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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Spatiotemporal Reaction Dynamics Control in Two‐Photon Polymerization for Enhancing Writing Characteristics

Since 2001, 3D microfabrication based on two‐photon polymerization (TPP) has drawn extensive attention and interest in biology, optics, photonics, material science, and high‐energy physics. The in‐volume fabrication capability due to the threshold behavior of two‐photon absorption enables TPP higher flexibility compared with other nanofabrication techniques. However, as determined by the in‐volume fabrication feature as well as various reaction dynamics, the writing characteristics of TPP, such as throughput, accuracy, surface quality, and fabrication capability, are still limited. Herein, a comprehensive study is performed on the spatiotemporal behavior of reaction dynamics during TPP fabrication, mainly focusing on spatiotemporal characteristics of radical diffusion, photothermal effect, microscale mechanics, and voxel stacking process. Based on the study, a nonsequential fabrication method is established to simultaneously improve key writing characteristics of TPP and realize sharp features, high speeds, large overhang structure, and smooth surfaces. The method established in this work can be applied to improve the performance of functional devices for various fields.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Spatiotemporal Dynamics of the Relative Abundance of Soil Nutrient‐Degrading Enzyme‐Encoding Genes Across Continental US Ecoregions

Understanding the spatiotemporal patterns in the relative abundance of soil extracellular enzyme‐encoding genes is critical for predicting microbial responses to environmental change and their potential role in nutrient cycling. Yet, integrating novel metagenomic observations with spatiotemporal environmental gradients to infer regional patterns and future trajectories has remained unclear. To address this gap, we applied a machine learning (ML) approach, integrating soil metagenomic data with environmental variables—soil properties, topography, vegetation, and climate—to predict the relative abundance of enzyme‐encoding genes for soil carbon (C), nitrogen (N), and phosphorus (P) across surface soils of the continental United States. We assessed potential responses under future emission scenarios (SSP2‐4.5 and SSP5‐8.5) by comparing a baseline (1985–2014) to a future period (2071–2100). The ML model explained 57%–63% of baseline variation. Precipitation was identified as the most influential factor for the relative abundance of C‐ and N‐degrading enzyme‐encoding genes, while slope length, representing horizontal distance that water can travel downslope, was the primary driver for P‐degrading enzyme‐encoding genes abundance. Projections revealed spatially heterogeneous shifts across continental US ecoregions: the relative abundance of C‐ and N‐degrading enzyme‐encoding genes decreased in wetter ecoregions and increased in drier ecoregions under future climate, while P‐degrading enzyme‐encoding genes abundance decreased significantly in semiarid and Mediterranean ecoregions. This study demonstrates the utility of metagenomic data for mapping soil genetic potential and predicting its regional response to environmental change, to inform ecosystem management strategies.

extracellular enzyme-encoding genes↗
DOE OSTI2020

Spatiotemporal variations of albedo in managed agricultural landscapes: inferences to global warming impacts (GWI)

Albedo can be used to quantify ecosystem and landscape contributions to local and global climate. Such contributions are conventionally expressed as radiative forcing (RF) and global warming impact (GWI). We contextualize our results within landscape carbon production and storage to highlight the importance of changes in albedo for landscape GWI from multiple causes, including net ecosystem production (NEP) and greenhouse gas (GHG) emissions. Objective To examine the spatiotemporal changes in albedo (Δα) in contrasting managed landscapes through calculations of albedo-induced RF (RF Δα ) and GWI (GWI Δα ) under different climatic conditions. Methods We selected five contrasting landscapes within the Kalamazoo River watershed in southern Michigan USA as proof of concept. The daily MCD43A3 MODIS (V006) product was used to analyze the inter- and intra-annual variations of growing season albedo. In addition, the variations of RF Δα and GWI Δα were computed based on landscape composition and climate. Results The RF Δα (-5.6 W m -2 ) and GWI Δα (-1.3 CO 2eq ha -1 year -1 ) were high in forest-dominated landscapes, indicating cooling effects and CO 2eq mitigation impacts similar to crops. The CO 2eq mitigation of cropland-dominated landscapes was on average 52% stronger than forest-dominated landscapes. In the landscape with the highest proportion of forest, under dry and wet conditions CO 2eq mitigation was reduced by up to 24% and ~ 30%, respectively; in one cropland-dominated landscape wet conditions reduced CO 2eq mitigation by 23%. Conclusions Findings demonstrate that quantifying spatiotemporal changes in albedo in managed landscapes and under different climatic conditions is essential to understand how landscape modification affects RF Δα and GWI Δα and thereby contributes to ecosystem-level GWI.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Self-supervised learning of spatiotemporal thermal signatures in additive manufacturing using reduced order physics models and transformers

