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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Efficient Streaming Dynamic Mode Decomposition

We propose a reformulation of the streaming dynamic mode decomposition method that requires maintaining a single orthonormal basis, thereby reducing computational redundancy. The proposed efficient streaming dynamic mode decomposition method results in a constant-factor reduction in computational complexity and memory storage requirements. Numerical experiments on representative canonical dynamical systems show that the enhanced computational efficiency does not compromise the accuracy of the proposed method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Dynamic Mode Decomposition of Random Pressure Fields over Bluff Bodies

Fluctuating surface pressures on a bluff body exposed to a boundary layer flow generally are characterized as a spatiotemporally varying random field. In this paper, a dynamic mode decomposition (DMD) was applied to extract dominant features embedded in these random pressure fields. Utilizing an unsupervised machine learning algorithm, spatial modes and their temporal variations were grouped into different clusters at scales, e.g., macro, meso, and micro. A proper orthogonal decomposition (POD) of the experimental data was carried out to observe commonalities and distinctive perspectives each decomposition offers. Here, a comprehensive examination of the DMD/POD for their convergence criteria, data sufficiency, and modal components analysis was conducted. The physical interpretation of the spatiotemporal pressure field based on these decomposition schemes was discussed. At different scales, the DMD modes can capture the evolution of aerodynamic features, e.g., convection of vortices (or vortex tubes) and other structures. The distribution of energy among these three broad scales also reflects an energy cascade in pressure fluctuations akin to turbulence.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Image Gradient Decomposition for Parallel and Memory-Efficient Ptychographic Reconstruction

Ptychography is a popular microscopic imaging modality for many scientific discoveries and sets the record for highest image resolution. Unfortunately, the high image resolution for ptychographic reconstruction requires significant amount of memory and computations, forcing many applications to compromise their image resolution in exchange for a smaller memory footprint and a shorter reconstruction time. In this paper, we propose a novel image gradient decomposition method that significantly reduces the memory footprint for ptychographic reconstruction by tessellating image gradients and diffraction measurements into tiles. In addition, we propose a parallel image gradient decomposition method that enables asynchronous point-to-point communications and parallel pipelining with minimal overhead on a large number of GPUs. Our experiments on a Titanate material dataset (PbTiO3) with 16632 probe locations show that our Gradient Decomposition algorithm reduces memory footprint by 51 times. In addition, it achieves time-to-solution within 2.2 minutes by scaling to 4158 GPUs with a super-linear strong scaling efficiency at 364% compared to runtimes at 6 GPUs. This performance is 2.7 times more memory efficient, 9 times more scalable and 86 times faster than the state-of-the-art algorithm.

Wang, Xiao↗
DOE OSTI2022

Dynamic mode decomposition with core sketch

With the increase in collected data volumes, either from experimental measurements or high fidelity simulations, there is an ever-growing need to develop computationally efficient tools to process, analyze, and interpret these datasets. Modal analysis techniques have gained great interest due to their ability to identify patterns in the data and extract valuable information about the system being considered. Dynamic mode decomposition (DMD) relies on elements of the Koopman approximation theory to compute a set of modes, each associated with a fixed oscillation frequency and a decay/growth rate. Extracting these details from large datasets can be computationally expensive due to the need to implement singular value decomposition of the input data matrix. Sketching algorithms have become popular in numerical linear algebra where statistical theoretic approaches are utilized to reduce the cost of major operations. A sketch of a matrix is another matrix, which is significantly smaller, but still sufficiently approximates the original system. We put forth an efficient DMD framework, SketchyDMD, based on a core sketching algorithm that captures information about the range and corange (their mutual relationship) of input data. The proposed sketching-based framework can accelerate various portions of the DMD routines, compared to classical methods that operate directly on the raw input data. We conduct numerical experiments using the spherical shallow water equations as a prototypical model in the context of geophysical flows. In conclusion, we show that the proposed SketchyDMD is superior to existing randomized DMD methods that are based on capturing only the range of the input data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Transverse mode analysis for free-space laser beams using Bayesian analysis

