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At least 415 records · Page 23

A coupled hydrologic-agroeconomic modeling framework to evaluate adaptive irrigation strategies under groundwater withdrawal restrictions

Growing groundwater scarcity requires integrated tools to capture interactions among hydrology, agricultural production, markets, and land use. This study presents an iterative modeling framework that couples hydrologic, crop-yield, and economic models to capture two-way feedback among water availability, agricultural production, and market responses under groundwater constraints. The primary goal of this paper is to describe the methodological development of the coupled framework and demonstrate the significance of iterative model interaction. Applied to the western United States, we evaluated adaptive responses to restricting groundwater use beyond recharge levels, represented through changes in irrigation management and expansion or shrinkage of crop markets through land reallocation. Results demonstrate that the iterative coupling converges to stable equilibrium responses within 10 iterations. At equilibrium, deficit irrigation emerges as the dominant adaptation strategy in California, with irrigation levels stabilizing at approximately 70% of full irrigation demand, while Arizona and New Mexico experience stronger yield sensitivities. Early iterations produce commodity price increases of up to 10% for fruit and vegetable crops; however, these responses moderate as land allocation and production patterns adjust across regions. Deficit irrigation and spatial reallocation of irrigated land partially offset production losses, with variability observed across different states: California maintains yields primarily via deficit irrigation, whereas Arizona and New Mexico will rely mainly on reducing irrigated area to absorb the shock. By capturing feedback between biophysical and economic processes, this approach highlights how irrigation strategies and land-use decisions evolve under water stress and provides a transferable platform for evaluating water management policies.

54 ENVIRONMENTAL SCIENCES↗

Seismic response of vertical dry storage casks under three-dimensional earthquake motions

Ensuring the long-term seismic safety of dry storage casks (DSCs) is becoming increasingly critical as these systems evolve from temporary to de facto permanent repositories for spent nuclear fuels. Traditional seismic soil–structure interaction (SSI) assessment methods use one-dimensional deconvolution or simplified boundary conditions to model incident waves. Although computationally appealing, simplifying assumptions may alter the seismic risk by neglecting the full complexity of three-dimensional (3D) wave propagation effects. To address this challenge, this paper introduces a novel high-fidelity computational framework that leverages the Domain Reduction Method (DRM) with perfectly matched layers (PML) to accurately transfer complex, 3D seismic wavefields from regional-scale fault-rupture simulations into local-scale finite element models of DSCs. Using broadband, physics-based ground motions from a generic M w 7.0 strike-slip event, both single-cask and multi-cask configurations were investigated under near- and far-field conditions. Emphasis is placed on capturing complex SSI, spatial variability in the ground motion, and nonlinear phenomena such as cask rocking and sliding. Numerical results demonstrate that near-field conditions, where forward directivity and fling-step effects dominate, lead to significantly higher DSC rocking and sliding. Far-field cases, by contrast, generally exhibit modest responses. Incorporating SSI tends to amplify or alter DSC response spectra and introduce response variability, which underscores the need for site-specific evaluations and robust modeling approaches to ensure the seismic integrity of DSCs in interim spent fuel storage installations.

Das, Tonmoy↗

Explainable machine learning for incipient anomaly detection in compact molten salt heat exchanger with overlapping feature distributions

High-temperature molten salt-cooled reactors (MSCRs) are a promising next-generation nuclear technology option, offering efficient power conversion and inherent safety features. However, the reliability of these systems depends on the robust operation of heat exchangers (HXs), which are susceptible to failure due to temperature gradients and channel plugging caused by fluid freezing. Conventional monitoring methods, relying on inlet and outlet measurements, lack the spatial resolution needed to detect early-stage faults. We propose a novel design of a compact salt-to-salt matrix-type HX design consisting of interleaved arrays of parallel tubes, with integrated synthetic fiber optic distributed temperature sensing (DTS) to enable localized detection of incipient faults. To evaluate performance of this design, we generate high-fidelity synthetic data using heat transfer computational modeling to simulate channel plugging, and introduce sensor noise for realistic modeling of measurements. The dataset comprises of 97% normal operation and 3% anomaly cases, with each anomaly class representing 1% of the data. These early anomalies result in overlapping temperature profiles between normal and faulty channels, producing a non-separable dataset that challenges traditional classification techniques. We benchmark eight supervised machine learning (ML) models and demonstrate that XGBoost achieves the highest performance. To improve transparency, we develop an explainability framework combining Shapley values and partially ordered sets (POSETs) to quantify and structurally analyze feature importance. This approach identifies both dominant predictors and ambiguous feature relationships, enhancing trust and interpretability. Our results highlight the potential of combining DTS and explainable ML with intelligent feature selection to improve predictive maintenance and ensure operational resilience in advanced nuclear systems.

