Empirical Model Research Articles

AI based optimal analysis of electro-osmotic peristaltic motion of non-Newtonian fluid with chemical reaction using artificial neural networks and response surface methodology

PurposeThe present work is devoted to investigating the sensitivity analysis of the electroosmotic peristaltic motion of non-Newtonian Casson fluid with the effect of the chemical reaction and magnetohydrodynamics through the porous medium. The main focus is on flow efficiency quantities such as pressure rise per wavelength, frictional forces on the upper wall and frictional forces on the lower wall. This initiative is to bridge the existing gap in the available literature.Design/methodology/approachThe governing equations of the problem are mathematically formulated and subsequently simplified for sensitivity analysis under the assumptions of a long wavelength and a small Reynolds number. The simplified equations take the form of coupled nonlinear differential equations, which are solved using the built-in Matlab routine bvp4c. The response surface methodology and artificial neural networks are used to develop the empirical model for pressure rise per wavelength, frictional forces on the upper wall and frictional forces on the lower wall.FindingsThe empirical model demonstrates an excellent fit with a coefficient of determination reaching 100% for responses, frictional forces on the upper wall and frictional forces on the lower wall and 99.99% for response, for pressure rise per wavelength. It is revealed through the sensitivity analysis that pressure rise per wavelength, frictional forces on the upper wall and frictional forces on the lower wall are most sensitive to the permeability parameter at all levels.Originality/valueThe objective of this study is to use artificial neural networks simulation and analyze the sensitivity of electroosmotic peristaltic motion of non-Newtonian fluid with the effect of chemical reaction.

Enhancement the electrical and linear/nonlinear optical properties of ZnCo2O4 through Al3+doping

In this work, ZnCo2O4 samples doped with Al3+ were prepared by the hydrothermal method. Synchrotron x-ray diffraction, FTIR, SEM and EDX techniques were used to investigate the structure, microstructure, morphology, and composition of ZnCo2-xAlxO4 samples. Rietveld analysis revealed a nearly inverse structure for the undoped ZnCo2O4. The degree of inversion is increased upon increasing the Al3+ doping content. Al ions preferentially occupy the octahedral B-site. The optical band gaps of ZnCo2−xAlxO4 samples are 1.9, 1.8 and 1.91 eV for x = 0, 0.1 and 0.2, respectively. Different empirical models were used to obtain the refractive index of the different samples using their corresponding optical band gaps. The increase in non-linear optical values in the sample with x = 0.1 suggested that this sample could be used in a variety of photonic applications. The effects of frequency and temperature on the dielectric permittivity, impedance and electrical modulus of different samples were explored. All samples exhibited the non-Debye relaxation phenomenon. The relaxation time of each sample is affected with the frequency and temperature. The Cole-Cole curve is employed to reduce uncertainty regarding the electrical characteristics of these ZnCo2−xAlxO4 samples induced by grain boundaries or bulk grain. The substitution of Al3+ ions can regulate the conductivity of the ZnCo2O4 sample.

An Empirical Model For Intrinsic Alignments: Insights From Cosmological Simulations

We extend current models of the halo occupation distribution (HOD) to include a flexible, empirical framework for the forward modeling of the intrinsic alignment (IA) of galaxies. A primary goal of this work is to produce mock galaxy catalogs for the purpose of validating existing models and methods for the mitigation of IA in weak lensing measurements. This technique can also be used to produce new, simulation-based predictions for IA and galaxy clustering. Our model is probabilistically formulated, and rests upon the assumption that the orientations of galaxies exhibit a correlation with their host dark matter (sub)halo orientation or with their position within the halo. We examine the necessary components and phenomenology of such a model by considering the alignments between (sub)halos in a cosmological dark matter only simulation. We then validate this model for a realistic galaxy population in a set of simulations in the Illustris-TNG suite. We create an HOD mock with Illustris-like correlations using our method, constraining the associated IA model parameters, with the between our model’s correlations and those of Illustris matching as closely as 1.4 and 1.1 for orientation–position and orientation–orientation correlation functions, respectively. By modeling the misalignment between galaxies and their host halo, we show that the 3-dimensional two-point position and orientation correlation functions of simulated (sub)halos and galaxies can be accurately reproduced from quasi-linear scales down to . We also find evidence for environmental influence on IA within a halo. Our publicly-available software provides a key component enabling efficient determination of Bayesian posteriors on IA model parameters using observational measurements of galaxy-orientation correlation functions in the highly nonlinear regime.