Reliable and rapid assessment of the transverse mode quality of a free-space laser beam has a wide range of applications in laser development, research, and utilization. It has become even more important with recent advances in developing orbital angular momentum photon beams across a broad spectral region. In this work, a general modal analysis method for a free-space multimode laser beam has been developed based on Bayesian analysis. After transforming mode decomposition into a linear system problem, a Gaussian probabilistic model is used to find a closed-form solution. The method is found to be robust with the presence of Gaussian noise. Prior knowledge about the mode content can be incorporated into the method to improve the solution for situations when coherent disturbances or contamination are present in the laser beam. This method can be used to analyze the mode content for laser beams in different bases, such as Hermite–Gaussian (HG) modes and Laguerre–Gaussian (LG) modes. Three applications of this method are presented: a detailed modal analysis of the beam image from the incoherent intensity addition of HG modes and two examples of mode decomposition using the complex wavefront from the coherent superposition of HG and LG modes. The feasibility of this method is demonstrated using various simulation results. Based on digital images of a laser beam recorded without complex wavelength-limiting optics, in principle, this method can be used in a wide wavelength range from infrared to ultraviolet, and possibly x ray.

Liu, Peifan (ORCID:0000000273816153)↗
DOE OSTI2021

Towards Reconstruction of Complex Flow Fields Using Unit Flows

Many complex turbulent flows in nature and engineering can be qualitatively regarded as being constituted of multiple simpler unit flows. The objective of this work is to characterize the coherent structures in such complex flows as a combination of constituent unitary flow structures for the purpose of reduced-order representation. While turbulence is clearly a non-linear phenomenon, we aim to establish the degree to which the optimally weighted superposition of unitary flow structures can represent the complex flow structures. The rationale for investigating such superposition stems from the fact that the large-scale coherent structures are generated by underlying flow instabilities that may be reasonably described using linear analysis. Clearly, the degree of validity of superposition will depend on the flow under consideration. In this work, we take the first step toward establishing a procedure for investigating superposition. Experimental data of single and triple tandem jets in crossflow are used to demonstrate the procedure. A composite triple tandem jet flow field is generated from optimal superposition of single jet data and compared against ‘true’ triple jet data. Direct comparisons between the true and composite fields are made for spatial, temporal, and kinetic energy content. The large-scale features (obtained from proper orthogonal decomposition or POD) of true and composite tandem jet wakes exhibit nearly 70% agreement in terms of modal eigenvector correlation. Corresponding eigenvalues reveal that the kinetic energy of the flow is also emulated with only a slight overprediction. Temporal frequency features are also examined in an effort to completely characterize POD modes. The proposed method serves as a foundation for more rigorous and robust dimensional reduction in complex flows based on unit flow modes.

Kristo, Paul J.↗
DOE OSTI2022

Cyber-Attack Identification of Synchrophasor Data Via VMD and Multifusion SVM

A large amount of synchrophasor data in the wide area measurement system (WAMS) needs to be collected and transmitted to the phasor data concentrator, thereby increasing the possibility of being attacked by hackers. The attacked data are therefore hidden into the normal synchrophasor data so that the synchrophasor data based application will be affected. To remedy this problem, an identification framework is proposed to detect the data cyber-attack in WAMS utilizing variational mode decomposition (VMD) and multifusion support vector machine (MSVM). First, VMD is used to transform the attacked data into multiple modal components. Thereafter, a novel MSVM is employed to classify the deterministic features using the proposed linear combined multikernel (LCM). Further, this LCM can fuse multiple types of features, including the time, frequency, and statistical domains of the synchrophasor data. Utilizing the actual data from FNET/GridEye, different experiments are conducted under multiple attack strengths and types. The results demonstrate that the identification framework has higher precision and robustness compared with other conventional classifiers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Structural response reconstruction using a system-equivalent singular vector basis

