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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

Designing an Optimal Sensor Network via Minimizing Information Loss

Optimal experimental design is a classic topic in statistics, with many well-studied problems, applications, and solutions. The design problem we study is the placement of sensors to monitor spatiotemporal processes, explicitly accounting for the temporal dimension in our modeling and optimization. We observe that recent advancements in computational sciences often yield large datasets based on physics-based simulations, which are rarely leveraged in experimental design. We introduce a novel model-based sensor placement criterion, along with a highly-efficient optimization algorithm, which integrates physics-based simulations and Bayesian experimental design principles to identify sensor networks that “minimize information loss” from simulated data. Our technique relies on sparse variational inference and (separable) Gauss-Markov priors, and thus may adapt many techniques from Bayesian experimental design. We validate our method through a case study monitoring air temperature in Phoenix, Arizona, using state-of-the-art physics-based simulations. Our results show our framework to be superior to random or quasi-random sampling, particularly with a limited number of sensors. We conclude by discussing practical considerations and implications of our framework, including more complex modeling tools and real-world deployments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Convolutional Neural Network for Segmenting Micro-X-ray Computed Tomography Images of Wood Cellular Structures

To further enhance the performance of wood products, improved tools are needed to study in situ cellular scale phenomena like mechanical deformations and moisture swelling. Micro-X-ray computed tomography (μXCT) using brilliant synchrotron light sources now has the spatial and temporal resolution for real-time visualization of phenomena in three-dimensional cellular structures. However, the tradeoff for speed includes the loss of intensity contrast between different types of materials within the imaged structure, such as cell wall and air in wood. This loss of contrast prevents traditional histogram-based segmentation methods from being used effectively. A new convolutional neural network (CNN) approach was therefore developed to segment fast μXCT images of wood into cell wall and air volumes. The fast μXCT and segmentation were demonstrated in the study of moisture swelling in loblolly pine (Pinus taeda) earlywood and latewood cellular structures conditioned at 0%, 33%, 75%, and 95% relative humidity (RH). The CNN segmentation results had a mean intersection over union (IoU) metric accuracy of 96%. Initial analysis of the swelling in the latewood revealed cell walls swelled about 25% when conditioned from 0% to 95% RH. Additionally, the widths of ray cell lumina in the transverse plane of latewood could be observed to increase at higher RH. The segmentation method presented here will facilitate future quantitative analyses in in situ μXCT studies of wood and other similar cellular materials.

Arzola-Villegas, Xavier (ORCID:0000000305369766)↗
DOE OSTI2016

5 kHz thermometry in a swirl-stabilized gas turbine model combustor using chirped probe pulse femtosecond CARS. Part 2. Analysis of swirl flame dynamics

We have performed a detailed analysis of the temperature field in a turbulent swirl flame operating with a self-excited thermo-acoustic instability. The temperature field was measured using 5 kHz chirped-probe-pulse (CPP) femtosecond (fs) coherent anti-Stokes Raman scattering (CARS). The measurements are described in detail in the part 1 companion article. In this paper, part 2, a detailed analysis of the time-resolved temperature measurements and simultaneous pressure measurements is performed to provide insight into the dynamics and structure of the swirl-stabilized flame. Here this work is the first to capture the dynamics of the flame, flow, and coupled flow-flame processes using high-fidelity, spatially- and temporally-resolved thermometry in a flame of practical relevance. The time-averaged contour plot of the temperature field indicates that the flame is very flat and stabilizes approximately 10 mm downstream of the burner face. In this region, there are very significant temperature fluctuations indicating a very high level of unsteadiness. The temperature probability distribution functions (PDFs) are clearly bimodal in this region near the injector face. A Fourier analysis of the temperature time series revealed multiple coherent oscillatory modes. The strongest oscillation was found to be coherent and in-phase with an acoustic resonance at 314 Hz, as expected from the Rayleigh criteria for the unstable flame. An analysis of the phase-conditioned average temperature fields clearly shows an axial pumping of low-temperature reactants, which are consumed after a convective delay and result in a spike in the global heat-release rate. Continued analysis also revealed a 438 Hz oscillation that was found to correspond with the dynamics of convective transport by a helical precessing vortex core (PVC). The structure of the PVC, and its interaction with the flame, were studied based on the presence of this characteristic frequency in the power spectral densities computed throughout the flow. The precision and time-resolution of the CPP fsCARS measurements was also sufficient to enable computation of the integral time-scales as well as the PDFs of the temporal temperature gradients. A sample of state space trajectories were used to provide insight into the nature of coupling between the narrowband acoustic resonance and the broadband spectrum of turbulent flame processes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Multiscale high-order/low-order (HOLO) algorithms and applications

