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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 1,333 records · Page 74

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DOE OSTI2023

Optimization of metal-supported solid oxide electrolysis cells with infiltrated catalysts

We report metal-supported solid oxide electrolysis cells (MS-SOECs) are being developed for steam-to-hydrogen electrolysis, especially for utilization of dynamic or intermittent electrical power from renewable sources. Various aspects of the electrocatalyst processing and composition, and metal support structure were explored. Catalyst materials, infiltration temperature and infiltration cycles were optimized for high performance and durability. Numerous catalyst materials were screened for both oxygen and steam electrodes. The oxygen catalyst had moderate impact on both initial cell performance and durability. Reducing Ni content in the steam electrode had little effect on durability, but reduced initial performance. Ex-situ XRD analysis and cell assessment of catalyst infiltration temperature revealed that the optimal range is 750–850 °C. The best cell performance and durability was achieved with LSCF-SDC oxygen electrocatalyst and SDC-Ni (60:40 vol%) steam electrocatalyst infiltrated 11 times at 800 °C and operated at 700 °C. At low steam content, a significant mass transport limitation on the steam side results in limiting current behavior. Thinner and more porous metal supports were implemented, and found to improve steam mass transport at low steam content, relevant for SOECs operating under high H 2 recycle rate or high steam utilization.

08 HYDROGEN↗

DOE OSTI2026

Characterizing Defect Dynamics in Silicon Carbide Using Symmetry-Adapted Collective Variables and Machine Learning Interatomic Potentials

Silicon carbide (SiC) divacancies are attractive candidates for spin-defect qubits possessing long coherence times and optical addressability. The high activation barriers associated with SiC defect formation and motion pose challenges for their study by first-principles molecular dynamics. In this work, we develop and deploy machine learning interatomic potentials (MLIPs) to accelerate defect dynamics simulations while retaining ab initio accuracy. We employ an active learning strategy comprising symmetry-adapted collective variable discovery and enhanced sampling to compile configurationally diverse training data, calculation of energies and forces using density functional theory (DFT), and training of an E(3)-equivariant MLIP based on the Allegro model. Here, the trained MLIP reproduces DFT-level accuracy in defect transition activation free energy barriers, enables the efficient and stable simulation of multidefect 216-atom supercells, and permits an analysis of the temperature dependence of defect thermodynamic stability and formation/annihilation kinetics to propose an optimal annealing temperature to maximally stabilize VV divacancies.

Computer simulations↗

DOE OSTI2022

Nanoscale defect evaluation framework combining real-time transmission electron microscopy and integrated machine learning-particle filter estimation

Observation of dynamic processes by transmission electron microscopy (TEM) is an attractive technique to experimentally analyze materials’ nanoscale phenomena and understand the microstructure-properties relationships in nanoscale. Even if spatial and temporal resolutions of real-time TEM increase significantly, it is still difficult to say that the researchers quantitatively evaluate the dynamic behavior of defects. Images in TEM video are a two-dimensional projection of three-dimensional space phenomena, thus missing information must be existed that makes image’s uniquely accurate interpretation challenging. Therefore, even though they are still a clustering high-dimensional data and can be compressed to two-dimensional, conventional statistical methods for analyzing images may not be powerful enough to track nanoscale behavior by removing various artifacts associated with experiment; and automated and unbiased processing tools for such big-data are becoming mission-critical to discover knowledge about unforeseen behavior. We have developed a method to quantitative image analysis framework to resolve these problems, in which machine learning and particle filter estimation are uniquely combined. The quantitative and automated measurement of the dislocation velocity in an Fe-31Mn-3Al-3Si autunitic steel subjected to the tensile deformation was performed to validate the framework, and an intermittent motion of the dislocations was quantitatively analyzed. The framework is successfully classifying, identifying and tracking nanoscale objects; these are not able to be accurately implemented by the conventional mean-path based analysis.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Ultrafast conformational dynamics of Rydberg-excited N -methyl piperidine