Here, this paper develops a novel method for reconstructing the full-field response of structural dynamic systems using sparse measurements. The singular value decomposition is applied to a frequency response matrix relating the structural response to physical loads, base motion, or modal loads. The left singular vectors form a non-physical reduced basis that can be used for response reconstruction with far fewer sensors than existing methods. The contributions of the singular vectors to measured response are termed singular-vector loads (SVLs) and are used in a regularized Bayesian framework to generate full-field response estimates and confidence intervals. The reconstruction framework is applicable to the estimation of single data records and power spectral densities from multiple records. Reconstruction is successfully performed in configurations where the number of SVLs to identify is less than, equal to, and greater than the number of sensors used for reconstruction. In a simulation featuring a seismically excited shear structure, SVL reconstruction significantly outperforms modal FRF-based reconstruction and successfully estimates full-field responses with as few as two uniaxial accelerometers. SVL reconstruction is further verified in a simulation featuring an acoustically excited cylinder. Finally, response reconstruction and uncertainty quantification are performed on an experimental structure with three shaker inputs and 27 triaxial accelerometer outputs.

42 ENGINEERING↗
DOE OSTI2022

Physics-constrained deep learning of nonlinear normal modes of spatiotemporal fluid flow dynamics

In this study, we present a physics-constrained deep learning method to discover and visualize from data the invariant nonlinear normal modes (NNMs) which contain the spatiotemporal dynamics of the fluid flow potentially containing strong nonlinearity. Specifically, we develop a NNM-physics-constrained convolutional autoencoder (NNM-CNN-AE) integrated with a multi-temporal-step dynamics prediction block to learn the nonlinear modal transformation, the NNMs containing the spatiotemporal dynamics of the flow, and reduced-order reconstruction and long-time future-state prediction of the flow fields, simultaneously. In test cases, we apply the developed method to analyze different flow regimes past a cylinder, including laminar flows with low Reynolds number in transient and steady states (RD = 100) and high Reynolds number flow (RD = 1000), respectively. The results indicate that the identified NNMs are able to reveal the nonlinear spatiotemporal dynamics of these flows, and the NNMs-based reduced-order modeling consistently achieves better accuracy with orders of magnitudes smaller errors in construction and prediction of the nonlinear velocity and vorticity fields, compared to the linear proper orthogonal decomposition (POD) method and the Koopman-constrained-CNN-AE using the same number or dimension of modes. We perform an analysis of the modal energy distribution of NNMs and find that compared to POD modes, the few fundamental NNMs capture a very high level of total energy of the flow, which is advantageous for reduced-order modeling and representation of the complex flows. Finally, we discuss the potentials and limitations of the presented method.

Mechanics↗
DOE OSTI2022

Investigation of methods for targeted search of dominant higher modes in subcritical systems

The reactor physics modeling of subcritical systems requires a substantially different approach from that of a critical reactor due to the presence of the so-called higher modes. Earlier investigations showed that the dominant higher modes lie in the inner part of the eigenvalue spectrum, making the conventional eigenvalue searches unfeasible and calling for a targeted search. This paper investigates the possibility of targeted and multitudinous eigenvalue calculation by testing the Krylov-Schur and the Dynamic Modes Decomposition (DMD) method for simple, analytically tractable problems. While the DMD method clearly showed its efficiency in a one-group homogeneous problem, modal analysis of a three-dimensional reflected reactor in two-groups approximation demonstrated that the increasing complexity poses challenges for both methods. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Development and implementation of an in situ high-resolution isotopic microscope for measuring metabolic interactions in soil mesocosms (Final Report)

