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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Photoconversion in Disordered Semiconductors: Spatial, Spectral, and Temporal Insights through Nonlinear Microscopy (Final Technical Report)

Conversion of solar photons to useable energy requires two processes to occur successfully in a light-harvesting material: 1) the initial photoexcited state must efficiently produce excited states that are free to move, and 2) those states must be transported to a new location where they can do useful work before any potential is lost as heat. The core focus of this project examined the spatial and energetic dynamics of these two primary events of photoconversion in solution-processed semiconducting materials. The electronic properties of these systems are complex and strongly influenced by the cumulative interaction of local morphology and composition, along with defects, interfaces, and trap sites. It is therefore important to interrogate these systems at the local level, where structural and compositional heterogeneity can be precisely correlated to variation in functionality. Utilizing ultrafast laser spectroscopy coupled together with optical microscopy, this project examined how excited states and free charges spatially and energetically evolve on length scales between 10 nanometers and 10 microns and on timescales from 50 femtoseconds to 1 nanosecond. Insights gained from measurements made on these characteristic length and time scales provided new insight into how macroscopic functionality in disordered photovoltaic materials emerges from the interplay of nanoscale and mesoscale interactions.

14 SOLAR ENERGY↗
DOE OSTI2020

Nonlinear Multiobjective MPC-Based Optimal Operation of a High Consistency Refining System in Papermaking

As one of the most important unit in the papermaking industry, the high consistency (HC) refining system is confronted with challenges such as improving pulp quality, energy saving, and emissions reduction in its operation processes. Here in this correspondence, an optimal operation of HC refining system is presented using nonlinear multiobjective model predictive control strategies that aim at set-point tracking objective of pulp quality, economic objective, and specific energy (SE) consumption objective, respectively. First, a set of input and output data at different times are employed to construct the subprocess model of the state process model for the HC refining system, and then the Wiener-type model can be obtained through combining the mechanism model of Canadian Standard Freeness and the state process model that determines their structures based on Akaike information criterion. Second, the multiobjective optimization strategy that optimizes both the set-point tracking objective of pulp quality and SE consumption is proposed simultaneously, which uses NSGA-II approach to obtain the Pareto optimal set. Furthermore, targeting at the set-point tracking objective of pulp quality, economic objective, and SE consumption objective, the sequential quadratic programming method is utilized to produce the optimal predictive controllers. In conclusion, the simulation results demonstrate that the proposed methods can make the HC refining system provide a better performance of set-point tracking of pulp quality when these predictive controllers are employed. In addition, while the optimal predictive controllers orienting with comprehensive economic objective and SE consumption objective, it has been shown that they have significantly reduced the energy consumption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

DRIPS: A framework for dimension reduction and interpolation in parameter space

Reduced-order models are often used to describe the behavior of complex systems, whose simulation with a full model is too expensive, or to extract salient features from the full model’s output. We introduce a new model-reduction framework DRIPS (dimension reduction and interpolation in parameter space) that combines the offline local model reduction with the online parameter interpolation of reduced-order bases (ROBs). The offline step of this framework relies on dynamic mode decomposition (DMD) to build a low-rank linear surrogate model, equipped with a local ROB, for quantities of interest derived from the training data generated by repeatedly solving the (nonlinear) high-fidelity model for multiple parameter points. The online step consists of the construction of a parametric reduced-order model for each target/test point in the parameter space, with the interpolation of ROBs done on a Grassman manifold and the interpolation of reduced-order operators done on a matrix manifold. The DMD component enables DRIPS to model (typically low-dimensional) quantities of interest directly, without having to access the (typically high-dimensional and possibly nonlinear) operators in a high-fidelity model that governs the dynamics of the underlying high-dimensional state variables, as required in projection-based reduced-order modeling. A series of numerical experiments suggests that DRIPS yields a model reduction, which is computationally more efficient than the commonly used projection-based proper orthogonal decomposition; it does so without requiring a prior knowledge of the governing equation for quantities of interest. Furthermore, for the nonlinear systems considered, DRIPS is more accurate than Gaussian-process interpolation (Kriging).

