Large-scale Field Research Articles

Coherent amplification of the Okhotsk high, Korean trough, and northwestern Pacific subtropical high during heavy rainfall over Japan in August 2021

In August 2021, rain front stagnation in Japan resulted in prolonged and disastrous rainfall across the entire country. During the heavy rainfall period, the large-scale atmospheric field over the East Asian–western North Pacific region was characterized by meridional tripolar circulation anomalies: the Okhotsk high (OH), the trough over the Korean Peninsula (Korean trough), and the northwestern Pacific subtropical high (NWPSH). Simultaneously, tropical convective activity was enhanced over the eastern Indian Ocean and suppressed over the tropical western–central Pacific. This study investigates the dynamic mechanism of linkage of the extratropical tripolar anomalies and the effects of tropical convective modulation using a reanalysis dataset, a cutoff low detection scheme, the potential vorticity inversion method, and numerical experiments. Upper-tropospheric blocking over eastern Siberia connected to the surface OH is conducive to the stagnation of synoptic depressions, including cutoff lows and troughs, over the Korean Peninsula, contributing to the development and maintenance of the quasi-stationary Korean trough. Rossby waves emanating from the Korean trough excite an anticyclonic anomaly over the northwestern Pacific. This upper-level anomalous anticyclone acts to enhance the surface NWPSH through zonal heat transport, accompanied by a northward tilting structure with height. Simultaneously, the tropical intraseasonal oscillation is amplified over the Indo–western Pacific Ocean sector under the negative-phase Indian Ocean dipole and multi-year La Niña conditions. The combination of enhanced convection over the eastern Indian Ocean and suppressed convection across the tropical western–central Pacific reinforces the NWPSH. The anomalous circulation associated with the extratropical tripolar pattern and concurrent tropical heat forcing causes more moisture transport, convergence, and anomalous ascent, which contribute to heavy rainfall in Japan. These results suggest that the dynamically correlated amplification of tropical and extratropical circulation anomalies plays a crucial role in precipitation variability in East Asia.

Constrained nonlinear amplitude-variation-with-offset inversion for reservoir dynamic changes estimation from time-lapse seismic data

We develop a novel elastic amplitude-variation-with-offset (AVO) inversion process to estimate the fluid saturation and effective pressure or variations in these properties from time-lapse seismic data sets. These changes occur in oil and gas reservoirs caused by fluid injection or hydrocarbon production that leads to changes in the elastic wave properties, reflectivity, and seismic response. Our method is based on a seismic forward model that consists of a linearized AVO equation and a rock-physics model. The AVO equation links the elastic wave properties to seismic reflection amplitudes, whereas the rock-physics model maps the saturation and pressure into seismic velocities and density. The inversion approach relies on the gradient descent technique to estimate the unknown variables by searching for the minimum of the least-squares misfit between the observed and modeled data. The first-order gradient equations of the least-squares data misfit function with respect to effective pressure and water saturation are derived by using the adjoint-state method and the chain rule. The optimization method used to minimize the misfit function and obtain the best optimal solution is a limited-memory quasi-Newton algorithm. This inversion process allows us to incorporate prior constraints by using the logistic function to map the model variables to a bounded range. To achieve a stable solution to ill-posed inversion problems, optimal regularization weights are applied. The application of the developed workflow on 1D synthetic and real well-log data from the Edvard Grieg oil field simulating various saturation-pressure conditions during production demonstrates the validity of the approach with different noise levels. The inversion is then applied to a 2D synthetic data set modeled from the reservoir model of the Smeaheia field, a potential site for a large-scale offshore CO2 storage field located in the North Sea. Our results illustrate that our inversion method efficiently and accurately estimates reservoir saturation and pressure variations.