Many of our planet’s ecosystems rely on the activities of soil microbial communities. These microbes have significant and wide-ranging effects: they metabolize carbon and other nutrients, interact with plants and fungi, and perform other processes important to soil health. However, our ability to directly observe the enzymatic and metabolic activities of microbes within soil is currently limited, not only by the complexity of soil microbial communities themselves, but also by the lack of experimental tools to study them and their molecular interactions in situ. These challenges hinder our understanding of the life-sustaining processes of biomass decomposition and the manner in which it contributes to the movement of freed carbon within soil ecosystems. Here we aimed to develop a novel ultrahigh-resolution isotopic microscope that combines complementary imaging modalities to gain insights into metabolic cycling in soil. This final report covers both the portion of this work that was initially completed at the University of North Carolina at Chapel Hill from 2018 to 2019 (as award DE-SC0019012) and then from 2020 to 2022 (2023 in NCE) at the University of Massachusetts Chan Medical School. This goal was to build an integrated platform consisting of fluorescence microscopy, Raman microspectroscopy, and nanospray desorption electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (nanoDESI-FTICR-MS) to directly investigate microbial activities and molecular transformations occurring in soil by exploiting the use of both fluorescent labels and stable isotope probing. Specifically, our instrument was conceived as integrating: (A) fluorescence detection to localize soil microbes, identify bacteria taking up polysaccharides, and monitor gene expression of enzymes involved in decomposition; (B) Raman microspectroscopy to determine which microbes incorporate decomposition products into their biomass, as well as which decomposers are metabolically active; and (C) nanoDESI-FTICR-MS imaging to spatially probe, in real-time, the metabolites in the surrounding area, which we expect will reveal the distributions of the products of the enzymatic breakdown of polysaccharides, as well as specialized metabolites acting as cell-cell signals between decomposers. To enable these measurements, we exploited fluorescence-based probes to map the microbes that are enzymatically active (per A) and utilized stable isotope-labeled substrates to visualize both the microbial and molecular fate of decomposed biomass (B and C, respectively). To accomplish our goal, we combined the expertise of a multidisciplinary group of scientists to pursue the construction of this microscope and to investigate scientific questions that would be facilitated by such a capability. We aimed to employ this spatially informative, high-resolution isotopic microscope to visualize the critical steps of biomass degradation and the molecular fate of other environmentally relevant substrates within soil mesocosms. Accordingly, this technology will enhance our understanding of the microbial and metabolic interactions occurring within soil communities that are relevant to carbon degradation and other soil processes. The instrument has been developed and housed at EMSL, where it will be available to the entire EMSL User Base, enabling a variety of related DOE-relevant systems to be interrogated in the future by diverse scientific research groups.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Mapping microhabitats of lignocellulose decomposition by a microbial consortium

The leaf-cutter ant fungal garden ecosystem is a naturally evolved model system for efficient plant biomass degradation. Degradation processes mediated by the symbiotic fungus Leucoagaricus gongylophorus are difficult to characterize due to dynamic metabolisms and spatial complexity of the system. Herein, we performed microscale imaging across 12-µm-thick adjacent sections of Atta cephalotes fungal gardens and applied a metabolome-informed proteome imaging approach to map lignin degradation. This approach combines two spatial multiomics mass spectrometry modalities that enabled us to visualize colocalized metabolites and proteins across and through the fungal garden. Spatially profiled metabolites revealed an accumulation of lignin-related products, outlining morphologically unique lignin microhabitats. Metaproteomic analyses of these microhabitats revealed carbohydrate-degrading enzymes, indicating a prominent fungal role in lignocellulose decomposition. Integration of metabolome-informed proteome imaging data provides a comprehensive view of underlying biological pathways to inform our understanding of metabolic fungal pathways in plant matter degradation within the micrometer-scale environment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Determination of latent dimensionality in international trade flow