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Multi-frequency progressive refinement for learned inverse scattering

Interpreting scattered acoustic and electromagnetic wave patterns is a computational task that enables remote imaging in a number of important applications, including medical imaging, geophysical exploration, sonar and radar detection, and nondestructive testing of materials. However, accurately and stably recovering an inhomogeneous medium from far-field scattered wave measurements is a computationally difficult problem, due to the nonlinear and non-local nature of the forward scattering process. We design a neural network, called Multi-Frequency Inverse Scattering Network (MFISNet), and a training method to approximate the inverse map from far-field scattered wave measurements at multiple frequencies. We consider three variants of MFISNet, with the strongest performing variant inspired by the recursive linearization method — a commonly used technique for stably inverting scattered wavefield data — that progressively refines the estimate with higher frequency content. MFISNet outperforms past methods in regimes with high-contrast, heterogeneous large objects, and inhomogeneous unknown backgrounds.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Using an Explainable Machine Learning Approach to Characterize Earth System Model Errors: Application of SHAP Analysis to Modeling Lightning Flash Occurrence

Abstract Computational models of the Earth System are critical tools for modern scientific inquiry. Efforts toward evaluating and improving errors in representations of physical and chemical processes in these large computational systems are commonly stymied by highly nonlinear and complex error behavior. Recent work has shown that these errors can be effectively predicted using modern Artificial Intelligence (A.I.) techniques. In this work, we go beyond these previous studies to apply an explainable A.I. technique to not only predict model errors but also move toward understanding the underlying reasons for successful error prediction. We use XGBoost classification trees and SHapley Additive exPlanations analysis to explore the errors in the prediction of lightning occurrence in the NASA Goddard Earth Observing System model, a widely used Earth System Model. This explainable error prediction system can effectively predict the model error and indicates that the errors are strongly related to convective processes and the characteristics of the land surface.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Twin-pulse seeding enables pump-probe capabilities in the EUV to soft X-ray spectrum at synchrotron light sources

Having previously reported that separating the two stages of echo-enabled harmonic generation (EEHG) with one or more bending magnet (BM) sections allows the BMs to serve as the desired source of momentum compaction, here we demonstrate that this arrangement can greatly reduce the total energy modulation required by any 4th generation synchrotron light source, leading to higher repetition rates as well as stronger coherent radiation output power, with significant benefits. Since the EEHG beamline performance is mainly determined by the momentum compaction, beam emittances and beta functions of a storage ring lattice, allowing for different separations between the two stages is a straightforward way to increase the momentum compaction of chicane 1. This also enables pump-probe capabilities in a novel context, where twin-pulse seeding on the same electron bunch would allow two distinct radiation pulses with an adjustable delay in the range of 0.1 to 10 ps. In the twin-pulse seeding scheme, the same electron bunch could undergo modulation from two distinct laser pulses. Later stages would produce independent harmonics in subsequent straight sections. There are two variations of this twin-pulse seeding scheme, supporting different scientific applications. With a common modulation in stage 1, the first option allows simultaneously two independent radiation sources, with a full coverage of the EUV (2.5 to 50 nm) to soft X-ray (1.25 to 2.5 nm) spectrum; for the second option, the same stage 2 undulator could generate two coherent pulses both fitting within the FEL bandwidth, or at distinct harmonics. We present particle tracking simulation studies based on the APS-U lattice, including quantum excitation and radiation damping. These simulations indicate that there is no degradation of the modulated longitudinal phase space even when the two stages are separated by as many as 10 BM sections.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Detecting hidden transient events in noisy nonlinear time-series

The information impulse function (IIF), running Variance, and local Hölder Exponent are three conceptually different time-series evaluation techniques. These techniques examine time-series for local changes in information content, statistical variation, and point-wise smoothness, respectively. Using simulated data emulating a randomly excited nonlinear dynamical system, this study interrogates the utility of each method to correctly differentiate a transient event from the background while simultaneously locating it in time. Computational experiments are designed and conducted to evaluate the efficacy of each technique by varying pulse size, time location, and noise level in time-series. Our findings reveal that, in most cases, the first instance of a transient event is more easily observed with the information-based approach of IIF than with the Variance and local Hölder Exponent methods. While our study highlights the unique strengths of each technique, the results suggest that very robust and reliable event detection for nonlinear systems producing noisy time-series data can be obtained by incorporating the IIF into the analysis.

