Upscaling Approach Research Articles

Upscaling and downscaling approaches for early season rice yield prediction using Sentinel-2 and machine learning for precision nitrogen fertilisation

Early season yield prediction could support rice farmers in adopting precision agriculture for nitrogen fertilisation management. Remote sensing and machine learning (ML) can be used to predict and map crop yield during phenological stages relevant to nitrogen application, like tillering in rice, at both within-field and field scales. This study evaluated the transferability of ML models in early season yield prediction through upscaling and downscaling approaches. The effects of two prediction times (tillering and ripening stages) and training/testing set sizes on ML models performance were evaluated over five rice growing seasons (from 2018 to 2022) in northern Italy, using whole-field-average yields and yield maps. Vegetation indices from Sentinel-2 imagery using the Google Earth Engine platform fed five ML algorithms (Cubist-CUB, Gaussian Process Regression-GPR, Neural Network-NNET, Random Forest-RF, and Support Vector Machines-SVM). ML algorithms were trained with yield maps and tested with whole-field-average yields to obtain a downscaling approach, while the opposite was done to obtain an upscaling approach. The downscaling approach showed higher accuracy than upscaling approach. Ripening stage predictions were more accurate than tillering stages, although the downscaling approach showed small differences between tillering and ripening stages. The highest tillering stage accuracy was achieved by SVM for both downscaling and upscaling approaches with 20 % and 27.8 % of Normalized Root Mean Square Error (NRMSE), and 0.99 and 0.99 of Simple Additive Weighting (SAW) score, respectively. Set size and data distribution effected ML models accuracy, with the highest performance achieved by RF and GPR with 0.80 and 1.00 of SAW score for the downscaling and upscaling approaches, respectively. This study demonstrated how ML models and downscaling approach could support rice farmers to calculate the nitrogen dose using the predicted yield at the tillering stage, enabling them to apply a site-specific nitrogen fertilisation based on the within-field yield prediction variability.

Preparation and characterization of carboxymethyl microcrystalline cellulose from pineapple leaf fibre

Highly functional and robust biobased materials are still in research to produce valuable composites for various applications. The literature shows the gap of new raw biobased materials in market which can functionally tuned and structurally modified for development of 2d/3d architectures. Thus, in the present study, very cheap, easily available agricultural waste, pineapple leaf fiber (PL-raw) was used for the isolation of microcrystalline cellulose (PL-MCC) and further functionalized using upscaled chemical approach to carboxymethyl microcrystalline cellulose (PL-CMMCC). Very advanced techniques like Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), scanning electron microscopy (SEM), energy dispersive X-ray analysis (EDX), X-ray diffraction analysis (XRD), and differential scanning calorimetry (DSC) served to characterize the raw material, high crystalline PL-MCC, and modified carboxy methyl MCC. FTIR determined presence of different absorbed peak at approximately 1620.2 cm−1, and at 1423.8 cm−1, carboxyl groups were assigned to PL-CMMCC. On the other hand, the XRD findings verified that PL-CMMCC’s crystalline structure has decreased. Analysis by SEM revealed a damaged surface morphology for PL-CMMCC. Following chemical treatments, the EDX analysis revealed that each fiber sample contained a highly pure cellulose elemental composition. Thus, results explain the utilization of agricultural waste, pineapple leaf fiber to high valuable products like highly crystalline PL-MCC, in addition further modification of PL-MCC could leads to formation of highly functional material that could be used for other applications too in future.

Improving the simulation accuracy of summer maize growth and yield by pixel-based parameterization based on assimilating upscaled MODIS LAI