We review the state of the art in the formulation, implementation, and performance of so-called high-order/low-order (HOLO) algorithms for challenging multiscale problems. HOLO algorithms attempt to couple one or several high-complexity physical models (the high-order model, HO) with low-complexity ones (the low-order model, LO). The primary goal of HOLO algorithms is to achieve nonlinear convergence between HO and LO components while minimizing memory footprint and managing the computational complexity in a practical manner. Key to the HOLO approach is the use of the LO representations to address temporal stiffness, effectively accelerating the convergence of the HO/LO coupled system. The HOLO approach is broadly underpinned by the concept of nonlinear elimination, which enables segregation of the HO and LO components in ways that can effectively use heterogeneous architectures. The accuracy and efficiency benefits of HOLO algorithms are demonstrated with specific applications to radiation transport, gas dynamics, plasmas (both Eulerian and Lagrangian formulations), and ocean modeling. Across this broad application spectrum, HOLO algorithms achieve significant accuracy improvements at a fraction of the cost compared to conventional approaches. It follows that HOLO algorithms hold significant potential for high-fidelity system scale multiscale simulations leveraging exascale computing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Deep learning interfacial momentum closures in coarse-mesh CFD two-phase flow simulation using validation data

Multiphase flow phenomena have been widely observed in the industrial applications while it remains a challenging yet unsolved problems. Three-dimensional computational fluid dynamics (CFD) approaches resolve the flow fields on a finer special and temporal scales which can complement the dedicated experimental study. However, closures have to be introduced to reflect the underlying physics in multiphase flow. Among them, the interfacial forces, including drag, lift, turbulent dispersion and wall lubrication forces, play in important role on the bubble’s distribution and migration in liquid-vapor two-phase flow. Development of those closures traditionally rely on the experimental data and analytical derivation with simplified assumptions which usually cannot deliver a universal solution across wide range of flow conditions. In this paper, a data-driven approach, named as Feature Similarity Measurement (FSM), is developed and applied to improve the simulation capability of two-phase flow with coarse-mesh CFD approach. Interfacial momentum transfer in adiabatic bubbly flow serves as the focus of the present study. Both a mature and a simplified set of interfacial closures are taken as the low fidelity data. Experimental data and fine mesh CFD simulations results are adopted as high-fidelity data. Qualitative and quantitative analysis are performed in this paper which reveals that FSM can substantially improve the prediction of coarse mesh CFD model regardless of the choice of interfacial closures and it provides scalability and consistency across discontinuous flow regimes. Furthermore, it demonstrates that data-driven method can aid the multiphase flow modeling by exploring the connections between local physical features and simulation errors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Multiscale Temporal Variability of the Global Air‐Sea CO 2 Flux Anomaly

Abstract The global air‐sea CO 2 flux (F) impacts and is impacted by a plethora of climate‐related processes operating at multiple time scales. In bulk mass transfer formulations, F is driven by physico‐ and bio‐chemical factors such as the air‐sea partial pressure difference (∆pCO 2 ), gas transfer velocity, sea surface temperature, and salinity–all varying at multiple time scales. To de‐convolve the impact of these factors on variability in F at different time scales, time‐resolved estimates of F were computed using a global data set assembled between 1988 and 2015. The F anomalies were defined as temporal deviations from the 28‐year time‐averaged value. Spectral analysis revealed four dominant timescales of variability in F–subseasonal, seasonal, interannual, and decadal with relative amplitude differences varying across regions. A second‐order Taylor series expansion was then conducted along these four timescales to separate drivers across differing regions. The analysis showed that on subseasonal timescales, wind speed variability explains some 66% of the global F anomaly and is the dominant driver. On seasonal, interannual, and decadal timescales, the ∆pCO 2 effect controlled by the ∆pCO 2 anomaly, explained much of the F anomaly. On decadal timescales, the F anomaly was almost entirely governed by the ∆pCO 2 effect with large contributions from high latitudes. The main drivers across timescales also dominate the regional F anomaly, particularly in the mid‐high latitude regions. Finally, the driver of the ∆pCO 2 effect was closely connected with the relative strength of atmospheric pCO 2 and the nonthermal component of oceanic pCO 2 anomaly associated with dissolved inorganic carbon and alkalinity.