42 ENGINEERING↗
DOE OSTI2024

Adamantine 1.0: A Thermomechanical Simulator for Additive Manufacturing

Adamantine is a thermomechanical simulation code that is written in C++ and built on top of deal.II (Arndt et al., 2023), p4est (Burstedde et al., 2011), ArborX (Lebrun-Grandié et al., 2020), Trilinos (The Trilinos Project Team, 2020), and Kokkos (Trott et al., 2022). Adamantine was developed with additive manufacturing in mind and it is particularly well adapted to simulate fused filament fabrication, directed energy deposition, and powder bed fusion. Adamantine employs the finite element method with adaptive mesh refinement to solve a nonlinear anisotropic heat equation, enabling support for various additive manufacturing processes. It can also perform elastoplastic and thermoelastoplastic simulations. It can handle materials in three distinct phases (solid, liquid, and powder) to accurately reflect the physical state during different stages of the manufacturing process. To enhance simulation accuracy, adamantine incorporates data assimilation techniques (Asch et al., 2016). This allows it to integrate experimental data from sensors like thermocouples and infrared (IR) cameras. This combined approach helps account for errors arising from input parameters, material properties, models, and numerical calculations, leading to more realistic simulations that reflect what occurs in a particular print.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Real-Time FPGA Implementation For Frequency Sweep Interferometry In The LBNF Complex

The Long Baseline Neutrino Facility (LBNF) will deliver a 2.4 MW muon neutrino beam from Fermilab to the Deep Underground Neutrino Experiment (DUNE), requiring unprecedented precision in beamline alignment to achieve DUNE's neutrino oscillation measurement goals. Vertical misalignments of beamline components as small as 0.5 mm can contribute 6-7\% uncertainty in predicted neutrino flux, necessitating sub-0.1 mm alignment monitoring capabilities. The Horn Location Sensor (HLS) system employs frequency sweep interferometry (FSI) in a distributed hydrostatic leveling network to achieve the required precision under harsh radiation conditions up to 5000 kRad/year. Traditional FSI implementations suffer from laser sweep nonlinearities that degrade resolution and require computationally intensive post-processing corrections using gas reference cells. This work presents a real-time FPGA-based implementation of the HLS data acquisition and processing system using a sweep tracker interferometer for dynamic sweep linearization. The system utilizes a PYNQ-Z2 FPGA with programmable logic implementing parallel 16k-point FFT processing across four channels, synchronized by the sweep tracker signal to eliminate post-processing requirements. Spectral performance testing demonstrates significant improvements in peak sharpness compared to traditional fixed-frequency digitization. The FPGA implementation enables real-time displacement monitoring with processing speeds orders of magnitude faster than software-based approaches, essential for the operational requirements of LBNF's eventual distributed sensor network. This advancement in real-time FSI processing directly supports DUNE's precision neutrino physics program by providing the rapid feedback necessary for maintaining stringent beamline alignment tolerances during high-power beam operations.

Rossel, A. Jacob [Fermilab; Unlisted]↗
DOE OSTI2023

Real-time infrared spectroscopy coupled with blind source separation for nuclear waste process monitoring

On-line infrared absorbance spectroscopy enables rapid measurement of solution-phase molecular species. Many spectra-to-concentration models exist for spectral data, with some models able to handle overlapping spectral bands and nonlinearities. However, model accuracy is limited by the quality of training data used in model fitting. The process spectra of nuclear waste simulants at the Savannah River Site display incongruity between training and process spectra; the glycolate spectral signature in the training data does not match the glycolate signature in Savannah River National Laboratory process data. A novel blind source separation algorithm is proposed that preprocesses spectral data so that process spectra more closely resemble training spectra, thereby improving model quantification accuracy when unexpected sources of variation appear in process spectra. The novel blind source separation preprocessing algorithm is shown to improve nitrate quantification from an R 2 of 0.934 to 0.988 and from 0.267 to 0.978 in two instances analyzing nuclear waste simulants from the Slurry Receipt Adjustment Tank and Slurry Mix Evaporator cycle at the Savannah River Site.