Prantikos, Konstantinos [Argonne National Laborato↗

Realizing a topological diode effect on the surface of a topological Kondo insulator

Introducing the concept of topology into material science has sparked a revolution from classic electronic and optoelectronic devices to topological quantum devices. The latter has potential for transferring energy and information with unprecedented efficiency. Here, we demonstrate a topological diode effect on the surface of a three-dimensional material, SmB 6 , a candidate topological Kondo insulator. The diode effect is evidenced by pronounced rectification and photogalvanic effects under electromagnetic modulation and radiation at radio frequency. Our experimental results and modeling suggest that these prominent effects are intimately tied to the spatially inhomogeneous formation of topological surface states (TSS) at the intermediate temperature. This work provides a manner of breaking the mirror symmetry (in addition to the inversion symmetry), resulting in the formation of pn-junctions between puddles of metallic TSS. Further, this effect paves the way for efficient current rectifiers or energy-harvesting devices working down to radio frequency range at low temperature, which could be extended to high temperatures using other topological insulators with large bulk gap.

36 MATERIALS SCIENCE↗

Information transfer with a gravitating bath

Late-time dominance of entanglement islands plays a critical role in addressing the information paradox for black holes in AdS coupled to an asymptotic non-gravitational bath. A natural question is how this observation can be extended to gravitational systems. To gain insight into this question, we explore how this story is modified within the context of Karch-Randall braneworlds when we allow the asymptotic bath to couple to dynamical gravity. We find that because of the inability to separate degrees of freedom by spatial location when defining the radiation region, the entanglement entropy of radiation emitted into the bath is a time-independent constant, consistent with recent work on black hole information in asymptotically flat space. If we instead consider an entanglement entropy between two sectors of a specific division of the Hilbert space, we then find non-trivial time-dependence, with the Page time a monotonically decreasing function of the brane angle---provided both branes are below a particular angle. However, the properties of the entropy depend discontinuously on this angle, which is the first example of such discontinuous behavior for an AdS brane in AdS space.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Dependent scattering and fractal microstructure determine the transparency of aerogel monoliths

This study reveals how dependent scattering and microstructure significantly affect electromagnetic wave propagation through aerogel monoliths, contributing to their transparency. Light scattering by particle ensembles is considered “dependent” when the scattering properties rely not only on particle size and optical constants but also on their spatial distribution, typically occurring when the average interparticle distance is small in comparison with the wavelength of incident radiation. Addressing dependent scattering requires solving Maxwell’s equations for complex heterogeneous structures, which is computationally demanding and usually limited to sample thicknesses on the same scale as the wavelength. This study combines computer-generated ambigel microstructures of fractal aggregates of polydisperse nanoparticles and the radiative transfer with reciprocal transaction method to predict the transmittance of thick ambigel slabs. Transmittance measurements of ambiently dried aerogel monoliths (ambigels) with porosities from about 50% to 90% closely matched the predicted values for their digital twins. However, ignoring dependent scattering or particle aggregation led to inaccurate predictions. This study validated the computational framework, and its findings offer insights for designing photonic metamaterials and analyzing their interactions with electromagnetic waves.

