3D Field Research Articles

Analysis of leakage flux, losses, and temperature in large synchronous generator end zone under the multi‐layer screen thickness based on novel iterative method

AbstractThe multi‐layer screen is the key component in the large synchronous generator end zone. The leakage flux, losses, and temperature of end components are significantly affected by the thickness of multi‐layer screen in the synchronous generator. To investigate the influence of multi‐layer screen thickness on the end leakage flux, losses, and temperature in the synchronous generator end zone, 1407MVA nuclear power synchronous generator is studied. Three‐dimensional transient electromagnetic field model of synchronous generator end zone is established. Three‐dimensional transient electromagnetic field in the end zone of 1407MVA synchronous generator with the multi‐layer screen is calculated based on the novel iterative method. The flux density of end components is compared and studied in the end zone under the variation of multi‐layer screen thickness. Influence of the different thicknesses of multi‐layer screen on the losses of the shield plate, screen, finger plate, and stator end core is researched. The losses of end components obtained from 3D end electromagnetic field calculation are applied to the end zone as the heat source in the three‐dimensional fluid and thermal coupled field. The temperature distribution of the end components is determined. The accuracy of the calculated results is validated by the experimental values.

Design of passive and structural conductors for tokamaks using thin-wall eddy current modeling

A new three-dimensional electromagnetic modeling tool (ThinCurr) has been developed using the existing PSI-Tet finite-element code in support of conducting structure design work for both the SPARC and DIII-D tokamaks. Within this framework a 3D conducting structure model was created for both the SPARC and DIII-D tokamaks in the thin-wall limit. This model includes accurate details of the vacuum vessel and other conducting structural elements with realistic material resistivities. This model was leveraged to support the design of a passive runaway electron mitigation coil (REMC), studying the effect of various design parameters, including coil resistivity, current quench duration, and plasma vertical position, on the effectiveness of the coil. The REMC is a non-axisymmetric coil designed to passively drive large non-axisymmetric fields during the plasma disruption thereby destroying flux surfaces and deconfining RE seed populations. These studies indicate that current designs should apply substantial 3D fields at the plasma surface during future plasma current disruptions as well as highlight the importance of having the REMC conductors away from the machine midplane in order to ensure they are robust to off-normal disruption scenarios.

Evaluating the Representations of Atmospheric Rivers and Their Associated Precipitation in Reanalyses With Satellite Observations

AbstractAtmospheric rivers (ARs) are filaments of enhanced horizontal moisture transport in the atmosphere. Due to their prominent role in the meridional moisture transport and regional weather extremes, ARs have been studied extensively in recent years. Yet, the representations of ARs and their associated precipitation on a global scale remains largely unknown. In this study, we developed an AR detection algorithm specifically for satellite observations using moisture and the geostrophic winds derived from 3D geopotential height field from the combined retrievals of the Atmospheric Infrared Sounder and the Advanced Microwave Sounding Unit on NASA Aqua satellite. This algorithm enables us to develop the first global AR catalog based solely on satellite observations. The satellite‐based AR catalog is then combined with the satellite‐based precipitation (Integrated Muti‐SatellitE Retrievals for GPM) to evaluate the representations of ARs and AR‐induced precipitation in reanalysis products. Our results show that the spreads in AR frequency and AR length distribution are generally small across data sets, while the spread in AR width is relatively larger. Reanalysis products are found to consistently underestimate both mean and extreme AR‐related precipitation. However, all reanalyses tend to precipitate too often under AR conditions, especially over low latitude regions. This finding is consistent with the “drizzling” bias which has plagued generations of climate models. Overall, the findings of this study can help to improve the representations of ARs and associated precipitation in reanalyses and climate models.

