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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Wireless Frequency‐Multiplexed Acoustic Array‐Based Acoustofluidics

Abstract Acoustofluidics has shown great potential in enabling on‐chip technologies for driving liquid flows and manipulating particles and cells for engineering, chemical, and biomedical applications. To introduce on‐demand liquid sample processing and micro/nano‐object manipulation functions to wearable and embeddable electronics, wireless acoustofluidic chips are highly desired. This paper presents wireless acoustofluidic chips to generate acoustic waves carrying sufficient energy and achieve key acoustofluidic functions, including arranging particles and cells, generating fluid streaming, and enriching in‐droplet particles. To enable these functions, the wireless acoustofluidic chips leverage mechanisms, including inductive coupling‐based wireless power transfer (WPT), frequency multiplexing‐based control of multiple acoustic waves, and the resultant acoustic radiation and drag forces. For validation, the wirelessly generated acoustic waves are measured using laser vibrometry when different materials (e.g., bone, tissue, and hand) are inserted between the WPT transmitter and receiver. Moreover, the wireless acoustofluidic chips successfully arrange nanoparticles into different patterns, align cells into parallel pearl chains, generate streaming, and enrich in‐droplet microparticles. This research is anticipated to facilitate the development of embeddable wireless on‐chip flow generators, wearable sensors with liquid sample processing functions, and implantable devices with flow generation and acoustic stimulation abilities for engineering, veterinary, and biomedical applications.

Li, Jiali↗
DOE OSTI2020

Geometry-independent microstructure optimization for electron beam powder bed fusion additive manufacturing

The ability to manipulate the formation of microstructure is one of the potential advantages of additive manufacturing. Yet, the additive manufacturing process is riddled with complex interactions between processing parameters, geometry, and spot melting sequence which makes the task of controlling microstructure challenging. This paper proposes a method to solve the microstructure texture optimization problem by leveraging established optimization techniques and increasingly fast and accurate analytical heat transfer models. Respectively, we apply two different optimization algorithms, genetic algorithm and concurrent greedy line search, in order to induce arbitrarily shaped microstructure regions and we use a heat transfer model to evaluate the fitness of solutions in an iterative approach that is far more efficient than pure trial and error. The objective of the optimization algorithm is to control local thermal conditions – hence microstructure formation – during the melting process by manipulating the order and the dwell time of all the points of the infill pattern sequence. We then analyze the optimized sequence to determine important path characteristics that affect the microstructure formation. Through examples, we show that relative order and frequency, as measured by weighted average and variance of spot order, are important factors that influence the final characteristics.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Characterizing Ultrafast Intersystem Crossing Pathways in Molecular Pt Dimers Using Time-Resolved Wide-Angle X-ray Scattering

Vibronic coupling between transition metal charge transfer states is a potential mechanism for enhancing the intersystem crossing (ISC) rate. Vibronic coupling-driven ISC has been observed in Pt­(II) dimer complexes, where the trajectory across excited-state pathways is tuned by atomic displacements via Pt–Pt stretching vibrations. Time-resolved wide-angle X-ray scattering (TR-WAXS) was utilized to quantify the Pt–Pt contraction following metal–metal-to-ligand charge transfer (MMLCT) excitation in Pt dimers with different bridging ligands. Both complexes exhibit Pt–Pt bond formation with a decrease in Pt–Pt distance of ∼ 0.25 Å and coherent vibrational wavepackets (CVWPs) encoded in the Pt–Pt contraction of both dimers. However, the complexes exhibit different time-dependent evolution of their CVWPs. Analysis of interference patterns between different CVWPs is used to track the trajectory across the excited-state surfaces. Furthermore, this work demonstrates that the interference between CVWPs in ultrafast TR-WAXS encodes indirect information regarding electronic excited-states to reveal the Pt dimer bridge-dependent ISC mechanism.