Yalcin, Refet A. (ORCID:0000000339973494)↗

Convection in Scaled Turbine Internal Cooling Passages With Additive Manufacturing Roughness

Abstract Additive manufacturing processes, such as direct metal laser sintering (DMLS), enable the creation of novel turbine cooling internal passages and systems. However, the DMLS method produces a significant and unique surface roughness. Previous work in scaled passages analyzed pressure losses and friction factors associated with the rough surfaces, as well as investigated the velocity profiles and turbulent flow characteristics within the passage. In this study, the heat transfer characteristics of scaled additively manufactured surfaces were measured using infrared (IR) thermography. Roughness panels were CNC machined from plates of aluminum 6061 to create near isothermal roughness elements when heated. Fluid resistance differences between the aluminum roughness panels and roughness panels constructed from ABS plastic using the same roughness patterns from McClain et al. (2020, “Flow in a Simulated Turbine Blade Cooling Channel With Spatially Varying Roughness Caused by Additive Manufacturing Orientation,” ASME Turbo Expo 2020, Turbomachinery Technical Conference and Exposition, Virtual Conference, Sept. 21–25, GT2020-16069) were investigated. Finally, the overall thermal performance enhancements and friction losses were assessed through the calculation of surface averaged “global thermal performance” ratios. The global thermal performance characterizations indicate results in-line with those found for traditional commercial roughness and slightly below traditional internal passage convection enhancement methods such as swirl chambers, dimples, and ribs. The passages investigated in this study do not include compressibility effects or the long-wavelength artifacts and channel geometric deviations observed by Wildgoose et al. (2020, “Impact of Additive Manufacturing on Internal Cooling Channels with Varying Diameters and Build Directions,” ASME Turbo Expo 2020, Turbomachinery Technical Conference and Exposition, Virtual Conference, Sept. 21–25, GT2020-15049). However, the results of this study indicate that, based on the roughness augmentation alone, artificial convective cooling enhancers such as turbulators or dimples may still be required for additively manufactured turbine component cooling.

Engineering↗

NRC Multiphysics Analysis Capability Deployment FY21: Part 3

This report details the progress and activities of Idaho National Laboratory (INL) on the Nuclear Regulatory Commission (NRC) project “Development and Modeling Support for Advanced Non-Light Water Reactors.” The deliverables completed for this report are: Deliverable 1c: the capability to model gas mixtures was added to Pronghorn. A test problem mimicking the conditions achieved in a depressurized loss of forced cooling (DLOFC) event was solved with both RELAP-5 and Pronghorn. Pronghorn employed a finite vol ume method with the Kurganov-Tadmor discretization. The comparison between the mass fraction spatial profiles computed with RELAP-5 and Pronghorn clearly shows the presence of numerical artifacts (i.e., overly diffusive behavior at low Mach numbers). We confirmed that the problem disappears at higher Mach numbers. We recommend future work on the implementation of a low Mach finite volume formulation to better treat low Mach number problems. Deliverable 2a: we demonstrated two approaches to model the radiation/conduction/natural convection heat transfer across a stagnant gas for the PBMR-400 design using Pronghorn. The first approach is based on the net radiation method, which relies on the computation of view factors with the Multiphysics Object-Oriented Simulation Environment (MOOSE) ray tracing capability. The second method is a traditional thermal resistance approach. The test problems include both 2D and 3D geometries. In all cases, the results show very good agreement during a DLOFC transient. This confirms that the faster thermal resistance method produces solutions that are equivalent to the net radiation method for this geometry. Deliverable 3d: we demonstrated the use of the advection kernel for the delayed neutron precursor equation in Griffin with a 2D MSFR model. The results appear physical but further verification is recommended. We also recommend the addition of conjugate heat transfer to compute the temperatures and model the thermomechanic behavior of the reflectors and other structures. Significant memory and performance issues were encountered in the 3D axisymmetric model. Future work is recommended in this area. Task 8g: this task allows multidimensional MOOSE applications to be coupled to system codes (RELAP-7 and SAM). We implemented a faster multiphysics iteration coupling algorithm, which provides an overall 6× acceleration of the 3D-1D coupling of the core multidi- mensional fluid flow solver and the 1D primary and secondary loop model.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

An Overview of the Circular Economy Lifecycle Assessment and VIsualization (CELAVI) Framework

