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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Verification of Data-Driven Models of Physical Phenomena using Interpretable Approximation

Machine-learned models, specifically neural networks, are increasingly used as “closures” or “constitutive models” in engineering simulators to represent fine-scale physical phenomena that are too computationally expensive to resolve explicitly. However, these neural net models of unresolved physical phenomena tend to fail unpredictably and are therefore not used in mission-critical simulations. In this report, we describe new methods to authenticate them, i.e., to determine the (physical) information content of their training datasets, qualify the scenarios where they may be used and to verify that the neural net, as trained, adhere to physics theory. We demonstrate these methods with neural net closure of turbulent phenomena used in Reynolds Averaged Navier-Stokes equations. We show the types of turbulent physics extant in our training datasets, and, using a test flow of an impinging jet, identify the exact locations where the neural network would be extrapolating i.e., where it would be used outside the feature-space where it was trained. Using Generalized Linear Mixed Models, we also generate explanations of the neural net (à la Local Interpretable Model agnostic Explanations) at prototypes placed in the training data and compare them with approximate analytical models from turbulence theory. Finally, we verify our findings by reproducing them using two different methods.

42 ENGINEERING↗
NASA NTRS2005

The Hierarchical Data Format for EOS (HDF-EOS)

HDF is a file format and a software library for data storage, management, exchange, and archiving. It is written and maintained by the National Center for Supercomputing Applications (NCSA). HDF5 has a very simple but versatile data model which is compatible with most competing formats. Through its grouping and linking mechanisms, the HDF5 data model enables complex data relationships and dependencies. HDF5 accommodates the inclusion of many common types of metadata and arbitrary types and quantities of user-defined metadata.

Ullman, Richard↗
DOE OSTI2021

Non-intrusive data-driven model reduction for differential–algebraic equations derived from lifting transformations

In this paper we present a non-intrusive data-driven approach for model reduction of nonlinear systems. The approach considers the particular case of nonlinear partial differential equations (PDEs) that form systems of partial differential–algebraic equations (PDAEs) when lifted to polynomial form. Such systems arise, for example, when the governing equations include Arrhenius reaction terms (e.g., in reacting flow models) and thermodynamic terms (e.g., the Helmholtz free energy terms in a phase-field solidification model). Using the known structured form of the lifted algebraic equations, the approach computes the reduced operators for the algebraic equations explicitly, using straightforward linear algebra operations on the basis matrices. The reduced operators for the differential equations are inferred from lifted snapshot data using operator inference, which solves a linear least squares regression problem. The approach is illustrated for the nonlinear model of solidification of a pure material. The lifting transformations reformulate the solidification PDEs as a system of PDAEs that have cubic structure. The operators of the lifted system for this solidification example have affine dependence on key process parameters, permitting us to learn a parametric reduced model with operator inference. Numerical experiments show the effectiveness of the resulting reduced models in capturing key aspects of the solidification dynamics.

42 ENGINEERING↗
DOE OSTI2024

Autonomous control for Heat-Pipe microreactor using Data-Driven model predictive control

To enable a self-regulating capability for heat pipe (HP) microreactors, an anticipatory control strategy achieved via model predictive control (MPC) could proactively respond to potential disturbances and deviations in operating setpoints. This paper demonstrates data-driven methods for predicting the distribution and transient of temperatures and heat fluxes at selected components and regions in a 37-HP system, based on which the optimal control actions in response to changes in user-defined setpoints can be found. We present the development and validation of linear state-space model, feedfoward, and recurrent neural networks. Here, we compare the performance of MPCs with different modeling approaches in terms of following setpoints for temperatures and averaged output heat fluxes. The accuracies of the three data-driven models are similar, but the control actions initiated by neural-network-based MPC can better adapt to drastic changes in setpoints yet generate the smallest errors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

AGGREGATE: dAta-driven modelinG preservinG contRollable dEr for outaGe mAnagemenT and rEsiliency (Final Report)