Characterization and prediction of InSAR-derived ground motion with ICA-assisted LSTM model

Interferometric Synthetic Aperture Radar (InSAR) is a highly effective and widely used approach for monitoring large-scale ground deformation. The precise and timely prediction of deformation holds significant importance in mitigating and preventing geological hazards, particularly considering the long revisit cycle of satellites and the considerable time required for data processing. In this study, we propose a strategy that predicts spatiotemporal InSAR time series based on Independent Component Analysis (ICA) and the Long Short-Term Memory (LSTM) machine learning model. Unlike traditional methods that rely on physical or statistical models, the proposed strategy leverages the power of ICA and LSTM to achieve accurate predictions without such dependencies. ICA is employed to decompose and capture the InSAR displacement signals of interest caused by various natural or anthropogenic processes and to characterize each individual signal. The spatiotemporal unsupervised K-mean cluster method is then applied to partition large-scale deformation fields into homogeneous subregions, considering the spatial variations and temporal nonlinearities of time series. This process facilitates the refinement of the model, thereby enhancing the accuracy of large-scale predictions. The neural network models are then individually constructed for each cluster, and the optimal parameters are determined through a grid search strategy. Subsequently, the proposed framework is implemented and assessed using two datasets featuring distinct deformation patterns: Case I involves land subsidence in Willcox Basin, USA, while Case II focuses on post-seismic deformation following the 12 November 2017 Mw 7.3 Sarpol-e Zahab earthquake. The results demonstrate that our proposed ICA-assisted LSTM outperforms the original LSTM model on large-scale deformation prediction, with the average prediction accuracy for one-step prediction (12 days in our case) being improved by 34% and 17% for cases I and II, respectively. Furthermore, we perform iterative predictions on the spatiotemporal InSAR measurements with varying temporal characteristics for the subsequent five steps using Sentinel-1 data and evaluate its performance and limitations. The successful prediction of land subsidence and post-seismic deformation provides further evidence that the proposed prediction strategy can be effectively employed in monitoring other large-scale geohazards characterized by prolonged and gradual deformation. This capability enables expedited decision-making and timely implementation of risk mitigation measures.

Motivating Experts to Contribute to Digital Public Goods: A Personalized Field Experiment on Wikipedia

We conducted a large-scale personalized field experiment to examine how match quality, recognition, and social impact influence domain experts’ contributions to Wikipedia. Forty-five percent of the experts expressed willingness to contribute in the baseline condition, whereas 51% (a 13% increase over the baseline) expressed interest when they received a signal that an article matched their expertise. However, none of the treatments had a significant effect on actual contributions. Instead experts contributed longer and better comments when the actual match between a recommended Wikipedia article and an expert's expertise, measured by cosine similarity, was higher, when they had higher reputation, and when the original article was longer. These findings suggest that match quality between volunteers and tasks is critically important in encouraging contributions to digital public goods and likely to volunteering in general. This paper was accepted by David Simchi-Levi, behavioral economics and decision analysis. Funding: This work was supported by the National Science Foundation through [Grant SES-1620319] awarded to Carnegie Mellon University and the University of Michigan. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4852 .

THE EARTH’S MAGNETIC FIELD AND THE LARGE-SCALE MAGNETIC FIELD OF THE SUN: THE SOLAR-TERRESTRIAL CONNECTION

The paper presents the results of a joint analysis of the Earth's main magnetic field (core field) BIGRF and the large-scale magnetic field (LSMF) of the Sun. Selected 11- and 22-year periods of LSMF and BIGRF variations are well manifested in both fields and are usually modulated by solar activity. Even 11-year cycles for which the direction of the Sun's magnetic field coincides with the direction of the Earth's magnetic field are characterized by the minimum values of sanspot numbers, and odd cycles with opposite directions of magnetic fields have larger values of sunspot numbers. The rotation rate of two- and four-sector structure of the Sun source of LSMF varied with about 11-year and 22-year cycle. Longer changes in the magnetic fields of the Sun and the Earth with a period of about 75 years have also been revealed. The rotation periods of the Sun source global field (28.0-28.5 days) were maximum at the middle of the 20th century in the period 1940÷1960 years. This maximum of solar activity corresponds to temporal gradient of geomagnetic field. It is shown that the gradient of the geomagnetic field BIGRF depends on the rate of change in the length of the day. So, according to the results of the study, the rotation modes of the Sun and the Earth cause different periodic changes in their magnetic fields.