Accurately simulating maize (Zea mays L.) yield at the regional scale is of paramount importance for making informed policy adjustments and for ensuring food security. Sobol sensitivity analysis was used in this study to screen sensitive parameters of a process-based crop model (Chinese Agricultural Meteorological Model, CAMM). An upscaling approach was utilized to reduce the intra-pixel heterogeneity error of MODIS LAI. Then upscaled MODIS LAI data were assimilated into the CAMM model through a 4DVar assimilation algorithm to optimize pixel-level sensitive parameters, thereby improving the simulation accuracy of the summer maize growth process and yield. Specific leaf area (SLATB_0, SLATB_1) during the period from emergence to tasseling of summer maize exhibited the strongest impact on summer maize yield. Additionally, the average LAI value within the 85–95 % range of ordered LAI values for small pixels within large pixels effectively reduced the intra-pixel heterogeneity error of MODIS LAI, and pixel-based parameterization of SLATB_0 and SLATB_1 at a larger pixel scale (0.0625°) was achieved. Based on yields recorded at agrometeorological stations from 2015 to 2020, assimilated yields in both data assimilation scheme 1 (DA1, optimization of only the sensitive parameter SLATB_0) and scheme 2 (DA2, simultaneous optimization of sensitive parameters SLATB_0 and SLATB_1) exhibited higher accuracy than schemes without data assimilation (with r values of 0.41–0.72 and NRMSE values of 19–30 %). Furthermore, DA2 showed greater simulation accuracy (r: 0.64–0.93, NRMSE: 9–21 %) than DA1 (r: 0.61–0.91, NRMSE: 12–23 %). Upscaling remotely sensed LAI products can significantly reduce the uncertainties of LAI data at a larger pixel scale, and assimilating these LAI data into crop models can effectively increase the simulation accuracy of crop growth and development processes at the regional scale.

Multi-scale FE analysis of coupled load-moisture mechanical behavior of saturated asphalt pavements considering transversely isotropic permeability

Assessing the collective impacts of external loading and void pressure on the mechanical behavior of porous media presents a significant challenge due to its inherent heterogeneity and complex multi-physical field coupling mechanisms. This study addresses this challenge by developing a novel multiscale hydraulic-mechanical modeling framework to investigate the structural response of water-saturated asphalt pavement under sequential coupling hydro-mechanical loading. The framework comprises three key components. Firstly, incorporating an upscaling homogenization approach to establish the linkage of material properties between different scales; secondly, developing a downscaling transfer procedure to transfer the structural response across scales for insight into its multiphysics mechanisms; and finally, proposing a new sequential coupling algorithm in multiscale simulations for comprehensive multi-field coupling calculations. The primary outcomes of this study demonstrate that asphalt concrete (AC)-graded pavements are susceptible to “down-top” cracks under hydro-mechanical loading, while open graded friction course (OGFC)-graded pavements have the potential to develop both “top-down” and “down-top” cracks. In AC-graded pavements, increasing the hydraulic head reduces stress concentrations, while in OGFC-graded pavements, changes in the permeability coefficient have a lesser impact on mechanical response. At the mesoscopic level, tensile stress concentrations in the asphalt mortar decrease significantly at higher temperatures. Furthermore, the OGFC-graded RVE model exhibits higher tensile stresses in the asphalt mortar compared to the AC-graded RVE model.

Intercomparison of global foliar trait maps reveals fundamental differences and limitations of upscaling approaches

Foliar traits such as specific leaf area (SLA), leaf nitrogen (N), and phosphorus (P) concentrations play important roles in plant economic strategies and ecosystem functioning. Various global maps of these foliar traits have been generated using statistical upscaling approaches based on in-situ trait observations. Here, we intercompare such global upscaled foliar trait maps at 0.5° spatial resolution (six maps for SLA, five for N, three for P), categorize the upscaling approaches used to generate them, and evaluate the maps with trait estimates from a global database of vegetation plots (sPlotOpen). We disentangled the contributions from different plant functional types (PFTs) to the upscaled maps and quantified the impacts of using different plot-level trait metrics on the evaluation with sPlotOpen: community weighted mean (CWM) and top-of-canopy weighted mean (TWM). We found that the global foliar trait maps of SLA and N differ drastically and fall into two groups that are almost uncorrelated (for P only maps from one group were available). The primary factor explaining the differences between these groups is the use of PFT information combined with remote sensing-derived land cover products in one group while the other group mostly relied on environmental predictors alone. The maps that used PFT and corresponding land cover information exhibit considerable similarities in spatial patterns that are strongly driven by land cover. The maps not using PFTs show a lower level of similarity and tend to be strongly driven by individual environmental variables. Upscaled maps of both groups were moderately correlated to sPlotOpen data aggregated to the grid-cell level (R = 0.2–0.6) when processing sPlotOpen in a way that is consistent with the respective trait upscaling approaches, including the plot-level trait metric (CWM or TWM) and the scaling to the grid cells with or without accounting for fractional land cover. The impact of using TWM or CWM was relevant, but considerably smaller than that of the PFT and land cover information. The maps using PFT and land cover information better reproduce the between-PFT trait differences of sPlotOpen data, while the two groups performed similarly in capturing within-PFT trait variation.Our findings highlight the importance of explicitly accounting for within-grid-cell trait variation, which has important implications for applications using existing maps and future upscaling efforts. Remote sensing information has great potential to reduce uncertainties related to scaling from in-situ observations to grid cells and the regression-based mapping steps involved in the upscaling.