The AGGREGATE project team successfully developed and validated various modules for outage management. Brief summaries of each module are provided to showcase their strength for outage management and restoration for a distribution system with a high penetration of connected distribution energy resources (DERs). In recent years, inverter-based DERs have been widely deployed in distribution system. A most of behind-the-meter (BTM) solar power generation is not visible to the utility. The data-driven DER and load estimation modules are using machine learning (ML) and artificial intelligence (AI) to manage this issue, which provides an opportunity for distribution system operators (DSOs) to operate systems and make decisions in real-time for a distribution system with a high penetration of DERs deployed. Also, the estimated DER and true load can be further leveraged in network aggregation and cold-load pick up estimation for reducing the computing complexity and providing for fast restoration. After load demand and DER power generations have been estimated, the information will support topology and state estimation (SE). The topology estimation module demonstrated the viability of mixed integer linear programming (MILP) formulation to estimate the most likely operational radial topology and outage sections using power flow measurements, historical/estimated load and DERs data and smart meter ping measurements. Formulation includes continuous (power flow, load and DERs data) and binary measurements (smart meter ping measurements) in a single formulation. Errors in continuous data and binary data are modeled as normal distribution and Bernoulli distribution, respectively. In the future distribution grid, the power injection from controllable DERs will be essential for efficient and resilient grid operation. However, determining the optimal DER injections and restoration actions is dependent on knowledge of the system states. State estimation (SE), already the cornerstone of transmission energy management systems, will become commonplace in distribution management systems as more measurements become available from deployment of automated metering infrastructure (AMI). Observability analysis is the first step in SE, as it determines the sufficiency of the available measurements for accurately estimating the current system states. A new type of pseudo-measurement called a Correlational Measurement (CM) is introduced in this module, to enhance the observability of the system to enable more accurate SE. CMs encapsulate knowledge of correlation between demand patterns for similar classes of loads as well as injection patterns for same-technology renewable DERs. During grid contingency scenarios, DERs have been traditionally disconnected, without any fault ride-through capabilities. However, with new regulations and better technology, it is feasible for these resources to contribute to the grid’s restoration after an adverse event and hence enhance resilience. The controllability module proposes a two-step restoration scheme for the power system restoration process by leveraging additional degrees of freedom in power electronics interfaced DERs for mitigating voltage problems. In a resilience mode without the utility system, the distribution grid relies on DERs to serve critical load. In such a severe event with multiple faults on the distribution feeders, actuation of various protective devices (PDs) divides the distribution system into electrical islands. The undetected actuated PDs due to fault current contributions from DERs can delay the restoration process, thereby reducing the system resilience. The Advanced Outage Management (AOM) and the Advanced Feeder Restoration (AFR) modules developed in this project provide improved system resilience with multiple DERs. AOM identifies the faulted sections and actuated PDs in a distribution system with DERs by incorporating smart meter data. The most credible outage scenario including fault locations, PD actuations, and fault indicator (FI) failures is identified by a set of binary integer linear programming incorporating hypotheses. The AFR module serves to restore a distribution system with available energy resources taking into consideration the availability of utility sources and DERs. By partitioning the system into islands, critical load will be served with the available generation resources within islands based on the solution of a MILP. When the utility systems become available, the optimal path will be determined by a spanning tree search algorithm that reconnects these islands back to substations and restores the remaining load. The transmission and distribution (T&D) co-simulation module was used to validate the effect of a control action performed on the distribution side assets as it propagates to the transmission side. This ensures that the control action performed results in a feasible operating point on both the transmission and the distribution system. In addition to validation, the team used the T&D co-simulation module to demonstrate how distribution system assets can be used to mitigate issues on the transmission system. Specifically, the team demonstrated that appropriate switching operations on the distribution side can alleviate the line overload condition on the transmission side without causing new operational constraint violations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Distribution System Dataset Generator for AI Applications [SWR-24-75]

This software is a simple, light-weight python package to generate pytorch compatible machine learning graph dataset representing electric power distribution system. User is able to use these graph datasets to test their graph generation artificial intelligence (AI) models, link prediction AI models, graph classification AI models and so much more. This package uses grid-data-models (https://github.com/NREL-Distribution-Suites/grid-data-models) as input data format for power distribution system. NREL-Ditto (https://github.com/NREL-Distribution-Suites/ditto) tool can be leveraged to transform popular distribution system file formats such as opendss, cyme and synergi to grid-data-models.

Duwadi, Kapil↗
DOE OSTI2022

Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inference

Operator inference learns low-dimensional dynamical-system models with polynomial nonlinear terms from trajectories of high-dimensional physical systems (non-intrusive model reduction). Here, this work focuses on the large class of physical systems that can be well described by models with quadratic and cubic nonlinear terms and proposes a regularizer for operator inference that induces a stability bias onto learned models. The proposed regularizer is physics informed in the sense that it penalizes higher-order terms with large norms and so explicitly leverages the polynomial model form that is given by the underlying physics. This means that the proposed approach judiciously learns from data and physical insights combined, rather than from either data or physics alone. Additionally, a formulation of operator inference is proposed that enforces model constraints for preserving structure such as symmetry and definiteness in linear terms. Numerical results demonstrate that models learned with operator inference and the proposed regularizer and structure preservation are accurate and stable even in cases where using no regularization and Tikhonov regularization leads to models that are unstable.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2018