Computational chemistry↗
DOE OSTI2021

Infrared spectroscopy of RG–Co + (H 2 O) complexes (RG = Ar, Ne, He): The role of rare gas “tag” atoms

RG n –Co + (H 2 O) cation complexes (RG = Ar, Ne, He) are generated in a supersonic expansion by pulsed laser vaporization. Here, complexes are mass-selected using a time-of-flight spectrometer and studied with infrared laser photodissociation spectroscopy, measuring the respective mass channels corresponding to the elimination of the rare gas “tag” atom. Spectral patterns and theory indicate that the structures of the ions with a single rare gas atom have this bound to the cobalt cation opposite the water moiety in a near-C 2v arrangement. The O–H stretch vibrations of the complex are shifted compared to those of water because of the metal cation charge-transfer interaction; these frequencies also vary systematically with the rare gas atom attached. The efficiencies of photodissociation also vary with the rare gas atoms because of their widely different binding energies to the cobalt cation. The spectrum of the argon complex could only be measured when at least three argon atoms were attached. In the case of the helium complex, the low binding energy allows the spectra to be measured for the low-frequency H–O–H scissors bending mode and for the O–D stretches of the deuterated analog. The partially resolved rotational structure for the antisymmetric O–H and O–D stretches reveals the temperature of these complexes (6 K) and establishes the electronic ground state. The helium complex has the same 3 B 1 ground state as the tag-free complex studied previously by Metz and co-workers (2013), but the A rotational constant is contaminated by vibrational averaging from the bending motion of the helium.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Building a FAIR data ecosystem for incorporating single-cell transcriptomics data into agricultural genome to phenome research

Introduction The agriculture genomics community has numerous data submission standards available, but the standards for describing and storing single-cell (SC, e.g., scRNA- seq) data are comparatively underdeveloped. Methods To bridge this gap, we leveraged recent advancements in human genomics infrastructure, such as the integration of the Human Cell Atlas Data Portal with Terra, a secure, scalable, open-source platform for biomedical researchers to access data, run analysis tools, and collaborate. In parallel, the Single Cell Expression Atlas at EMBL-EBI offers a comprehensive data ingestion portal for high-throughput sequencing datasets, including plants, protists, and animals (including humans). Developing data tools connecting these resources would offer significant advantages to the agricultural genomics community. The FAANG data portal at EMBL-EBI emphasizes delivering rich metadata and highly accurate and reliable annotation of farmed animals but is not computationally linked to either of these resources. Results Herein, we describe a pilot-scale project that determines whether the current FAANG metadata standards for livestock can be used to ingest scRNA-seq datasets into Terra in a manner consistent with HCA Data Portal standards. Importantly, rich scRNA-seq metadata can now be brokered through the FAANG data portal using a semi-automated process, thereby avoiding the need for substantial expert curation. We have further extended the functionality of this tool so that validated and ingested SC files within the HCA Data Portal are transferred to Terra for further analysis. In addition, we verified data ingestion into Terra, hosted on Azure, and demonstrated the use of a workflow to analyze the first ingested porcine scRNA-seq dataset. Additionally, we have also developed prototype tools to visualize the output of scRNA-seq analyses on genome browsers to compare gene expression patterns across tissues and cell populations. This JBrowse tool now features distinct tracks, showcasing PBMC scRNA-seq alongside two bulk RNA-seq experiments. Discussion We intend to further build upon these existing tools to construct a scientist-friendly data resource and analytical ecosystem based on Findable, Accessible, Interoperable, and Reusable (FAIR) SC principles to facilitate SC-level genomic analysis through data ingestion, storage, retrieval, re-use, visualization, and comparative annotation across agricultural species.

Genetics & Heredity↗
DOE OSTI2022

Multi-Kernel Adaptive Support Vector Machine for Scalable Predictive Maintenance

Application of data-driven solutions across an industry is challenging, since the data are often stored locally, and increasing privacy and security concerns restrict access to the data. In addition, it is highly unlikely that all potential data patterns are captured in a single data source. Because it is highly unlikely that all potential data patterns are captured in a single data source, machine learning (ML) models developed from a single source cannot be robust enough. An alternative is to train the ML model at each source and develop a distributed knowledge discovery and aggregation approach to build global knowledge. In this paper, we develop and demonstrate a distributed ML model, federated transfer learning (FTL), using a multi-kernel-based adaptive support vector machine (MK-A-SVM). For federated learning (FL), the multi-kernel (MK) approach enables feature-specific model aggregation under data heterogeneity; whereas for transfer learning (TL) the adaptive model enables utilization of an aggregated model from a different task. The proposed approach is validated using nuclear power plant (NPP) vertical motor-driven pump data to predict the health condition of vertical motor-driven pumps as an anomaly detection. The efficiency of the proposed approach is also quantified and compared with neural network.