An eco-friendly and up-scalable approach to extract canthaxanthin from yeast cells

Canthaxanthin is a naturally occurring ketocarotenoid pigment present in plants, algae, bacteria and some fungi. In addition to its coloring role, canthaxanthin has an excellent antioxidant activity, thus having additional market demands in the feed, food, cosmetic and pharmaceutical industries. Canthaxanthin can be directly isolated from its natural source or produced by chemical synthesis, but these strategies either result in low yields, or use hazardous solvents, respectively. Therefore, the biosynthesis of canthaxanthin using microbial cell factories is becoming an advantageous alternative. Furthermore, microbial synthesis represents an economic and sustainable approach as it enables the use of agriculture and industrial wastes as substrates. In this work, the extraction, recovery and purification of canthaxanthin from modified yeasts using food grade solvents and up-scalable methodologies was studied. The resulting canthaxanthin-enriched extract was characterized (UV-Vis, PXRD and SEM) and quantified (HPLC), resulting in a canthaxanthin purity of 43.7 % (w/w).

Development of Nanopillar Arrays Nanopatterning Without Lift‐Off for Transferable GaN‐Based µLEDs

AbstractThe mass production of µLEDs requires an upscaling approach on 200 mm wafers, which implies the deployment of a technology that achieves zero defectivity without liftoff. In this report, Nanoimprint lithography (NIL) processing is successfully optimized for nanostructuring GaN‐based Silicon‐On‐Insulator (SOI) substrates. The etching of SiO2/GaN/AlN/Si/SiO2 layers using different plasmas is conducted and multi‐layer nanopillars 100–200 mm in diameter are fabricated. This approach generates zero‐defect arrays of pillars, which is particularly advantageous for the growth process. In addition, the SiO2 at the bottom of the pillar allows it to twist during the subsequent GaN regrowth, as this layer becomes soft at the growth temperature >1000 °C. This ability to deform enables a coalescence of pillars into layers with reduced dislocation density. As a result, high‐quality GaN microplatelets and µLEDs are grown via a bottom‐up approach based on pendeoepitaxy using metal–organic vapor phase epitaxy (MOVPE). The fabricated µLEDs have a very smooth surface with a roughness of 0.6 nm which facilitated the implementation of an easy and simple transfer protocol. Adhesive tape and metalmetal bonding, are used to bond the µLEDs onto a metal‐coated silicon substrate. The reported findings offer exciting new insights into the development of high‐performance displays.

An up-scaled biotechnological approach for phosphorus-depleted rye bran as animal feed

Side streams from the milling industry offer excellent nutritional properties for animal feed; yet their use is constrained by the elevated phosphorus (P) content, mainly in the form of phytate. Biotechnological P recovery fosters sustainable P management, transforming these streams into P-depleted animal feed through enzymatic hydrolysis. The enzymatic P mobilization not only enables P recovery from milling by-products but also supports the valorization of these streams into P-depleted animal feeds. Our study presents the scalability and applicability of the process and characterizes the resulting P-depleted rye bran as animal feed component. Batch mode investigations were conducted to mobilize P from 100 g to 37.1 kg of rye bran using bioreactors up to 400 L. P reductions of 89% to 92% (reducing from 12.7 gP/kg to 1.41–1.28 gP/kg) were achieved. In addition, High Performance Ion Chromatography (HPIC) analysis showed complete depletion of phytate. The successful recovery of the enzymatically mobilized P from the process wastewater by precipitation as struvite and calcium hydrogen phosphate is presented as well, achieving up to 99% removal efficiency. Our study demonstrates a versatile process that is easily adaptable, allowing for a seamless implementation on a larger scale.Graphical