Deciphering the physical basis of the intermediate-scale instability

We study the underlying physics of cosmic ray (CR)-driven instabilities that play a crucial role for CR transport across a wide range of scales, from interstellar to galaxy cluster environments. By examining the linear dispersion relation of CR-driven instabilities in a magnetised electron–ion background plasma, we establish that both the intermediate and gyroscale instabilities have a resonant origin, and show that these resonances can be understood via a simple graphical interpretation. These instabilities destabilise wave modes parallel to the large-scale background magnetic field at significantly distinct scales and with very different phase speeds. Furthermore, we show that approximating the electron–ion background plasma with either magnetohydrodynamics (MHD) or Hall-MHD fails to capture the fastest-growing instability in the linear regime, namely the intermediate-scale instability. This finding highlights the importance of accurately characterising the background plasma for resolving the most unstable wave modes. Finally, we discuss the implications of the different phase speeds of unstable modes on particle–wave scattering. Further work is needed to investigate the relative importance of these two instabilities in the nonlinear, saturated regime and to develop a physical understanding of the effective CR transport coefficients in large-scale CR hydrodynamics theories.

Unified treatment of mean-field dynamo and angular-momentum transport in magnetorotational instability-driven turbulence.

Magnetorotational instability-driven (MRI-driven) turbulence and dynamo phenomena are analyzed using direct statistical simulations. Our approach begins by developing a unified mean-field model that combines the traditionally decoupled problems of the large-scale dynamo and angular momentum transport in accretion disks. The model consists of a hierarchical set of equations, capturing up to the second-order correlators, while a statistical closure approximation is employed to model the three-point correlators. We highlight the web of interactions that connect different components of stress tensors-Maxwell, Reynolds, and Faraday-through shear, rotation, correlators associated with mean fields, and nonlinear terms. We determine the dominant interactions crucial for the development and sustenance of MRI turbulence. Our general mean-field model for the MRI-driven system allows for a self-consistent construction of the electromotive force, inclusive of inhomogeneities and anisotropies. Within the realm of large-scale magnetic field dynamo, we identify two key mechanisms-the rotation-shear-current effect and the rotation-shear-vorticity effect-that are responsible for generating the radial and vertical magnetic fields, respectively. We provide the explicit (nonperturbative) form of the transport coefficients associated with each of these dynamo effects. Notably, both of these mechanisms rely on the intrinsic presence of large-scale vorticity dynamo within MRI turbulence.

Large-scale field data-based battery aging prediction driven by statistical features and machine learning

Large-scale field data-based battery aging prediction driven by statistical features and machine learning

Study on configurational operation strategy of ground heat exchangers under the effect of groundwater flow

In large-scale ground source heat pump (GSHP) systems, the accumulation of thermal energy resulting from seasonal load imbalances has emerged as a significant impediment. The implementation of a rational operation strategy can effectively mitigate heat accumulation in soil. This study investigates the impact of the configurational operation strategy for large-scale vertical ground heat exchanger (GHE) on the spatial distribution of ground temperature under the influence of groundwater flow. The internal heat source model is proposed to describe heat transfer process of GHE. Sandbox experiment is conducted to verify the model with maximum deviation of 3 %. The validated model is then utilized to simulate soil temperature profile in the large-scale GHE field over a 10-year operational period, considering a matrix of three configuration strategies and three operation strategies. The results shows that trapezoidal configurations combined with operation Strategy 3 has a minimal temperature increment of 2.9 °C in soil temperature. The contribution of this study lies in provision of a methodology for mitigating thermal accumulation in large-scale GHE field.

