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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Improving Lidar Turbulence Estimates for Wind Energy

Remote sensing devices (e.g., lidars) are quickly becoming a cost-effective and reliable alternative to meteorological towers for wind energy applications. Although lidars can measure mean wind speeds accurately, these devices measure different values of turbulence intensity (TI) than an instrument on a tower. In response to these issues, a lidar TI error reduction model was recently developed for commercially available lidars. The TI error model first applies physics-based corrections to the lidar measurements, then uses machine-learning techniques to further reduce errors in lidar TI estimates. The model was tested at two sites in the Southern Plains where vertically profiling lidars were collocated with meteorological towers. This presentation primarily focuses on the physics-based corrections, which include corrections for instrument noise, volume averaging, and variance contamination. As different factors affect TI under different stability conditions, the combination of physical corrections applied in L-TERRA changes depending on the atmospheric stability during each 10-minute time period. This stability-dependent version of L-TERRA performed well at both sites, reducing TI error and bringing lidar TI estimates closer to estimates from instruments on towers. However, there is still scatter evident in the lidar TI estimates, indicating that there are physics that are not being captured in the current version of L-TERRA. Two options are discussed for modeling the remainder of the TI error physics in L-TERRA: machine learning and lidar simulations. Lidar simulations appear to be a better approach, as they can help improve understanding of atmospheric effects on TI error and do not require a large training data set.

17 WIND ENERGY↗
DOE OSTI2017

Engineering Inertial and Primary-Frequency Response for Distributed Energy Resources: Preprint

We propose a framework to engineer synthetic-inertia and droop-control parameters for distributed energy resources (DERs) so that the system frequency in a network composed of DERs and synchronous generators conforms to prescribed transient and steady-state performance specifications. Our approach is grounded in a second-order lumped-parameter model that captures the dynamics of synchronous generators and frequency-responsive DERs endowed with inertial and droop control. A key feature of this reduced-order model is that its parameters can be related to those of the originating higher-order dynamical model. This allows one to systematically design the DER inertial and droop-control coefficients leveraging classical frequency-domain response characteristics of second-order systems. Time-domain simulations validate the accuracy of the model-reduction method and demonstrate how DER controllers can be designed to meet steady-state-regulation and transient-performance specifications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

Understanding coking on Platinum catalysts by in situ TEM

Light alkenes are widely use as building blocks for the production of rubber, plastics, and other polymers, and are currently produced by steam cracking of alkanes or naphtha at high temperatures. However, these processes exhibit low alkene selectivity and produce significant amounts of methane and coke. Catalytic dehydrogenation of C2-C5 alkanes offers an attractive alternative because it utilizes low-cost reactants and can be carried out with high selectivity(1). Platinum is known to be the most effective transition metal for dehydrogenation of light alkanes. However, in the absence of a promoting element such as Sn, platinum catalysts deactivate rapidly due to coke formation, making it difficult to measure the true initial activity in comparison with bimetallic PtSn catalysts(2). Furthermore, it is difficult to assess composition and nanoparticle size effects with catalysts synthesized by traditional impregnation methods. We have developed a colloidal synthesis in which we form the metal particles ex-situ, enabling stricter control over the nucleation and growth of nanoparticles(3). Following immobilization and reduction, model catalysts of fixed composition and size are used in this study. This talk will present the effects of composition (PtxSny) and size (2-8 nm) on ethane dehydrogenation at low residence-time, a novel method that we show to significantly reduce coke formation and measure the true activity of these catalysts. Catalysts composition and size are characterized by XRD, TEM, and EDX. The results show that the addition of Sn favors higher ethene production and selectivity, even in the absence of coke formation. Furthermore, the reaction is more favored on large particles that can be explained by poisoning of corner sites on small particles. In conjunction with these studies, we have examined coke formation on Pt particles in-situ using a Titan 80-300 ETEM to better understand the location of carbon nucleation along with the formation of multiple graphene layers. If these issues are resolved, design of future catalysts to limit deactivation can be achieved(4). However, imaging challenges to avoid beam effects exist and obtaining an optimal imaging strategy to observe carbon formation on Pt will be discussed.