Quantitative assessment of the scale conversion from instantaneous to daily GPP under various sky conditions based on MODIS local overpassing time

ABSTRACT With the increasing frequency of extreme events, daily gross primary productivity (GPP) is necessary to be accurately assessed to determine when and how much it is affected. Fluctuating environmental conditions contribute to diverse diurnal photosynthetic patterns influenced by changing atmospheric factors, including solar radiation, CO2 concentrations, and leaf temperature. This complexity underscores the challenge of accurately estimating daily GPP. We quantitatively assessed three temporal upscaling approaches – cosine of the solar zenith angle, extraterrestrial solar irradiance, and photosynthetic active radiation(PAR) – under varying sky conditions. Additionally, we introduced a novel correction method for universal temporal upscaling ratios. Instantaneous GPP values from the FLUXNET2015 dataset, acquired around the MODIS overpassing time, were utilized. The upscaled daily GPP exhibited minimal deviation from the half-hourly GPPs collected between 11 am and 1 pm, with increased discrepancies for GPPs later in the afternoon. The PAR-based approach demonstrated superior accuracy in the afternoon, effectively capturing incoming radiation changes due to clouds. Instant environmental variables-GPP relationships were weak under clear skies but exhibited moderate-to-high positive correlations under cloudy conditions. Analyzing the impact of the diffuse ratio of incoming photosynthetic active radiation on instantaneous GPP revealed limited enhancement at short time scales. On a daily scale, all three temporal upscaling approaches, under different ranges of clearness index, consistently underestimated daily GPP. We proposed a novel correction method, normalizing the difference between maximum and minimum air temperature in a day, notably reduced errors by approximately 10.69 and 21.07 gC m−2 d−1 for cosine of the solar zenith angle and extraterrestrial solar irradiance-based temporal upscaling approaches. We recommend adopting the corrected temporal upscaling factor globally due to its simplicity and improved accuracy.

Investigating Correlations and the Validation of SMAP-Sentinel L2 and In Situ Soil Moisture in Thailand.

Soil moisture plays a crucial role in various hydrological processes and energy partitioning of the global surface. The Soil Moisture Active Passive-Sentinel (SMAP-Sentinel) remote-sensing technology has demonstrated great potential for monitoring soil moisture with a maximum spatial resolution of 1 km. This capability can be applied to improve the weather forecast accuracy, enhance water management for agriculture, and managing climate-related disasters. Despite the techniques being increasingly used worldwide, their accuracy still requires field validation in specific regions like Thailand. In this paper, we report on the extensive in situ monitoring of soil moisture (from surface up to 1 m depth) at 10 stations across Thailand, spanning the years 2021 to 2023. The aim was to validate the SMAP surface-soil moisture (SSM) Level 2 product over a period of two years. Using a one-month averaging approach, the study revealed linear relationships between the two measurement types, with the coefficient of determination (R-squared) varying from 0.13 to 0.58. Notably, areas with more uniform land use and topography such as croplands tended to have a better coefficient of determination. We also conducted detailed soil core characterization, including soil-water retention curves, permeability, porosity, and other physical properties. The basic soil properties were used for estimating the correlation constants between SMAP and in situ soil moistures using multiple linear regression. The results produced R-squared values between 0.933 and 0.847. An upscaling approach to SMAP was proposed that showed promising results when a 3-month average of all measurements in cropland was used together. The finding also suggests that the SMAP-Sentinel remote-sensing technology exhibits significant potential for soil-moisture monitoring in certain applications. Further validation efforts and research, particularly in terms of root-zone depths and area-based assessments, especially in the agricultural sector, can greatly improve the technology's effectiveness and usefulness in the region.