Performance and configuration optimization of proton exchange membrane fuel cell considering dual symmetric Tesla valve flow field

Aiming at the advantages of Tesla valve channel (TVC), such as low resistance and smooth flow, a new bionic-like channel structure with dual symmetric (DS) TVC as cathode is proposed. Firstly, through a series of structural parameters optimization, including experimental verification of the model, the arrangement of modules in the channel, the internal angle and the number of modules, the best design parameters are obtained. Secondly, the mass spatial distribution characteristics of proton exchange membrane fuel cell (PEMFC) with large-scale dual symmetric Tesla valve flow field (DSTVFF) are investigated. Finally, the DSTVFF structure is improved by baffles with a front inclination angle of 30° to reduce the contact area of the ridge. The simulation results indicate that the pressure drop of the fourth DSTVC is small and the oxygen concentration distribution is uniform. The increase of the inner angle promotes the convection effect, and the power density at the angle of 30° is 17.6 % higher than that at the angle of 15°. Reasonable number of modules can increase the activation area of catalyst layer (CL) and enhance the output performance of fuel cell (FC).

Constraining an eddy energy dissipation rate due to relative wind stress for use in energy budget-based eddy parameterisations

Abstract. A geostrophic eddy energy dissipation rate due to the interaction of the large-scale wind field and mesoscale ocean currents, or relative wind stress, is derived here for use in eddy energy budget-based eddy parameterisations. We begin this work by analytically deriving a relative wind stress damping term and a baroclinic geostrophic eddy energy equation. The time evolution of this analytical eddy energy in response to relative wind stress damping is compared directly with a baroclinic eddy in a general circulation model for both anticyclones and cyclones. The dissipation of eddy energy is comparable between each model and eddy type, although the numerical model diverges from the analytical model at around day 150, likely due to the presence of non-linear baroclinic processes. A constrained dissipation rate due to relative wind stress is then proposed using terms from the analytical eddy energy budget. This dissipation rate depends on the potential energy of the eddy thermocline displacement, which also depends on eddy length scale. Using an array of ocean datasets, and computing two forms for the eddy length scale, a range of values for the dissipation rate are presented. The analytical dissipation rate is found to vary from 0.25 to 4 times that of a constant dissipation rate employed in previous studies. The dissipation rates are generally enhanced in the Southern Ocean but smaller in the western boundaries. This proposed dissipation rate offers a tool to parameterise the damping of total eddy energy in coarse resolution global climate models and may have implications for a wide range of climate processes.

Soil microbiome indicators can predict crop growth response to large-scale inoculation with arbuscular mycorrhizal fungi

Alternative solutions to mineral fertilizers and pesticides that reduce the environmental impact of agriculture are urgently needed. Arbuscular mycorrhizal fungi (AMF) can enhance plant nutrient uptake and reduce plant stress; yet, large-scale field inoculation trials with AMF are missing, and so far, results remain unpredictable. We conducted on-farm experiments in 54 fields in Switzerland and quantified the effects on maize growth. Growth response to AMF inoculation was highly variable, ranging from −12% to +40%. With few soil parameters and mainly soil microbiome indicators, we could successfully predict 86% of the variation in plant growth response to inoculation. The abundance of pathogenic fungi, rather than nutrient availability, best predicted (33%) AMF inoculation success. Our results indicate that soil microbiome indicators offer a sustainable biotechnological perspective to predict inoculation success at the beginning of the growing season. This predictability increases the profitability of microbiome engineering as a tool for sustainable agricultural management.

Control of Solar Energy Systems

This review deals with the control of parabolic trough collector (PTC) solar power plants. After a brief introduction, we present a description of PTC plants. We then provide a short literature review and describe some of our experiences. We also describe new control trends in PTC plants. Recent research has focused on (a) new control methods using mobile sensors mounted on drones and unmanned ground vehicles as an integral part of the control systems; (b) spatially distributed solar irradiance estimation methods using a variable fleet of sensors mounted on drones and unmanned ground vehicles; (c) strategies to achieve thermal balance in large-scale fields; (d) new model predictive control algorithms using mobile solar sensor estimates and predictions for safer and more efficient plant operation, which allow the effective integration of solar energy and combine coalitional and artificial intelligence techniques; and (e) fault detection and diagnosis methods to ensure safe operation.