Lung-inspired hybrid flow field to enhance PEMFC performance: A case of dual optimization by response surface and artificial intelligence

The design and optimization of flow field play a crucial role in the development of proton exchange membrane fuel cells (PEMFC). This study presents a modular tri-layer lung-inspired hybrid flow field (LHFF) design that incorporates 2D and 3D flow field advantages. The key structural parameters of LHFF mainly encompass G, D, and S of reactant distribution layer and A of directional transport layer. The LHFFs with different G have been investigated, and the G = 2 LHFF exhibits a 16.55% enhancement in maximum net power density compared to conventional parallel flow field. Then the response surface methodology (RSM) and artificial intelligence methodology (AIM) have been employed to optimize the D, S, and A structure parameters of LHFF to determine the optimal inlet position of water removal layer. The LHFFs optimized by RSM and AIM show a further increase in maximum net power density by 3.58% and 4.10%, respectively. The optimized LHFFs achieve a trade-off among species distribution, water management, and pressure drop, with high consistency between numerical and experimental results. It demonstrates the reliability of artificial intelligence in optimizing PEMFC flow field. Therefore, the optimization strategies presented here hold a promising solution to improve the flow fields in other electrochemical systems.

Experimental studies on the extent of 3D and 2D stress–strain states at sharp V-notches

A sharp V-notched plate under plane stress loading conditions is always accompanied by a strong three-dimensional (3D) stress–strain region close to the notch tip, followed by a 3D-2D transition state and a region dominated by the 2D stress–strain field. Experimental studies on planar V-notched bodies constitute an important part of notch fracture mechanics and therefore it is important to know the extents of the above fields for accurate measurement of field variables. In the present work, experimental verification of the extent of the 3D stress–strain field, 3D-2D transition zone and plane stress zone ahead of the tip of a sharp V-notch has been carried out in order to suggest a minimum radius required for identification of the plane stress dominant zone. Knowing this value apriori is vital for accurate sampling of the 2D field variables in any experimental study on the sharp V-notched configurations. In order to achieve this, strain gage experiments on the single-edge notched tensile (SENT) specimens have been conducted. The above zones have been identified by studying the error in the measured mode I NSIF of the SENT specimen. The experimental results of the current study agree well with the results of published numerical works. The results clearly show that the minimum radius of the plane stress dominant zone has been found to be 1.25 times the thickness of the plate. Results also show that 1.5 times the thickness is the most conservative and dependable value for this radius. The results of the present investigation also indicate that not only the identification of the plane stress region, but also the locations of the sampling points within the plane stress region are important for achieving the required accuracy.

BMS modular covariance and structure constants

Two-dimensional (2d) field theories invariant under the Bondi-Metzner-Sachs algebra, or 2d BMSFTs in short, are putative holographic duals of Einstein gravity in 3d asymptotically flat spacetimes. When defined on a torus, these field theories come equipped with a modified modular structure. We use the modular covariance of the BMS torus two-point function to develop formulae for different three-point structure constants of the field theory. These structure constants indicate that BMSFTs follow the eigenstate thermalization hypothesis, albeit with some interesting changes to usual 2d CFTs. The singularity structures of the structure constants contain information on perturbations of cosmological horizons in 3d asymptotically flat spacetimes, which we show can also be obtained as a limit of BTZ quasinormal modes.

Assessment of the Impact of Turbo Factor on Image Quality and Tissue Volumetrics in Brain Magnetic Resonance Imaging Using the Three-Dimensional T1-Weighted (3D T1W) Sequence.