Environmental Sciences & Ecology↗
DOE OSTI2017

Asynchronous space–time domain decomposition method with localized uncertainty quantification

The computational cost associated with uncertainty quantification of engineering problems featuring localized phenomenon can be reduced by confining the random variability of the model parameters within a region of interest. Here in this case, a localized treatment of mesh and time resolutions is required to capture the effect of the confined material uncertainty on the global response. We present a computational approach for localized uncertainty quantification with the capability of asynchronous treatment of mesh and time resolutions. In particular, we allow each subdomain to have its local uncertainty representation and the corresponding mesh and time resolutions. As a result, computing resources can be directed toward a small region of interest where a model with high spatial and temporal resolutions is required. To verify the numerical implementation, we consider elastic wave propagation in an axially loaded beam. Moreover, we perform convergence studies with respect to the spatial and temporal discretizations as well as the size of an uncertain subdomain. A projectile impacting a composite sandwich plate is considered as an engineering application for the proposed method.

42 ENGINEERING↗
DOE OSTI2018

An adaptive, implicit, conservative, 1D-2V multi-species Vlasov–Fokker–Planck multi-scale solver in planar geometry

We consider a 1D-2V Vlasov–Fokker–Planck multi-species ionic description coupled to fluid electrons. We address temporal stiffness with implicit time stepping, suitably preconditioned. To address temperature disparity in time and space, we extend the conservative adaptive velocity-space discretization scheme proposed in [Taitano et al., J. Comput. Phys., 318, 391–420, (2016)] to a spatially inhomogeneous system. Here in this approach, we normalize the velocity-space coordinate to a temporally and spatially varying local characteristic speed per species. We explicitly consider the resulting inertial terms in the Vlasov equation, and derive a discrete formulation that conserves mass, momentum, and energy up to a prescribed nonlinear tolerance upon convergence. Our conservation strategy employs nonlinear constraints to enforce these properties discretely for both the Vlasov operator and the Fokker–Planck collision operator. Numerical examples of varying degrees of complexity, including shock-wave propagation, demonstrate the favorable efficiency and accuracy properties of the scheme.

1D2V↗
DOE OSTI2013

Understanding the Irradiation Behavior of Zirconium Carbide

Zirconium carbide (ZrC) is being considered for utilization in high-temperature gas-cooled reactor fuels in deep-burn TRISO fuel. Zirconium carbide possesses a cubic B1-type crystal structure with a high melting point, exceptional hardness, and good thermal and electrical conductivities. The use of ZrC as part of the TRISO fuel requires a thorough understanding of its irradiation response. However, the radiation effects on ZrC are still poorly understood. The majority of the existing research is focused on the radiation damage phenomena at higher temperatures (>450{degree}C) where many fundamental aspects of defect production and kinetics cannot be easily distinguished. Little is known about basic defect formation, clustering, and evolution of ZrC under irradiation, although some atomistic simulation and phenomenological studies have been performed. Such detailed information is needed to construct a model describing the microstructural evolution in fast-neutron irradiated materials that will be of great technological importance for the development of ZrC-based fuel. The goal of the proposed project is to gain fundamental understanding of the radiation-induced defect formation in zirconium carbide and irradiation response by using a combination of state-of-the-art experimental methods and atomistic modeling. This project will combine (1) in situ ion irradiation at a specialized facility at a national laboratory, (2) controlled temperature proton irradiation on bulk samples, and (3) atomistic modeling to gain a fundamental understanding of defect formation in ZrC. The proposed project will cover the irradiation temperatures from cryogenic temperature to as high as 800{degree}C, and dose ranges from 0.1 to 100 dpa. The examination of this wide range of temperatures and doses allows us to obtain an experimental data set that can be effectively used to exercise and benchmark the computer calculations of defect properties. Combining the examination of radiation-induced microstructures mapped spatially and temporally, microstructural evolution during post-irradiation annealing, and atomistic modeling of defect formation and transport energetics will provide new, critical understanding about property changes in ZrC. The behavior of materials under irradiation is determined by the balance between damage production, defect clustering, and lattice response. In order to predict those effects at high temperatures so targeted testing can be expanded and extrapolated beyond the known database, it is necessary to determine the defect energetics and mobilities as these control damage accumulation and annealing. In particular, low-temperature irradiations are invaluable for determining the regions of defect mobility. Computer simulation techniques are particularly useful for identifying basic defect properties, especially if closely coupled with a well-constructed and complete experimental database. The close coupling of calculation and experiment in this project will provide mutual benchmarking and allow us to glean a deeper understanding of the irradiation response of ZrC, which can then be applied to the prediction of its behavior in reactor conditions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2014

