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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Proximate ferromagnetic state in the Kitaev model material α-RuCl 3

α-RuCl 3 is a major candidate for the realization of the Kitaev quantum spin liquid, but its zigzag antiferromagnetic order at low temperatures indicates deviations from the Kitaev model. We have quantified the spin Hamiltonian of α-RuCl 3 by a resonant inelastic x-ray scattering study at the Ru L 3 absorption edge. In the paramagnetic state, the quasi-elastic intensity of magnetic excitations has a broad maximum around the zone center without any local maxima at the zigzag magnetic Bragg wavevectors. This finding implies that the zigzag order is fragile and readily destabilized by competing ferromagnetic correlations. The classical ground state of the experimentally determined Hamiltonian is actually ferromagnetic. The zigzag state is stabilized by quantum fluctuations, leaving ferromagnetism – along with the Kitaev spin liquid – as energetically proximate metastable states. The three closely competing states and their collective excitations hold the key to the theoretical understanding of the unusual properties of α-RuCl 3 in magnetic fields.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Models and Strategies for Optimal Demand Side Management in the Chemical Industries

Deregulation and the increase of renewable electricity generation from wind and solar photovoltaics have transformed the U.S. electricity market. Economic and environmental benefits notwithstanding, the presence of renewables has increased variability and uncertainty on the supply side of the grid. Managing demand, rather than generation – a strategy referred to as “demand response (DR)” – is an attractive approach for mitigating this imbalance. DR efforts aim to reduce electricity usage during peak demand times, lessening stress on the grid. Industrial users are particularly attractive entities for DR participation since they present large, localized loads that can provide significant relief on grid demand and –unlike other large loads, such as buildings – are minimally dependent on human needs and preferences. In this project, we accomplished three main objectives. (1) We developed data-driven low-order DR scheduling-relevant dynamic models of chemical processes. Concurrently, we studied the formulation and solution of the associated optimal DR production scheduling problems. (a) A prototype air separation unit (ASU) model was used to generate simulated operating data for initial modeling efforts, which enabled the later use of industrial data for data-driven modeling. (b) We utilized Hammerstein-Wiener (HW) and Finite Step Response (FSR) models to represent nonlinear plant dynamics. (c) The HW models were linearized using exact linearization so they could potentially be embedded in power system models, which are formulated as mixed integer linear programs (MILPs). (d) We solved DR optimization problems under uncertainty and found that even naïve predictions of electricity price and product demand led to significant cost savings benefits. (2) Our DR scheduling optimization problem formulations are amenable to real-time solution. (a) We utilized Lagrangian Relaxation (LR) to efficiently solve the optimization problem by decoupling subproblems linked by complicating constraints. (b) We have achieved computation times for the 3-day DR scheduling problem of an ASU as low as 1.88 minutes. (3) Our representations of the DR behavior of chemical process as grid-level batteries were embedded in power system models. (a) For a small-scale grid, we found that incorporating the dynamics of the chemical plant in the optimal power flow calculations resulted in better resource management leading to up to 15% and 46% cost reduction for the grid and chemical plant operations, respectively, during periods of power line congestion. We have published several works dedicated to modeling and solving DR optimization problems from the user side. These were published in top peer-reviewed journals and are summarized in this report. The most recent work (and papers in preparation) considers DR scheduling from the grid side. Future efforts will consider networked plants (e.g., air separation units operating on a common pipeline) for DR participation, which is expected to amplify the capabilities of industrial DR participants to perform load-shifting. Our consideration of uncertainty in DR has inspired future directions in this area as well: we plan to develop multistage methods to fully account for the effects of uncertainty in DR scheduling.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Control-Oriented Modeling of Lithium-Ion Batteries

Battery management systems (BMSs), which monitor and optimize performance while ensuring safety, require control-oriented models, i.e., models tailored to the design and implementation of estimation and control algorithms. Physics-based electrochemical models describe detailed battery phenomena, but are too computationally intensive for use in estimation and control applications. Single particle models (SPMs), which retain some of the physics of electrochemical models, are often used for control-oriented battery modeling since they are computationally efficient; however, they are only valid over very low frequency ranges and C-rates. Empirical equivalent circuit models (ECMs) are also used for control-oriented battery modeling since they are computationally efficient and can describe battery behavior over wide frequency ranges; however, they provide no physical understanding of the battery and, therefore, have limited applicability. Further, fractional order terms (e.g., Warburg impedances) are often employed, making the models unwieldy for use in the time domain. This work provides a control-oriented battery model that combines the benefits of SPM and ECM models, while overcoming their limitations. The proposed model incorporates some of the battery physics found in electrochemical models, can easily be used in both the time and frequency domains, and describes battery behavior over its entire frequency range. A linearized single particle model, which incorporates key electrochemical parameters, is used for modeling battery physics at very low frequencies. For low frequencies, integer-order linear systems are used to approximate diffusion physics described by Warburg impedances, and high frequency behavior is modeled by the double layer capacitance effect. The proposed battery model is more computationally efficient than full electrochemical models since it does not require the solution of PDEs, is accurate for a wider frequency range than the SPM considered in this paper, and does not suffer from the unwieldiness and limited applicability of empirical ECMs. Finally, the model is validated in the time and frequency domains via a comparison to pseudo-two-dimensional (P2D) model simulations and experimental data.