Crouse, Steven H.↗
DOE OSTI2020

The Complexity of Nonlinear Flow and non-Fickian Transport in Fractures Driven by Three-Dimensional Recirculation Zones

Subsurface fluid flow and solute transport are typically described by first-order or linear rate laws. However, deviations from the first-order rate laws (or anomalous behavior) are typical and lead to nonlinear flow and non-Fickian transport phenomena. The shared underlying mechanisms for these “anomalous” flow and transport phenomena, which are actually typical of fractures, are seldom analyzed. We investigated the co-occurrence of nonlinear flow and non-Fickian transport through numerical simulation experiments of pore-scale flow and solute transport processes through three-dimensional natural fractures. In these fractures, 3-D recirculation zones (RZs) developed and grew with increasing flow rates. Flow nonlinearity resulted from the RZ development and growth, which shrank the main flow channel. Moreover, the same RZs also captured solutes from the main flow channel and released them back later. This retardation resulted in non-Fickian transport. Thus, nonlinear flow and non-Fickian transport are intertwined via their shared dependence on RZs.

58 GEOSCIENCES↗
DOE OSTI2021

Reconnection and particle acceleration in three-dimensional current sheet evolution in moderately magnetized astrophysical pair plasma

Magnetic reconnection, a plasma process converting magnetic energy to particle kinetic energy, is often invoked to explain magnetic energy releases powering high-energy flares in astrophysical sources including pulsar wind nebulae and black hole jets. Reconnection is usually seen as the (essentially two-dimensional) nonlinear evolution of the tearing instability disrupting a thin current sheet. To test how this process operates in three dimensions, we conduct a comprehensive particle-in-cell simulation study comparing two- and three-dimensional evolution of long, thin current sheets in moderately magnetized, collisionless, relativistically hot electron–positron plasma, and find dramatic differences. We first systematically characterize this process in two dimensions, where classic, hierarchical plasmoid-chain reconnection determines energy release, and explore a wide range of initial configurations, guide magnetic field strengths and system sizes. We then show that three-dimensional (3-D) simulations of similar configurations exhibit a diversity of behaviours, including some where energy release is determined by the nonlinear relativistic drift-kink instability. Thus, 3-D current sheet evolution is not always fundamentally classical reconnection with perturbing 3-D effects but, rather, a complex interplay of multiple linear and nonlinear instabilities whose relative importance depends sensitively on the ambient plasma, minor configuration details and even stochastic events. It often yields slower but longer-lasting and ultimately greater magnetic energy release than in two dimensions. Intriguingly, non-thermal particle acceleration is astonishingly robust, depending on the upstream magnetization and guide field, but otherwise yielding similar particle energy spectra in two and three dimensions. Although the variety of underlying current sheet behaviours is interesting, the similarities in overall energy release and particle spectra may be more remarkable.

Physics↗
DOE OSTI2023

Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties

Here we introduce a denoising diffusion algorithm to discover microstructures with nonlinear fine-tuned properties. Denoising diffusion probabilistic models are generative models that use diffusion-based dynamics to gradually denoise images and generate realistic synthetic samples. By learning the reverse of a Markov diffusion process, we design an artificial intelligence to efficiently manipulate the topology of microstructures to generate a massive number of prototypes that exhibit constitutive responses sufficiently close to designated nonlinear constitutive behaviors. To identify the subset of microcstructures with sufficiently precise fine-tuned properties, a convolutional neural network surrogate is trained to replace high-fidelity finite element simulations to filter out prototypes outside the admissible range. Results of this study indicate that the denoising diffusion process is capable of creating microstructures of fine-tuned nonlinear material properties within the latent space of the training data. More importantly, this denoising diffusion algorithm can be easily extended to incorporate additional topological and geometric modifications by introducing high-dimensional structures embedded in the latent space. Numerical experiments are conducted on the open-source mechanical MNIST data set (Lejeune, 2020). Consequently, this algorithm is not only capable of performing inverse design of nonlinear effective media, but also learns the nonlinear structure–property map to quantitatively understand the multiscale interplay among the geometry, topology, and their effective macroscopic properties.