42 ENGINEERING↗
DOE OSTI2021

Automatically adaptive stabilized finite elements and continuation analysis for compaction banding in geomaterials

Under compressive creep, viscoplastic solids experiencing internal mass transfer processes can accommodate singular cnoidal wave solutions as material instabilities at the stationary wave limit. These instabilities appear when the loading rate is significantly faster than the material's capacity to diffusive internal perturbations, leading to localized failure features (e.g., cracks and compaction bands). These cnoidal waves, generally found in fluids, have strong nonlinearities that produce periodic patterns. Due to the singular nature of the solutions, the applicability of the theory is currently limited. Additionally, practical simulation tools require proper regularization to overcome the challenges that singularity induces. We focus on the numerical treatment of the governing equation using a nonlinear approach building on a recent adaptive stabilized finite element method. This automatic refinement method provides an error estimate that drives mesh adaptivity, a crucial feature for the problem at hand. We compare the performance of this adaptive strategy against analytical and standard finite element solutions. We then investigate the sensitivity of the diffusivity ratio, the parameter controlling the process, and identify multiple possible solutions with several stress peaks. We also show the evolution of the spacing between peaks for all solutions as a function of that parameter.

42 ENGINEERING↗
DOE OSTI2023

Modeling the co-assembly of binary nanoparticles

Abstract In this work, we present a binary assembly model that can predict the co-assembly structure and spatial frequency spectra of monodispersed nanoparticles with two different particle sizes. The approach relies on an iterative algorithm based on geometric constraints, which can simulate the assembly patterns of particles with two distinct diameters, size distributions, and at various mixture ratios on a planar surface. The two-dimensional spatial-frequency spectra of the modeled assembles can be analyzed using fast Fourier transform analysis to examine their frequency content. The simulated co-assembly structures and spectra are compared with assembled nanoparticles fabricated using transfer coating method are in qualitative agreement with the experimental results. The co-assembly model can also be used to predict the peak spatial frequency and the full-width at half-maximum bandwidth, which can lead to the design of the structure spectra by selection of different monodispersed particles. This work can find applications in fabrication of non-periodic nanostructures for functional surfaces, light extraction structures, and broadband nanophotonics.

Mohanty, Saurav (ORCID:0009000302592855)↗
DOE OSTI2020

Real-time Intrusion Detection for High-bandwidth Research Networks using Unsupervised Deep Learning

Nowadays, data generated by large-scale scientific experiments are on the scale of petabytes per month. These data are transferred through dedicated high-bandwidth networks across distributed collaboration sites for processing, storage, and analysis. Like general purpose networks, research networks experience attacks. However, monitoring traffic in such high-bandwidth network environments is challenging with conventional deep packet inspection (DPI)- based network intrusion detection systems (NIDS). Recently, machine learning (ML) algorithms have shown potential in detecting anomalous patterns with better generalizability and faster speed. Compared to the general purpose networks, traffic movement in research network is less volatile and more likely to benefit from the ML-based anomaly analysis. We present ThunderSecure, an efficient AI-powered system for detecting anomalies in 100G research networks. ThunderSecure implements a high-throughput packet processing infrastructure using multi-cores and GPUs. It takes time-series statistics from a variety of network measurements and uses 2D Convolutional Neural Network (CNN) to explore both spatial and temporal dependencies in network data stream. Patterns of science data traffic are learned with a one-class neural network, which blends an Adversarial Autoencoder (AAE) with Gaussian mixture density estimation. Testing traffic flows exhibiting significant deviations from the learned baseline of normality are marked as anomalies. We trained ThunderSecure on hundreds of billions of science data packets recorded from a 100G research network at Fermi National Accelerator Laboratory. The detection performance was evaluated on traffic captured from the same research network days and weeks after the training with different types of attack flows injected. To the best of our knowledge, this is the first ML work in the area of network anomaly detection that has been validated on such extreme scale datasets. Results show that ThunderSecure can recognize science data traffic that are captured long after the training and make nearly certain detection on those with anomalous flows injected, even in the case when the anomaly-to-normal mixing ratio is 0.1%.