25 ENERGY STORAGE↗
NASA NTRS1990

Identification and control of a flexible beam using modal domain sensor

The use of a distributed-effect, modal domain optical fiber sensor (MD sensor) as a component in an active control system to suppress vibrations in a flexible beam is discussed. The sensor model is integrated into the model for a flexible structure. Based on this system model, a control system with a dynamic compensator is designed to add damping to the low order modes of the flexible structure. To verify the modeling procedure, a cantilevered beam was instrumental with a piezoelectric actuator and an MD sensor. It is shown that experimental responses closely match simulated responses for both open loop and closed loop tests.

Cox, David E.↗
NASA NTRS2017

Computationally Efficient Beam Elements for Accurate Stresses in Sandwich Laminates and Laminated Composites with Delaminations

Laminated composites are prone to delamination failure due to the lack of reinforcement through the thickness. Therefore, during the design process the initiation and propagation of delaminations should be accounted for as early as possible. This paper presents computationally efficient nine degree-of-freedom (dof) and eight-dof shear locking-free beam elements using the mixed form of the refined zigzag theory (RZT(sup(m))). The corresponding nine-dof and eight-dof elements use the anisoparametric and constrained anisoparametric interpolation schemes, respectively, to eliminate shear locking in slender beams. The advantage of the present element over previous RZT beam elements is that no post-processing is required to accurately model the transverse shear stress while maintaining the computational efficiency of a low-order beam element. Comparisons with high-fidelity finite element models and three-dimensional elasticity solutions show that the elements can robustly and accurately predict the displacement field, axial stress and transverse shear stress through the thickness of a sandwich beam or a composite laminate with an embedded delamination. In fact, the accuracy and computational efficiency of predicting stresses in laminates with embedded delaminations make the present elements attractive choices for RZT-based delamination initiation and propagation methodologies available in the literature.

R. M. J. Groh↗
DOE OSTI2022

Simulating Atmospheric Boundary Layer Turbulence with Nek5000/RS

We present large-eddy-simulation (LES) modeling approaches for the simulation of atmospheric boundary layer turbulence that are of direct relevance to wind energy production. In this report, we study a GABLS benchmark problem using high-order spectral element code Nek5000/RS, which is supported under the DOE’s Exascale Computing Project (ECP) Center for Efficient Exascale Discretizations (CEED) project, targeting application simulations on various acceleration-device based exascale computing platforms [1, 2]. We demonstrate our newly developed subgrid-scale (SGS) models based on high-pass filter (HPF), mean-field eddy viscosity (MFEV), and Smagorinsky (SMG) with no-slip and traction boundary conditions, provided with low-order statistics, convergence and turbulent structure analysis. The model fidelity and scaling performance of Nek5000/RS on DOE’s leadership computing platforms in comparison to those of AMR-Wind, a block-structured second-order finite-volume code with adaptive-mesh-refinement capabilities, are discussed in [3].