42 ENGINEERING↗
DOE OSTI2021

The data-driven future of high-energy-density physics

High-energy-density physics is the field of physics concerned with studying matter at extremely high temperatures and densities. Such conditions produce highly nonlinear plasmas, in which several phenomena that can normally be treated independently of one another become strongly coupled. The study of these plasmas is important for our understanding of astrophysics, nuclear fusion and fundamental physics—however, the nonlinearities and strong couplings present in these extreme physical systems makes them very difficult to understand theoretically or to optimize experimentally. Here we argue that machine learning models and data-driven methods are in the process of reshaping our exploration of these extreme systems that have hitherto proved far too nonlinear for human researchers. Furthermore, from a fundamental perspective, our understanding can be improved by the way in which machine learning models can rapidly discover complex interactions in large datasets. From a practical point of view, the newest generation of extreme physics facilities can perform experiments multiple times a second (as opposed to approximately daily), thus moving away from human-based control towards automatic control based on real-time interpretation of diagnostic data and updates of the physics model. To make the most of these emerging opportunities, we suggest proposals for the community in terms of research design, training, best practice and support for synthetic diagnostics and data analysis.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Deterministic modeling of hybrid nonlinear effects in epsilon-near-zero thin films

In nonlinear optics, significant effort is concentrated on improving the strength and efficiency of interactions; however, experimentally investigating nonlinear materials is a complex, time-consuming, and costly investment. Moreover, it is often challenging to isolate, study, and optimize material parameters in an experiment due to complexities in the growth process. Recently, epsilon-near-zero materials have received a great deal of attention as promising nonlinear optical materials, but like many up-and-coming materials, the ability to explore and optimize their properties has been challenging. Here, we establish a framework to rapidly evaluate the performance of nonlinear epsilon-near-zero materials for both inter- and intraband effects in silico, requiring only an energy-momentum (E-k) diagram, linear optical properties, and experimental conditions. Measured nonlinear reflection and transmission in gallium-doped zinc oxide films are compared to the numerical framework for both intra- and interband excitation to verify accuracy across wavelength and irradiance while two figures of merit (FoMs) are introduced to quickly evaluate the performance of films without a full numerical framework. This capability is used to predict the performance of highly doped gallium nitride, cadmium oxide, zinc oxide, and indium tin oxide films, and efficient intra- and interband operation conditions are identified. Through this numerical framework and the FoMs, the exploration of unstudied epsilon-near-zero materials is enabled without the need for a nonlinear experiment, thereby accelerating the search for more efficient nonlinear materials and excitation conditions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

When physics-informed data analytics outperforms black-box machine learning: A case study in thickness control for additive manufacturing

Aerosol jet printing (AJP) has emerged as a promising noncontact additive manufacturing method for high-resolution printing for a wide range of material systems. A key challenge limiting the broader adoption of AJP in the material science community is the lack of methods to precisely control thickness. Herein, we develop a model-based design of experiment (MBDoE) framework that integrates physics-informed models, nonlinear regression, and information criteria to postulate, select and calibrate the best model to describe and optimize the AJP manufacturing process. Starting with already available data from system commissioning (e.g., prior single variable sensitivity analysis), four candidate physics-informed models are postulated and trained. MBDoE identifies a single additional optimal experiment to validate these predictive models with quantified uncertainties, which are then used to determine the best experimental conditions to control printed film thickness. As a comparative benchmark, the analysis is repeated using the same dataset with nonparametric Gaussian process regression (GPR) model that does not incorporate physical information. Using MBDoE principles, we find that only five experiments are necessary to calibrate the nonlinear physics-informed parametric model, and with said limited data, this model outperforms the black-box machine learning GPR model. This key result underscores an emerging trend in the data science community: incorporating physical information into predictive models often drastically reduces the data requirements. Leveraging MBDoE further increased the data efficiency. By design, the proposed data science framework is general in nature and can be easily extended to other experimental and additive manufacturing systems beyond AJP.

Aerosol jet printing↗
DOE OSTI2024

FTL: Transfer Learning Nonlinear Plasma Dynamic Transitions in Low Dimensional Embeddings (FTL) v1.0

Fusion Transfer Learning (FTL) model provides a new paradigm to study high-dimensional dynamical behaviors, such as those in fusion plasma systems. The knowledge transfer process leverages a pre-trained neural encoder-decoder network, initially trained on linear simulations, to effectively capture nonlinear dynamics. The low-dimensional embeddings extract the coherent structures of interest, while preserving the inherent dynamics of the complex system. Experimental results highlight FTL's capacity to capture transitional behaviors and dynamical features in plasma dynamics -- a task often challenging for conventional methods. The model developed in this study is generalizable and can be extended broadly through transfer learning to address various magnetohydrodynamics (MHD) modes.

Bai, Zhe↗