We have observed the ultrafast conformational dynamics of electronically excited N-methyl piperidine (NMP) using time-resolved Rydberg fingerprint spectroscopy. Optical excitation at various wavelengths ranging from 212 nm to 229 nm leads to the 3s or 3p Rydberg states and induces coherent oscillatory motions with periods of about 700 fs. Furthermore, these coherent motions survive the internal conversion from 3p to 3s but then dephase on a time scale of a few oscillations. Intramolecular vibrational energy redistribution on a picosecond time scale leads to an equilibrium between two conformeric structures that are separated in binding energy by 0.09 eV. Model calculations using the DFT-SIC method are in excellent agreement with the experiments and identify the conformers as the chair and twist structures of NMP. The analysis of the equilibrium parameters at long time delays as a function of excitation wavelength allows for the extraction of thermodynamic parameters for the conformeric transformation. We derive an enthalpy of the chair to twist reaction in the 3s excited state of 62 meV with an entropy of 19.70 J mol –1 K –1 . An activation energy of 276 meV is also obtained with a kinetic model.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2023

Gauge constrained algorithm of variational discrete action theory at N = 3 for the multiorbital Hubbard model

The recently developed variational discrete action theory (VDAT) provides a systematic variational approach to the ground state of the quantum many-body problem, where the quality of the solution is controlled by an integer N, and increasing N monotonically approaches the exact solution. VDAT can be exactly evaluated in the d = ∞ multiorbital Hubbard model using the self-consistent canonical discrete action theory (SCDA), which requires a self-consistency condition for the integer time Green's functions. Previous work demonstrates that N = 3 accurately captures multiorbital Mott/Hund physics at a cost similar to the Gutzwiller approximation. Here we employ a gauge constraint to automatically satisfy the self-consistency condition of the SCDA at N = 3, yielding an even more efficient algorithm with enhanced numerical stability. We derive closed form expressions of the gauge constrained algorithm for the multiorbital Hubbard model with general density-density interactions, allowing VDAT at N = 3 to be straightforwardly applied to the seven-orbital Hubbard model. We present results and a performance analysis using N = 2 and N = 3 for the SU⁡(2⁢N orb ) Hubbard model in d = ∞ with N orb = 2–8, and compare to numerically exact dynamical mean-field theory solutions where available. Finally, the developments in this work will greatly facilitate the application of VDAT at N = 3 to strongly correlated electron materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2020

High Speed, Localized Multi-Point Strain Measurements on a Containment Vessel at 1.7 MHz Using Swept-Wavelength Laser-Interrogated Fiber Bragg Gratings

Dynamic elastic strain in ~1.8 and 1.0 m diameter containment vessels containing a high explosive detonation was measured using an array of fiber Bragg gratings. The all-optical method, called real-time localized strain measurement, recorded the strain for 10 ms after detonation with additional measurements being sequentially made at a rate of 1.7 MHz. A swept wavelength laser source provided the repetition rate necessary for such high-speed measurements while also providing enough signal strength and bandwidth to simultaneously measure 8 or more unique points on the vessel’s surface. The data presented here arethen compared with additional diagnostics consisting of a fast spectral interferometer and an optical backscatter reflectometer to show a comparison between the local and global changes in the vessel strain, both dynamically and statically to further characterize the performance of the localized strain measurement. The results are also compared with electrical resistive strain gauges and finite element analysis simulations.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Magnetosphere Evolution and Precursor-driven Electromagnetic Signals in Merging Binary Neutron Stars

