Physical Model Research Articles

Cropland Microclimate And Leaf-Nesting Behavior Shape The Growth of Caterpillar Under Future Warming.

Predicting performance responses of insects to climate change is crucial for biodiversity conservation and pest management. While most projections on insects' performance under climate change have used macro-scale weather station data, few incorporated the microclimates within vegetation that insects inhabit and their feeding behaviors (e.g., leaf-nesting: building leaf nests or feeding inside). Here, taking advantage of relatively homogenous vegetation structures in agricultural fields, we built microclimate models to examine fine-scale air temperatures within two important crop systems (maize and rice) and compared microclimate air temperatures to temperatures from weather stations. We deployed physical models of caterpillars and quantified effects of leaf-nesting behavior on operative temperatures of two Lepidoptera pests: Ostrinia furnacalis (Pyralidae) and Cnaphalocrocis medinalis (Crambidae). We built temperature-growth rate curves and predicted the growth rate of caterpillars with and without leaf-nesting behavior based on downscaled microclimate changes under different climate change scenarios. We identified widespread differences between microclimates in our crop systems and air temperatures reported by local weather stations. Leaf-nesting individuals in general had much lower body temperatures compared to non-leaf-nesting individuals. When considering microclimates, we predicted leaf-nesting individuals grow slower compared to non-leaf nesting individuals with rising temperature. Our findings highlight the importance of considering microclimate and habitat-modifying behavior in predicting performance responses to climate change. Understanding the thermal biology of pests and other insects would allow us to make more accurate projections on crop yields and biodiversity responses to environmental changes.

Digital twin model of a large scale hot molten metal ladle pouring system

AbstractIn steel-making processes, large quantities (frequently exceeding 300 t) of liquid metal are transferred between vessels. In Basic Oxygen Steel (BOS) making process, metal is poured from Hot Metal (HM) ladles, utilising overhead gantry cranes, into furnaces for further processing. Due to the large quantities of liquid metal poured, this operation poses significant safety concerns associated with metal spillage and releases of heat emissions. This can further lead to damage being caused to surrounding infrastructure. Pouring automation can reduce the likelihood of metal spillage, optimising ladle movement for reduction in heat emission releases. Given the hazardous nature of this operation, robust testing and evaluation of automated crane pouring movements is required prior to their application. A digital twin (DT) model of an overhead gantry crane/HM ladle system is presented here, intended to provide a safe testing environment for controlled pouring movement and serve as a testbed for control system design studies. Accurate crane movement is achieved using multi-body dynamics, solving for non-linearities present due to rigid joint frictional components. The flow rate of HM is estimated through the application of a dynamic model, allowing the modelling of system dynamics due to differences in HM pouring weights. The devised DT model is evaluated by simulating real crane movement and making a comparison on the resultant changing HM weight inside the ladle. The devised DT removes the need for construction of a physical model or performing tests directly on the HM pouring system.

Performance optimization and testing of a novel energy coupled actuated high-speed valve based on peak-and-hold driving strategy

High-speed on/off valve acts as critical enabling component of digital hydraulics, which has been challenging the traditional hydraulic system by providing efficient and effective hydraulic control solutions. The high-speed on/off valve that ideally fits the digital hydraulic system is expected to achieve fast response and long stroke with affordable energy consumption. This not only requires developing advanced actuation mechanism but also demands extensive study on the driving strategy. This study focuses on investigating the Peak-and-hold driving strategy applied to a novel high speed on/off valve-energy coupling actuated valve (ECAV), in order to achieve target performance with optimized energy consumptions. This work has developed an electro-mechanical coupled physics model based on commercial finite element solver COMSOL. This model helps to investigate the performance of ECAV under different peak-and-hold driving parameters and thereby recommend the optimal peak-and-hold driving strategy. Then, the experimental testing on the prototype ECAV has been conducted to validate the simulated performances. The measurement results showed that the prototype ECA using the optimal peak & hold solution demonstrated significant advantage in energy saving targeting at response of 10 ms for 1.5 mm stroke, especially compared to a typical step input signal.