Factors affecting the adoption of high technology in vegetable production in Hanoi, Vietnam

High-tech vegetable production is becoming a priority in agricultural development in Vietnam in the context of digital economic development. This study aims at identifying factors involving the adoption of high technology in vegetable production by local farmers in Hanoi. We used the theory of planned behavior and other farmers’ personal, social and economic factors to develop empirical model and hypotheses. Primary data was collected from a survey of 450 vegetable producers in Hanoi using cluster sampling method combined with random selection. Then, binary logit model was used to analyze the impact of influencing factors. Results showed that there were 7 factors having significant influences the decision to apply technology in vegetable production of farmers including attitude on high tech production, access to information, size of farm, member of extension organization, education level, access to credit and perceived behavior control, in which attitude variable was the most influential factor. Main management implications raised included enhancing access to technological information, providing demostration visits, giving more extension services, improving social inclusion and implementing hi- tech training for farmers in vegetable production.

An Unmet goal? A Scale Development and Model test of the role of Inclusive Leadership

AbstractThere is a lack of evidence on whether inclusive leadership (IL) truly fosters inclusion for those in historically marginalized groups. Further, a conceptually up-to-date and practically useful measure of IL is lacking. Thus, in two separate phases, we first present a newly developed measure of IL and then test an empirical model. In Phase 1, results from five demographically diverse samples demonstrate reliability and validity evidence of a 10-item IL scale. In Phase 2, use this scale to examine how IL may support members of historically marginalized groups by fostering (a) interpersonal behaviors that uphold inclusive work environments (allyship, antiracism) and (b) individual experiences of inclusion (workgroup inclusion, authenticity). Path analysis results from a multi-wave study support the influence of IL on these outcomes and also demonstrate support for IL’s indirect effects on outcomes that may be disproportionately high (emotional exhaustion) and low (empowerment) for members of marginalized groups. Moderation analyses reveal many of the hypothesized relationships are similar across groups representing marginalized identities on the bases of race, sex, and the intersection of race and sex. These results add evidence to the importance of IL for fostering inclusive workplaces, especially for those who face the greatest barriers to inclusion.

Effect of 2-hydroxyethylammonium carboxylate protic ionic liquids on the solubility and cytotoxicity of indomethacin

Recently, there is a particular interest to utilize protic ionic liquids (PILs) in drug solubility. This study is exploring the effect of three protic ionic liquids (PILs) based on 2-hydroxyethylammonium carboxylate [2-hydroxyethylammonium acetate (MEAA), 2-hydroxyethylammonium lactate (MEAL), and 2-hydroxyethylammonium propionate (MEAP)] on the solubility of the very poorly soluble drug in water, indomethacin (IMC). The shake flask method was used to measure the experimental solubility of IMC at the different temperatures range (298.15–313.15) K. The results demonstrate significantly enhancment the solubility of IMC in PILs compared to pure water, with an approximate increase of 200 times. The experimental solubility data have been correlated using the empirical models which showed the performance as the order: Modified Apelblat–Jouyban–Acree > Van’t Hoff–Jouyban–Acree > Modified Apelblat equations and also the performance for the Wilson model indicated as the order (absolute relative deviation): 2-hydroxyethylammonium acetate (3.030) > 2-hydroxyethylammonium propionate (3.239) > 2-hydroxyethylammonium lactate (7.665). Then the thermodynamic dissolution properties were obtained by usage of Gibbs and Van’t Hoff equations to investigate the thermodynamic behavior of the IMC in the aqueous solution PILs. Eventually, the cytotoxicity of the co-solvents (PILs) under study was evaluated using a standard MTT assay. The results showed that the cell viability percentage increased in the following order: MEAA < MEAP < MEAL. These findings indicated that these PILs had low to moderate toxicity. It is noteworthy that the functional groups of the anions were not the only determinant factor of the cytotoxicity. Other factors encompassing concentration, exposure time, and cell line characteristics also had significant effects.