NASA's Carbon Cycle OSSE Initiative - Informing Future Space-Based Observing Strategies Through Advanced Modeling and Data Assimilation

Land and ocean carbon sinks absorb half of human CO2 emissions. The fate of these sinks in a changing world is unknown, introducing large uncertainties in climate projections. Satellite measurements of atmospheric CO2 are required to better understand the processes governing carbon uptake. Careful planning of future missions using Observing System Simulation Experiments (OSSEs) can help ensure that they meet the needs of the scientific and policy communities. NASA's Carbon Cycle OSSE Initiative brings together researchers from multiple universities and NASA centers to create model-derived data products in support of informed mission planning.

Ott, Lesley↗
NASA NTRS2004

Variable Stiffness Spar Wind-Tunnel Model Development and Testing

The concept of exploiting wing flexibility to improve aerodynamic performance was investigated in the wind tunnel by employing multiple control surfaces and by varying wing structural stiffness via a Variable Stiffness Spar (VSS) mechanism. High design loads compromised the VSS effectiveness because the aerodynamic wind-tunnel model was much stiffer than desired in order to meet the strength requirements. Results from tests of the model include stiffness and modal data, model deformation data, aerodynamic loads, static control surface derivatives, and fuselage standoff pressure data. Effects of the VSS on the stiffness and modal characteristics, lift curve slope, and control surface effectiveness are discussed. The VSS had the most effect on the rolling moment generated by the leading-edge outboard flap at subsonic speeds. The effects of the VSS for the other control surfaces and speed regimes were less. The difficulties encountered and the ability of the VSS to alter the aeroelastic characteristics of the wing emphasize the need for the development of improved design and construction methods for static aeroelastic models. The data collected and presented is valuable in terms of understanding static aeroelastic wind-tunnel model development.

Florance, James R.↗
NASA NTRS1993

The IRGen infrared data base modeler

IRGen is a modeling system which creates three-dimensional IR data bases for real-time simulation of thermal IR sensors. Starting from a visual data base, IRGen computes the temperature and radiance of every data base surface with a user-specified thermal environment. The predicted gray shade of each surface is then computed from the user specified sensor characteristics. IRGen is based on first-principles models of heat transport and heat flux sources, and it accurately simulates the variations of IR imagery with time of day and with changing environmental conditions. The starting point for creating an IRGen data base is a visual faceted data base, in which every facet has been labeled with a material code. This code is an index into a material data base which contains surface and bulk thermal properties for the material. IRGen uses the material properties to compute the surface temperature at the specified time of day. IRGen also supports image generator features such as texturing and smooth shading, which greatly enhance image realism.

Bernstein, Uri↗
NASA NTRS2018

The Atmosphere-Ocean Interface Layer of NASA's Goddard Earth Observing System Model and Data Assimilation System Volume 51

The Goddard Earth Observing System (GEOS) general circulation model (GCM) includes modules for sea surface temperature (SST) diurnal warming and cool-skin layers. To support the application of a coupled atmosphere-ocean data assimilation capability, the GCM needs to be flexible enough to support both coupled atmosphere ocean general circulation model (AOGCM) and atmosphere-only (AGCM) configurations, with only minor configuration changes at the user interface. This document presents a formulation of an atmosphere-ocean interface layer (AOIL) that serves this purpose. Previous work by Akella et al. (2017) described a version of a model for near-surface temperature variations, including both both diurnal warming and cool-skin effects, that has been used since 2017 in the near-real-time GEOS FP (forward processing) weather analysis and forecasting system. The diurnal cycle of SST in that version of the GEOS atmospheric data assimilation system (ADAS) undergoes a sharp decay in the late afternoon (local time). The updated AOIL presented here includes a modification of the similarity function used in the diurnal warming model. Results from offline model runs illustrate an improvement in the near-surface (less than 0:5m depth) diurnal cycle compared to the original formulation. The new formulation requires minimal parameter tuning, and the improvements are robust across long (several month) simulation periods. This new model formulation, however, retains some deficiences from the previous module, such as a small warm bias in calm wind conditions for water depths below 1m. Our future work would include surface salinification and sea-ice into the AOIL.

