures and collaboration levels further differentiate algorithms from one another. This study evaluated six controllers in two drive cycle-based scenarios using an electric powertrain model for energy analysis. Individual-vehicle and string performance were examined, including string stability and length. Algorithms with greater access to information generally performed best. Although collaboration affected energy use only slightly, it made a greater impact on string length.

Dollar, R. Austin↗
NASA NTRS1990

DSIM: A distributed simulator

Discrete event-driven simulation makes it possible to model a computer system in detail. However, such simulation models can require a significant time to execute. This is especially true when modeling large parallel or distributed systems containing many processors and a complex communication network. One solution is to distribute the simulation over several processors. If enough parallelism is achieved, large simulation models can be efficiently executed. This study proposes a distributed simulator called DSIM which can run on various architectures. A simulated test environment is used to verify and characterize the performance of DSIM. The results of the experiments indicate that speedup is application-dependent and, in DSIM's case, is also dependent on how the simulation model is distributed among the processors. Furthermore, the experiments reveal that the communication overhead of ethernet-based distributed systems makes it difficult to achieve reasonable speedup unless the simulation model is computation bound.

Goswami, Kumar K.↗
DOE OSTI2024

A framework for strategic discovery of credible neural network surrogate models under uncertainty

The widespread integration of deep neural networks in developing data-driven surrogate models for high-fidelity simulations of complex physical systems highlights the critical necessity for robust uncertainty quantification techniques and credibility assessment methodologies, ensuring the reliable deployment of surrogate models in consequential decision-making. Here, this study presents the Occam Plausibility Algorithm for surrogate models (OPAL-surrogate), providing a systematic framework to uncover predictive neural network-based surrogate models within the large space of potential models, including various neural network classes and choices of architecture and hyperparameters. The framework is grounded in hierarchical Bayesian inferences and employs model validation tests to evaluate the credibility and prediction reliability of the surrogate models under uncertainty. Leveraging these principles, OPAL-surrogate introduces a systematic and efficient strategy for balancing the trade-off between model complexity, accuracy, and prediction uncertainty. The effectiveness of OPAL-surrogate is demonstrated through two modeling problems, including the deformation of porous materials for building insulation and turbulent combustion flow for ablation of solid fuels within hybrid rocket motors.

42 ENGINEERING↗
DOE OSTI2023

Enabling off-highway diesel engine downsizing and performance improvement using electrically assisted turbocharging

Internal combustion engine (ICE) downsizing through various turbocharging configurations is generally known by the powertrain design community as an effective means to reduce frictional losses, increase waste heat recovery, and improve fuel efficiency while increasing engine power density. However, often is the case that turbocharging strategies, including variable geometry turbochargers, and regulated two-stage turbochargers, incur the performance tradeoff between transient response and fuel economy (pumping losses) at high engine speeds. For off-highway vehicles having particularly transient and high-powered duty cycles, efforts to improve this tradeoff and increase operational flexibility have turned to evaluating various electrified air system architectures. In this study, a 4.5 L diesel-ICE configured with an electrically driven compressor (eBooster ® ) is placed in series with a conventional turbocharger and integrated into a 48 V mild-hybrid powertrain architecture. The objective of this powertrain configuration is to enable engine downsizing by 34%, replacing the current 6.8 L ICE platform with the hybridized 4.5 L ICE concept. The commercial 1-D simulation software GT-SUITE is used for powertrain system development and system optimization. Development and validation of the GT-SUITE model and air system controls is concurrently supported through experimental data collection. The simulation model development includes using machine learning methods for optimizing exhaust gas dilution, injection timing, and eBooster ® power to improve steady-state and transient brake-specific fuel consumption while minimizing criteria pollutant emissions. It was found that total specific fluid consumption over the standardized non-road transient engine duty cycle could be reduced by 18% over the current 6.8 L engine by using an optimized eBoosted 4.5 L engine. The hybridized 4.5 L engine concept concurrently showed sufficient transient response capability and nearly an order of magnitude reduction in duty cycle total soot production.

25 ENERGY STORAGE↗
DOE OSTI2024

Real-Time Neuromorphic Processing of Spatiotemporal Data for Scientific Discovery

Spiking Neural Networks (SNNs) are brain-inspired computing models incorporating unique temporal dynamics and event-driven processing. Rich dynamics in both space and time offer great challenges and opportunities for efficient processing of sparse spatiotemporal data compared with conventional artificial neural networks (ANNs). Under this context, the goal of this project is to develop spiking neural network based neuromorphic computing to enable energy-efficient real-time learning and processing of spatiotemporal data. This report summarizes the key results on network architecture design, training methods, and SNN hardware acceleration achieved under this project, demonstrating the promise of spiking neural networks.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Modeling the Effective Elasticity of Anisotropic Porous Materials

The development and optimization of composite materials designed for thermal protection of NASA’s spacecraft require understanding their physical response to high-enthalpy environments. To predict their macro-scale properties and behavior, high-fidelity 3D simulations are performed at the microscale on realistic representations of these composites. The digital microstructures are generated either synthetically or through X-ray micro-computed tomography reconstructions. One of the main challenges in the prediction of the structural response of heatshield materials is the computation of the effective elasticity of the fibrous composite, as well as the understanding of the deformation and stresses generated at the microscale. These are driven by the fiber layout within the microstructure and the distribution of the infused matrix. In this effort, the micro-mechanical linear elastic behavior of fibrous ablators is modeled using a numerical method based on the Multi-Point Stress Approximation (MPSA) finite volume scheme, a generalization of the more commonly used Multi-Point Flux Approximation (MPFA) that was presented at the 10th Ablation Workshop. To predict the behavior of fibrous and woven architectures, algorithms that compute the local fiber orientation are used. The implementation of the MPSA was verified using analytical solutions, engineering test cases, and compared against legacy Finite Element Analysis (FEA) software. The stress analysis models were then applied to real geometries used by NASA in thermal protection systems such as fibrous preforms and woven materials and the results were compared to experimental data.

Elasticity↗