Study of wellbore instability in shale formation considering the effect of hydration on strength weakening

Shale formations often contain a high proportion of clay minerals, which, upon contact with drilling fluid, undergo hydration expansion. This leads to wellbore instability, a problem that poses significant challenges globally. This study aims to investigate the variation of mechanical properties of shale with respect to hydration time. We employ an empirical model that relates shale strength parameters to the time of drilling through geological formations. Additionally, we consider both shear failure along the wellbore boundary and shear sliding along bedding planes in the analysis. We establish a predictive model for wellbore instability in shale formations. The model quantitatively analyzes the variation of wellbore collapse pressure with drilling time. The research findings indicate that, when the influence of bedding is considered, both the wellbore collapse pressure and the optimal well trajectory undergo significant changes, in addition, for some wellbore trajectories, the collapse pressure can increase by more than 30%. Therefore, it is essential to account for the influence of bedding in wellbore stability analysis in shale formations. As the bedding dip angle changes, both the numerical values and distribution range of wellbore collapse pressure and the optimal well trajectory change noticeably. Changes in bedding dip direction, however, do not affect the numerical values of collapse pressure but do influence the distribution region of the optimal well trajectory. Thus, in wellbore trajectory design within shale formations, it is crucial to determine the orientation of bedding and adjust the well trajectory accordingly to enhance wellbore stability. Furthermore, shale hydration does not impact the optimal well trajectory for a block, but with prolonged hydration, the minimum drilling fluid density required to maintain wellbore stability gradually increases. This suggests that hydration intensifies the weakening effect on bedding plane strength. The research results are helpful to understand the effect of hydration on shale wellbore stability and ensure shale wellbore stability during drilling cycle.

Estimation of potato yield using a semi-mechanistic model developed by proximal remote sensing and environmental variables

Timely monitoring of potato yield status is crucial for guiding field management and ensuring the sustainability of food trade. In recent years, non-destructive remote sensing techniques have emerged as one of the most promising methods for crop yield monitoring. However, most existing potato yield estimation models have primarily been tested at specific growth stages within a single ecosystem, highlighting the urgent need for a model that can be applied across different ecological environments. In this study, we developed a semi-physical model based on hierarchical linear modeling (HLM) to automatically correct yield estimation based on remotely sensed vegetation indices using environmentally relevant variables such as net solar radiation at the surface (ssr), air temperature at 2 m above the surface (t2m), and soil water content in the root zone (swvl). The accuracy and transferability of the model were further evaluated by comparing it with aboveground destructive sampling methods, conventional empirical models, and machine learning approaches. The results demonstrated that site-specific relationships exist between aboveground agronomic traits parameters like biomass and leaf area index (LAI) with respect to yield estimation. Conventional linear statistical and machine learning methods faced challenges in transferring their constructed yield estimation models across regions and years. However, the HLM approach exhibited excellent generalization ability across all trial samples. In the middle and later stages of potato growth, the standardized LAI determining index proved to be the most effective vegetation for predicting yield, while ssr, sktmax, and shallow swvl were identified as key environmental variables affecting yield. By integrating these variables using HLM modeling techniques, we achieved optimal generalization performance (R2 = 0.57, RMSE = 7.65 t/ha, NRMSE = 25.02 % during tuber growth stage; R2 = 0.60, RMSE = 7.37 t/ha, NRMSE = 24.13 % during starch accumulation stage). This study underscores the importance of considering environmental factors when constructing a reliable model for estimating potato yield at different spatial and temporal scales using remote sensing technology.

Empirical models for calculating soil wetting patterns under surface drip irrigation systems: A comprehensive analysis

