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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Physics-Informed and Data-Driven Prediction of Residual Stress in Three-Dimensional Machining

Efficient and reliable prediction of machining-induced residual stress (RS) is a key requirement for truly integrated computational materials engineering (ICME). Currently available process modeling approaches, including empirical, analytical, and numerical methodologies lack predictive power and require substantial calibration and validation data. Moreover, most model-based approaches consider only two-dimensional (2D) (i.e., orthogonal), cutting processes. Meanwhile, industrial processes such as milling, turning, and drilling are inherently three-dimensional (3D). The present work attempts to bridge the gap between 2D and 3D through careful consideration of the process physics, including geometric, kinematic, and size-effect constraints to realize robust prediction of how RS develops in 3D machining. Using a novel in-situ experimental technique and digital image correlation (DIC) to determine equivalent Hertzian contact widths, contact pressures, and friction coefficients, the proposed methodology leverages a discretized conversion algorithm that includes multi-pass shakedown effects. This paper presents a semi-analytical model to predict machining-induced RS in 3D turning operations, which are used representatively for 3D processes more generally. Rather than follow a ‘brute force’ 3D FEM approach or conduct countless experiments to train a purely data-driven machine learning algorithm, the proposed approach builds on previous 2D modeling work. Through careful consideration of the process physics, including complex geometry/kinematic considerations of 3D turning, the authors demonstrated an experimentally calibrated approach, as well as validation based on published RS data. Model predictions and previously published measurement data of RS depth profiles for turning of Inconel 718 were compared for a range of process parameters. Correlation between the proposed 3D model and validation data was found to be within the margin of experimental error for most conditions. The proposed model appears to capture the overall behavior of 3D RS depth profiles with acceptable accuracy, particularly the key metrics of near-surface stress, peak stress magnitude and location, as well as overall stress profile depth. This report presents a physics-informed, data-driven approach for efficient calibration of a 2D model for machining-induced RS through DIC analysis of in-situ characterized subsurface displacement fields.

42 ENGINEERING↗
DOE OSTI2024

Toward ultra-efficient high-fidelity predictions of wind turbine wakes: Augmenting the accuracy of engineering models with machine learning

This study proposes a novel machine learning (ML) methodology for the efficient and cost-effective prediction of high-fidelity three-dimensional velocity fields in the wake of utility-scale turbines. The model consists of an autoencoder convolutional neural network with U-Net skipped connections, fine-tuned using high-fidelity data from large-eddy simulations (LES). The trained model takes the low-fidelity velocity field cost-effectively generated from the analytical engineering wake model as input and produces the high-fidelity velocity fields. The accuracy of the proposed ML model is demonstrated in a utility-scale wind farm for which datasets of wake flow fields were previously generated using LES under various wind speeds, wind directions, and yaw angles. Comparing the ML model results with those of LES, the ML model was shown to reduce the error in the prediction from 20% obtained from the Gauss Curl hybrid (GCH) model to less than 5%. In addition, the ML model captured the non-symmetric wake deflection observed for opposing yaw angles for wake steering cases, demonstrating a greater accuracy than the GCH model. The computational cost of the ML model is on par with that of the analytical wake model while generating numerical outcomes nearly as accurate as those of the high-fidelity LES.

Mechanics↗
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 OSTI2024

Liquid lithium divertor analysis using coupled plasma material interaction model

A liquid lithium divertor can improve performance of future fusion devices by creating efficient power exhaust and improving the energy confinement via pumping of the hydrogen isotopes. In addition, significantly higher heat fluxes can be handled if controlled vapor shielding is used to redistribute the divertor heat flux over a wider area. Design and optimization of such a system calls for an analysis model which includes a strong two-way coupling between the plasma and divertor material. The incoming plasma heat and particle flux will affect the divertor surface temperature, which is a defining factor of the lithium evaporative and sputtered flux going into the plasma. Results of the coupled model based on the plasma edge code SOLPS-ITER and the computational fluid dynamics (CFD) code ANSYS-CFX will be presented for different configurations. An analytical slab flow model is used as a heat transfer boundary condition for SOLPS, defining particle flux from the wall via calculation of the surface temperature. At the final step, results of the SOLPS analysis are verified using a 3D CFD magnetohydrodynamics (MHD) analysis which uses heat and particle flux from SOLPS as a boundary condition. In addition to plasma heat flux, both analytical and CFD temperature models include several plasma material interaction effects, such as lithium evaporation, condensation and sputtering based on deuterium target flux. New adatom sputtering model based on the available experimental data is presented. Analytical model is expanded to include free surface axisymmetric configurations. Results of parametric studies of the divertor configurations with different lithium inlet temperature and velocity will be presented leading to the optimal design resulting in the lowest possible lithium contamination in the core.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Progress report on analytic and numerical studies of x-ray-induced impulse