17 WIND ENERGY↗
DOE OSTI2021

Autoignition study of iso -cetane/tetralin blends at low temperature

iso-Cetane and tetralin are the two fuel components commonly considered in literature diesel surrogates, and are hydrocarbon classes representative of iso-alkanes and naphthoaromatics, respectively. Since both surrogate components are involved in the key reactions controlling the autoignition reactivity of diesel surrogates, autoignition studies of iso-cetane, tetralin, and their blending behavior at low temperatures can be helpful in developing/refining chemical kinetic models of diesel surrogates, in order to better predict diesel ignition response under low temperature combustion conditions. In this investigation, autoignition experiments of iso-cetane, tetralin, and their binary mixtures at varying blending ratios have been conducted in a rapid compression machine (RCM) in the temperature/pressure range of 630–930 K and 10–20 bar. For the neat fuel components, the newly-acquired RCM data have been compared with the literature experimental results at some overlapping conditions. In addition, a chemical kinetic model describing the oxidation of iso-cetane and tetralin compiled/updated from the diesel surrogate model developed by Lawrence Livermore National Laboratory is used to simulate the experimental datasets. It is shown that this model exhibits good agreement with the experimental results of tetralin and also predicts the total ignition delay times of iso-cetane at lower temperatures reasonably well. However, the current model fails to fully capture the increase of total ignition delay time with increasing amount of tetralin in the binary blends observed in the present RCM experiments. Therefore, chemical kinetic analyses of the current model are conducted to help identify possible reasons leading to the discrepancies between simulations and experiments. Furthermore, the chemical kinetic interactions between iso-cetane and tetralin are explored by including additional cross-reactions among iso-cetane, tetralin, and their fuel fragments in the current model to assess their effects on model predictions. As a result, based on the chemical kinetic analysis results, future direction for model refinements is discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Spectroscopic Evidence of Kondo-Induced Quasiquartet in CeRh 2 As 2

CeRh 2 As 2 is a new multiphase superconductor with strong suggestions for an additional itinerant multipolar ordered phase. The modeling of the low-temperature properties of this heavy-fermion compound requires a quartet Ce 3 + crystal-field ground state. Here, we provide the evidence for the formation of such a quartet state using x-ray spectroscopy. Core-level photoelectron and x-ray absorption spectroscopy confirm the presence of Kondo hybridization in CeRh 2 As 2 . The temperature dependence of the linear dichroism unambiguously reveals the impact of Kondo physics for coupling the Kramer’s doublets into an effective quasiquartet. Nonresonant inelastic x-ray scattering data find that the | Γ 7 − ⟩ state with its lobes along the 110 direction of the tetragonal structure ( x y orientation) contributes most to the multiorbital ground state of CeRh 2 As 2 . Published by the American Physical Society 2024

Christovam, Denise S. (ORCID:0000000250698107)↗
NASA NTRS1991

Directivity and prediction of low frequency rotor noise

Acoustic data obtained over a large horizontal plane under the model rotor and digitally filtered in order to determine the low-frequency content near the blade passage frequency is analyzed. Focus is placed on the directivity of low-frequency noise, and the changes in directivity as a function of the descent glide slope angle and advance ratio are presented and compared with predicted directivity results. The differences between the data and prediction are discussed for two observer positions, one below and on the rotor axis, and the other 60 degrees down from the horizontal. It is demonstrated that for the latter position, blade-vortex interaction noise is strong when it occurs, and the loading at the low frequencies is significantly affected during blade-vortex interactions.

Burley, C. L.↗
DOE OSTI2021

Investigation of Nonlinear Difference-Frequency Wave Excitation on a Semisubmersible Offshore-Wind Platform with Bichromatic-Wave CFD Simulations: Preprint

The natural surge and pitch frequencies of semisubmersible offshore wind platforms are typically designed to be below the wave frequencies to avoid direct excitation. However, surge or pitch resonance can be excited by the nonlinear low-frequency loads generated by irregular incident waves. Second-order potential-flow models with added Morison drag have been found to underpredict this low-frequency excitation and response. As part of the OC6 project, Computational Fluid Dynamics (CFD) simulations were performed to enable a better understanding of the low-frequency loads and the limitations of lower-fidelity models. The focus of the current paper is to set up a computationally cost-effective CFD simulation of a fixed semisubmersible platform to investigate nonlinear difference-frequency loads and establish the corresponding uncertainty in the results. Due to the high computing cost, CFD simulations of irregular waves can be challenging. Instead, simulations were performed with bichromatic waves having a shorter repeat period. A preliminary comparison with Quadratic Transfer Functions from second-order potential-flow theory shows that CFD models consistently predict higher nonlinear wave loads at the difference frequency, likely due to flow separation and viscous drag not accounted for in potential-flow theory.

2nd order↗
NASA NTRS1977

Hierarchy of simulation models for a turbofan gas engine

Steady-state and transient performance of an F-100-like turbofan gas engine are modeled by a computer program, DYNGEN, developed by NASA. The model employs block data maps and includes about 25 states. Low-order nonlinear analytical and linear techniques are described in terms of their application to the model. Experimental comparisons illustrating the accuracy of each model are presented.