Microstructure control via additive manufacturing has enormous potential as manufacturers, materials scientists, and designers alike seek to exploit novel fabrication technologies to improve component performance. Recent works have demonstrated the feasibility of producing materials with controlled microstructures across various length scales. However, the experimental approach towards exploring the process-structure space can be laborious and costly. This is particularly true if also considering scan pattern optimization which is well suited for processes such as powder bed fusion electron beam melting. In this work we propose an approach for encoding additive manufacturing layer-wise thermal response signatures using self-supervised representation learning. Thermal simulations from a reduced order model are utilized to estimate the spatiotemporal response during printing. A machine learning framework, using video-transformers, is utilized to efficiently distill spatiotemporal patterns into a compact latent space representation. This latent state representation encodes the relevant physics which is then utilized to establish a data-driven process-structure model for an additively manufactured Ni-based superalloy. In conclusion, the proposed methodology could potentially be used towards in-situ process monitoring, scan pattern experimental design, and component qualification.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Cause identification of electromagnetic transient events using spatiotemporal feature learning

This paper presents a spatiotemporal feature learning method for cause identification of electromagnetic transient events in power grids. The proposed method is formulated based on the availability of time-synchronized high-frequency measurements and using the convolutional neural network as the spatiotemporal feature representation along with softmax function for the classification. Despite the existing threshold-based, or energy-based events analysis methods, such as support vector machine autoencoder, and tapered multi-layer perceptron neural network, the proposed feature learning is carried out with respect to both time and space. The effectiveness of the proposed feature learning and the subsequent cause identification is validated through the Electromagnetic Transients Program (EMTP) simulation of different events such as line energization, capacitor bank energization, lightning, fault, and high-impedance fault in the IEEE 30-bus, and the real-time digital simulation of the Western System Coordinating Council (WSCC) 9-bus system.

42 ENGINEERING↗
DOE OSTI2023

Historical spatiotemporal changes in fire danger potential across biomes

This study 1) identifies the seasons and biomes that exhibit significant (1980–2019) changes in fire danger potential, as quantified by the Canadian Fire Weather Index (FWI); 2) explores what types of fire behavior potentials may be contributing to changes in fire danger potential, as quantified by the United States Energy Release Component (ERC) and the Ignition Component (IC); 3) provides spatiotemporal insight on how fire danger potential and fire behavior potential are responding in relation to changes in seasonal precipitation totals and seasonal mean air temperature across biomes. Time series of these fire potentials, as well as seasonal mean temperature, and seasonal precipitation totals are generated using data from the 0.25° ECMWF spatial resolution Reanalysis 5th Generation (ERA5) and the Climatic Research Unit gridded Time Series (CRU TS). The Mann-Kendall test is then applied to identify significant spatiotemporal trends across each biome. Results indicate that the September–November season (SON) exhibits the greatest rate of increase in fire danger potential, followed by the June–August season (JJA), December, January–February season (DJF), and March–May season (MAM), and this is predominant over the Tropical and Subtropical Moist Broadleaf Forest Biome, as well as all vegetation types of the temperate biomes. Similarly, the temperate biomes experience the greatest rate of increase in fire intensity potential and ignition potential, but prevalent during the DJF and MAM seasons. Furthermore, there is a significant positive correlation between fire danger potential and seasonal mean air temperature during JJA in the Northern Hemisphere for the temperate biomes in North America and Europe, as well as the Tropical and Subtropical biomes in Africa. Our analysis provides quantitative insight as to how fire danger potential and fire behavior potential have been responding to changes in seasonal mean temperature and seasonal precipitation totals across different ecoregions around the world.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Tracking Spatiotemporal Electric Potential in Batteries Using High-Resolution Operando X‑ray Transmission Imaging