Treatment of complex limb fractures with 3D printing technology combined with personalized plates: a retrospective study of case series and literature review

BackgroundIn recent years, 3D printing technology has made significant strides in the medical field. With the advancement of orthopedics, there is an increasing pursuit of high surgical quality and optimal functional recovery. 3D printing enables the creation of precise physical models of fractures, and customized personalized steel plates can better realign and more comprehensively and securely fix fractures. These technologies improve preoperative diagnosis, simulation, and planning for complex limb fractures, providing patients with better treatment options.Patients and methodsFive typical cases were selected from a pool of numerous patients treated with 3D printing technology combined with personalized custom steel plates at our hospital. These cases were chosen to demonstrate the entire process of printing 3D models and customizing individualized steel plates, including details of the patients' surgeries and treatment procedures. Literature reviews were conducted, with a focus on highlighting the application of 3D printing technology combined with personalized custom steel plates in the treatment of complex limb fractures.Results3D printing technology can produce accurate physical models of fractures, and personalized custom plates can achieve better fracture realignment and more comprehensive and robust fixation. These technologies provide patients with better treatment options.ConclusionThe use of 3D printing models and personalized custom steel plates can improve preoperative diagnosis, simulation, and planning for complex limb fractures, realizing personalized medicine. This approach helps reduce surgical time, minimize trauma, enhance treatment outcomes, and improve patient functional recovery.

A Systematic Review of Physical Modelling Techniques, Developments and Applications in Slope Stability Analyses

AbstractAn extensive evaluation of published articles suggests a lack of research on a systematic literature review relating to physical modelling techniques, developments and application in slope stability studies. However, there is growing interest in using physical model tests in slope stability investigations. The present study systematically reviews the methodologies and applications of physical modelling in slope stability research. The Scopus database was used to identify relevant studies which employed physical model tests in slope stability investigations. A combination of fifteen keywords was used to identify relevant articles. A PRISMA-P method for conducting a systematic review was adopted. Articles were screened and analysed, and extracted data were re-organised using an Excel sheet. Data relating to research objectives, physical model test techniques, instrumentation, scaling laws, numerical modelling, results, and findings were extracted and analysed. The systematic review highlights gaps requiring further studies, particularly in slope reinforcements using vegetation and strength deterioration of reinforcements performance under repeated loading exposures. It is found that scholars have not fully addressed the influence of loss of water on pore water regimes and its impact on stability when vegetation is applied as reinforcements. It is also found that the development of slope materials for soil slopes in physical model tests relies on the artificial development of such materials with minimal consideration of their long-term behaviour. Although other options, such as bio-cementation and desiccation techniques, which simulate the natural environment of the slopes, are neglected in slope material development, the present study recommends that future studies consider such techniques.

The oblique parameters from arbitrary new fermions

We compute the six oblique parameters S, T, U, V, W, X in a New Physics Model with an arbitrary number of new fermions, in arbitrary representations of SU(2) × U(1), and mixing arbitrarily among themselves. We show that S and U are automatically finite, but T is finite only if there is a specific relation between the masses of the new fermions and the representations of SU(2) × U(1) that they sit in. We apply our general computation to two illustrative cases.

Research on electrical capacitance tomography (ECT) detection of cerebral hemorrhage based on symmetrical cancellation method