The 3D T1W turbo field echo sequence is a standard imaging method for acquiring high-contrast images of the brain. However, the contrast-to-noise ratio (CNR) can be affected by the turbo factor, which could affect the delineation and segmentation of various structures in the brain and may consequently lead to misdiagnosis. This study is aimed at evaluating the effect of the turbo factor on image quality and volumetric measurement reproducibility in brain magnetic resonance imaging (MRI). Brain images of five healthy volunteers with no history of neurological diseases were acquired on a 1.5 T MRI scanner with varying turbo factors of 50, 100, 150, 200, and 225. The images were processed and analyzed with FreeSurfer. The influence of the TFE factor on image quality and reproducibility of brain volume measurements was investigated. Image quality metrics assessed included the signal-to-noise ratio (SNR) of white matter (WM), CNR between gray matter/white matter (GM/WM) and gray matter/cerebrospinal fluid (GM/CSF), and Euler number (EN). Moreover, structural brain volume measurements of WM, GM, and CSF were conducted. Turbo factor 200 produced the best SNR (median = 17.01) and GM/WM CNR (median = 2.29), but turbo factor 100 offered the most reproducible SNR (IQR = 2.72) and GM/WM CNR (IQR = 0.14). Turbo factor 50 had the worst and the least reproducible SNR, whereas turbo factor 225 had the worst and the least reproducible GM/WM CNR. Turbo factor 200 again had the best GM/CSF CNR but offered the least reproducible GM/CSF CNR. Turbo factor 225 had the best performance on EN (-21), while turbo factor 200 was next to the most reproducible turbo factor on EN (11). The results showed that turbo factor 200 had the least data acquisition time, in addition to superior performance on SNR, GM/WM CNR, GM/CSF CNR, and good reproducibility characteristics on EN. Both image quality metrics and volumetric measurements did not vary significantly (p > 0.05) with the range of turbo factors used in the study by one-way ANOVA analysis. Since no significant differences were observed in the performance of the turbo factors in terms of image quality and volume of brain structure, turbo factor 200 with a 74% acquisition time reduction was found to be optimal for brain MR imaging at 1.5 T.

Experimental investigation of in situ combustion (ISC) in heavy oil thermal recovery

With the intensification of global climate change, there is an urgent need to change the energy structure of oil and gas to reduce the impact on the environment. As a thermally enhanced oil recovery technique, in situ combustion (ISC) is very important for reducing carbon emissions in the petroleum industry through the comprehensive utilization of oil displacement and storage in reservoirs. In this paper, the similarity parameters of a three-dimensional (3D) physical simulation of ISC were determined, the temperature field changes in the combustion process were monitored in real time, and a 3D field map was obtained. The oxidation characteristics of heavy oil were examined via thermogravimetry (TG), and the results showed that the oxidation process of heavy oil had three stages. The experimental results indicated that ISC could greatly increase the reservoir temperature, improve the quality of crude oil through high-temperature cracking, reduce the viscosity of crude oil and enhance the oil fluidity. The recovery factor from the ISC reached 53.27%. The formation of the coking zone was caused by various chemical and physical changes that occurred at high temperatures. Moreover, the formation process led to a concentration of the remaining oil in the small-aperture channels in the coking zone; this reduced the fluidity and increased the production difficulty. This study could provide guidance for the development of heavy oil reservoirs.

Asymptotically correct 3D displacement of the Mooney–Rivlin model using VAM

The current work focuses on the nonlinear analytical analysis (dimensional reduction and recovery relations) of a hyperelastic plate governed by the compressible Mooney–Rivlin model using the Variational Asymptotic Method (VAM). The geometric nonlinearity is accommodated through finite deformations, and generalized 3D warping functions, while material nonlinearity through the hyperelastic material model. VAM mathematically splits the 3D nonlinear elastic problem into the 1D through the thickness analysis and 2D nonlinear plate analysis, using the inherent small parameters. These are the geometric small parameter (ratio of thickness to the characteristic dimension), and the physical small parameter (moderate strains). This work results include the derivation of closed-form analytical expressions of 3D warping functions, 2D nonlinear constitutive relation, and recovery relation to express the 3D displacement field for a plate. The 2D nonlinear constitutive relation is given as an input to the in-house developed 2D nonlinear finite element analysis of the reference surface to determine the 2D displacements and 2D strains. In order to validate the current theory, standard test cases are solved and compared with 3D nonlinear Finite Element Analysis (FEA).