S8-4 Reaching beyond competitive sports. How International Sports Federation can use their main competitions to promote physical activity. A World Athletics pilot project

Abstract Purpose International Sports Federations (IF) are global governing bodies for each respective sport and their main duties are to organize international competitions, regulate and promote their sport. Despite their objective of promoting sports participation, their activity mostly focused on competitive/elite sports so far. International competitions not only represent the epitome of each sport but could also be used as an opportunity to improve the health of those living in the hosting city or the staff working for the local organising committee (LOC). The purpose of this pilot project was to measure the benefits of active commuting (AC) and leisure time physical activity (LTPA) of the staff involved in the World Athletics Championships Oregon22 during the two weeks of competition. Project description World Athletics partnered with StravaMetro□ to measure AC between official accommodation and competition venues, as well as the staff LTPA, during the Championships. Before the event, World Athletics staff was briefed on the project purposes and given information on AC options available in Eugene (pedestrian and cycling paths, bike rental). Staff members were encouraged to record their AC and LTPA through the Strava App. During the 2 weeks, a total of 18,656 walking/running trips (both AC and LTPA) were recorded in Eugene. This represents a 225.4% increase compared to the same period of the previous year. Furthermore, 2690 cycling commuting trips were recorded, representing a 143% increase. Conclusions This pilot project demonstrates that IF can efficiently use their main events to promote AC and LTPA to their staff and the workforce involved. Encouraging AC, improves the health of those active, reduces traffic related air pollution, road traffic, and transportation costs for the LOC. An upscaled approach adopted in the early phase of the event planning, involving spectators would amplify observed results and their health-related benefits. The post analysis by StravaMetro□ of the walking and cycling routes used by staff and spectators can also allow the host city to better define and create protected routes which represent a tangible legacy. These objectives require a strong collaboration between the IF, LOC, city government and the national institutions promoting AC and HEPA.

An efficient analytical approach for steady-state upscaling of relative permeability and capillary pressure

Upscaling in petroleum reservoir simulation captures the dynamic behavior of fine-scale models at the coarse-scale in a volume average sense. Traditional upscaling of immiscible two-phase flow can be implemented by solving the fluid flow equations at steady state in the capillary or viscous limit in fine scale, which requires modeling the single-phase flow numerically many times and is computationally demanding. In this study, an analytical approach is proposed, to replace the numerical simulation, for computing the relative permeability. The key idea is to utilize the perturbation expansion technique and Fourier analysis to derive an explicit expression of the equivalent permeability. The analytical expression accounts for spatial correlations and is more accurate than the simple averaging and renormalization methods. It matches well with the numerical method in all cases for the upscaling of the relative permeability and capillary pressure. The developed method is also validated by comparing the pressure and saturation results from the coarse-scale and fine-scale models. In the base case of SPE10 benchmark test, the numerical upscaling takes 436 s, whereas the analytical upscaling takes 13.6 s, which is about 30 times faster than the numerical method. Moreover, the analytical coefficients just need to be computed once for the whole space in a given geostatistical model, which naturally leads to improved efficiency and is clearly favorable for petroleum reservoir simulations.

Cost-efficient cell clarification using an intensified fluidized bed centrifugation platform approach.

The intensification of industrial biopharmaceutical production and the integration of process steps pave the way for patients to access affordable treatments. The predominantly batchwise biomanufacturing of established cell clarification technologies, stainless steel disc stack centrifugation (DSC) and single-use (SU) depth filtration (DF), pose technological and economical bottlenecks, that include low biomass loading capacities and low product recoveries. Therefore, a novel SU-based clarification platform was developed combining fluidized bed centrifugation (FBC) with integrated filtration. The feasibility of this approach was investigated for high cell concentration with more than 100E6 cells/mL. Furthermore, scalability to 200 L bioreactor scale was tested for moderate cell concentrations. In both trials, low harvest turbidities (4 NTU) and superior antibody recoveries (95%) were achieved. The impact on the overall economics of industrial SU biomanufacturing using an up-scaled FBC approach was compared with DSC and DF technologies for different process parameters. As a result, the FBC showed to be the most cost-effective alternative for annual mAb production below 500 kg. In addition, the FBC clarification of increasing cell concentrations was found to have minimal impact on overall process costs, in contrast to established technologies, demonstrating that the FBC approach is particularly suitable for intensified processes.