In a variety of high-energy-density (HED) systems, x-rays of a given energy are used to generate shockwaves, bulk motion, and impulse in materials. As radiation energy is deposited within the material, the material is heated. The number of photons of a certain wavelength absorbed is determined by the spectral intensity of the radiation and the material’s opacity evaluated at that wavelength. This heating results in a pressure increase dictated by the material’s equation of state. Depending on the intensity of the absorbed radiation, it may also cause the material to change phase into a liquid, gas, or plasma. The increased pressure drives the heated surface layer to blow off, imparting impulse and sending a compressive wave into the bulk of the material. Additionally, the compression wave interacts with the solid boundary of the material, resulting in a tensile wave that may cause the material to spall. The impulse generated by the deposition of x-ray energy within the sample can be modeled using purely analytical methods, e.g. the Bethe, Bade, Averell, and Yost (BBAY) model. However, the blow-off process is rather complicated, and proper modeling efforts must account for material ejected by spallation, vaporization, jetting, and plasma ablation. For this reason, analytical models have an unclosed term describing the final energy of the blown-off material Ef(z). Prior modeling efforts have arbitrarily fixed this at some value or modeled it using a limiting set of thermodynamic assumptions. The work we are currently performing uses validated simulations using sophisticated photon transport, equation of state, and strength models/data to provide a fit for Ef(z) that is useful for predictive calculation of impulse. We will apply our methodology and show results for different materials and x-ray sources. This work is particularly useful for the design of experiments studying x-ray impulse generation. The present report is outlined as follows. Section 1.2 describes a series of HED experiments investigating x-ray-generated impulse in materials. Section 1.3 describes the computational approach we employ in this study, and presents validation results against the aforementioned experiments. Section 1.4 introduces analytical models for impulse generation, as well as our method for utilizing impulse from simulations to close the models. We discuss the concept of impulse-spectrum sensitivity, it’s application to uncertainty quantification, and derive a very useful analytical expression for it in 1.5. We then summarize recent progress in this project and discuss future work in section 1.6.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Interaction energy and isosteric heat of adsorption between hydrogen and magnesium diboride

Hydrogen storage materials form a crucial research topic for future energy utilization employing hydrogen and among those of interest magnesium diboride (MgB 2 ) has shown its prevalence. In this study, a first-principles analytical adsorption model of one hydrogen molecule in the vicinity of various magnesium diboride crystal surfaces was developed in order to obtain surface thermodynamic properties as a function of molecular and lattice properties. Henry's law constant (K H ) and isosteric heat of adsorption (ΔH ads ) indicators of the affinity between a gaseous molecule and a solid surface are thus calculated. The results in this paper not only address questions pertaining to the first stage of hydrogen storage processes but also advance the understanding of physisorption thermodynamics of a neutral molecule (H 2 ) coming in contact with a layered metallic-like surface (MgB 2 ). Although the model is built from a framework of classical calculations, quantum effects are incorporated as the fractional charge of the ions on the free surfaces, which is essential for the calculation of analytic thermodynamic values that approximate calculations from other methods. To benchmark our theoretical models, periodic density functional calculations were performed to determine the interactions between H 2 and different MgB 2 surfaces from first-principles. By considering both the top and sublayers of MgB 2 in calculating interaction energy, we have analytically and computationally calculated the interaction energies of H 2 molecules and MgB 2 's terminated planes, and witnessed the strong dependence of interaction energies on surface charges. We have also observed a dipole flipping phenomenon which explains the discontinuity seen in the interaction energy graph of Mg(0001). Both analytical and computational results showed heat of adsorption at zero coverage varying at a very low range (<7 kJ mol -1 ).