A circular economy emphasizes the efficient use of all resources (e.g., materials, land, water). Despite anticipated overall benefits to society, the transition to a circular economy is likely to create regional differences in impacts. Current tools are unable to fully evaluate these potential externalities, which will be important for informing research prioritization and regional decision making. This presentation will review and contrast nine current impact assessment methods and describe their granularity, scope, data requirements, and capabilities within the context of identifying regional and sectoral transfers of impacts that could result from transitioning to a circular economy for energy systems. The review of current approaches highlights the need for a new hybrid circularity assessment framework that leverages multiple methods. We describe one such approach: the Circular Economy Lifecycle Assessment and VIsualization (CELAVI) framework. CELAVI uses system dynamics to model material flows for multiple circular economy pathways, network theory to track the spatial and sectoral flow of functional units across a graph, and discrete event simulation to to step through time and evaluate lifecycle assessment data at each time step. The framework is designed to be flexible and scalable enough to accommodate multiple energy materials and multiple energy technologies. CELAVI's hybridization of multiple existing methods yields additional capabilities, which may help answer questions about how flows between the technosphere and ecosphere may evolve if the circularity of energy systems is modified.

circular economy↗

Spatially Enhanced Electrostatic Doping in Graphene Realized via Heterointerfacial Precipitated Metals

Forming heavily-doped regions in 2D materials, like graphene, is a steppingstone to the design of emergent devices and heterostructures. Here, a selective-area approach is presented to tune the work-function and carrier density in monolayer graphene by spatially synthesizing sub-monolayer gallium beneath the 2D-solid. The localized metallic gallium is formed via precipitation from an underlying diamond-like carbon (DLC) film that is spatially implanted with gallium-ions. By controlling the interfacial precipitation process with annealing temperature, spatially precise ambipolar tuning of the graphene work-function is achieved, and the tunning effect preserved upon cooling to ambient conditions. Consequently, charge carrier densities from ≈1.8 × 10 10 cm -2 (hole-doped) to ≈7 × 10 13 cm -2 (electron-doped) are realized, confirmed by in situ and ex situ measurements. The theoretical studies corroborated the role of gallium at the heterointerface on charge transfer and electrostatic doping of the graphene overlayer. Specifically, sub-monolayer gallium facilitates heavy n-doping in graphene. Extending this doping strategy to other implantable elements in DLC provides a new means of exploring the physics and chemistry of highly-doped 2D materials.

36 MATERIALS SCIENCE↗

Viewing convection as a solar farm phenomenon broadens modern power predictions for solar photovoltaics

We report heat mitigation for large-scale solar photovoltaic (PV) arrays is crucial to extend lifetime and energy harvesting capacity. PV module temperature is dependent on site-specific farm geometry, yet common predictions consider panel-scale and environmental factors only. Here, we characterize convective cooling in diverse PV array designs, capturing combined effects of spatial and atmospheric variation on panel temperature and production. Parameters, including row spacing, panel inclination, module height, and wind velocity, are explored through wind tunnel experiments, high-resolution numerical simulations, and operating field data. A length scale based on fractal lacunarity encapsulates all aspects of arrangement (angle, height, etc.) in a single value. When applied to the Reynolds number Re within the canonical Nusselt number heat transfer correlation, lacunarity reveals a relationship between convection and farm-specific geometry. This correlation can be applied to existing and forthcoming array designs to optimize convective cooling, ultimately increasing production and PV cell life.

14 SOLAR ENERGY↗

Performance of Microreactor Test Article with Embedded Sensors During Testing in The Single Primary Heat Extraction and Removal Emulator