Longenbaker, W. E.↗
DOE OSTI2026

Higher-order LaSDI: Reduced order modeling with multiple time derivatives

Solving complex partial differential equations (PDEs) is essential across scientific disciplines but often requires numerical models that can be prohibitively expensive in time-sensitive applications. Reduced-order models (ROMs) address this challenge by exploiting low-dimensional structure to create fast approximations. The Latent Space Dynamics Identification (LaSDI) framework has demonstrated success in learning ROMs for parameterized PDE families, but remains limited to first-order systems. Here, in this paper, we propose Higher-Order LaSDI (HLaSDI), which extends the LaSDI framework to PDEs with arbitrary order of time derivatives. This generalization significantly expands the applicability of LaSDI-based methods to systems previously outside their scope, including hyperbolic PDEs. We demonstrate HLaSDI’s accuracy and efficiency on several linear and nonlinear benchmark problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Cost function for low-dimensional manifold topology assessment

Abstract In reduced-order modeling, complex systems that exhibit high state-space dimensionality are described and evolved using a small number of parameters. These parameters can be obtained in a data-driven way, where a high-dimensional dataset is projected onto a lower-dimensional basis. A complex system is then restricted to states on a low-dimensional manifold where it can be efficiently modeled. While this approach brings computational benefits, obtaining a good quality of the manifold topology becomes a crucial aspect when models, such as nonlinear regression, are built on top of the manifold. Here, we present a quantitative metric for characterizing manifold topologies. Our metric pays attention to non-uniqueness and spatial gradients in physical quantities of interest, and can be applied to manifolds of arbitrary dimensionality. Using the metric as a cost function in optimization algorithms, we show that optimized low-dimensional projections can be found. We delineate a few applications of the cost function to datasets representing argon plasma, reacting flows and atmospheric pollutant dispersion. We demonstrate how the cost function can assess various dimensionality reduction and manifold learning techniques as well as data preprocessing strategies in their capacity to yield quality low-dimensional projections. We show that improved manifold topologies can facilitate building nonlinear regression models.

42 ENGINEERING↗
DOE OSTI2022

The influence of thermochemistry on the reactivity of propane, the pentane isomers and $n$-heptane in the low temperature regime

In this study, the influence of thermochemistry on the reactivity of fuels at low temperatures (600–1000 K) is studied here. Specifically, the effect of different sets of thermochemistry on chemical model predictions is explored, where various sets are calculated at different levels of theory in addition to recently updated group additivity values. Experimentally measured ignition delay times for propane, the pentane isomers and n-heptane are simulated using NUIGMech1.2 and replacing the thermochemistry of the low-temperature species with the calculated values. For propane, three different thermochemistry sets were calculated, namely CCSD(T)-F12/TZ-F12//B2PLYP-D3/TZ//B2PLYP-D3/TZ (QM1), CCSD(T)-F12/TZ-F12//B2PLYP-D3/TZ//ωB97X-D/TZ (QM2) and B2PLYP-D3/TZ/ωB97X-D/6-31G*//ωB97X-D/6-31G* (QM3). The QM2 results provide parameters to optimize new group additivity (NGA) values which are used to calculate the fourth set of thermochemistry. The model predictions using these four sets are compared to those using NUIGMech1.2 for propane. As the QM1 and QM2 calculations are expensive, the thermochemistry calculated from the QM3 and NGA calculations are used in the pentane isomer and n-heptane models. For all of the models, it is found that the thermochemistry of the species involved in the low-temperature reaction sequence ($RH$, $\dot{R}$, $RO_2H$, $R\dot{O}_2$, $\dot{Q}OOH$ and $\dot{O}_2QOOH$ species) significantly affect fuel reactivity. The NGA values were developed based on all of these species except $\dot{O}_2QOOH$ radicals. The thermochemistry of $\dot{O}_2QOOH$ species cannot be accurately calculated with the NGA representations due to the importance of non-next-nearest neighbor interactions of –OOH substitution. Further development of the NGA method to capture such interactions is in progress. Overall, the model developed using the NGA thermochemistry shows better agreement with experimental data than the model using thermochemistry from affordable and prominent QM methods, such as QM3. Based on the results presented for propane, the pentane isomers and n-heptane, the thermochemistry calculated using the NGA method can be used to model the oxidation of higher order hydrocarbons at low temperatures.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

VpROM: a novel variational autoencoder-boosted reduced order model for the treatment of parametric dependencies in nonlinear systems