AbstractAccurate estimation of the surface wetted radius (R) and vertical wetted depth (Z) of wetting patterns in drip irrigation systems is crucial for ensuring that the designs of such systems are effective. This study compared 14 empirical models for estimating drip irrigation wetting patterns by assessing their accuracy using published measurement data and HYDRUS‐2D/3D simulations. The technique for order of preference by similarity to the ideal solution (TOPSIS) was employed to comprehensively rank the models. The results indicate that the empirical model proposed by Fan et al. (2023) (FY) exhibited the highest accuracy when the estimations of R and measured and simulated values were compared, with mean absolute error (MAE), root mean square error (RMSE), Nash–Sutcliffe modelling efficiency (NSE), and percent bias (PB) values of 2.2 cm, 3.4 cm, 0.79, and −7.1% and 5.2 cm, 7.0 cm, 0.50, and −14.1%, respectively. The empirical model proposed by Amin and Ekhmaj (2006) (AE) demonstrated the highest accuracy when the estimations of Z were compared with measured and simulated values, with MAE, RMSE, NSE and PB values of 1.7 cm, 2.0 cm, 0.95 and 4.15% and 4.4 cm, 5.9 cm, 0.82 and 4.7%, respectively. The comprehensive rankings of available models in the present study indicate that the FY model is the most universally applicable, followed by the Li et al. (2022) (LY) model, with comprehensive indices of 0.960 and 0.936, respectively. This research can aid in the selection of universally applicable, reliable and straightforward empirical models for estimating wetting patterns in drip irrigation systems.

Future changes in global rainfall erosivity: Insights from the precipitation changes

Quantifying future changes in rainfall erosivity is essential to address potential water erosion risks. This study reveals a significant power function relationship between rainfall erosivity and precipitation both globally and across various climatic zones. Building on this foundation, new empirical models were developed to project relative changes in rainfall erosivity. These models, predicted on the understanding that General Circulation Models (GCMs) provide more accurate predictions of climate change trends than exact meteorological values, utilizes the ratio of modeled historical precipitation to future precipitation under various scenarios as the variable, formulated as a power function. Data from three GCMs runs from both the Coupled Model Intercomparison Project phase 5 (CMIP5) and CMIP6–including historical, mid-term, and long-term periods–were employed. The analysis suggests that by the end of the 21st century, global annual rainfall erosivity could increase by 2.6% under the Shared Socio-Economic Pathway (SSP)1–2.6 scenario, by 3.5% under SSP2-4.5, and by a significant 6.4% under SSP5-8.5, relative to the 2000–2010 baseline. Furthermore, over 76% of the global land area is projected to experience an increase in rainfall erosivity over the century. Regions with projected changes in rainfall erosivity, whether increase or decrease, are likely to face more pronounced changes from mid-century onwards. The CMIP6 exhibits improved model consistency over its predecessor, CMIP5, indicating a greater water erosion risk as global warming progresses. These projections offer insights for strategies to combat soil degradation due to climate changes.

Validation of Simulated Statistical Characteristics of Magnetosphere‐Ionosphere Coupling in Global Geospace Simulations Over an Entire Carrington Rotation

AbstractWe study the statistical features of magnetosphere‐ionosphere (M‐I) coupling using a two‐way M‐I model, the GT configuration of the Multiscale Atmosphere Geospace Environment (MAGE) model. The M‐I coupling characteristics, such as field‐aligned current, polar cap potential, ionospheric Joule heating, and downward Alfvénic Poynting flux, are binned according to the interplanetary magnetic field clock angles over an entire Carrington Rotation event between 20 March and 16 April 2008. The MAGE model simulates similar distributions of field‐aligned currents compared to empirical Weimer/AMPS models and Iridium observations and reproduces the Region 0 current system. The simulated convection potential agrees well with the Weimer empirical model and displays consistent two‐cell patterns with SuperDARN observations, which benefit from more extensive data sets. The Joule heating structure in MAGE is generally consistent with both empirical Cosgrove and Weimer models. Moreover, our model reproduces Joule heating enhancements in the cusp region, as presented in the Cosgrove model and observations. The distribution of the simulated Alfvénic Poynting flux is consistent with that observed by the FAST satellite in the dispersive Alfvén wave regime. These M‐I coupling characteristics are also binned by the Kp indices, indicating that the Kp dependence of these patterns in the M‐I model is more effective than the empirical models within the Carrington Rotation. Furthermore, the MAGE simulation exhibits an improved M‐I current‐voltage relation that closely resembles the Weimer model, suggesting that the updated global model is significantly improved in terms of M‐I coupling.