Currently, high-dimensional data is ubiquitous in data science, which necessitates the development of techniques to decompose and interpret such multidimensional (aka tensor) datasets. Finding a low dimensional representation of the data, that is, its inherent structure, is one of the approaches that can serve to understand the dynamics of low dimensional latent features hidden in the data. Moreover, decomposition methods with non-negative constraints are shown to extract more insightful factors. Nonnegative RESCAL is one such technique, particularly well suited to analyze self-relational data, such as dynamic networks found in international trade flows. Particularly, non-negative RESCAL computes a low dimensional tensor representation by finding the latent space containing multiple modalities. Furthermore, estimating the dimensionality of this latent space is crucial for extracting meaningful latent features. Here, to determine the dimensionality of the latent space with non-negative RESCAL, we propose a latent dimension determination method which is based on clustering of the solutions of multiple realizations of non-negative RESCAL decompositions. We demonstrate the performance of our model selection method on synthetic data. We then apply our method to decompose a network of international trade flows data from International Monetary Fund and shows that with a correct latent dimension determination, the resulting features are able to capture relevant empirical facts from economic literature.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Spectral proper orthogonal decomposition of active wake mixing dynamics in a stable atmospheric boundary layer

Recent advancements in the use of active wake mixing (AWM) to reduce wake effects on downstream turbines open new avenues for increasing power generation in wind farms. However, a better understanding of the fluid dynamics underlying AWM is still needed to make wake mixing a reliable strategy for wind farm flow control. In this work, a spectral proper orthogonal decomposition (SPOD) is used to analyze the dynamics of coherent flow structures that are induced in the wake through blade pitch actuation. The data are generated using the ExaWind software suite to perform large eddy simulations of an NREL 2.8 MW turbine operating in a stable atmospheric boundary layer. SPOD tracks the modal behavior of flow structures from their generation in the turbine induction field through their growth in the near-wake region and to their subsequent evolution and energy transfers in the far wake. SPOD is shown to be a useful tool in the context of AWM because it translates the wavenumber and frequency inputs to the turbine controller to structures in the wake. A decomposition of the radial shear stress flux in the wake is also developed using SPOD to measure the contribution of coherent flow structures to mean flow turbulent entrainment and wake recovery. The effectiveness of AWM is connected to its ability to excite inherent structures in the wake of the turbine that arise using baseline controls. The effects of AWM on blade loading are also analyzed by connecting the axial force along the blade to the SPOD analysis of the turbine induction field. Lastly, the performance of different AWM strategies is demonstrated in a two-turbine array.

17 WIND ENERGY↗
DOE OSTI2025

Modal dynamics of wind turbine wake meandering from lidar observations

Horizontal scans from nacelle-mounted lidars provide time series measurements of wind turbine wakes across diverse atmospheric conditions, enabling analysis of coherent turbulent structures that influence wake meandering through proper orthogonal decomposition (POD). While low-order modes capture the most energetic turbulent structures, our analysis reveals that they do not necessarily dominate wake meandering dynamics. We evaluate more than 16,000 combinatorial reconstructions of the flow field for each inflow case, demonstrating that mode relevance depends on mode symmetry, turbulent kinetic energy content, and inflow characteristics. Cases with low turbulence intensity and large integral timescales show stronger correlations between POD modes and wake meandering, whereas higher turbulence conditions (turbulence intensity > 7%) are less effectively described by reduced-order models. However, the qualitative similarity of POD modes across varied atmospheric conditions suggests the potential existence of a semi-universal basis for representing wind turbine wakes, with implications for improving engineering wake models.

17 WIND ENERGY↗
DOE OSTI2025

Interface instabilities in hafnium hydride entrained iron metal matrix composites