Modelling of a passive hydraulic lock concept for the mitigation transfer tubes in a SFR core

To improve the safety of the integrated Sodium-cooled Fast Reactor, recent safety complementary devices can prevent or mitigate the consequences of a hypothetical severe accident consecutive to a loss of reactor cooling sequence. In particular, the M−TT device is set by empty transfer tubes, in place of some fuel sub-assemblies present in the reactor core, using an innovative passive hydraulic lock. It is based on the concept of hydraulic diode controlled by the flowrate of primary pumps. This system is dedicated to provide an easy way to discharge corium towards the core catcher in the hypothetical case of severe accident. In bonus, concerning the prevention of severe accident, it opens a direct flow path between the hot pool, equipped with decay heat removal exchangers, and the cold pool. This can lead to an easier passage of colder sodium towards the cold pool to promote core cooling by natural convection in case of the loss of the primary flow. This paper is devoted to the evaluation of the assessment of the hydraulic lock function and of the small magnitudes of the perturbations induced by our device on the thermal–hydraulic behavior of the reactor during normal operations. Through an analytical analysis and an up-scaling numerical approach, ranging from the local scale (CFD) to the system one, we claim that the lock function is kept during the normal situations at various power regimes. The leak flow represents no more than 1% of the primary pump flow. In addition, no important reactor thermal–hydraulic perturbation is brought by adding transfer tubes in the core. This is true for normal steady states, whatever the power is, but also for a full-power loss of forced flow accidental transient leading to the natural convection cooling.

Alloyed RexMo1 – xS2 Nanoflakes with Enlarged Interlayer Distances for Hydrogen Evolution

Molybdenum sulfide (MoS2) has attracted significant attention due to its great potential as a low-cost and efficient catalyst for the hydrogen evolution reaction. Developing a facile, easily upscalable, and inexpensive approach to produce catalytically active nanostructured MoS2 with a high yield would significantly advance its practical application. Colloidal synthesis offers several advantages over other preparation techniques to overcome the low reaction yield of exfoliation and drawbacks of expensive equipment and processes used in chemical vapor deposition. In this work, we report an efficient synthesis of alloyed RexMo1–xS2 nanoflakes with an enlarged interlayer distance, among which the composition Re0.55Mo0.45S2 exhibits excellent catalytic performance with overpotentials as low as 79 mV at 10 mA/cm2 and a small Tafel slope of 42 mV/dec. Density functional theory calculations prove that enlarging the distance between layers in the RexMo1–xS2 alloy can greatly improve its catalytic performance due to a significantly reduced free energy of hydrogen adsorption. The developed approach paves the way to design advanced transition metal dichalcogenide-based catalysts for hydrogen evolution and to promote their large-scale practical application.

Flow through porous metamaterials formed by TPMS-based unit cells: Effects of advection

The design of metamaterials based on triply periodic minimal surfaces (TPMS) is currently a very active field of research. An upscaling approach is used here to study the flow in TPMS-based porous media, with focus on the effects of advection. The effective medium permeability, function of the Reynolds number Re of the flow through the pores, is numerically evaluated for varying porosity θ, for six types of TPMS-based structures, namely Gyroid, I-WP, Schwarz P, Split P, Fischer–Koch S, and Neovius. Inertial effects are found to be significant; for instance, the permeability is reduced by 15−50% (according to the surface type) as Re increases from 0 to 50000, when θ=0.98.

Core scale investigation of fluid flow in the heterogeneous porous media based on X-ray computed tomography images: Upscaling and history matching approaches

In this paper, experiments and simulations were performed on outcrop samples from Lagoa Salgada in Rio de Janeiro, Brazil, as a possible analog to one of the most typical Brazilian Pre-salt carbonate reservoirs rocks. The rocks were microbial carbonates where plugs comprising two main facies were sampled, simplified as fine-grained and vugular facies. The plugs were utilized to study the impact of pore geometry with both experimental and simulation approaches on recovery factor, saturation profile, and relative permeability estimations. To provide direct visualization of the geometry, description of pore structure, and calculation of concentration profiles, computed tomography (CT) imaging was integrated with experimental measurements of petrography and core flooding. The injection of two pore volumes of formation water resulted in a recovery factor between 28 and 34 percent for the plug samples. Furthermore, based on porosity generated by dry and wet CT, as well as saturation profiles resulting from CT data collected during drainage and imbibition processes along the length of the plugs, it is revealed that the distribution of these properties was diverse and heterogeneous. An algorithm was used to process the 2D tomography images of the samples to remove the region related to the exterior parts. The images were then stacked to create a 3D fine-scale grid to simulate the porous media and the fluid flow by applying rules for the segmentation of rock types, porosity, and permeability estimations of each grid block. Course-scale grids were created by applying upscaling techniques to reduce computation time. Simulated produced fluid cuts for different upscaled models were compared with the experimental results from core flooding. A history-matching technique was then applied to match experimental and simulation results, calculating the relative permeability of two main defined facies and creating an updated model capable of assessing past and present performance and future forecasting. Since relative permeability is essential for accurate simulation, estimating these curves in the heterogeneous pre-salt reservoir considering different facies, greatly influences reasonable future prediction performance and the ability to make informed operational decisions.