Swarm Optimisation to Model the Surface Roughness of an AISI 4340 Turning using the Hot Machining Process

Given that surface roughness is used to determine product quality, it is a crucial consideration in turning machining. Moreover, it considerably affects the cost of machining. This study forecasts surface roughness values for AISI 304 stainless-steel hot lathe machining using the particle swarm optimisation (PSO) methodology. The workpiece is heated to 100, 150 or 200 degrees Celsius before being turned. Afterwards, the depth, speed and feeding rate of cutting are adjusted to determine the surface roughness of the workpiece. The feeding rate is determined to be the most considerable influence in raising the surface roughness value, followed by cutting depth, cutting speed and workpiece temperature. In terms of accuracy, empirical modelling performs better. The PSO methodology illustrates an effective and straightforward method that can be applied to calibrate different empirical machining models.

Political Regime and Governance on climate Change. Evidence from the Western Balkan Economies

Abstract The political environment plays a crucial role in steering societies to address the challenges posed by global warming and contributing to global initiatives toward a resilient and environmentally sustainable future. Political regimes in different places in the democracy-authoritarianism spectrum show varying degrees of effort to reduce their nation's respective carbon emissions. This paper aims to determine the role of political regimes along with governance dimensions in climate change mitigation. The analysis uses the V-Dem database and the World Governance Indicators (WGI) to conceptualise and measure each country's political system and its institutional quality of governance, respectively. The empirical model uses a panel database of the variables of interest for the years 1998-2022 regarding the Western Balkan (WB) economies. The target group of countries sparks our interest due to their significant challenges, such as widespread pollution, political regime changes, and severe governance problems. Furthermore, integrating the Environmental Kuznets Curve (EKC) hypothesis, which suggests a complex relationship between economic growth and environmental sustainability, the analysis provides evidence that the relationship between CO2 emissions and GDP per capita follows a quadratic trend, suggesting that as GDP per capita increases, the associated CO2 emissions may not change linearly but instead follow a parabolic pattern. WB countries also share the ambition of EU accession and hold the EU candidate status. To the extent these states manage to overcome their institutional issues, they will be in the position to implement the EU environmental acquis efficiently, which will amount to a critical step in the region's environmental health. Policymakers and scholars of the field will benefit from this paper since it determines the impact of political systems precisely matched with empirical results to derive conclusive answers regarding the political systems and climate change debate.

Experimental study on the dynamic characteristics of biodiesel/ethanol droplets impacting upon the inclined heated wall

The investigation of droplet impingement plays a crucial role in understanding the dynamics of fuel impact on the walls inside engine combustion chambers. To study the dynamics and heat transfer characteristics of two-component mixed droplets impacting upon an inclined stainless steel heated wall, the effect of wall tilt angle (0°–40°), Weber numbers (We, ranging from 50 to 210), and wall temperatures (Tw, between 25 and 350 °C) on the heat transfer models and spreading diameters is conducted. Through quantitative analysis of high-speed images captured during the experiments, it is observed that the spreading diameter of droplet impact on the inclined wall is closely correlated with both the wall temperature and We of the droplet. The wall tilt angle is found to modify the heat transfer state transition of the droplet to some extent. Additionally, the influence of gravity results in significant differences between the front and back spreading coefficients of the droplet. Based on the available data, a new empirical model is proposed that incorporates temperature terms and inclination to predict the maximum spreading coefficient of droplets impact on an inclined heated wall. Compared with experimental data of this study and in the literature, the maximum prediction error is less than 12.3%.

Mathematical modeling of routine biological parameters kinetics in order to predict durable benefit for first-line immunotherapy in metastatic or unresectable melanoma.