We detail new force-free simulations to investigate magnetosphere evolution and precursor electromagnetic (EM) signals from binary neutron stars. Our simulations fully follow a representative inspiral motion, capturing the intricate magnetospheric dynamics and their impact on EM outflows. We explore a range of stellar magnetic moment orientations and relative strengths, finding that the magnetospheres and Poynting flux evolution are strongly configuration dependent. The Poynting flux exhibits pulsations at twice the orbital frequency, 2Ω, and is highly anisotropic, following a power-law dependence on orbital frequency. The index ranges from 1 to 6, shaped by the intricate dynamics of the magnetospheres. Furthermore, we present the first computation of (1) the EM forces acting on the star surfaces, revealing the presence of torques that, for highly magnetized stars, could influence the orbital dynamics or break the crust; (2) the high-energy emission signals from these systems by adopting the established isolated pulsar theory. Assuming curvature radiation in the radiation-reaction limit, we find that photons could reach TeV–PeV energies in the last ∼ms for magnetic field strengths 10 10 –10 15 G. However, our analysis of single photon magnetic pair production suggests that these photons are unlikely to escape, with the MeV band emerging as a promising observational window for precursor high-energy emission. In this framework, we construct high-energy emission skymaps and light curves, exploring observational implications. Finally, we propose potential precursor radio emission and delayed afterglow echoes from magnetized outflows, which may contribute to late-time rebrightening in short gamma-ray bursts or to orphan afterglows.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Autonomous Intelligent Charging/Discharging of Electric Vehicles using Distributed Multi-Agent ADMM Framework for Grid Ancillary Services

The increasing popularity of Electric Vehicles (EVs) in the distribution grid along with technological advancement in EV electronics such as vehicle to grid (V2G) technique has enabled them to participate in grid ancillary services. To achieve this, the EVs need to establish a contract with third-party aggregators and connect to a charging unit, either residential or commercial. At any time they are connected, the EVs can decide to take part in the ancillary services program offered to them by the aggregators. If agreed, the aggregators will use the EVs as a power source capable of charging/discharging power according to the input signal, and in return, they will be compensated. This inter-temporal nature of charging/discharging is also transforming the traditional optimal power flow (OPF) problem into a dynamic OPF problem. This chapter aims at developing a multi-layer time-dependent optimization algorithm to utilize EV potential and provide ancillary services while maximizing its utilization function. Specifically, in the upper layer, an autonomous distributed ADMM algorithm is developed to optimize the cost for charging/discharging EVs while using them to regulate the voltage at each bus in the distribution grid. The distributed ADMM algorithm is also expanded to the lower layer where the individual EVs active and reactive power is controlled for voltage regulation while maintaining the desired state of the charge of the vehicle at the end of the charging period. Here, the effectiveness and performance improvement of the proposed multi-layer algorithm is illustrated through analytical analysis and simulation results.

Rahman, Towfiq↗

DOE OSTI2020

Searching for solitons in heavy-ion reactions near the fermi energy

Solitons are special shape-conserving hydrodynamical solutions that appear in many areas of physics. Here, we explore the existence of such solutions in microscopic descriptions of the heavy ion reaction 12 C + 28 Si → 12 C*+ 7α in the range E/A = 10 – 65 MeV/u. After recognizing the centrality of the collision and time-reversibility as fundamental requirements for the presence of solitonic 12 C, we utilize the Hybrid α-Cluster model for our analysis with a novel methodology. Our results suggest soliton production for E/A = 25 – 45 MeV/u in the forward direction, with a total cross section being at least in the order of a few μb. This, apart from being encouraging for possible experimental studies, maybe connected to possible toroid states of 28 Si at low angular momenta.

Heavy ion reactions↗

DOE OSTI2024

Dynamic Modeling of Power Conversion Stages for an Exascale Supercomputer

In this paper a power conversion and energy consumption model for an exascale supercomputer is investigated. Power consumption, energy loss and efficiency are derived for the 27.2 MW liquid-cooled, centralized, High Performance Computing (HPC) power system, which is supplied directly from the 480 V three-phase mains. Two energy conversion stages are analyzed, measured and modeled. The model is developed in order to be adapted and implemented in a digital twin platform utilizing a Resource Allocator and Power Simulator (RAPS) module. RAPS enables estimation of potential energy savings in the direct AC power supply architecture via both conventional rectifier load sharing (commonly used in HPC systems), as well as smart rectifier load sharing. Moreover, besides the direct AC supply architecture analysis, the full direct DC supply architecture with with 1 kV DC bus were also studied. Comparison of 10 hour time frame operation of the system, with direct 480 V AC voltage supply with conventional and smart load sharing and medium dc voltage supply were done. For the direct AC supply architecture, with conventional and smart load sharing the predicted power loss was approximately 840 kW and 820 kW, respectively and the predicted total system efficiency was 92.87% and 93.05%, respectively. For the direct DC supply architecture with the 1000 V DC supply bus power loss was approximately 340 kW and the predicted total system efficiency was 97.02%.