The formation of significant concentration gradients across electrolytes in batteries affects the rate at which electrochemical reactions occur. In this work, we use high-resolution operando X-ray transmission imaging to capture spatiotemporal salt concentration profiles c(x,t) in a symmetric cell comprising a polymer electrolyte sandwiched between two lithium–indium alloy electrodes during a constant-current experiment followed by open-circuit relaxation. The decay of open-circuit potential is related to the concentration dependence of the potential across concentration cells, U. We show how operando c(x,t) data can be used to calculate the spatiotemporal electric potential “inside” the polarized electrolyte. We track the spatial- and time-dependent cell potential during the constant-current step and distinguish its two contributions: a concentration overpotential governed by U. and an ohmic contribution governed by ionic conductivity. Over most of the time window, the concentration overpotential dominates. At steady state, it is a factor of 7 larger than the ohmic contribution. Such findings indicate that efforts to design new polymer electrolytes should focus on minimizing concentration gradients.

Electrical conductivity↗
DOE OSTI2020

Spatiotemporal DNA methylome dynamics of the developing mouse fetus

Cytosine DNA methylation is essential for mammalian development but understanding of its spatiotemporal distribution in the developing embryo remains limited1,2. Here, as part of the mouse Encyclopedia of DNA Elements (ENCODE) project, we profiled 168 methylomes from 12 mouse tissues or organs at 9 developmental stages from embryogenesis to adulthood. We identified 1,808,810 genomic regions that showed variations in CG methylation by comparing the methylomes of different tissues or organs from different developmental stages. These DNA elements predominantly lose CG methylation during fetal development, whereas the trend is reversed after birth. During late stages of fetal development, non-CG methylation accumulated within the bodies of key developmental transcription factor genes, coinciding with their transcriptional repression. Integration of genome-wide DNA methylation, histone modification and chromatin accessibility data enabled us to predict 461,141 putative developmental tissue-specific enhancers, the human orthologues of which were enriched for disease-associated genetic variants. These spatiotemporal epigenome maps provide a resource for studies of gene regulation during tissue or organ progression, and a starting point for investigating regulatory elements that are involved in human developmental disorders.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics

Traditional data-driven deep learning models often struggle with high training costs, error accumulation, and poor generalizability in complex physical processes. Physics-informed deep learning (PiDL) addresses these challenges by incorporating physical principles into the model. Most PiDL approaches regularize training by embedding governing equations into the loss function, yet this depends heavily on extensive hyperparameter tuning to weigh each loss term. To this end, we propose to leverage physics prior knowledge by “baking” the discretized governing equations into the neural network architecture via the connection between the partial differential equations (PDE) operators and network structures, resulting in a PDE-preserved neural network (PPNN). This method, embedding discretized PDEs through convolutional residual networks in a multi-resolution setting, largely improves the generalizability and long-term prediction accuracy, outperforming conventional black-box models. The effectiveness and merit of the proposed methods have been demonstrated across various spatiotemporal dynamical systems governed by spatiotemporal PDEs, including reaction-diffusion, Burgers’, and Navier-Stokes equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Simulations of wavelength-multiplexed holography for single-shot spatiotemporal characterization of NIF’s advanced radiographic capability (ARC) laser

We simulate the use of a newly developed single-shot wavelength-multiplexed holography-based diagnostic, STRIPED FISH, to fully characterize the as-delivered laser pulses of the National Ignition Facility’s Advanced Radiographic Capability (NIF-ARC) laser. To that end, we have performed simulations of the NIF-ARC pulse incorporating (a) a time-integrated spatial-profile measurement and a complete temporal-intensity-and-phase measurement using a frequency resolved optical gating, but without any spatiotemporal pulse characterizations, and (b) simulated first-order spatiotemporal distortions, which could be measured on a single shot if a STRIPED FISH device were deployed.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Spatiotemporal dispersion compensation for a 200-THz noncollinear optical parametric amplifier

A noncollinear optical parametric amplifier (NOPA) can produce few-cycle femtosecond laser pulses that are ideally suited for time-resolved optical spectroscopy measurements. However, the nonlinear-optical process giving rise to ultrabroadband pulses is susceptible to spatiotemporal dispersion problems. Here, we detail refinements, including chirped-pulse amplification (CPA) and pulse-front matching (PFM), that minimize spatiotemporal dispersion and thereby improve the properties of ultrabroadband pulses produced by a NOPA. Here, the description includes a rationale behind the choices of optical and optomechanical components, as well as assessment protocols. We demonstrate these techniques using a 1 kHz, second-harmonic Ti:sapphire pump configuration, which produces ~5-fs duration pulses that span from about 500 to 800 nm with a bandwidth of about 200 THz. To demonstrate the utility of the CPA-PFM-NOPA, we measure vibrational quantum beats in the transient–absorption spectrum of methylene blue, a dye molecule that serves as a reference standard.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Demonstration of full-scale spatiotemporal diagnostics of solid-density plasmas driven by an ultra-short relativistic laser pulse using an X-ray free-electron laser