Gong, Qian↗
DOE OSTI2022

Covert MOF-Based Photoluminescent Tags via Tunable Linker Energetics

We report that the optical anticounterfeiting tags utilize the photoluminescent properties of materials to encode unique patterns, enabling identification and validation of important items and assets. These tags must combine optical complexity with ease of production and authentication to both prevent counterfeiting and to remain practical for widespread use. Metal–organic frameworks (MOFs) based on polynuclear, rare earth clusters are ideal materials platforms for this purpose, combining fine control over structure and composition, with tunable, complex energy transfer mechanisms via both linker and metal components. Here we report the design and synthesis of a set of heterometallic MOFs based on combinations of Eu, Nd, and Yb with the tetratopic linker 1,3,6,8-tetrakis(4-carboxyphenyl)pyrene. The energetics of this linker facilitate the intentional concealment of the visible emissions from Eu while retaining the infrared emissions of Nd and Yb, creating an optical tag with multiple covert elements. Unique to the materials system reported herein, we document the occurrence of a previously not observed 11-metal cluster correlated with the presence of Yb in the MOFs, coexisting with a commonly encountered 9-metal cluster. We demonstrate the utility of these materials as intricate optical tags with both rapid and in-depth screening techniques, utilizing orthogonal identifiers across composition, emission spectra, and emission decay dynamics. This work highlights the important effect of linker selection in controlling the resulting photoluminescent properties in MOFs and opens an avenue for the targeted design of highly complex, multifunctional optical tags.

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

ENSO Impact on Winter Precipitation in the Southeast United States through a Synoptic Climate Approach

The ENSO impact on winter precipitation in the Southeast United States was analyzed from the perspective of daily weather types (WTs). We calculated the dynamic contribution associated with the change in frequency of the WTs and the thermodynamic contribution due to changes in the spatial patterns of the environmental fields of the WTs. Six WTs were obtained using a k-means clustering analysis of 850 hPa winds in reanalysis data from November to February of 1948–2022. All the WTs can only persist for a few days. The most frequent winter weather type is WT1 (shallow trough in Eastern U.S.), which can persist or likely transfer to WT4 (Mississippi River Valley ridge). WT1 becomes less frequent in El Niño years, while the frequency of WT4 does not change much. WTs 2–6 correspond to a loop of eastward propagating waves with troughs and ridges in the mid-latitude westerlies. Three WTs with a deep trough in the Southeast U.S., which are WT2 (east coast trough), WT3 (off east coast trough) and WT6 (plains trough), become more frequent in El Niño years. The more frequent deep troughs (WTs 2, 3 and 6) and less frequent shallow trough (WT1) result in above-normal precipitation in the coastal Southeast U.S. in the winter of El Niño years. WT5 (off coast Carolina High), with maximum precipitation extending from Mississippi Valley to the Great Lakes, becomes less frequent in El Niño years, which corresponds to the below-normal precipitation from the Great Lakes to Upper Mississippi and Ohio River Valley in El Niño years, and vice versa in La Niña years. The relative contribution of the thermodynamic and dynamic contribution is location dependent. On the east coast, the two contributions are similar in magnitude.

Qian, Jian-Hua↗
DOE OSTI2020

Direct-write orientation of charge-transfer liquid crystals enables polarization-based coding and encryption

Abstract Optical polarizers encompass a class of anisotropic materials that pass-through discrete orientations of light and are found in wide-ranging technologies, from windows and glasses to cameras, digital displays and photonic devices. The wire-grids, ordered surfaces, and aligned nanomaterials used to make polarized films cannot be easily reconfigured once aligned, limiting their use to stationary cross-polarizers in, for example, liquid crystal displays. Here we describe a supramolecular material set and patterning approach where the polarization angle in stand-alone films can be precisely defined at the single pixel level and reconfigured following initial alignment. This capability enables new routes for non-binary information storage, retrieval, and intrinsic encryption, and it suggests future technologies such as photonic chips that can be reconfigured using non-contact patterning.

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

Tailored Graphene Micropatterns by Wafer-Scale Direct Transfer for Flexible Chemical Sensor Platform

2D materials, such as graphene, exhibit great potential as functional materials for numerous novel applications due to their excellent properties. The grafting of conventional micropatterning techniques on new types of electronic devices is required to fully utilize the unique nature of graphene. However, the conventional lithography and polymer-supported transfer methods often induce the contamination and damage of the graphene surface due to polymer residues and harsh wet-transfer conditions. In this work, a novel strategy to obtain micropatterned graphene on polymer substrates using a direct curing process is demonstrated. Employing this method, entirely flexible, transparent, well-defined self-activated graphene sensor arrays, capable of gas discrimination without external heating, are fabricated on 4 in. wafer-scale substrates. Finite element method simulations show the potential of this patterning technique to maximize the performance of the sensor devices when the active channels of the 2D material are suspended and nanoscaled. This study contributes considerably to the development of flexible functional electronic devices based on 2D materials.