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

Entanglement and seniority

In quantum mechanics entanglement is the most striking phenomenon which has no counterpart in classical systems. Although, different approaches have already been developed to study correlations in the case of indistinguishable particles, the exploration of the so-called mode entanglement is still in its initial stage in nuclear physics. Study of mode-entanglement in the seniority model, derivation of analytic formulas for the one-body reduced density matrix of states with seniority $ν$ = 0, 1, 2, and $ν$ = 3, and also determination of the particle number dependence of the one-body reduced density matrix for arbitrary seniority. In addition, comparison of the predictions of the seniority model with the results of the shell model to gain insight into the structure and correlations of the ground and lowest yrast states. In the seniority model the analytic results are calculated using the quasi-spin formalism. The numerical shell model calculations are carried out in the standard shell model framework using an inert core. The applied realistic effective interactions are derived earlier using the $G$-matrix formalism. The density matrix renormalization group method is also applied in order to directly calculate the mode entropies. In the seniority model simple analytical expressions are given for the mode entropies. The peculiar behavior of the half-filled shells and the seniority-zero states are revealed. Numerical results are presented for the lightest stable calcium isotopes and for 94 Ru nucleus. For 94 Ru, the seniority model accounts for the 0$g$ 9/2 mode entropies, but seniority mixing is important for certain yrast states. Furthermore, interaction induced quantum fluctuations decrease the occupation of the 0$f$ 5/2 , 1$p$ 3/2 and 1$p$ 1/2 shells, and amount in non-negligible mode entropies on these shells, too, clearly outside the scope of the simple (0$g$ 9/2 ) 4 seniority model. The 0$f$ 7/2 shell-based seniority model is more accurate for the Ca isotopes, but seniority mixing is substantial for some 44 Ca yrast states, too. Mode and one-body entanglement entropies are useful tools to investigate the structure of quantum correlations in nuclei.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

On data-driven energy flexibility quantification: A framework and case study

Building energy flexibility is an important resource for a sustainable and resilient power grid, and an important measure to reduce utility costs for building owners. Quantifying energy flexibility for existing buildings can provide critical insights in optimizing their operation. Data-driven methods for building energy modeling and analytics are gaining popularity due to the increasingly available sensor and meter infrastructure, affordable computational resources, and advanced modeling algorithms. However, their application in quantifying the energy flexibility of real buildings is still limited due to the heterogeneous data types and limited data availability. Here, this study proposes a framework for building-level data-driven energy flexibility quantification that considers different levels of data availability and use cases. Two case studies with real building data collected at different scales were conducted to demonstrate the proposed framework for different purposes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Advanced Tritium Process Analytics and Optimization via a Digital Twin, SRNL-TR-2023-00550

Improving the process knowledge and understanding of TCAP can be achieved by incorporating advanced tools. This project addresses “Advanced analytics for modeling, forecasting, & optimization of the tritium refinement process” and is being applied to the TCAP process. The value of digitization and advanced analytics for TCAP data are tracking of gas mixtures & inventories is presently a manual effort that is rather cumbersome. Incorporating some automation into the data capture will reduce errors and storing data in an accessible database that can be used for tracking as well as process improvements. In addition, the database approach will enable longer term history to be maintained rather than the current practice of deleting data after six months. The digitized and automated data will allow for models to be developed for the process and will enable the forecasting and optimization.

Korinko, Paul S.↗
DOE OSTI2024

Leveraging design of experiments to build chemometric models for the quantification of uranium (VI) and HNO3 by Raman spectroscopy

Partial least squares regression (PLSR) and support vector regression (SVR) models were optimized for the quantification of U(VI) (10–320 g L −1 ) and HNO 3 (0.6–6 M) by Raman spectroscopy with optimized calibration sets chosen by optimal design of experiments. The designed approach effectively minimized the number of samples in the calibration set for PLSR and SVR by selecting sample concentrations with a quadratic process model, despite complex confounding and covarying spectral features in the spectra. The top PLS2 model resulted in percent root mean square errors of prediction for U(VI), HNO 3 , and NO 3 − of 3.7%, 3.6%, and 2.9%, respectively. PLS1 models performed similarly despite modeling an analyte with a majority linear response (i.e., uranyl symmetric stretch) and another with more covarying vibrational modes (i.e., HNO 3 ). Partial least squares (PLS) model loadings and regression coefficients were evaluated to better understand the relationship between weaker Raman bands and covarying spectral features. Support vector machine models outperformed PLS1 models, resulting in percent root mean square error of prediction values for U(VI) and HNO 3 of 1.5% and 3.1%, respectively. The optimal nonlinear SVR model was trained using a similar number of samples (11) compared with the PLSR model, even though PLS is a linear modeling approach. The generic D-optimal design presented in this work provides a robust statistical framework for selecting training set samples in disparate two-factor systems. This approach reinforces Raman spectroscopy for the quantification of species relevant to the nuclear fuel cycle and provides a robust chemometric modeling approach to bolster online monitoring in challenging process environments.