Effect of turbulent coherent motion on the onset of particle transport using synchronized 3D PIV and PTV

A series of laboratory experiments was conducted to understand the role of unsteady coherent motions of turbulent flow in the onset of particle motion. Turbulent eddies having coherent structures constantly interact with sediment particles within the bottom boundary layer. Therefore, a 3D turbulent flow field and grain trajectories were obtained by the simultaneous operation of three-dimensional (3D) particle image velocimetry and particle tracking velocimetry. Characteristics of the grain motions were investigated with the various particles in the various flow conditions. Based on the Stokes number of sediments, the spectral density of flow field revealed that turbulent eddies in the production subrange initiate grain motion. The proper orthogonal decomposition extracted the flow fields in the production subrange and the quadrant analysis was used to define dominant turbulent event initiating particle motions in the different flow conditions. The events of outward interaction and sweep initiated particle motion under low-velocity conditions, whereas those of sweep and ejection frequently occurred at the onset of particle motion under high-velocity conditions. Based on the results of the quadrant analysis, we proposed a transition curve that determines the dominant turbulent event depending on the flow condition on the conventional Shields diagram

SFAMNet: A scene flow attention-based micro-expression network

Tremendous progress has been made in facial Micro-Expression (ME) spotting and recognition; however, most works have focused on either spotting or recognition tasks on the 2D videos. Until recently, the estimation of the 3D motion field (a.k.a scene flow) for the ME has only become possible after the release of the multi-modal ME dataset. In this paper, we propose the first Scene Flow Attention-based Micro-expression Network, namely SFAMNet. It takes the scene flow computed using the RGB-D flow algorithm as the input and predicts the spotting confidence score and emotion labels. Specifically, SFAMNet is an attention-based end-to-end multi-stream multi-task network devised to spot and recognize the ME. Besides that, we present a data augmentation strategy to alleviate the small sample size problem during network learning. Extensive experiments are performed on three tasks: (i) ME spotting; (ii) ME recognition; and (iii) ME analysis on the multi-modal CAS(ME)3 dataset. Empirical results indicate that depth is vital in capturing the ME information and the effectiveness of the proposed approach. Our source code is publicly available at https://github.com/genbing99/SFAMNet.

S-matrix on effective string and compactified membrane

Expanding Nambu–Goto action near infinitely long string vacuum one can compute scattering amplitudes of 2d massless fields representing transverse string coordinates. As was shown in (Dubovsky et al 2012 J. High Energy Phys. JHEP09(2012)044), the resulting S-matrix is integrable (provided appropriate local counterterms are added), in agreement with known free string spectrum and also with an interpretation of the static-gauge NG action as a deformation of a free massless theory. We consider a generalization of this computation to the case of a membrane, expanding its 3d action near an infinite membrane vacuum that has cylindrical shape (we refer to such membrane as ‘compactified’). Representing 3d fields as Fourier series in S 1 coordinate we get an effective 2d model in which the massless string modes are coupled to an infinite KK tower of massive 2d modes. We find that the resulting 2d S-matrix is not integrable already at the tree level. We also compute 1-loop scattering amplitude of massless string modes with all compactified membrane modes propagating in the loop. The result is UV finite and is a non-trivial function of the kinematic variables. In the large momentum limit or when the radius of S 1 is taken to infinity we recover the expression for the 1-loop scattering amplitude of the uncompactified membrane. We also consider a 2d model which is the deformation to the free theory with the same massless plus infinite massive tower of modes. The corresponding 2d S-matrix is found, as expected, to be integrable. Contribution to the special issue of Journal of Physics A: ‘Fields, Gravity, Strings and Beyond: In Memory of Stanley Deser’

Integrated experimental and computational analysis of porous media combustion by combining gas-phase synchrotron [formula omitted]CT, IR-imaging, and pore-resolved simulations