e21549 Background: PD1, CTLA4 and LAG3 blockade have shown remarkable efficacy in melanoma patients. Predictive biomarkers are needed to predict response, prevent unneeded toxicity and reduce costs. Despite collective efforts, no biomarker is robust or accessible enough for routine use. Our objective was to evaluate mathematical modeling-based kinetic markers derived from routine biological parameters as early predictors of durable benefit for first-line immunotherapy in advanced melanoma. Our secondary objective was to assess if these modeling could predict progression-free survival (PFS). Methods: We retrospectively included all consecutive, treatment-naïve, metastatic or unresectable melanoma patients treated with PD1 blockade alone or in combination with CTLA4 or LAG3 blockade between April 2020 and 2022 in our department. Clinical, biological and radiological data were collected. The primary endpoint was durable clinical benefit (DCB) defined as partial or complete response at 6 months or stable disease of at least 6 months. 11 simple blood markers and their on-treatment kinetics were considered. 4 empirical mathematical models (constant, linear, double exponential and hyperbolic) were fitted to the data, allowing to reject the constant null hypothesis for all of them. The association of the best-models coefficients with DCB and PFS was assessed using logistic and Cox regression. To do so, the data was truncated at 1, 2 and 3 months and the model coefficients were retrieved using Bayesian estimation with a population prior identified on the full-kinetics dataset. This study was approved by our local IRB. Results: 61 patients were included with 24 months median follow-up. DCB was observed for 57% of them. A low model-based value at baseline using 1 month truncated data was significantly associated with DCB for neutrophils (OR = 4.54[1.69-12.5], p &lt; 0.0001) and - neutrophils/lymphocytes ratio (NLR) (OR = 3.03 [1.47-6.67], p = 0.003). Model-based results increased AUC versus baseline raw data: 0.755 versus 0.699 for neutrophils and 0.76 versus 0.7 for NLR. An elevated model-based at baseline using 1 month truncated data was significantly associated with PFS for neutrophils (HR = 1.56 [1.25-1.96], p &lt; 0.0001), AST (HR = 1.71 [1.24-2.35], p &lt; 0.0001), LDH (HR = 2.0 [1.44-2.77], p &lt; 0.0001), CPR (HR = 1.75 [1.14-2.67], p = 0.01) and ALT (HR = 1.37 [1.05-1.78], p = 0.02). At 1 month, the kinetics parameters on creatinine (HR = 1.38 [1.05-1.82], p = 0.02), albumin (HR = 0.68 [0.48-0.97], p = 0.03) and monocytes (HR = 1.49 [1.02-2.18], p = 0.04) were significantly associated with PFS. Conclusions: Modeling the kinetics of routine biological parameters shows potential ability to predict DCB. Perspectives include model improvement, kinetics machine learning integrative model development as well as a toxicity prediction.

An effective stress-based DSC model for predicting hydromechanical shear behavior of unsaturated collapsible soils subjected to initial shear stress

Evaluation of hydromechanical shear behavior of unsaturated soils is still a challenging issue. The time and cost needed for conducting precise experimental investigation on shear behavior of unsaturated soils have encouraged several investigators to develop analytical, empirical, or semi-empirical models for predicting the shear behavior of unsaturated soils. However, most of the previously proposed models are for specimens subjected to the isotropic state of stress, without considering the effect of initial shear stress. In this study, a hydromechanical constitutive model is proposed for unsaturated collapsible soils during shearing, with consideration of the effect of the initial shear stress. The model implements an effective stress-based disturbed state concept (DSC) to predict the stress-strain behavior of the soil. Accordingly, material/state variables were defined for both the start of the shearing stage and the critical state of the soil. A series of laboratory tests was performed using a fully automated unsaturated triaxial device to verify the proposed model. The experimental program included 23 suction-controlled unsaturated triaxial shear tests on reconstituted specimens of Gorgan clayey loess wetted to different levels of suctions under both isotropic and anisotropic stress states. The results show excellent agreement between the prediction by the proposed model and the experimental results.

Experimental and Model-Based Approach to Evaluate Solvent Effects on the Solubility of the Pharmaceutical Artemisinin

The separation and purification of plant-based Active Pharmaceutical Ingredients (API) from extracts is a crucial part in pharmaceutical process development. For the purification of the antimalarial drug component artemisinin (ARTE) from an Artemisia anna L. toluene extract, antisolvent crystallization is considered. Solubilities of ARTE in binary solvent mixtures of toluene and two potential antisolvents, n-heptane and ethanol, were determined at temperatures from 278.15 K to 313.15 K. The experimental work was supported by the application of various models, utilizing varying amounts of experimental input data. The goal was the identification of models that are able to predict solubilities in binary solvent mixtures sufficiently accurate and, thus, can help to reduce the experimental effort for future solvent screenings. In this study, we applied the PC-SAFT model both with and without fitting the binary interaction parameter kij between ARTE and the respective solvent, as well as the empirical Jouyban-Acree model. From the experiments, n-heptane demonstrated to be a promising antisolvent, while ethanol acted more as a cosolvent. All models tested were capable of distinguishing between effective and ineffective antisolvents. The purely predictive PC-SAFT model applied with kij = 0 exhibited the largest deviation from the experimental data. This was followed by the PC-SAFT model including fitted kij values, based on at least four experimental data points. The Jouyban-Acree model fitted the data most accurately. Its parametrization required a minimum of ten experimental data points.