Trend Analysis in Biomedical Texts via Vector Space Model Synonym Extraction

Accurate synonym extraction is a highly desired tool in computational semantics and natural language processing (NLP). Intrinsic to many current language models is the Vector Space Model (VSM) whereby linguistic properties are modeled by real vectors of small length compared to the document size. Here, several vector models are discussed. Utilizing the open source Word2Vec tool, temporal trends in biomedical abstracts from MEDLINE have been analyzed via a unique transformation matrix method. Additionally, accuracy of the word vector method and clustering of temporal trends are discussed.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

An integrated numerical model for coupled poro-hydro-mechanics and fracture propagation using embedded meshes

Integrated models for fluid-driven fracture propagation and general multiphase flow in porous media are valuable to the study and engineering of several systems, including hydraulic fracturing, underground disposal of waste, and geohazard mitigation across such applications. This work extends the coupled model multiphase flow and poromechanical model of Ren et al. (2018) to admit fracture propagation (FP). The coupled XFEM-EDFM scheme utilizes a separate fracture mesh that is embedded on a static background mesh. The onset and dynamics of fracture propagation are governed by the equivalent stress intensity factor (SIF) criterion. A domain-integral method (J integral) is applied to compute this information. Additionally, an adaptive time-marching scheme is proposed to rapidly restrict and grow temporal resolution to match the underlying time-scales. The proposed model is verified with analytical solutions, and shows the capability to accurately and adaptively co-simulate fluid transport and deformation as well as the propagation of multiple fractures.

42 ENGINEERING↗
DOE OSTI2021

Time-Resolved Geometric Feature Tracking Elucidates Laser-Induced Keyhole Dynamics

In this report, during laser melting of metals, localized metal evaporation resulting in the formation of a keyhole shaped cavity can occur if processing conditions are chosen with high power density. An unstable keyhole can have deleterious effects in certain applications (e.g., laser powder bed fusion) as it increases the likelihood of producing defects such as porosity. In this work, we propose a pipeline that enables complete segmentation and extraction of various geometric features in keyholing conditions. In situ synchrotron high-speed X-ray visualization at the Advanced Photon Source provides large datasets of experimental images with a high spatio-temporal resolution across a range of laser parameters for Ti-6Al- 4 V. Computer vision image processing techniques were used to extract time-resolved quantitative geometric features (e.g., depth, width, front wall angle) throughout keyhole evolution which were subsequently analyzed to understand the relationship between the variation of local keyhole geometry and processing conditions. This analysis is the first to employ a data-driven approach to further our understanding of the keyholing process regime.

36 MATERIALS SCIENCE↗
DOE OSTI2018

A class of efficient difference method for time fractional reaction–diffusion equation

The fractional reaction–diffusion equation has profound physical background and theoretical connotation, and its numerical methods are of great scientific significance and practical value. In this paper, a class of explicit–implicit (E–I) difference method and implicit–explicit (I–E) difference method are proposed for one-dimensional time fractional reaction–diffusion equation. The methods are constructed by combining classical explicit scheme and classical implicit scheme. The existence, uniqueness and convergence of solutions are given for E–I and I–E schemes. Theoretical analysis and numerical experiments show that the E–I and I–E schemes are unconditionally stable, with second-order spatial accuracy and 2−α order temporal accuracy. Compared with the classical implicit difference method with the similar accuracy, the computation time of the presented methods is improved by nearly 41%. It is shown that the E–I difference method and I–E difference method are effective for solving the time fractional reaction–diffusion equation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

The fast and the furious: Ultrafast hot electrons in plasmonic metastructures. Size and structure matter