Porous media combustion is a promising combustion technology that enables operation over an extended range of conditions, with a variety of fuels and low pollutant emissions. However, the lack of understanding of the interstitial combustion processes remains a major obstacle towards the application of this technology and the development of predictive models. To understand these processes, this study presents an integrated experimental and computational effort to examine porous media combustion. Specifically, a new experimental diagnostic method is employed that simultaneously combines synchrotron X-ray micro-computed tomography (μCT) and short-wavelength infrared thermometry. With this approach, the surface temperature and the 3D gas-phase temperature field are simultaneously measured inside a porous burner with micrometer spatial resolution. These measurements are then integrated into 3D pore-resolved simulations. This integrated approach enables a direct evaluation of current modeling capabilities, and a unique analysis of interstitial combustion in porous foams under realistic heating conditions. In light of these results, we discover that about 80% of the fuel consumption inside the matrix pores occurs far enough from the porous surface to be characteristic of a decoupled regime of preheated, but adiabatic flamelets. The remaining fuel consumption near the pore surface occurs in a non-adiabatic combustion mode. This insight and the proposed approach contribute to our fundamental understanding of porous media combustion, and suggest that multi-regime models could more accurately describe porous burners.

Dynamic ultrasound focusing and centimeter-scale ex vivo tissue ablations with a CMUT probe developed for endocavitary HIFU therapies.

Thermal ablation of localized prostate tumors via endocavitary Ultrasound-guided High Intensity Focused Ultrasound (USgHIFU) faces challenges that could be alleviated by better integration of dual modalities (imaging/therapy). Capacitive Micromachined Ultrasound Transducers (CMUTs) may provide an alternative to existing piezoelectric technologies by exhibiting advanced integration capability through miniaturization, broad frequency bandwidth and potential for high electro-acoustic efficiency. An endocavitary dual-mode USgHIFU probe was built to investigate the potential of using CMUT technologies for transrectal prostate cancer ablative therapy. The USgHIFU probe included a planar 64-element annular HIFU CMUT array (fHIFU = 3 MHz) surrounding a 256-element linear imaging CMUT array. Acoustic characterization of the HIFU array included 3D pressure field mapping and radiation force balance measurements. Ex vivo proof-of-concept experiments consisted in generating HIFU thermal ablations with the CMUT probe on porcine liver tissues. The planar CMUT probe enabled HIFU dynamic focusing (distance range: 32 - 72 mm) while providing acoustic surface intensities of 1 W/cm2 that allowed producing elementary ex vivo ablations in depth of liver tissue (L×W ≈ 10 mm × 5 mm). Combinations of dynamic focusing, along with probe rotation and translation produced larger thermal ablations (L×W ≈ 20 mm × 20 mm) by juxtaposing multiple elementary ablations, consistent with expected results obtained through numerical modeling. The technical feasibility of using a USgHIFU probe, fully-developed using CMUTs for tissue ablation purposes, was demonstrated. The HIFU-CMUT array showed tissue ablation capabilities with volumes compatible with localized cancer targeting thus providing assets for further development of focal therapies.

Trace-wise coherent noise suppression via a self-supervised blind-trace deep-learning scheme

Seismic data denoising via supervised deep learning is effective and popular but requires noise-free labels, which are rarely available. Blind-spot networks circumvent this requirement by training directly on noisy data and have been demonstrated to be a powerful suppressor of random noise. In this work, we expand the methodology of blind-spot networks to create a blind-trace network that successfully removes trace-wise coherent noise. An extensive synthetic analysis illustrates the denoising procedure’s robustness to varying noise levels and varying numbers of noisy traces within shot gathers. It is demonstrated that the network can accurately learn to suppress the noise when up to 60% of the original traces are noisy. Furthermore, our procedure is implemented on the Stratton 3D field data set and demonstrates the restoration of the previously corrupted direct arrivals. In addition to trace-wise noise suppression, we adapt the blind-spot networks to the successful suppression of colored Gaussian noise, which exhibits varying coherent properties in time and spatial axes. Our adaptation of the blind-spot networks paves the way for its use in other applications, such as the suppression of coherent noise arising from wellsite activity, passing vessels, or nearby industrial activity.