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

Multiscale plasticity of geomaterials predicted via constrained optimization‐based granular micromechanics

Abstract A general framework to derive nonlinear elastic and elastoplastic material models from granular micromechanics is proposed, where a constraint‐based variational structure is introduced to classical grain contact‐based homogenization methods of hyperelasticity. Like the classical hyperelastic methods, reference solutions for closed‐form hyperelastic material models are analytically derived from the grain‐scale contact mechanics. However, unlike prior methods, the proposed homogenization framework defines closed‐form hyperelastoplastic material models that extend multiscale variational methods to granular plasticity. The proposed framework is used to develop novel granular micromechanics‐based macroscopic models for a Mises type solid, Drucker–Prager type plasticity, and grain‐contact cohesive‐debonding with a deviatorically and volumetrically coupled nonlinearly elastic response. Macroscopic plastic parameters and yield criteria are explicitly related to their microscale counterparts, for example, the friction coefficient governing intergranular slip. Numerical examples and comparison to measurements from the literature, including triaxial compaction of concrete, are provided to investigate model predictions and demonstrate calibration to experimental data.

Bryant, E. C.↗
DOE OSTI2020

A New Approach to Distributed Hypothesis Testing and Non-Bayesian Learning: Improved Learning Rate and Byzantine-Resilience

Here, we study a setting where a group of agents, each receiving partially informative private signals, seek to collaboratively learn the true underlying state of the world (from a finite set of hypotheses) that generates their joint observation profiles. To solve this problem, we propose a distributed learning rule that differs fundamentally from existing approaches, in that it does not employ any form of “belief-averaging”. Instead, agents update their beliefs based on a min-rule. Under standard assumptions on the observation model and the network structure, we establish that each agent learns the truth asymptotically almost surely. As our main contribution, we prove that with probability 1, each false hypothesis is ruled out by every agent exponentially fast, at a network-independent rate that is strictly larger than existing rates. We then develop a computationally-efficient variant of our learning rule that is provably resilient to agents who do not behave as expected (as represented by a Byzantine adversary model) and deliberately try to spread misinformation.

42 ENGINEERING↗
DOE OSTI2020

Validation of Combined Analytical Methods to Predict Slip in Cylindrical Roller Bearings

This paper presents a combination of models that together calculate the cage and roller speeds of a cylindrical roller bearing. The models consider elastohydrodynamic lubrication and contact elasticity between the roller and raceway, roller centrifugal forces, hydrodynamic lubrication at the cage pocket, and frictional forces. Using these models, the predicted cage and roller speeds and the extent of slip are compared to measurements acquired on cylindrical roller bearings in a commercial gearbox in steady-state and transient operating conditions of a wind turbine. In steady-state conditions at low wind speeds and low lubricant temperatures, cage and roller slip up to 60% occur in the loaded zone of the bearing. In the unloaded zone, up to 80% roller slip occurs. Cage and roller slip then decrease as the lubricant temperature and wind speed increases. In general, the analytical model results match experimental measurements within 10% for lubricant temperatures above 40 degrees C and wind speeds over 10 meters per second. The analytical model is further evaluated during a transient start-up event and highly dynamic emergency stop event and also used to examine changes in the bearing design or lubricant properties. Furthermore, roller and cage slip in cylindrical bearings is a combined effect of the bearing design, applied load, shaft speed, and lubricant properties and temperature, and can be quickly evaluated with the combined analytical models.

17 WIND ENERGY↗
DOE OSTI2025

Studying the Power Sources behind Type Ic Supernovae

Astrophysical transients can be powered by a broad range of energy sources, including shock heating (internal and external shocks), decay of radioactive isotopes, and long-lived central engines (magnetar and fallback). The dominant energy source for astrophysical transients depends on the nature of the explosive engine and its progenitor. To model all transients, light-curve codes must include all of these energy sources. Here, we present a supernova light-curve code implementing analytic source models to compare the role of different energy sources in these transients. To demonstrate the utility of this code, we conduct an extensive study of Type Ic broad-line (Ic-BL) supernovae. A diverse set of energy sources have been linked to Ic-BL supernovae, making them an excellent candidate for this light-curve code. In this paper, we explore which features of the explosion (mass, velocity, etc.) affect the Type Ic supernovae light curves, focusing on shock-interaction and radioactive-decay energy sources. Although the explosion properties under both energy sources can be tuned to match the peak emission, matching the light-curve evolution in many Ic-BL supernovae requires fine-tuned conditions. We find that shock interactions in the stellar wind are likely to be the dominant energy source at peak for these supernovae.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Performance evaluation of a wrapped around condenser for heat pump water heater applications

In this report, accurate performance evaluation of a wrapped-around condenser for heat pump water heaters (WHPWH) is critical since the COP of the system depends heavily on thermal stratification and condenser design. An analytical, quasi-steady state heat balance method has been developed to determine the optimal spacing between adjacent channels, tube diameter, tube shape, and total refrigerant charge amount for the condenser. The heat transfer rate is compared among three approaches: the refrigerant thermodynamic model, the condenser-wrap fin model, and the analytical natural convection model on the inside of the tank for the three regions based on the refrigerant phase (e.g., super-heated, saturated, or subcooled). The heat transfer rate was predicted by a combination of slug-plug and annular flow for a D-shaped helical tube. Experimental data were used as boundary conditions for validating the model. The rate of heat transfer based on tube shape and tank wall temperatures was compared by CFD analysis. Parametric analysis indicates a tradeoff between refrigerant mass, pressure drop, tube diameter, and tube length to maximize the heat transfer rate. The model suggests the saturated region length can be extended by 400%, and the condenser pressure drop can be reduced by 23% with an optimal spacing pattern.