Non-linear magnetic buoyancy instability and turbulent dynamo

ABSTRACT Stratified discs with strong horizontal magnetic fields, are susceptible to magnetic buoyancy instability (MBI). Modifying the magnetic field and gas distributions, this can play an important role in galactic evolution. The MBI and the Parker instability, in which MBI is exacerbated by cosmic rays, are often studied using an imposed magnetic field. However, in galaxies and accretion discs, the magnetic field is continuously replenished by a large-scale dynamo action. Using non-ideal MHD equations, we model a section of the galactic disc (we neglect rotation and cosmic rays considered elsewhere), in which the large-scale field is generated by an imposed α-effect of variable intensity to explore the interplay between dynamo instability and MBI. The system evolves through three distinct phases: the linear (kinematic) dynamo stage, the onset of linear MBI when the magnetic field becomes sufficiently strong and the non-linear, statistically steady state. Non-linear effects associated with the MBI introduce oscillations which do not occur when the field is produced by the dynamo alone. The MBI initially accelerates the magnetic field amplification but the growth is quenched by the vertical motions produced by MBI. We construct a 1D model, which replicates all significant features of 3D simulations to confirm that magnetic buoyancy alone can quench the dynamo and is responsible for the magnetic field oscillations. Unlike similar results obtained with an imposed magnetic field, the non-linear interactions do not reduce the gas scale height, so the consequences of the magnetic buoyancy depend on how the magnetic field is maintained.

Large-scale flow field and aerosol particle transport investigations in a classroom using 2D-Shake-The-Box Lagrangian Particle Tracking

Infections with COVID-19 in enclosed public spaces, where virus-laden aerosol particles can accumulate over time, have significantly contributed to the rapid spread of the virus. It is therefore of great importance to understand the transport and dispersion process of aerosol particles in such spaces, especially against the background of future pandemics. In this work, we present a Lagrangian-Particle-Tracking experiment to assess the mixed convective flow in a classroom with different ventilation strategies. For this purpose, thermal plumes were created by heated dummies, and a collimated LED light-sheet with ∼0.4 m thickness was used for illumination of helium filled soap bubbles (HFSB) acting as passive tracer particles. In this way, the Lagrangian trajectories of the particles were recorded at two approximately 4.2 m × 2.8 m large fields using the novel 2D-Shake-The-Box-Method. As a result, time-resolved trajectories of over 300,000 simultaneously tracked HFSB have been reconstructed, so that both small-scale and large-scale properties of the flow are visualized quantitatively across the entire cross-section of the room. The trajectories show that the thermal plumes create lengthwise circulating vortices, which cannot be destroyed across the entire cross-section of the room by opening or tilting a window. Furthermore, the mixing in the room through the operation of an air purifier is higher compared to opening a window, which suggests that this strategy in combination with its air filtering capability is the most effective strategy to prevent infections.

From 65 to 103, Older Adults Experience Virtual Reality Differently Depending on Their Age: Evidence from a Large-Scale Field Study in Nursing Homes and Assisted Living Facilities.

There is growing interest in applications of virtual reality (VR) to improve the lives of older adults, but the limited research on older adults and VR largely treats older adults as a monolith, ignoring the substantial differences across 65 to 100+ year olds that may affect their experience of VR. There are also few existing studies examining the experiences and challenges facing those who facilitate VR for older adults (e.g., caregiving staff). We address these limitations through two studies. In study 1, we explore variation within older adults' experiences with VR through a field study of VR use among a large (N = 245) and age-diverse (Mage = 83.6 years, SDage = 7.9, range = 65-103 years) sample of nursing home and assisted living facility residents across 10 U.S. states. Age was negatively associated with the extent to which older adults enjoyed VR experiences. However, the negative relationship between age and older adults' attitudes toward VR was significantly less negative than the relationship between age and their attitudes toward other technologies (cell phones and voice assistants). In study 2, we surveyed caregiving staff (N = 39) who facilitated the VR experiences for older adult residents and found that the caregiving staff generally enjoyed the activity relative to other activities and felt it to be beneficial to their relationship with residents.