Ergodic sampling: Acquisition design to maximize information from limited samples

AbstractData acquisition using equal spacing has been a standard practice in geophysics. The dense uniform sampling derived from Nyquist–Shannon sampling includes redundant samples, and it is sufficient but not necessary to adequately record target signals. We propose an ergodic sampling, which avoids the redundant samples in the dense uniform sampling and possesses the ability to capture the sufficiently similar information content as does Nyquist sampling. Ergodicity means that a key part of the system can represent the average performance of the entire system. Our ergodic samples are a critical subset of dense uniform samples and can represent the full uniform Nyquist samples. To find such a critical subset, we first examine the properties of different sampling patterns, including the sampling interval distribution, sampling angle distribution, areal sample density, and resolution in the spectral domain. We define the information sampling ability of sampling patterns based on these properties. The concept of information sampling ability that we have proposed serves as the criterion to compare different sampling patterns and assess their sampling performances. The sampling patterns with the same information sampling ability have the same capability to gather information, even though the appearance of sampling patterns may be different. We formulate an optimization problem to find this critical subset of sample locations, which has the fewest number of samples but has a similar information sampling ability as that of the desired dense uniform samples. This critical subset is irregularly located, has the optimized properties and forms the ergodic sampling pattern. We define this process of sampling design and associated understanding as the ergodic sampling. Ergodic sampling can be applied to gain two major benefits in practice. First, this approach can save a significant number of samples. We demonstrate ergodic sampling using one‐dimensional synthetic data and a two‐dimensional field geophysical dataset. The simulations confirm that, compared with other sampling strategies, ergodic sampling can use fewer samples to acquire the same amount of information, so that we can save cost. Alternatively, with the same budget, we can use the same number of samples through ergodic sampling to acquire more information. The new ergodic sampling can lead to a new generation of economic and efficient geophysical data acquisition, which could assist in increasing the discovery rate in resource exploration, tackling more earth science problems with a limited budget and can also benefit the environment in the process by reducing the invasiveness in potentially sensitive regions.

Spherical Gauss-Laguerre beam propagation in 4D space-time.

In this paper, what we believe to be a novel class of beams, which are referred to as the spherical Gauss-Laguerre beams, are proposed. The beams propagate stably in the anomalous dispersive media, within which the second order derivative with respect to t could be combined with the two-dimensional (2D) Laplacian operator in the transverse direction and forms a three-dimensional (3D) Laplacian operator, which describes the beam propagation in the z direction within the four-dimensional (4D) x-y-z-t space-time. The wave equation is solved by the variable separation method and the analytical expression for the spherical Gauss-Laguerre beams is derived. The beams have a 3D Gaussian field distribution with a variable beam waist with respect to the propagation distance. Unlike any 2D spatial vortex beams, the 3D beams could possess either the spatial vortex or the spatiotemporal optical vortex (STOV) by choosing the vortex plane in the 3D x-y-t space-time. The derived spherical Gauss-Laguerre beam expression in the 4D space-time is verified by the numerical simulations with excellent agreement.

Quality analysis of diffraction grating based 3D light field display system: Identifying optimal system parameters

Revealing the relationship between the parameters and display performance metrics is crucial to designing a high-quality three-dimensional (3D) light field display (LFD) system. However, there is currently no such analysis available for the diffraction grating (DG)-based 3D LFD system. In this paper, the light field mathematical model of the DG-based 3D LFD system is established and the influence of the main parameters of the DG-based 3D LFD system on the display quality is analyzed. A quality analysis methodology for the reconstructed light field of the DG-based 3D LFD system is proposed. This quality analysis methodology can be adopted as a design criterion for the DG-based 3D LFD system.