Electrochemical Oxidation for Treatment of PFAS in Contaminated Water and Fractionated Foam-A Pilot-Scale Study.

Per- and polyfluoroalkyl substances (PFAS) are persistent synthetic contaminants that are present globally in water and are exceptionally difficult to remove during conventional water treatment processes. Here, we demonstrate a practical treatment train that combines foam fractionation to concentrate PFAS from groundwater and landfill leachate, followed by an electrochemical oxidation (EO) step to degrade the PFAS. The study combined an up-scaled experimental approach with thorough characterization strategies, including target analysis, PFAS sum parameters, and toxicity testing. Additionally, the EO kinetics were successfully reproduced by a newly developed coupled numerical model. The mean total PFAS degradation over the designed treatment train reached 50%, with long- and short-chain PFAS degrading up to 86 and 31%, respectively. The treatment resulted in a decrease in the toxic potency of the water, as assessed by transthyretin binding and bacterial bioluminescence bioassays. Moreover, the extractable organofluorine concentration of the water decreased by up to 44%. Together, these findings provide an improved understanding of a promising and practical approach for on-site remediation of PFAS-contaminated water.

Machine learning assisted two-phase upscaling for large-scale oil-water system

The computation of two-phase upscaled functions entails solving time-dependent flow and transport equations over target regions, which is usually the most time-demanding component in the overall two-phase upscaling procedure. For large-scale reservoir models with a great number of coarse grid blocks, it can be very computationally expensive to calculate the two-phase upscaled functions for each individual coarse block. To address this problem, we develop a machine learning assisted upscaling (MLAU) approach, in which the two-phase upscaling is only performed for representative coarse blocks selected by a convolutional neural network (CNN) based clustering model, while the two-phase upscaled functions are quickly predicted for the rest of the coarse blocks using a regression algorithm. The performance of MLAU approach was assessed with three cases involving Gaussian, channelized and SPE 10 sector models, respectively. Numerical results have shown that the MLAU approach consistently provides coarse-scale results with close agreement with the results using full flow-based upscaling. Because two-phase numerical upscaling is only applied for representative coarse blocks (about 5% in each case), the speedups relative to the full flow-based upscaling are significant, ranging from 6.2 to 13.5. Compared to the fine-scale simulations, the speedups range from 27.0 to 47.2.

Upscaling soil moisture from point scale to field scale: Toward a general model

AbstractField‐scale soil moisture measurements are valuable but rarely available because the resolution of most satellite soil moisture products is too coarse, while most in situ sensors provide only point‐scale data. Previous upscaling approaches for such data are mostly site‐specific, and none are suitable to upscale data from the thousands of stations in existing monitoring networks. To help fill this gap, this research aims to develop a more broadly applicable upscaling approach using data from the Marena, Oklahoma, In Situ Sensor Testbed and a cosmic‐ray neutron rover. Rover survey data were used to measure average soil moisture for the ∼64‐ha field on 12 dates in 2019–2020. Statistical modeling was used to identify the soil, terrain, and vegetation properties influencing the relationships between the field‐scale rover data and point‐scale in situ data from six monitoring sites. Site‐specific linear upscaling models estimated the field average soil moisture with root mean squared error (RMSE) values ranging from 0.007 to 0.017 cm3 cm−3, but such models are not transferrable between sites. To create a more general model, Least Absolute Shrinkage and Selection Operator regression was used with a leave‐one‐out cross‐validation approach to identify the key predictors for upscaling. The resulting parsimonious model required only the point‐scale observations and sand content data and achieved RMSE values ranging from 0.006 to 0.031 cm3 cm−3 for the six monitoring sites. The texture‐based model demonstrated reasonable accuracy and is a promising step toward a general model that could be broadly applied for upscaling point‐scale in situ monitoring stations.