Reduced Order Models (ROMs) are of considerable importance in many areas of engineering in which computational time presents difficulties. Established approaches employ projection-based reduction, such as Proper Orthogonal Decomposition. The limitation of the linear nature of such operators is typically tackled via a library of local reduction subspaces, which requires the assembly of numerous local ROMs to address parametric dependencies. Our work attempts to define a more generalisable mapping between parametric inputs and reduced bases for the purpose of generative modeling. We propose the use of Variational Autoencoders (VAEs) in place of the typically utilised clustering or interpolation operations, for inferring the fundamental vectors, termed as modes, which approximate the manifold of the model response for any and each parametric input state. The derived ROM still relies on projection bases, built on the basis of full-order model simulations, thus retaining the imprinted physical connotation. However, it additionally exploits a matrix of coefficients that relates each local sample response and dynamics to the global phenomena across the parametric input domain. The VAE scheme is utilised for approximating these coefficients for any input state. This coupling leads to a high-precision low-order representation, which is particularly suited for problems where model dependencies or excitation traits cause the dynamic behavior to span multiple response regimes. Moreover, the probabilistic treatment of the VAE representation allows for uncertainty quantification on the reduction bases, which may then be propagated to the ROM response. The performance of the proposed approach is validated on an open-source simulation benchmark featuring hysteresis and multi-parametric dependencies, and on a large-scale wind turbine tower characterised by nonlinear material behavior and model uncertainty.

Conditional VAEs↗
NASA NTRS1988

Developments in the simulation of a geopotential research mission

An essential element of any satellite system that will be used to recover information about high degree and order terms in the geopotential model of the earth is one or more low altitude (about 160 km) satellites equipped with a drag compensation mechanism. To study the effects of various error sources and to test the new theoretical and numerical techniques that will be developed for such a mission, two simulated scenarios have been used with a geopotential model complete to degree and order 360 and Encke's method to numerically integrate the equations of motion. The two scenarios include the low-low dual satellite system with integrated, one-way Doppler measurements and the single satellite system with gradiometer measurements. The simulations include a reference orbit which is assumed to be available from conventional tracking systems.

Schutz, B. E.↗
DOE OSTI2021

Learning to Correct Climate Projection Biases

The fidelity of climate projections is often undermined by biases in climate models due to their simplification or misrepresentation of unresolved climate processes. While various bias correction methods have been developed to post-process model outputs to match observations, existing approaches usually focus on limited, low-order statistics, or break either the spatiotemporal consistency of the target variable, or its dependency upon model resolved dynamics. We develop a Regularized Adversarial Domain Adaptation (RADA) methodology to overcome these deficiencies, and enhance efficient identification and correction of climate model biases. Instead of pre-assuming the spatiotemporal characteristics of model biases, we apply discriminative neural networks to distinguish historical climate simulation samples and observation samples. The evidences based on which the discriminative neural networks make distinctions are applied to train the domain adaptation neural networks to bias correct climate simulations. We regularize the domain adaptation neural networks using cycle-consistent statistical and dynamical constraints. An application to daily precipitation projection over the contiguous United States shows that our methodology can correct all the considered moments of daily precipitation at approximately $1^\circ$ resolution, ensures spatiotemporal consistency and inter-field correlations, and can discriminate between different dynamical conditions. Our methodology offers a powerful tool for disentangling model parameterization biases from their interactions with the chaotic evolution of climate dynamics, opening a novel avenue toward big-data enhanced climate predictions.

58 GEOSCIENCES↗
NASA NTRS1991

A new technique for oil backstreaming contamination measurements

Due to the large size and the number of diffusion pumps, space simulation chambers cannot be easily calibrated by the usual test dome method for measuring backstreaming from oil diffusion pumps. In addition, location dependent contamination may be an important parameter of the test. The backstreaming contamination was measured in the Space Power Facility (SPF) near Sandusky, OH, the largest space simulation vacuum test chamber in the U.S.. Small clean silicon wafers placed at all desired measurement sites were used as contamination sensors. The facility used diffusion pumps with DC 705 oil. The thickness of the contamination oil film was measured using ellipsometry. Since the oil did not wet the silicon substrate uniformly, two analysis models were developed to measure the oil film: (1) continuous, homogeneous film; and (2) islands of oil with the islands varying in coverage fraction and height. In both cases, the contamination film refractive index was assumed to be that of DC 705. The second model improved the ellipsometric analysis quality parameter by up to two orders of magnitude, especially for the low coverage cases. Comparison of the two models shows that the continuous film model overestimates the oil volume by less than 50 percent. Absolute numbers for backstreaming are in good agreement with published results for diffusion pumps. Good agreement was also found between the ellipsometric results and measurements done by x-ray photoelectron spectroscopy (XPS) and by scanning electron microscopy (SEM) on examples exposed to the same vacuum runs.

Alterovitz, S. A.↗