Wojda, Rafal↗

DOE OSTI2021

Unraveling the implications of finite specimen size on the interpretation of dynamic experiments for polycrystalline aluminum through direct numerical simulations

Normal and Pressure-shear plate impact (NPI and PSPI) experiments are popular experimental techniques for studying the mean-field macroscopic behavior of polycrystalline metals under high-rate dynamic loading. However, since both configurations rely upon geometry for subjecting the specimen to high strain rates, these experiments often involve a limited specimen size. Moreover, because of the inherent heterogeneities present within polycrystalline metals, it is difficult to ascertain if the size of the specimen and/or regions where measurements are made are sufficiently large for making representative inferences about the mean-field macroscopic properties from single-point velocity measurements. In the present study, we quantify the expected measurement variability on observable point measurements in NPI and PSPI experiments by carrying out direct numerical simulations (DNS) of statistically representative polycrystalline microstructures subjected to dynamic compression and compression-shear loading. In particular, we consider the role of specific material heterogeneities (e.g. the grain-to-grain difference in size, crystallographic orientation) on dispersion in the normal and transverse particle velocity records and on local fluctuations in key state variables (e.g. velocity, accumulated plastic strain) by incorporating these effects directly into a representative synthetic microstructure geometry and crystalline description of pure polycrystalline aluminum. The form of the present study is a large parametric investigation, consisting of ten ensembles of one hundred simulations. Each of the thousand simulations reflects a randomly realized synthetic microstructure in one of five cases of decreasing average grain size for the two loading configurations. Our analysis of the DNS results demonstrates that for both of these experimental configurations, the grain size directly correlates with the coefficient of variation (CV) in simulated point measurements, showing a convergent decrease in CV to zero (i.e. particle velocity record approaches the mean-field value) with decreasing grain size. Remarkably, the magnitude of variations in the particle velocity record is shown to be largest where the deviatoric stresses are most significant. In the case of NPI, this occurs at the elastic and plastic wavefront, whereas, in the case of PSPI, the magnitude of fluctuations are approximately constant throughout the experimental window time. The reasoning for the scatter in particle velocity due to the heterogeneous microstructure is demonstrated to be dependent on the mechanisms for accommodating deformation and on the interaction of reflection waves generated at sites of heterogeneities occurring at the scale of grains. Lastly, we develop a power-law description for the magnitude of scattering versus characteristic length, which provides a statistical framework for assessing the required number of grains per characteristic specimen dimension for minimizing scatter within these two experimental configurations (NPI, PSPI).

36 MATERIALS SCIENCE↗

DOE OSTI2020

Effects of Atwood and Reynolds numbers on the evolution of buoyancy-driven homogeneous variable-density turbulence

The evolution of buoyancy-driven homogeneous variable-density turbulence (HVDT) at Atwood numbers up to 0.75 and large Reynolds numbers is studied by using high-resolution direct numerical simulations. To help understand the highly non-equilibrium nature of buoyancy-driven HVDT, the flow evolution is divided into four different regimes based on the behaviour of turbulent kinetic energy derivatives. The results show that each regime has a unique type of dependence on both Atwood and Reynolds numbers. It is found that the local statistics of the flow based on the flow composition are more sensitive to Atwood and Reynolds numbers compared to those based on the entire flow. It is also observed that, at higher Atwood numbers, different flow features reach their asymptotic Reynolds-number behaviour at different times. The energy spectrum defined based on the Favre fluctuations momentum has less large-scale contamination from viscous effects for variable-density flows with constant properties, compared to other forms used previously. The evolution of the energy spectrum highlights distinct dynamical features of the four flow regimes. Thus, the slope of the energy spectrum at intermediate to large scales evolves from -7/3 to -1 , as a function of the production-to-dissipation ratio. The classical Kolmogorov spectrum emerges at intermediate to high scales at the highest Reynolds numbers examined, after the turbulence starts to decay. Finally, the similarities and differences between buoyancy-driven HVDT and the more conventional stationary turbulence are discussed and new strategies and tools for analysis are proposed.