42 ENGINEERING↗
DOE OSTI2023

Kondo Lattice Model of Magic-Angle Twisted-Bilayer Graphene: Hund’s Rule, Local-Moment Fluctuations, and Low-Energy Effective Theory

For this work, we apply a generalized Schrieffer-Wolff transformation to the extended Anderson-like topological heavy fermion (THF) model for the magic-angle (θ=1.05°) twisted bilayer graphene (MATBLG) [Phys. Rev. Lett. 129, 047601 (2022)], to obtain its Kondo lattice limit. In this limit localized $\mathcal{f}$ electrons on a triangular lattice interact with topological conduction $\mathcal{c}$ electrons. By solving the exact limit of the THF model, we show that the integer fillings ν=0, ±1, ±2 are controlled by the heavy $\mathcal{f}$ electrons, while ν=±3 is at the border of a phase transition between two $\mathcal{f}$-electron fillings. For ν=0, ±1, ±2, we then calculate the Ruderman-Kittel-Kasuya-Yosida (RKKY) interactions between the $\mathcal{f}$ moments in the full model and analytically prove the SU(4) Hund’s rule for the ground state which maintains that two $\mathcal{f}$ electrons fill the same valley-spin flavor. Our (ferromagnetic interactions in the) spin model dramatically differ from the usual Heisenberg antiferromagnetic interactions expected at strong coupling. We show the ground state in some limits can be found exactly by employing a positive semidefinite “bond-operators” method. We then compute the excitation spectrum of the $\mathcal{f}$ moments in the ordered ground state, prove the stability of the ground state favored by RKKY interactions, and discuss the properties of the Goldstone modes, the (reason for the accidental) degeneracy of (some of) the excitation modes, and the physics of their phase stiffness. We develop a low-energy effective theory for the $\mathcal{f}$ moments and obtain analytic expressions for the dispersion of the collective modes. We discuss the relevance of our results to the spin-entropy experiments in TBG.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

MIND-MAC: Multi-Level In-memory Quasi Non-Destructive MAC Operation in Compact 2T-nC FeRAM for Efficient DNN Accelerator

We present MIND-MAC, a compact 2T-nC FeRAM architecture that performs multi-level, quasi-non-destructive in-memory multiply–accumulate (MAC) for deep neural networks. By exploiting voltage-controlled partial domain switching in MFM capacitors and read-transistor amplification, the cell stores multi-bit weights and gates bit-serial inputs to produce an accumulated current on shared lines. We combine TCAD-extracted parasitics with experimentally calibrated ferroelectric models in SPICE to validate device-/circuit-level behavior, and validate multi-level sensing and QNRO with measurements on a fabricated 2T-3C test vehicle. An analytical system model maps MIND-MAC to a 6-GB main-memory in-memory compute (IMC) architecture and benchmarks VGG13 inference in 61.08 ms at 964.99 mJ. Results indicate high density, reduced rewrite overhead, and energy efficiency, positioning 2T-nC FeRAM as a promising IMC candidate for next-generation AI hardware.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Improving Estimation of the Koopman Operator with Kolmogorov–Smirnov Indicator Functions

It has become common to perform kinetic analysis using approximate Koopman operators that transform high-dimensional timeseries of observables into ranked dynamical modes. The key to the practical success of the approach is the identification of a set of observables that form a good basis on which to expand the slow relaxation modes. Good observables are, however, difficult to identify a priori and suboptimal choices can lead to significant underestimations of characteristic time scales. Leveraging the representation of slow dynamics in terms of Hidden Markov Models (HMM), we propose a simple and computationally efficient clustering procedure to infer surrogate observables that form a good basis for slow modes. Here, we apply the approach to an analytically solvable model system as well as on three protein systems of different complexities. We consistently demonstrate that the inferred indicator functions can significantly improve the estimation of the leading eigenvalues of Koopman operators and correctly identify key states and transition time scales of stochastic systems, even when good observables are not known a priori.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