Wu, J↗
DOE OSTI2020

Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning

We assume that we are given a time series of data from a dynamical system and our task is to learn the flow map of the dynamical system. We present a collection of results on how to enforce constraints coming from the dynamical system in order to accelerate the training of deep neural networks to represent the flow map of the system as well as increase their predictive ability. In particular, we provide ways to enforce constraints during training for all three major modes of learning, namely supervised, unsupervised and reinforcement learning. In general, the dynamic constraints need to include terms which are analogous to memory terms in model reduction formalisms. Such memory terms act as a restoring force which corrects the errors committed by the learned flow map during prediction. For supervised learning, the constraints are added to the objective function. For the case of unsupervised learning, in particular generative adversarial networks, the constraints are introduced by augmenting the input of the discriminator. Finally, for the case of reinforcement learning and in particular actor-critic methods, the constraints are added to the reward function. In addition, for the reinforcement learning case, we present a novel approach based on homotopy of the action-value function in order to stabilize and accelerate training. We use numerical results for the Lorenz system to illustrate the various constructions.

Stinis, Panagiotis↗
DOE OSTI2022

Iterative Workflow for Quantification and Minimization of Reduced Chemistry-Induced Uncertainties in Reacting Flow Simulation

Reduced chemistry models mitigate computational cost but introduce two sources of uncertainties in reacting flow simulation, including chemical information loss due to model reduction, and approximation errors due to non-optimal projection. We present an iterative workflow for quantification and minimization of reduced chemistry-induced uncertainties in reacting flow simulations.

chemistry↗
DOE OSTI2016

Time-Dependent Sensitivity Analysis of OECD Benchmark using BISON and RAVEN

Developments in uncertainty quantification for nuclear simulations have decreased the computational cost required to perform accurate sensitivity analysis. This work demonstrates implementation of these methods in the RAVEN framework, allowing for time-dependent sensitivity analysis. By demonstration we consider an OECD benchmark case. We propagate uncertainties using RAVEN operating on the BISON fuels performance code. We then consider the time-evolution of output response sensitivity to uncertain inputs. We perform sensitivity analysis using time-based stochastic collocation for generalized polynomial chaos (SCgPC) and high-dimension model reduction (HDMR). While uncertainty quantification applied to end-of-cycle statistics is useful, we demonstrate that dominant sources of uncertainty for figures of merit may fluctuate throughout the simulation. Time-dependent sensitivity analysis allows more insight on key design parameters for fuel. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2010

The Role of Hierarchy in Response Surface Modeling of Wind Tunnel Data

This paper is intended as a tutorial introduction to certain aspects of response surface modeling, for the experimentalist who has started to explore these methods as a means of improving productivity and quality in wind tunnel testing and other aerospace applications. A brief review of the productivity advantages of response surface modeling in aerospace research is followed by a description of the advantages of a common coding scheme that scales and centers independent variables. The benefits of model term reduction are reviewed. A constraint on model term reduction with coded factors is described in some detail, which requires such models to be well-formulated, or hierarchical. Examples illustrate the consequences of ignoring this constraint. The implication for automated regression model reduction procedures is discussed, and some opinions formed from the author s experience are offered on coding, model reduction, and hierarchy.

DeLoach, Richard↗
NASA NTRS2009

Development and Validation of a Model for Hydrogen Reduction of JSC-1A

Hydrogen reduction of lunar regolith has been proposed as a viable technology for oxygen production on the moon. Hydrogen reduces FeO present in the lunar regolith to form metallic iron and water. The water may be electrolyzed to recycle the hydrogen and produce oxygen. Depending upon the regolith composition, FeO may be bound to TiO2 as ilmenite or it may be dispersed in glassy substrates. Some testing of hydrogen reduction has been conducted with Apollo-returned lunar regolith samples. However, due to the restricted amount of lunar material available for testing, detailed understanding and modeling of the reduction process in regolith have not yet been developed. As a step in this direction, hydrogen reduction studies have been carried out in more detail with lunar regolith simulants such as JSC-1A by NASA and other organizations. While JSC-1A has some similarities with lunar regolith, it does not duplicate the wide variety of regolith types on the moon, for example, it contains almost no ilmenite. Nonetheless, it is a good starting point for developing an understanding of the hydrogen reduction process with regolith-like material. In this paper, a model utilizing a shrinking core formulation coupled with the reactor flow is described and validated against experimental data on hydrogen reduction of JSC-1A.