Koster, Randal D.↗
DOE OSTI2025

Enhancing Drinking Water Quality Modeling: Leveraging Physics Informed Neural Networks for Learning with Imperfect Reaction Models and Partial Data

Chemical kinetics models, typically formulated as systems of ordinary or partial differential equations, are valuable tools for simulating drinking water quality. However, these models often face inaccuracies due to discrepancies between the laboratory and the real-world conditions, as well as limitations in experimental analytical methods, hindering the accurate representation of the true underlying chemical mechanisms. In this study, we propose a Physics Informed Neural Network (PINN), using the eXtreme Theory of Functional Connections, to improve the prediction of chemical concentrations over time. The PINN method accounts for imperfect chemical models and incorporates partial data to improve predictions. Focusing on reactions describing water disinfection residual and disinfectant byproduct formation, which are crucial for public health and regulatory compliance, we demonstrate that the PINN model is able to accurately predict the concentrations of chemical species across various pH values. Notably, the model extends its accuracy to predict concentrations of chemical species not originally included in its training data. The developed method can be extended to a variety of chemical systems, offering a wide array of potential applications.

13 HYDRO ENERGY↗
DOE OSTI2023

Utilizing data-based modeling with low life cycle GHG emissions algae biofuels for engine optimization

Aquatic microalgae are a highly promising feedstock for the production of biocrude and tailored biofuels, with distinct advantages over traditional terrestrial crops, such as reduced land use and avoidance of food production competition. However, unlocking their full potential requires the development of biofuels with low life cycle greenhouse emissions biofuels, such as algae biofuels, which can significantly reduce the environmental impact of the transportation systems without requiring a complete overhaul of existing engine technology. In this study, we employ cutting-edge data-based AI modeling techniques to optimize the performance of heavy-duty engines, with a focus on transitioning towards biofuels with low life cycle greenhouse emissions biofuels. Our methodology offers significant advantages over traditional sweep testing, enabling efficient and accurate optimization of engine performance with minimal time and resources consumption. Our findings demonstrate the potential of utilizing this approach, with up to 55% NOx emissions reductions and up to 2% reduction in fuel consumption compared to the baseline optimized point. Moving forward, we plan to utilize a 30% blend of algae biofuels with diesel fuel, with the ultimate goal of achieving up to 60% lifecycle GHG emissions. Lastly, we plan to compare the results with 100% renewable biodiesel to add an additional dimension of investigating the impact of fuel chemistry on engine optimization. Overall, this study underscores the vital importance of biofuels for reducing the carbon footprint of the transportation sector and supporting a sustainable future. By harnessing the power of data-based AI modeling with low life cycle greenhouse emissions biofuels, we can accelerate the adoption of more environmentally friendly transportation systems and reduce their impact on the planet. Our findings contribute to this transition and offer insights for developing efficient and effective strategies for addressing global climate change.

09 BIOMASS FUELS↗
DOE OSTI2024

Learning physics-based reduced-order models from data using nonlinear manifolds

Here we present a novel method for learning reduced-order models of dynamical systems using nonlinear manifolds. First, we learn the manifold by identifying nonlinear structure in the data through a general representation learning problem. The proposed approach is driven by embeddings of low-order polynomial form. A projection onto the nonlinear manifold reveals the algebraic structure of the reduced-space system that governs the problem of interest. The matrix operators of the reduced-order model are then inferred from the data using operator inference. Numerical experiments on a number of nonlinear problems demonstrate the generalizability of the methodology and the increase in accuracy that can be obtained over reduced-order modeling methods that employ a linear subspace approximation.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2013

Modeling Longitudinal Data Containing Non-Normal Within Subject Errors

The mission of the National Aeronautics and Space Administration’s (NASA) human research program is to advance safe human spaceflight. This involves conducting experiments, collecting data, and analyzing data. The data are longitudinal and result from a relatively few number of subjects; typically 10 – 20. A longitudinal study refers to an investigation where participant outcomes and possibly treatments are collected at multiple follow-up times. Standard statistical designs such as mean regression with random effects and mixed–effects regression are inadequate for such data because the population is typically not approximately normally distributed. Hence, more advanced data analysis methods are necessary. This research focuses on four such methods for longitudinal data analysis: the recently proposed linear quantile mixed models (lqmm) by Geraci and Bottai (2013), quantile regression, multilevel mixed–effects linear regression, and robust regression. This research also provides computational algorithms for longitudinal data that scientists can directly use for human spaceflight and other longitudinal data applications, then presents statistical evidence that verifies which method is best for specific situations. This advances the study of longitudinal data in a broad range of applications including applications in the sciences, technology, engineering and mathematics fields.

Feiveson, Alan↗