The chemical interactions in Fe–HfH 2 metal matrix composites (MMCs) are studied across multiple length scales to elucidate the decomposition of the parent phases and corresponding reaction zone physics during direct current sintering. Fe–HfH 2 composites were synthesized with increasing as-mixed hydride contents of Fe–25% HfH 2 , Fe–40% HfH 2 , Fe–55% HfH 2 , and Fe–70% HfH 2 (all in vol. %) to demonstrate the ability to achieve sintered MMCs with target hydride contents. Samples were probed across multiple length scales through a multi-modal workflow employing x-ray diffraction, scanning electron microscopy and segmentation analysis, and synchrotron techniques including hard x-ray fluorescence mapping and nanoprobe x-ray absorption near-edge structure measurements. Under the selected sintering temperature and pressure conditions, hydrogen evolution is seen to evolve through parallel paths: thermal decomposition from during the transformation of HfH 2 to HfH x<2 and through subsequent reaction with the Fe matrix leading to intermetallic phase formation. Specifically, HfFe and HfFe 2 intermetallic formation accelerates the release of hydrogen with a subsequent HfO 2 phase forming at grain boundaries. For this MMC, the consumption or loss of hydrogen can be considerable in compacts with initial hydride loading of 25%–40% HfH 2 approaching 83% hydrogen loss for the lower volume fraction composites. Increasing the volume fraction of HfH 2 to 70% enhanced the retained hydrogen content to 53% and attributed to the reduced interfacial area intrinsic to the increased HfH 2 loading in this MMC.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Harnessing on-machine metrology data for prints with a surrogate model for laser powder directed energy deposition

In this study, we leverage the massive amount of multi-modal on-machine metrology data generated from Laser Powder Directed Energy Deposition (LP-DED) to construct a comprehensive surrogate model of the 3D printing process. By employing Dynamic Mode Decomposition with Control (DMDc), a data-driven technique, we capture the complex physics inherent in this extensive dataset. This physics-based surrogate model emphasizes thermodynamically significant quantities, enabling us to accurately predict key process outcomes. The model ingests 21 process parameters, including laser power, scan rate, and position, while providing outputs such as melt pool temperature, melt pool size, and other essential observables. Furthermore, it incorporates uncertainty quantification to provide bounds on these predictions, enhancing reliability and confidence in the results. We then deploy the surrogate model on a new, unseen part and monitor the printing process as validation of the method. Our experimental results demonstrate that the predictions align with actual measurements with high accuracy, confirming the effectiveness of our approach. Furthermore, this methodology not only facilitates real-time predictions but also operates at process-relevant speeds, establishing a basis for implementing feedback control in LP-DED.

Digital twins↗
DOE OSTI2022

[Fe 4 S 4 ] cubane in sulfite reductases: new insights into bonding properties and reactivity

The dissimilatory sulfite reductase enzyme has very characteristic active site where the substrate binds to an iron site, ligated by a siroheme macrocycle and a thiol directly connected to a [Fe 4 S 4 ] cluster. This arrangement gives the enzyme remarkable efficiency in reducing sulfite and nitrite all the way to hydrogen sulfide and ammonia. For the first time we present a theoretical study where substrate binding modalities and activation are elucidated using active site models containing proton supply side chains and the [Fe 4 S 4 ] cluster. Density functional theory (DFT) was deployed in conjunction with the energy decomposition scheme (as implemented in AMS), the quantum theory of atoms in molecules (QTAIM), and conceptual DFT (cDFT) descriptors. We quantified the role of the electrostatic interactions inside the active site created by the side chains as well as the influence of the [Fe4S4] cluster on the substrate binding. Furthermore, using conceptual DFT results we shed light of the activation process, thus, laying foundation for further mechanistic studies. We found that the bonding of the ligands to the iron complex is dominated by electrostatic interactions, but the presence of the [Fe 4 S 4 ] cubane leads to substantial changes in electronic interaction. The spin state of the cubane, however, affects the binding energy only marginally. The conceptual DFT results show that the presence of the [Fe 4 S 4 ] cubane affects the reactivity of the active site as it is involved in electron transfer. This is corroborated by an increase in the electrophilicity index, thus making the active site more prone to react with the ligands. The interaction energies between the ligand and the siroheme group are also increased upon the presence of the cubane group, thus, suggesting that the siroheme group is not an innocent spectator but plays an active role in the reactivity of the dSIR active site.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