Natural chalcopyrite mitigates nitrous oxide emissions in sediment from coastal wetlands

Coastal wetlands are one of the most important natural sources of nitrous oxide (N2O). Previous studies have shown that copper-containing chemicals are able to reduce N2O emissions from these ecosystems. However, these chemicals may harm organisms present in coastal waters and sediment, and disturb the ecological balance of these areas. Here, we first investigated the physiological characteristics and genetic potential of denitrifying bacteria isolated from coastal wetlands. Based on an isolated denitrifier carrying a complete denitrification pathway, we tested the effect of the natural mineral chalcopyrite on N2O production by the bacteria. The results demonstrated that chalcopyrite addition lowers N2O emissions from the bacteria while increasing its N2 production rate. Among the four denitrification genes of the isolate, only nosZ gene expression was significantly upregulated following the addition of 2 mg L−1 chalcopyrite. Furthermore, chalcopyrite was applied to coastal wetland sediments. The N2O flux was significantly reduced in 50–100 mg L−1 chalcopyrite-amended sets relative to the controls. Notably, the dissolved Cu concentration in chalcopyrite-amended sediment remained within the limit set by the National Sewage Treatment Discharge Standard. qPCR and metagenomic analysis revealed that the abundance of N2O-reducing bacteria with the nosZ or nirK + nosZ genotype increased significantly in the chalcopyrite-amended groups relative to the controls, suggesting their active involvement in the reduction of N2O emissions. Our findings offer valuable insights for the use of natural chalcopyrite in large-scale field applications to reduce N2O emissions.

Approximations for the divergence of the reconstructed local large-scale structure velocity field and its possible implications for Cosmology

We characterize the peculiar velocity field of the local large-scale structure reconstructed from the 2M++ survey, by treating it as a fluid, extracting the divergence via different approximations over a range of averaged scales. This reconstructed field is important for cosmology, since it was used to correct the peculiar redshifts of the last SNIA compilation Pantheon+. The results have intriguing implications for the LLSS fluid dynamics and particularly for the “Tilted Cosmology” model, although those results have to be taken carefully as the velocity field could contain significant bias due to the reconstruction procedure. Those possible bias and its influence in our results are discussed. Representative values of the apparent deceleration parameter (q̃) are computed, in order to compare our results with the theoretical predictions of the tilted-universe scenario. We conclude that better velocity field reconstructions are necessary in order to constrain the parameters implied in LLSS research and alternative cosmologies.

Price Increases and Their Financial Consequences in International Business-to-Business Selling

Although price increases are common in business-to-consumer and business-to-business (B2B) markets, research examining the consequences of price increases on financial performance outcomes in B2B contexts is scarce. This study takes a relationship perspective on price increases and examines how a portfolio price increase affects the financial performance of the customer relationship and whether and how this effect varies between international business customers from different cultures. Based on objective data from 966 international B2B customers of a chemical goods company, this study examines a large-scale field intervention. Results show that higher portfolio price increases, although rooted in an increase of upstream costs, bring more severe harm to B2B customers’ sales revenue than lower portfolio price increases. These consequences vary with customers’ cultures as B2B customers with culture-specific communal norms are more susceptible to the magnitude of portfolio price increases than customers without such norms. International B2B companies, therefore, need to refrain from implementing uniform price increases and should consider their business customers’ cultural origin when designing and implementing price increases.

Influence of an Upstream Dynamic Pressure Pulse on Venus–Solar Wind Interaction by Multispecies Magnetohydrodynamic Simulation

As an unmagnetized planet, Venus does not have a global dipole magnetic field like Earth. The solar wind interacts directly with the Venusian ionosphere. In this paper, the interaction between solar wind and the Venusian upper atmosphere is investigated by conducting a three-dimensional multispecies magnetohydrodynamic (MHD) model, in which the Venusian ionosphere is developed self-consistently by considering chemical reactions among four major ion species (H+, O2 +, O+, and CO2 +). Based on this developed MHD model, we mainly study the effects of the impulsive solar wind dynamic pressure on the interaction process. Simulation results reveal that the locations of the bow shock and magnetic pileup boundary show an abrupt compression, then expansion, and finally shrinkage to reach a new steady state with the influence of the impulsive dynamic pressure. The recovery time of the large-scale magnetic field is a few minutes. In addition, the peak escape fluxes are enhanced by about 1 order of magnitude due to the increased solar wind dynamic pressure pulse.