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 OSTI2020

Distinct responses of Asian summer monsoon to black carbon aerosols and greenhouse gases

Black carbon (BC) aerosols emitted from natural and anthropogenic sources induce positive radiative forcing and global warming, which in turn significantly affect the Asian summer monsoon (ASM). However, many aspects of the BC effect on ASM remain elusive and largely inconsistent among previous studies, which is strongly dependent on different low-level thermal feedbacks over the Asian continent and the surrounding ocean. This study examines the response of ASM to BC forcing in comparison with the effect of doubled greenhouse gases (GHGs) by analyzing the Precipitation Driver Response Model Intercomparison Project (PDRMIP) simulations under an extreme high BC level (10 times modern global BC emissions or concentrations, labeled by BC $\times$ 10) from nine global climate models (GCMs). The results show that although BC and GHGs both enhance the ASM precipitation minus evaporation (P–E) (a 13.6 % increase for BC forcing and 12.1 % for GHGs from the nine-model ensemble, respectively), there exists a much larger uncertainty in changes in ASM P–E induced by BC than by GHGs. The summer P–E is increased by 7.7 % to 15.3 % due to these two forcings over three sub-regions including East Asian, South Asian, and western North Pacific monsoon regions. Further analysis of moisture budget reveals distinct mechanisms controlling the increases in ASM P–E induced by BC and GHGs. The change in ASM P–E by BC is dominated by the dynamic effect due to the enhanced large-scale monsoon circulation, whereas the GHG-induced change is dominated by the thermodynamic effect through increasing atmospheric water vapor. Radiative forcing of BC significantly increases the upper-level atmospheric temperature over the Asian region to enhance the upper-level meridional land-sea thermal gradient (MLOTG), resulting in a northward shift of the upper-level subtropical westerly jet and an enhancement of the low-level monsoon circulation; whereas radiative forcing of GHGs significantly increases the tropical upper-level temperature, which reduces the upper-level MLOTG and suppresses the low-level monsoonal circulation. Hence, our results indicate a different mechanism of BC climate effects under the extreme high BC level, that BC forcing significantly enhances the upper-level atmospheric temperature over the Asian region, determining ASM changes, instead of low-level thermal feedbacks as indicated by previous studies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

A High-Speed Rotational Diamond Anvil Cell for In Situ Analysis of Hierarchical Microstructural Evolution of Metallic Alloys during Extreme Shear Deformation

High speed shear deformation is ubiquitous in engineering applications, ranging from material processing methods such as friction stir processing/extrusion and in tribological contacts. However, analyzing the microstructural evolution of materials while they are undergoing high speed shear deformation have been a long-standing challenge. This led to predominant reliance on ex situ microscopy before and after shear deformation. But ex situ microscopy lacks the ability to analyze dynamic and transient hierarchical microstructural evolution mechanisms that could occur during shear deformation of materials. Therefore, to better understand the dynamic mechanisms of mass and energy transfer in materials under shear deformation, we developed a first of its kind high-speed rotational diamond anvil cell (HS-RDAC) for synchrotron-based in situ high-energy x-ray diffraction (XRD). We studied the time resolved lattice strain evolution, XRD peak broadening and changes in spatial variation of shear deformation induced alloying in pure metal and metal alloy sheets and powder mixture using the HS-RDAC. These in situ results were combined with detailed ex situ microstructural characterization before and after the shear deformation using transmission electron microscopy and atom probe tomography, which revealed the different stages of evolution of a shear deformation induced hierarchical nanostructure. Multiscale computational simulations including computational fluid dynamics, crystal plasticity, molecular dynamic simulation and density functional theory uncovered the mechanisms behind morphological changes, evolution of defect structures and changes in driving force for shear deformation induced intermixing. In conclusion, this in situ HS-RDAC capability, in combination with ex situ microstructural characterization and computational simulations, can provide new insights into the hierarchical microstructural evolution pathway during shear deformation.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Analysis of Transformer Magnetizing Reactance Using Describing Function in Direct Power Control of Back-to-Back Modular Multilevel Converter with Advanced Grid Support