Bridging 20 Years of Soil Organic Matter Frameworks: Empirical Support, Model Representation, and Next Steps

AbstractIn the past few decades, there has been an evolution in our understanding of soil organic matter (SOM) dynamics from one of inherent biochemical recalcitrance to one deriving from plant‐microbe‐mineral interactions. This shift in understanding has been driven, in part, by influential conceptual frameworks which put forth hypotheses about SOM dynamics. Here, we summarize several focal conceptual frameworks and derive from them six controls related to SOM formation, (de)stabilization, and loss. These include: (a) physical inaccessibility; (b) organo‐mineral and ‐metal stabilization; (c) biodegradability of plant inputs; (d) abiotic environmental factors; (e) biochemical reactivity and diversity; and (f) microbial physiology and morphology. We then review the empirical evidence for these controls, their model representation, and outstanding knowledge gaps. We find relatively strong empirical support and model representation of abiotic environmental factors but disparities between data and models for biochemical reactivity and diversity, organo‐mineral and ‐metal stabilization, and biodegradability of plant inputs, particularly with respect to SOM destabilization for the latter two controls. More empirical research on physical inaccessibility and microbial physiology and morphology is needed to deepen our understanding of these critical SOM controls and improve their model representation. The SOM controls are highly interactive and also present some inconsistencies which may be reconciled by considering methodological limitations or temporal and spatial variation. Future conceptual frameworks must simultaneously refine our understanding of these six SOM controls at various spatial and temporal scales and within a hierarchical structure, while incorporating emerging insights. This will advance our ability to accurately predict SOM dynamics.

Impact of rotor blade aerodynamics on tower vortex induced vibration of modern wind turbines

Modern wind turbines in standstill or idling could experience vortex induced vibrations (VIV); the origin of these vibrations is either shedding from the wind turbine blade referred to as blade VIV or shedding from tower termed as tower (or turbine) VIV. When the whole structure of the wind turbine is considered and the blades are pitched out, the dominant 1st order mode for blade deformation in edgewise direction leads to negligible motion of the tower top, while when the whole turbine vibrates with first tower mode, blades also undergo significant periodic translations and deformations. In this study, forced motion is used to study the impact of aerodynamic damping due to rotor moving with 1st tower mode. In the literature no comprehensive work is available to characterize the impact of blades aerodynamics in the turbine mode either using systematic tests or high-fidelity computational fluid dynamics (CFD). In this article high-fidelity numerical simulations are performed to compute the power injections from the rotor blades when the turbine moves in 1st tower mode. Depending on the inflow angle and the azimuthal position, blades can inject significant power. Thus, the estimated damping from tower-only empirical models could severely underestimate the total aerodynamic power if the power injection from the rotor is neglected.

Core performance predictions in projected SPARC first-campaign plasmas with nonlinear CGYRO

This work characterizes the core transport physics of SPARC early-campaign plasmas using the PORTALS-CGYRO framework. Empirical modeling of SPARC plasmas with L-mode confinement indicates an ample window of breakeven (Q &amp;gt; 1) without the need of H-mode operation. Extensive modeling of multi-channel (electron energy, ion energy, and electron particle) flux-matched conditions with the nonlinear CGYRO code for turbulent transport coupled to the macroscopic plasma evolution using PORTALS reveals that the maximum fusion performance to be attained will be highly dependent on the near-edge pressure. Stiff core transport conditions are found, particularly when fusion gain approaches unity, and predicted density peaking is found to be in line with empirical databases of particle source-free H-modes. Impurity optimization is identified as a potential avenue to increase fusion performance while enabling core-edge integration. Extensive validation of the quasilinear TGLF model builds confidence in reduced-model predictions. The implications of projecting L-mode performance to high-performance and burning-plasma devices is discussed, together with the importance of predicting edge conditions.