Integrated methodology to link geochemical and geophysical-lab data in a geophysical investigation of a slag heap for resource quantification

The increasing need to find alternative stocks of critical raw materials drives to revisit the residues generated during the former production of mineral and metallic raw materials. Geophysical methods contribute to the sustainable characterization of metallurgical residues inferring on their composition, zonation and volume(s) estimation. Nevertheless, more quantitative approaches are needed to link geochemical or mineralogical analyses with the geophysical data. In this contribution, we describe a methodology that integrates geochemical and geophysical laboratory measurements to interpret geophysical field data solving a classification problem. The final aim is to estimate volume(s) of different types of materials to assess the potential resource recovery. We illustrate this methodology with a slag heap composed of residues from a former iron and steel factory. First, we carried out a 3D field acquisition using electrical resistivity tomography (ERT) and induced polarization (IP), based on which, a sampling survey was designed. We conducted laboratory measurements of ERT, IP, spectral induced polarization (SIP), and X-ray fluorescence analysis, based on which, 4 groups of different chemical composition were identified. Then we carried out a 3D probabilistic classification of the field data, based on 2D kernel density estimators (for each group) fitted to the inverted data collocated with the samples. The estimated volumes based on the classification model were: 4.17 × 103 m3 ± 12 %, 1.888 × 105 m3 ± 12 %, 59.4 × 103 m3 ± 19 %, and 2.30 × 104 m3 ± 21% for the groups ordered with an increasing metallic content. The uncertainty ranges were derived from comparing the volumes with and without considering the probabilities associated to the classification. We found that a representative sampling and the definition of the KDE bandwidths are defining elements in the classification and ultimately the estimation of volumes. This methodology is suitable to quantitatively interpret geophysical data in terms of the geochemical composition of the materials, integrating uncertainties both in the classification and the estimation of volumes. Furthermore, several crucial elements in the investigation of metallurgical residues could be applied in a real case study, e.g., geophysical field acquisition, sampling and lab measurements.

Calibration of DART 3D model with UAV and Sentinel-2 for studying the radiative budget of conventional and agro-ecological maize fields

Quantifying the radiative budget (RB) of maize, the world's predominant cereal, is crucial to managing its growth in a context of global warming. Its microclimate including temperature, water, and energy flux depend on crop management practices. This paper compares the influence of agroecological practices (AE) and conventional agriculture (CA) on the RB of maize using three-dimensional (3D) radiative transfer modelling and remote sensing (RS) observations. We studied two neighbour maize fields in south-west France, respectively with AE and CA practices. Time series of photosynthetically active radiation absorbed (APAR) by plants (APARplant) and soil (APARsoil) were simulated with the DART radiative transfer model. First, realistic 3D models of the CA and AE fields were created and validated with a new method based on DART and Sentinel 2 and UAV reflectance and vegetation indices acquired in July 2019. At that date, around midday, APARplant was larger by 21.5 W/m2 in the AE field and APARsoil was larger by 20.1 W/m2 in the CA field. We explained these differences by differences in geometric and optical parameters (OP) between the two fields. OPsoil causes ≈45% of the differences in APARplant and APARsoil. The shape of plant causes ≈14% of the difference in APARplant and ≈17% in APARsoil. OPplant causes ≈14% of the difference in APARplant and ≈1% in APARsoil. Field geometry (row orientation, inter-row and plant distance) causes ≈7% of the difference in APARplant and 2% in APARsoil. It stresses the great role of OPsoil associated to AE practices. Because it integrates the non-linear effects of all above parameters, the LAI (Leaf Area Index) accounts for ≈40% difference in APARplant and APARsoil. The sensitivity of APAR to LAI is twice as low in the AE field than in the CA field. We showed that APAR differences between the AE and CA fields can be even greater with other maize row orientations. We also highlighted the role of the field model on simulated APAR by comparing the use of APAR simulated by a 3D field model and its corresponding 1D turbid field: 1D turbid models resulted in a 16% increase in APARplant and in similar APARsoil, compared to the 3D CA and AE models.