Hegde, U.↗
DOE OSTI2018

A Multi-scale Mathematical Model of Growth and Coalescence of Bubbles Beneath the Anode in an Aluminum Reduction Cell

Modeling of bubble shapes is challenging because of the wide range of length scales in aluminum reduction cells. A 3D multi-scale mathematical model was developed to understand the nucleation, growth and coalescence of bubbles beneath the anode and to investigate the transition of bubbles from the micro- to macro level. The motion of micro-bubbles is examined using the discrete bubble model (DBM) within a Lagrangian reference. An algorithm for the transition from discrete micro-bubbles to large bubbles, which are fully resolved by the volume of fluid (VOF) approach, is achieved using a user-defined function. The two-way coupling between discrete bubbles and continuous fluids is achieved by inter-phase momentum exchange. The model involves three kinds of bubble coalescence: micro-bubble coalescence is taken into account by the DBM; large bubbles swallowing up micro-bubbles are solved by the discrete-continuum transition model; the coalescence between large bubbles is handled by the VOF method. Numerical results show that the coverage and thickness of bubbles agree well with the experimental data in the literature. Bubble thickness stops increasing when the bubble elongates with the anode bottom, which is 4.0 to 4.5 mm. Meanwhile, two asymmetrical thick heads can be observed under the anode. Bubble release frequency increases with increasing current density.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Kinetic Model for the Reduction of Cu II Sites by NO + NH 3 and Reoxidation of NH 3 -Solvated Cu I Sites by O 2 and NO in Cu-SSZ-13

In this work, a kinetic model is developed for the reduction of Cu II sites by NO + NH 3 and the reoxidation of NH 3 -solvated Cu I sites by O 2 and NO in Cu-SSZ-13. Fourier transform infrared (FTIR) spectroscopy and spatially resolved capillary inlet mass spectrometry (SpaciMS) measurements during transient reactor experiments are utilized to identify the rate parameters associated with NO + NH 3 RHC (reduction half-cycle), proposed to occur via two distinct pathways involving adsorbed NH 3 and gas-phase NH 3 . The resulting NO + NH 3 RHC model is validated using spatiotemporal N 2 measurements covering a wide range of temperatures (200–450 °C) and space velocities (53,000–640,000 h –1 ). N 2 O formation is observed and modeled during NO + NH 3 RHC, with quantitative validation under standard selective catalytic reduction (SCR) conditions. Experimentally measured enthalpic and entropic changes associated with O 2 adsorption on NH 3 -solvated Cu I (ZCu(NH 3 ) 2 ) complexes [ Kamasamudram, K. Catal. Today 2010 , 151 (3–4), 212-222], along with activation energies estimated computationally for the intercage diffusion of ZCu(NH 3 ) 2 complexes [ Paolucci, C. Science 2017 , 357 (6 354), 898-903], are incorporated into a mean field kinetic model for the low-temperature oxidation half-cycle (OHC). Significant NH 3 release is observed during the isothermal oxidation of Cu I sites, attributed to desorption of NH 3 ligands from NH 3 -solvated Cu II dimers (Z 2 Cu 2 (NH 3 ) 4 O 2 ). Reduction of these dimeric complexes leads to the consumption of one NO/Cu II , contradicting the expected reduction stoichiometry. Inclusion of a global Arrhenius rate for the NO titration of Z 2 Cu 2 (NH 3 ) 4 O 2 complexes provides accurate representations of standard SCR on reduced and oxidized catalysts, predicting transient NO and NH 3 consumption between 150 and 250 °C as a function of hydrothermal aging. Deactivation of low-temperature standard SCR by NH 3 is observed at high NH 3 pressures, modeled via the formation of superoxo amino (ZCu(NH 3 ) 3 OO*) complexes during NH 3 titration of Z 2 Cu 2 (NH 3 ) 4 O 2 complexes [ Negri, C. J. Am. Chem. Soc. 2020 , 142 (37), 15884-15896]. The redox kinetic model presented here provides a foundational description of active site redox during low-temperature standard SCR, combining the recent kinetic, spectroscopic, and computational findings on the mechanism of standard SCR over Cu-SSZ-13.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Nonlinear dimension reduction for surrogate modeling using gradient information