Understanding the complex plasma dynamics in ultra-intense relativistic laser–solid interactions is of fundamental importance for applications of laser–plasma-based particle accelerators, the creation of high-energy-density matter, understanding planetary science, and laser-driven fusion energy. However, experimental efforts in this regime have been limited by the lack of accessibility of over-critical densities and the poor spatiotemporal resolution of conventional diagnostics. Over the last decade, the advent of femtosecond brilliant hard X-ray free-electron lasers (XFELs) has opened new horizons to overcome these limitations. Here, for the first time, we present full-scale spatiotemporal measurements of solid-density plasma dynamics, including preplasma generation with tens of nanometer scale length driven by the leading edge of a relativistic laser pulse, ultrafast heating and ionization at the main pulse arrival, the laser-driven blast wave, and transient surface return current-induced compression dynamics up to hundreds of picoseconds after interaction. These observations are enabled by utilizing a novel combination of advanced X-ray diagnostics including small-angle X-ray scattering, resonant X-ray emission spectroscopy, and propagation-based X-ray phase-contrast imaging simultaneously at the European XFEL-HED beamline station.

Huang, Lingen [Helmholtz-Zentrum Dresden-Rossendor↗
DOE OSTI2024

Deep learning-based spatiotemporal multi-event reconstruction for delay line detectors

Abstract Accurate observation of two or more particles within a very narrow time window has always been a challenge in modern physics. It creates the possibility of correlation experiments, such as the ground-breaking Hanbury Brown–Twiss experiment, leading to new physical insights. For low-energy electrons, one possibility is to use a Microchannel plate with subsequent delay lines for the readout of the incident particle hits, a setup called a Delay Line Detector. The spatial and temporal coordinates of more than one particle can be fully reconstructed outside a region called the dead radius. For interesting events, where two electrons are close in space and time, the determination of the individual positions of the electrons requires elaborate peak finding algorithms. While classical methods work well with single particle hits, they fail to identify and reconstruct events caused by multiple nearby particles. To address this challenge, we present a new spatiotemporal machine learning model to identify and reconstruct the position and time of such multi-hit particle signals. This model achieves a much better resolution for nearby particle hits compared to the classical approach, removing some of the artifacts and reducing the dead radius a factor of eight. We show that machine learning models can be effective in improving the spatiotemporal performance of delay line detectors.

Computer Science↗
DOE OSTI2023

Experimental generation of polarization entanglement from spontaneous parametric down-conversion pumped by spatiotemporally highly incoherent light

Here, we investigate the cross influence of the pump coherence on the entanglement produced from spontaneous parametric down-conversion (SPDC) in different degrees of freedom (DOFs). We experimentally demonstrate the generation of polarization entanglement from SPDC pumped by a spatiotemporally highly incoherent (STHI) light-emitting diode. Our quantum state tomography measurements using multimode collection fibers to avoid postselection yield a two-qubit state with the concurrence of 0.531 ± 0.006 and purity of 0.647 ± 0.005, in excellent agreement with our theoretically predicted concurrence of 0.552 and purity of 0.652. We find that using an STHI pump reduces the entanglement and purity of the output polarization two-qubit state due to a coupling between the spatiotemporal and polarization DOFs introduced by the birefringence and dispersion of the nonlinear crystal. The viability of SPDC with STHI pumps is important for two reasons: (i) STHI sources are ubiquitous and available at a wider range of wavelengths than lasers, and (ii) the generated STHI polarization-entangled two-photon states could potentially be useful in long-distance quantum communication schemes due to their robustness to scattering.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Spatiotemporal control of high-intensity laser pulses with a plasma lens

Spatiotemporal control encompasses a variety of techniques for producing laser pulses with dynamic intensity peaks that move independently of the group velocity. This controlled motion of the intensity peak offers a new approach to optimizing laser-based applications and enhancing signatures of fundamental phenomena. Here, we demonstrate spatiotemporal control with a plasma optic. A chirped laser pulse focused by a plasma lens exhibits a moving focal point, or “flying focus,” that can travel at an arbitrary, predetermined velocity. Unlike currently used conventional or adaptive optics, a plasma lens can be located close to the interaction region and can operate at an orders of magnitude higher, near-relativistic intensity. Published by the American Physical Society 2024