2D materials, Graphene, Microscale patterning, Che↗
DOE OSTI2022

OLEDs on planarized light outcoupling-enhancing structures in plastic

Light extraction from OLEDs remains a challenge. While consumer demand for OLEDs in display technology continues to grow as prices decline, commercial use of OLEDs in solid state lighting (SSL) applications is lagging due to low light extraction, which results in low efficiency. Previous studies have been directed toward increasing the light extraction factor η out , but cost effective approaches that will enable high throughput and potential upscaling, as well as elucidation of the extraction process, are still lagging. One promising approach is the use of buried light extraction enhancing patterns that are planarized with a high refractive index (RI) layer. When adding substrate mode extracting means, the highest external quantum efficiencies (EQEs) were achieved using a complex approach of planarized vacuum nano holes. Here we present η out -enhancing planarized extraction structures (PES) in plastic. While plastic substrates currently present long-term stability issues and other drawbacks, as we show, they provide a tool for simple, inexpensive, and rapid generation of various enhancing structures that importantly can be transferred to the preferred rigid and flexible glass substrates. Moreover, plastic substrates are attractive for biomedical applications. The successful structures we present are easily scalable periodic corrugations, e.g., with pitch a ~ 4.25 µm and pitch/depth ratio a/h ~2.4 planarized by a layer of RI ~1.9, resulting in maximal EQEs exceeding 60% for a green OLED and 48.5% for a white OLED. Comparable increased EQEs were achieved for structures with a ~ 15 and 16 µm, and a/h ~ 2.1 and 4, respectively. Enhanced extraction was also observed with a very simply fabricated quasiperiodic nanohole array, and surprisingly, with a random shallow design. The latter is at variance with simulations based on diffraction only. Results are discussed in terms of a, h, and structural parameters together with scattering matrix simulations to assess the effect of various PES parameters and the nanoparticle-embedded planarizing layer on η out . The experiments and simulations indicate contributions of diffraction, reflection, and scattering to EQE enhancement.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Erosion-Corrosion Detection in Carbon Steel Pipe Loops using SRNLs Thickness and Mass Loss Measurement Coupons - 20464

Structural health of waste transfer infrastructure is of utmost importance to DOE and communities surrounding their active sites. A Fitness-for-Service (FFS) program for the Waste Transfer System has been implemented by the US Department of Energy (DOE) for the purposes of evaluating and quantifying the degradation of critical components. Currently, the method of approach is to section and evaluate pipelines that have been removed at end service. In order to augment data collection methods applied to making lifespan estimates of waste transfer system components, Florida International University (FIU) has been investigating new-technology based strategies for quantifying the rate of degradation. Present research considers the option of a coupon system installed on a section of pipe at points of interest that will provide easy access to high resolution measurements of erosion/corrosion rates during operation. Researchers at Savannah River National Laboratory (SRNL) have developed this novel solution for in situ erosion and corrosion measurements in many applicable locations such as tanks, pipes, ducts, and injection points. The coupons are approximately 12.7 mm (1/2 inch) in diameter and 25.4 mm (1 inch) in length and can be placed in the pipe sections using Swagelok compression fittings. The coupon design allows for monitoring of the total degradation resulting from corrosion and erosion. High-resolution Ultrasonic Thickness (UT) testing allows in-situ monitoring of both degradation mechanisms. When removed from the system mass loss resulting from corrosion and erosion may also be evaluated. Experimental testing at FIU implemented the SRNL coupon system in a 6.10 meter (20 foot) pipe loop system comprised of 50.8 mm (2 inch) and 76.2 mm (3 inch) pipe sections in order to replicate expected use cases at the sites. Operational conditions were emulated by pumping abrasive materials through the pipeline at full capacity and collecting measurements from the SRNL coupons. Initial testing was conducted by circulating water followed by a mixture of sand and water for uniform wear rates. Thickness changes and mass loss were measured, and the surface exposed to the process fluid was observed for visual evidence of characteristic wear patterns resulting from an erosive environment. The test matrix consisted of several hours of operation and using fine, medium and coarse grain sands. Test results were analyzed to check for all three parameters (mass loss/gain, thickness change and surface patters) which could provide estimates of the remaining useful life of the pipe sections. Thus, the research presents the findings of this evaluation of the SRNL coupons as a means for conducting high accuracy, cost effective in-situ analysis of pipeline and waste transfer systems for erosion and corrosion susceptibility. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Modeling Global Vegetation Gross Primary Productivity, Transpiration and Hyperspectral Canopy Radiative Transfer Simultaneously Using a Next Generation Land Surface Model—CliMA Land