We introduce a method for the nonlinear dimension reduction of a high-dimensional function $u:{\mathbb{R}}^d\rightarrow{\mathbb{R}}$, $d\gg 1$. Our objective is to identify a nonlinear feature map $g:{\mathbb{R}}^d\rightarrow{\mathbb{R}}^m$, with a prescribed intermediate dimension $m\ll d$, so that $u$ can be well approximated by $f\circ g$ for some profile function $f:{\mathbb{R}}^m\rightarrow{\mathbb{R}}$. We propose to build the feature map by aligning the Jacobian $\nabla g$ with the gradient $\nabla u$, and we theoretically analyze the properties of the resulting $g$. Once $g$ is built, we construct $f$ by solving a gradient-enhanced least squares problem. Our practical algorithm uses a sample $\{{\textbf{x}}^{(i)},u({\textbf{x}}^{(i)}),\nabla u({\textbf{x}}^{(i)})\}_{i=1}^N$ and builds both $g$ and $f$ on adaptive downward-closed polynomial spaces, using cross validation to avoid overfitting. We numerically evaluate the performance of our algorithm across different benchmarks, and explore the impact of the intermediate dimension $m$. We show that building a nonlinear feature map $g$ can permit more accurate approximation of $u$ than a linear $g$, for the same input data set.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Modeling the Environment-Dependent Kinetics of Oxygen Reduction Reaction – a Continuum Model for Electric Double Layer

Here, for proton-exchange-membrane fuel cells (PEMFCs) to achieve broad commercialization, improved energy-conversion efficiency with minimal Pt-based electrocatalyst is required. Because the sluggish rate of oxygen reduction reaction (ORR) limits the efficiency of PEMFCs, the efficiency improvement requires a better understanding of ORR kinetics and mechanism to design better catalyst. To understand the ORR mechanism, theoretical and experimental analyses have been conducted. While previous studies reasonably explained the catalyst-dependent activity on single crystal catalysts in 0.1 M perchloric acid solution, the explicit effect of electrolyte and related microenvironments is not thoroughly understood. The change in the electrolyte alters the electric-double-layer (EDL) structure and thus the local microenvironment at the electrode/electrolyte interface. Thus, the structure of the EDL should be carefully analyzed to uncover the electrolyte-dependent reaction kinetics. In this talk, we propose a multiscale continuum model to predict the EDL structure and examine the effect of perchloric acid concentration on ORR activity on Pt (111). The model includes Density Potential Functional Theory (DPFT) for electron density and Modified Poisson Boltzmann equation for species’ density and electric potential. Also, the interaction between adsorbents and electric field is taken into account by minimizing the grand potential. After model validation with experimentally measured double-layer capacity data as a function of applied potential and concentration, the effect of the perchloric acid concentration (0.02 M – 0.2 M) on ORR activity is analyzed and discussed. It is shown that the model reproduces the specific activity obtained in the experiments when assuming the oxygen adsorption is limiting the rate, which can be attributed to the large energetic barrier for solvent reorganization. Then, extension of the model to PEMFC ionomer electrolytes will be introduced. Overall, the model framework and findings provide insights into the ORR mechanism and guidance on how to tailor catalyst materials for increased PEMFC performance.

30 DIRECT ENERGY CONVERSION↗
NASA NTRS1978

Procedures for generation and reduction of linear models of a turbofan engine

A real time hybrid simulation of the Pratt & Whitney F100-PW-F100 turbofan engine was used for linear-model generation. The linear models were used to analyze the effect of disturbances about an operating point on the dynamic performance of the engine. A procedure that disturbs, samples, and records the state and control variables was developed. For large systems, such as the F100 engine, the state vector is large and may contain high-frequency information not required for control. This, reducing the full-state to a reduced-order model may be a practicable approach to simplifying the control design. A reduction technique was developed to generate reduced-order models. Selected linear and nonlinear output responses to exhaust-nozzle area and main-burner fuel flow disturbances are presented for comparison.

Seldner, K.↗
DOE OSTI2024

Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields

Tensor datatypes representing field variables like stress, displacement, velocity, etc., have increasingly become a common occurrence in data-driven modeling and analysis of simulations. Numerous methods [such as convolutional neural networks (CNNs)] exist to address the meta-modeling of field data from simulations. As the complexity of the simulation increases, so does the cost of acquisition, leading to limited data scenarios. Modeling of tensor datatypes under limited data scenarios remains a hindrance for engineering applications. Here, in this article, we introduce a direct image-to-image modeling framework of convolutional autoencoders enhanced by information bottleneck loss function to tackle the tensor data types with limited data. The information bottleneck method penalizes the nuisance information in the latent space while maximizing relevant information making it robust for limited data scenarios. The entire neural network framework is further combined with robust hyperparameter optimization. We perform numerical studies to compare the predictive performance of the proposed method with a dimensionality reduction-based surrogate modeling framework on a representative linear elastic ellipsoidal void problem with uniaxial loading. The data structure focuses on the low-data regime (fewer than 100 data points) and includes the parameterized geometry of the ellipsoidal void as the input and the predicted stress field as the output. The results of the numerical studies show that the information bottleneck approach yields improved overall accuracy and more precise prediction of the extremes of the stress field. Additionally, an in-depth analysis is carried out to elucidate the information compression behavior of the proposed framework.