This review focuses on the generation of energetic (hot) electrons in plasmonic metastructures and nanomaterials, and their characterization through time-resolved spectroscopy. Excitation of hot electrons under illumination occurs in any metal or conductor, but their number will vary for each type of nanostructure. While plasmonic resonances are well described classically, the excitation of hot electrons (HEs) is a quantum process and its description requires further elaboration. Some potential applications for HEs lie in the fields of photo-catalysis and optoelectronics, and their study constitutes a very active interdisciplinary research direction that involves chemistry, physics and device engineering. Here we focus on some particular developments enabling the use of hot carriers for these applications. Particularly, we discuss the approaches and structures required to create hot carriers, the temporal dynamics of hot carrier formation and relaxation, and relevant theoretical methods used to compute the HE dynamics. The observations presented here support the conclusion that the shape of the nanostructure matters. Although metastructures with infrared gap plasmons can exhibit spatially extended hot spots and anomalously large numbers of non-thermalized HEs, most excited carriers in a plasmonic nanostructure typically have small excitation energies. Here in this paper, we discuss ways to strongly increase the number of high-energy electrons, highlighting the role of hot spots, system size, geometry and resonant frequencies. To generate HEs efficiently, we can take advantage of special geometries with hot spots, such as metamaterial absorbers with ultra-narrow gaps or nanostars. Furthermore, we discuss the applications in ultrafast electronics based on plasmon-enhanced photoemission and tunneling in the nonlinear regime. Considering the longer timescale phenomena, we also present studies on the coherent dynamics in a nanostructure after electron thermalization, showing acoustic breathing modes. In this paper, we review some key developments in the field of ultra-fast plasmonic dynamics and provide a perspective for its possible next steps.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Fluid temperature predictions of geothermal borefields using load estimations via state observers

Fluid temperature predictions of geothermal borefields usually involves temporal superposition of its characteristic g-function, using load aggregation schemes to reduce computational times. Assuming that the ground has linear properties, it can be modeled as a linear state space system where the states are the aggregated loads. However, the application and accuracy of these models is compromised when the borefield is already operating and its load history is not registered or there are gaps in the data. This paper assesses the performance of state observers to estimate the borefield load history and obtain accurate fluid predictions. Results show that both Time-Varying Kalman Filter (TVKF) and Moving Horizon Estimator (MHE) provide predictions with average and maximum errors below 0.1 $^\circ C$ and 1 $^\circ C$ respectively. MHE outperforms TVKF in terms of n-step ahead output predictions and load history profile at the expenses of about 5 times more computational time.

Figueroa, Iago C.↗
DOE OSTI2026

Evolving language of pediatric anxiety in electronic health records

Objectives This study aimed to identify and quantify semantic drift (ie, the change in semantic meaning over time) within expert-defined anxiety-related (AR) terminology and compare it to common electronic health record (EHR) vocabulary across longitudinal pediatric clinical notes. Materials and Methods A corpus of pediatric clinical notes from 2009 to 2022 was analyzed using computational methods. Semantic drift for each term was quantified using cosine similarity between annual temporal word embeddings. Contextual meaning was examined through changes in nearest neighbors across years. The Laws of Semantic Change were applied to assess the influence of word frequency and polysemy. Vocabulary terms were categorized as AR or common EHR. Results 98% of AR terminology maintained a cosine similarity between 0.00 and 0.50, indicating moderate semantic stability, whereas 90% of common EHR terms remained between 0.00 and 0.25, showing greater contextual stability overall. Frequent terms exhibited minimal change (Frequency Coefficient = 0.04), whereas highly polysemous or abbreviated terms showed less stability (Polysemy Coefficient = 0.630). AR terminology drifted more slowly than general EHR vocabulary (Type Coefficient = −0.179), further supported by significant year–type interactions (Coef = −0.09 to −0.523). Discussion Although anxiety-related terminology demonstrates slower semantic drift than general EHR vocabulary, subtle contextual shifts still occur that may affect downstream interpretability and retrieval in automated systems. Conclusion Continuous linguistic monitoring and adaptive modeling are essential to maintain semantic fidelity and ensure the long-term reliability of clinical decision support systems as healthcare documentation evolves.

Pediatric anxiety disorders↗
DOE OSTI2017

Spatial and temporal control of populations, branching ratios, and electronic coherences in LiH by a single one-cycle infrared pulse

Dynamical computations demonstrate considerable selectivity over the fragmentation channels of the LiH molecule via the polarization and the carrier envelope phase (CEP) of a single ultrashort one-cycle strong IR pulse. For aligned molecules, control of the CEP allows building nonstationary coherent electronic wave packets of contrasting ionic character, either Li δ +H δ - or Li δ -H δ +. Furthermore, the complementary coherences are maintained all the way to dissociation. The direction of the electric field at its maximum points either towards the Li or towards the H atom, which selectively steers the nuclear dynamics to specific dissociation products.

74 ATOMIC AND MOLECULAR PHYSICS↗