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Spatiotemporal dynamics of ionic reorganization near biological membrane interfaces

Electrical signals in excitable cells involve spatially localized ionic fluxes through ion channels and pumps on cellular lipid membranes. Common approaches to understand how these fluxes spread assume that the membrane and the surrounding electrolyte comprise an equivalent circuit of capacitors and resistors, which ignores the localized nature of transmembrane ion transport, the resulting ionic gradients and electric fields, and their spatiotemporal relaxation. Here, we consider a model of localized ion pumping across a lipid membrane, and use theory and simulation to investigate how the electrochemical signal propagates spatiotemporally in and out of plane along the membrane. The localized pumping generates long-ranged electric fields with three distinct scaling regimes along the membrane: a constant potential near-field region, an intermediate monopolar region, and a far-field dipolar region. Upon sustained pumping, the monopolar region expands radially in plane with a steady speed that is enhanced by the dielectric mismatch and the finite thickness of the lipid membrane. For unmyelinated lipid membranes in physiological settings, we find remarkably fast propagation speeds of ∼ 40 m / s , allowing faster ionic reorganization compared to bare diffusion. Together, our paper shows that transmembrane ionic fluxes induce transient long-ranged electric fields in electrolyte solutions, which may play hitherto unappreciated roles in biological signaling. Published by the American Physical Society 2025

Row, Hyeongjoo (ORCID:000000033623512X)↗
DOE OSTI2023

Spatiotemporally Adaptive Compression for Scientific Dataset with Feature Preservation – A Case Study on Simulation Data with Extreme Climate Events Analysis

Scientific discoveries are increasingly constrained by limited storage space and I/O capacities. For time-series simulations and experiments, their data often need to be decimated over timesteps to accommodate storage and I/O limitations. In this paper, we propose a technique that addresses storage costs while improving post-analysis accuracy through spatiotemporal adaptive, error-controlled lossy compression. We investigate the trade-off between data precision and temporal output rates, revealing that reducing data precision and increasing timestep frequency lead to more accurate analysis outcomes. Additionally, we integrate spatiotemporal feature detection with data compression and demonstrate that performing adaptive error-bounded compression in higher dimensional space enables greater compression ratios, leveraging the error propagation theory of a transformation-based compressor. To evaluate our approach, we conduct experiments using the well-known E3SM climate simulation code and apply our method to compress variables used for cyclone tracking. Our results show a significant reduction in storage size while enhancing the quality of cyclone tracking analysis, both quantitatively and qualitatively, in comparison to the prevalent timestep decimation approach. Compared to three state-of-the-art lossy compressors lacking feature preservation capabilities, our adaptive compression framework improves perfectly matched cases in TC tracking by 26.4-51.3% at medium compression ratios and by 77.3-571.1% at large compression ratios, with a merely 5–11% computational overhead.

Gong, Qian↗
DOE OSTI2022

Using Image Processing Techniques to Identify and Quantify Spatiotemporal Carbon Cycle Extremes

Rising atmospheric carbon dioxide due to human activities through fossil fuel emissions and land use changes have increased climate extremes such as heat waves and droughts that have led to and are expected to increase the occurrence of carbon cycle extremes. Carbon cycle extremes represent large anomalies in the carbon cycle that are associated with gains or losses in carbon uptake. Carbon cycle extremes could be continuous in space and time and cross political boundaries. Here, we present a methodology to identify large spatiotemporal extremes (STEs) in the terrestrial carbon cycle using image processing tools for feature detection. We characterized the STE events based on neighborhood structures that are three-dimensional adjacency matrices for the detection of spatiotemporal manifolds of carbon cycle extremes. We found that the area affected and carbon loss during negative carbon cycle extremes were consistent with continuous neighborhood structures. In the gross primary production data we used, 100 carbon cycle STEs accounted for more than 75% of all the negative carbon cycle extremes. This paper presents a comparative analysis of the magnitude of carbon cycle STEs and attribution of those STEs to climate drivers as a function of neighborhood structures for two observational datasets and an Earth system model simulation.

Sharma, Bharat↗