The nuclear industry is pursuing microreactors that can be factory assembled and deployed to remote regions for reliable power generation. One class of microreactors uses a monolithic metal core block coupled to heat pipes that use passive heat flow, increasing the surface area for heat transfer without requiring active coolant flow through the reactor core. Additional experimental testing is required to understand the heat rejection limitations of heat pipes and thermal stresses in the monolithic core block due to significant temperature gradients. This report describes the initial characterization and testing of a stainless-steel test article that was fabricated with embedded sensors to measure heat pipe performance limits, as well as spatially distributed temperatures and strains during electrically heated thermal testing to simulate nuclear heating. The electrically heated testing was performed in the Single Primary Heat Extraction and Removal Emulator facility located at Idaho National Laboratory. The ultimate goals of this work are to (1) accurately monitor temperature and strain distributions that result from differential thermal expansion in the test articles and (2) quantify the heat rejection limits of heat pipes as a function of operating temperature and working fluid during steady-state and transient operations. Initial tests focused on the feasibility of using advanced fiber-optic sensors and other sensor technologies to improve the understanding of temperature and strain distributions within the electrically heated experiments. However, these sensing capabilities could benefit the broader microreactor community if the sensors could be used to monitor component and system health during nuclear operations to inform a limited number of microreactor operators, ultimately reducing operation and maintenance costs and moving toward semiautonomous operation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

CFD Benchmark of Pressure Drop in a 61-Pin Wire-Wrapped Assembly with Blocked Channels Using NekRS

Thermal hydraulic behaviors of wire-wrapped rod bundles continue to be a subject of intense research. One of the leading next-generation designs, the sodium fast reactor, relies on a hexagonal assembly of wire-wrapped fuel pins. The issue of potential channel blockage has been raised as a safety concern due to the tightly packed arrangement of the fuel pins. This has led to several recent experimental and computational studies working to quantify the potential impact on the fluid flow and heat transfer behaviors of such blockages. The objective of the present study is to benchmark the high-fidelity NekRS CFD solver in predicting pressure drop for large blockages against available experimental data. A 61-pin wire-wrapped fuel assembly with two flow blockage configurations has been simulated and investigated at various low to moderate Reynolds numbers. The NekRS solver has been shown to yield exponentially decreasing spatial discretization errors with increasing polynomial order. All simulated results agreed well with measured data, which indicates that the overall methodology is adequate and consistent. The results of this benchmark study demonstrate the accuracy of NekRS for sodium fast reactor hydrodynamic simulations, increasing the confidence in its use for design, licensing, and analysis activities.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

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↗

Quantum Spin Torque Driven Transmutation of an Antiferromagnetic Mott Insulator

The basic model of spin-transfer torque (STT) in antiferromagnetic spintronics considers the exchange of angular momentum between quantum spins of flowing electrons and noncollinear-to-them localized spins treated as classical vectors. These vectors are assumed to realize Néel order in equilibrium, ↑ ↓ ∙∙∙ ↑ ↓, and their STT-driven dynamics is described by the Landau-Lifshitz-Gilbert (LLG) equation. However, many experimentally employed materials (such as archetypal NiO) are strongly electron-correlated antiferromagnetic Mott insulators (AFMIs) whose localized spins form a ground state quite different from the unentangled Néel state | ↑ ↓ ∙∙∙ ↑ ↓$\rangle$. The true ground state is entangled by quantum spin fluctuations, leading to the expectation value of all localized spins being zero, so that LLG dynamics of classical vectors of fixed length rotating due to STT cannot even be initiated. Instead, a fully quantum treatment of both conduction electrons and localized spins is necessary to capture the exchange of spin angular momentum between them, denoted as quantum STT. We use a recently developed time-dependent density matrix renormalization group approach to quantum STT to predict how injection of a spin-polarized current pulse into a normal metal layer coupled to an AFMI overlayer via exchange interaction and possibly small interlayer hopping—mimicking, e.g., topological-insulator/NiO bilayer employed experimentally—will induce a nonzero expectation value of AFMI localized spins. This new nonequilibrium phase is a spatially inhomogeneous ferromagnet with a zigzag profile of localized spins. The total spin absorbed by AFMI increases with electron-electron repulsion in AFMIs, as well as when the two layers do not exchange any charge.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Vegetation clumping modulates global photosynthesis through adjusting canopy light environment