This paper presents a Port Controlled Hamiltonian based direct power control architecture for a back-to-back modular multilevel converter system connecting two ac sources at different frequencies. The system features advanced grid support functionalities based on IEEE 1547-2018, implemented on the inverter side. The rectifier side controller ensures reference following for active and/or reactive powers and maintains the commanded dc bus voltage. The inverter side controller ensures power command following for active and reactive powers.The proposed control architecture is designed to suppress second harmonic oscillations in powers during unbalanced grid voltage sags by dynamically adjusting the currents on each ac side. This also ensures effective elimination of any second harmonic oscillations in the equivalent dc bus voltage. Validation is performed on an OPAL-RT real-time platform with case studies on unbalanced and balanced sags, demonstrating the controller's effectiveness during real time implementation. A reduced-scale laboratory prototype further verifies these case studies, with experimental results for balanced sags due to grid simulator limitations. The results confirm the robustness and efficiency of the proposed control strategy in ensuring stable and reliable operation under various grid conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Extrapolation of thermal conductivity in non-equilibrium molecular dynamics simulations to bulk scale

Predictions of the bulk scale thermal conductivity of solids using non-equilibrium molecular dynamics (MD) simulations have relied on the linear extrapolation of the thermal resistivity versus the reciprocal of the system length in the simulations. Several studies have reported deviation of the extrapolation from linearity near the micro-scale, raising a concern of its applicability to large systems. To investigate this issue, present work conducted extensive MD simulations of silicon with two different potentials (EDIP and Tersoff-II) for unprecedented length scales up to 10.3 μm and simulation times up to 530 ns. For large systems ≥0.35 μm in size the non-linearity of the extrapolation of the reciprocal of the thermal conductivity is mostly due to ignoring the dependence of the thermal conductivity on temperature. To account for such dependence, the present analysis fixes the temperature range for determining the gradient for calculating the thermal conductivity values. However, short systems ≤0.23 μm in size show significant non-linearity in the calculated thermal conductivity values using a temperature window of 500 ± 10 K from the simulations results with the EDIP potential. Since these system sizes are shorter than the mean phonon free path in EDIP (~0.22 μm), the nonlinearity may be attributed to phonon transport. For the MD simulations with the Tersoff-II potential there is no significant non-linearity in the calculated thermal conductivity values for systems ranging in size from 0.05 to 5.4 μm.

42 ENGINEERING↗

DOE OSTI2021

Microbial diversity analysis of two full-scale seawater desalination treatment trains provides insights into detrimental biofilm formation

Detrimental biofilms on RO membranes remain a crucial challenge for seawater desalination. Comparative analysis of 16S rRNA gene amplicon sequencing data revealed differences and commonalities of biofilm communities associated with unit operations in the two largest seawater desalination facilities in the U.S., the Claude "Bud" Lewis Carlsbad Desalination Plant and the Tampa Bay Seater Desalination facility. At both plants, feedwater collected at a single time point was a poor indicator of the RO membrane communities, which showed far greater taxa diversity. The analysis of prefilter cartridges from the Carlsbad plant revealed similarly high taxon diversity as the RO module biofilms, with relevant differences. Algal sequences were enriched on the prefilter cartridges as were sequences representing Bdellovibrionota, which are predatory bacteria. Sequences representing opportunistic Gammaproteobacteria (i.e., Shewanella, Woesia) were present in significantly higher relative abundance on the RO membranes than in the prefilter cartridges, suggesting growth of certain taxa in the RO modules. Untargeted metabolomics distinguished intra- and inter-desalination plant biofilm samples, highlighting the potential value of this tool for biofilm monitoring. These findings underscore the value of omics tools for effective microbial monitoring, to understand biofouling dynamics within RO desalination plants, and to provide insight for the development of ecologically-informed biofilm control measures.

59 BASIC BIOLOGICAL SCIENCES↗