Recent progress in satellite observations has provided unprecedented opportunities to monitor vegetation activity at global scale. However, a major challenge in fully utilizing remotely sensed data to constrain land surface models (LSMs) lies in inconsistencies between simulated and observed quantities. For example, gross primary productivity (GPP) and transpiration (T) that traditional LSMs simulate are not directly measurable from space, although they can be inferred from spaceborne observations using assumptions that are inconsistent with those LSMs. In comparison, canopy reflectance and fluorescence spectra that satellites can detect are not modeled by traditional LSMs. To bridge these quantities, we presented an overview of the next generation land model developed within the Climate Modeling Alliance (CliMA), and simulated global GPP, T, and hyperspectral canopy radiative transfer (RT; 400–2,500 nm for reflectance, 640–850 nm for fluorescence) at hourly time step and 1° spatial resolution using CliMA Land. CliMA Land predicts vegetation indices and outgoing radiances, including solar-induced chlorophyll fluorescence (SIF), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near infrared reflectance of vegetation (NIRv) for any given sun-sensor geometry. The spatial patterns of modeled GPP, T, SIF, NDVI, EVI, and NIRv correlate significantly with existing data-driven products (mean R 2 = 0.777 for 9 products). CliMA Land would be also useful in high temporal resolution simulations, for example, providing insights into when GPP, SIF, and NIRv diverge.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Holes in Optical Lightning Flashes: Identifying Poorly Transmissive Clouds in Lightning Imager Data

Space-based optical lightning sensors including the lightning imaging sensor (LIS) and geostationary lightning mapper (GLM) are pixelated imagers that detect lightning as transient increases in cloud top illumination. Detection requires optical emissions to escape the cloud top to space with sufficient energy to trigger a pixel on the imaging array. Through scattering and absorption, certain clouds are able to block most light from reaching the instrument, causing a reduction in detection efficiency (DE) and possibly location accuracy (LA). Radiant lightning emissions that illuminate large cloud top areas are used to examine scenarios where clouds block light from reaching orbit. In some cases, these anomalies in the spatial radiance distribution from the lightning pulse lead to “holes” in the optical lightning flash where certain pixels fail to trigger. Such holes are identified algorithmically in the Tropical Rainfall Measuring Mission satellite LIS record and the microphysical properties of the coincident storm region are queried. We find that holes primarily occur in tall (IR T b < 235 K) convection (87%) and overhanging anvil clouds (10%). The remaining 3% of holes occur in moderate-to-weak convection or in clear air breaks between stormclouds. We further demonstrate how an algorithm that assesses the spatial radiance patterns from energetic lightning pulses might be used to construct an optical transmission gridded stoplight product for GLM that could help operators identify clouds with a potentially reduced DE and LA.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Computational flow modeling of triply periodic minimal surfaces as feed channel spacers in ultra-high pressure reverse osmosis applications

Triply periodic minimal surfaces (TPMS) are a special class of mathematical surfaces characterized by a high surface area-to-volume ratio. They have generated considerable interest in fields such as acoustics, heat transfer, and membrane-based filtration processes. This study evaluates the performance of four different TPMS designs—Schoen Gyroid, Schoen Crossed Layers of Parallels (CLP), Schoen Transverse Crossed Layers of Parallels (tCLP), and Schwarz-Primitive—when used as feed channel spacers under ultra-high pressure reverse osmosis (UHPRO) conditions, at approximately 200 bar. Our experimentally validated computational fluid dynamics model reveal different flow patterns within the feed channels for each of the four TPMS designs, leading to varying hydrodynamic and permeation properties. Under the simulated UHPRO conditions, the Gyroid and tCLP designs yield up to a 23% increase in average permeate velocity and a 14% reduction in average membrane-surface concentration relative to a non-woven spacer of the same porosity. Furthermore, the enhanced performance comes with an increased feed channel pressure drop, although it only constitutes less than 4% of the operating pressure when extrapolated for a meter-long membrane module. Additionally, the study analyzes the effects of varying inlet velocity and spacer porosity on membrane performance. Overall, this research provides valuable insights into the potential use of TPMS spacers in UHPRO applications.

36 MATERIALS SCIENCE↗