artificial intelligence↗
NASA NTRS1989

Stochastic model of the NASA/MSFC ground facility for large space structures with uncertain parameters: The maximum entropy approach, part 2

A validated technology data base is being developed in the areas of control/structures interaction, deployment dynamics, and system performance for Large Space Structures (LSS). A Ground Facility (GF), in which the dynamics and control systems being considered for LSS applications can be verified, was designed and built. One of the important aspects of the GF is to verify the analytical model for the control system design. The procedure is to describe the control system mathematically as well as possible, then to perform tests on the control system, and finally to factor those results into the mathematical model. The reduction of the order of a higher order control plant was addressed. The computer program was improved for the maximum entropy principle adopted in Hyland's MEOP method. The program was tested against the testing problem. It resulted in a very close match. Two methods of model reduction were examined: Wilson's model reduction method and Hyland's optimal projection (OP) method. Design of a computer program for Hyland's OP method was attempted. Due to the difficulty encountered at the stage where a special matrix factorization technique is needed in order to obtain the required projection matrix, the program was successful up to the finding of the Linear Quadratic Gaussian solution but not beyond. Numerical results along with computer programs which employed ORACLS are presented.

Hsia, Wei Shen↗
DOE OSTI2017

Modal Substructuring of Geometrically Nonlinear Finite Element Models with Interface Reduction

Substructuring methods have been widely used in structural dynamics to divide large, complicated finite element models into smaller substructures. For linear systems, many methods have been developed to reduce the subcomponents down to a low order set of equations using a special set of component modes, and these are then assembled to approximate the dynamics of a large scale model. In this paper, a substructuring approach is developed for coupling geometrically nonlinear structures, where each subcomponent is drastically reduced to a low order set of nonlinear equations using a truncated set of fixedinterface and characteristic constraint modes. The method used to extract the coefficients of the nonlinear reduced order model (NLROM) is non-intrusive in that it does not require any modification to the commercial FEA code, but computes the NLROM from the results of several nonlinear static analyses. The NLROMs are then assembled to approximate the nonlinear differential equations of the global assembly. The method is demonstrated on the coupling of two geometrically nonlinear plates with simple supports at all edges. The plates are joined at a continuous interface through the rotational degrees-of-freedom (DOF), and the nonlinear normal modes (NNMs) of the assembled equations are computed to validate the models. The proposed substructuring approach reduces a 12,861 DOF nonlinear finite element model down to only 23 DOF, while still accurately reproducing the first three NNMs of the full order model.

42 ENGINEERING↗
NASA NTRS1991

Reduction of chemical reaction models

An attempt is made to reconcile the different terminologies pertaining to reduction of chemical reaction models. The approaches considered include global modeling, response modeling, detailed reduction, chemical lumping, and statistical lumping. The advantages and drawbacks of each of these methods are pointed out.

Frenklach, Michael↗
DOE OSTI2018

DuoModel: Leveraging Reduced Model for Data Reduction and Re-Computation on HPC Storage

High-performance computing (HPC) applications generate large amounts of floating-point data that need to be stored and analyzed efficiently to extract the insights and advance knowledge discovery. With the growing disparities between compute and I/O, optimizing the storage stack alone may not suffice to cure the I/O problem. There has been a strong push in the HPC communities to perform data reduction before data is transmitted to storage in order to lower the I/O cost. However, as of now, neither lossless nor lossy compressors can achieve the adequate reduction ratio that is desired by applications. This paper proposes DuoModel, a new approach that leverages the similarity between the full and reduced application models, and further improve the data reduction ratio. DouModel further improves the compression ratio of state-of-the-art compressors via compressing the differences (termed as delta) between the data products of the two models. For data analytics, the high fidelity data can be re-computed by launching the reduced model and applying the compressed delta. Our evaluations confirm that DuoModel can further push the limit of data reduction while the high fidelity of data is maintained.

Luo, Huizhang (ORCID:0000000323920267)↗