Abstract The spatial dispersion of photoelements within a vegetation canopy, quantified by the clumping index (CI), directly regulates the within‐canopy light environment and photosynthesis rate, but is not commonly implemented in terrestrial biosphere models to estimate the ecosystem carbon cycle. A few global CI products have been developed recently with remote sensing measurements, making it possible to examine the global impacts of CI. This study deployed CI in the radiative transfer scheme of the Community Land Model version 5 (CLM5) and used the revised CLM5 to quantitatively evaluate the extent to which CI can affect canopy absorbed radiation and gross primary production (GPP), and for the first time, considering the uncertainty and seasonal variation of CI with multiple remote sensing products. Compared to the results without considering the CI impact, the revised CLM5 estimated that sunlit canopy absorbed up to 9%–15% and 23%–34% less direct and diffuse radiation, respectively, while shaded canopy absorbed 3%–18% more diffuse radiation across different biome types. The CI impacts on canopy light conditions included changes in canopy light absorption, and sunlit–shaded leaf area fraction related to nitrogen distribution and thus the maximum rate of Rubisco carboxylase activity ( V cmax ), which together decreased photosynthesis in sunlit canopy by 5.9–7.2 PgC year −1 while enhanced photosynthesis by 6.9–8.2 PgC year −1 in shaded canopy. With higher light use efficiency of shaded leaves, shaded canopy increased photosynthesis compensated and exceeded the lost photosynthesis in sunlit canopy, resulting in 1.0 ± 0.12 PgC year −1 net increase in GPP. The uncertainty of GPP due to the different input CI datasets was much larger than that caused by CI seasonal variations, and was up to 50% of the magnitude of GPP interannual variations in the tropical regions. This study highlights the necessity of considering the impacts of CI and its uncertainty in terrestrial biosphere models.

59 BASIC BIOLOGICAL SCIENCES↗

Automatic Seismic Phase Picking Using Deep Learning for the EGS Collab project

Microseismic monitoring plays an important role in many energy-related and environmental industries.The microseismic event catalog and seismic structure of the subsurface are two of the primary outputsof the microseismic monitoring system. Though rough locations of microseismic events can be estimated automatically, obtaining high-resolution microseismic event locations requires a significant amount of human laborespecially on seismic phase picking. Unlike traditional automatic pickers that are usually less precise than human analysts, a fewrecently proposed algorithms based on deepneural networks(DNN)were able to match or surpass human performance for earthquake signals. Due to differences in the spatial scale of the study area, sensor sampling rate, and geometry of the monitoring system, it is not clear whether these deepneural networkmodels can be used to speed up microseismic data processing. In this paper, we adapted the DNN based technique for automatic phase picking of microseismic signals. We usedmicroseismic data recorded at the experiment 1 site of the enhancedgeothermal system (EGS) Collab project anddesigneda workflowthat we call transfer-learning aided double-difference tomography (TADT),thatcombines transfer learning and seismic tomography. We re-train an existing DNN with our data to obtain a newmodel using around 3500 seismogramsand associated manual phase picks. Thistransfer learnedmodel is able to reach human performance but muchfaster than human analysts. The transfer-learning-derived phase pickswereused to improve microseismic event locations and imagethesubsurface. The results are similar to or slightly better than those obtained with manual phase picks.

Chai, Chengping↗

Real-time and post-hoc compression for data from Distributed Acoustic Sensing

Distributed Acoustic Sensing (DAS) is an emerging sensing technology that records the strain-rate along fiber optic cables at high spatial and temporal resolution. This technique is becoming a popular tool in seismology, hydrology, and other subsurface monitoring applications. However, due to the large coverage (10’s of km) and high density of measurements (1m spacing at 100’s of Hz), a DAS installation could produce terabytes of data records per day. Because many DAS instruments are deployed in remote locations, this large data size poses significant challenges to its transfer and storage. In this paper, we explore lossless compression methods to reduce the storage requirement in both real-time and post-hoc scenarios. Here we propose a two-stage compression method to improve the compression ratio and compression speed. This two-stage compression method could reduce the storage requirement by 40%, which is 20% more than other lossless methods, such as ZSTD. We demonstrate that the compression method could complete its operation well before the DAS instrument needs to output the next file, making it suitable for real-time DAS acquisition. We also implement a parallel compression method for a post-hoc scenario and demonstrate that our method could effectively utilize a parallel computer. With 256 CPU cores, our parallel compression method achieves the speed of 26GB/second.

58 GEOSCIENCES↗