Currently, there is an urgent need for a fast and portable intracerebral hemorrhage (ICH) detection technology for pre-hospital emergency scenarios. Owing to the disproportionately elevated permittivity of blood compared to other brain tissues, Electrical Capacitance Tomography (ECT) offers a viable modality for mapping the spatial distribution of permittivity within the brain, thus facilitating the imaging-based identification of ICH. Currently, ECT is confined to time-differential imaging due to limited sensitivity, and this methodology requires non-hemorrhagic measurements for comparison, data that are frequently inaccessible in clinical contexts. To overcome this limitation, in accordance with the natural bilateral symmetry of the cerebral hemispheres, a symmetrical cancellation scheme is introduced. In this method, electrodes are uniformly arrayed around the cranial periphery and strategically positioned in a symmetrical manner relative to the sagittal suture. Subsequently, the measured capacitances for each electrode pair are subtracted from those of their symmetrical counterparts aligned with the sagittal suture. As a result, this process isolates the capacitance attributable solely to hemorrhagic events within a given hemisphere, permitting the absolute imaging of ICH. To assess the feasibility of this method, simulation and empirical imaging were conducted respectively on a numerical hemorrhage model and three physical models (a water-wrapped hemorrhage model, an isolated porcine fat-wrapped hemorrhage model, and an isolated porcine brain tissue-wrapped hemorrhage model). Traditional absolute imaging, time-differential imaging and symmetrical cancellation imaging were performed on all models. The results substantiate that the proposed imaging modality is capable of obtaining absolute imaging of ICH. But a mirrored artifact, symmetrical to the site of the actual hemorrhage image appeared in each of the imaging results. This mirror artifact was characterized by identical dimensions and an inverted pixel-value schema, an intrinsic consequence of the symmetrical cancellation imaging algorithm. The real image of hemorrhage can be ascertained through pre-judgment with the symptoms of the patient. Additionally, the quality of this imaging is seriously dependent on the precise alignment between the electrodes and the sagittal suture of the brain; even a minor deviation in symmetry could introduce excessive noises. Thus, the complicated operational procedures remain as challenges for practical application.

The Process of Digital Data Flow in RE/CAD/RP/CAI Systems Concerning Planning Surgical Procedures in the Craniofacial Area

This paper presents the process of digital data flow in RE/CAD/RP/CAI systems to develop models for planning surgical procedures in the craniofacial area. At the first RE modeling stage, digital data processing, segmentation, and the reconstruction of the geometry of the anatomical structures were performed. During the CAD modeling stage, three different concepts were utilized. The first concept was used to create a tool that could mold the geometry of the cranial vault. The second concept was created to prepare a prototype implant that would complement the anterior part of the mandibular geometry. And finally, the third concept was used to design a customized prototype surgical plate that would match the mandibular geometry accurately. Physical models were made using a rapid prototyping technique. A Bambu Lab X1 3D printer was used for this purpose. The process of geometric accuracy evaluation was carried out on manufactured prototypes of surgical plates made of ABS+, CPE, PLA+, and PETG material. In the geometric accuracy evaluation process, the smallest deviation values were obtained for the ABS plus material, within a tolerance of ±0.1 mm, and the largest were obtained for CPE (±0.2 mm) and PLA plus (±0.18 mm). In terms of the surface roughness evaluation, the highest value of the Sa parameter was obtained for the PLA plus material, which was 4.15 µm, and the lowest was obtained for the CPE material, equal to 3.62 µm. The knowledge of the flow of digital data and the identification of factors determining the accuracy of mapping the geometry of anatomical structures allowed for the development of a procedure that improves the modeling and manufacturing of anatomical structures within the craniofacial region.

Modeling Absorption Dynamics of Differently Shaped Gold Glioblastoma and Colon Cells Based on Refractive Index Distribution in Holotomographic Imaging.

Herein, it is demonstrated that the toxic effect of gold nanoparticles (Au NPs) on three different cancer cell lines (U-118 and LN-299 glioblastoma and HCT-116 colon) depends on their absorption dynamics by cells, related to the shapes of the NPs. This hypothesis is confirmed by showing that i) based on refractive index (RI) values, typical for cell components and gold nanoparticles, it is possible to show the absorption dynamics and accumulation locations of the latter ones inside and outside of the cells. Moreover, ii) the saturation of the accumulated Au NPs volume in the cells depends on the nanoparticle shape and is reached in the shortest time for star-shaped Au NPs (AuS NPs) and in the longest time for spherical Au NPs (AuSph NPs) and on the cancer cells, where the longest and the shortest saturation are noticed for HCT-116 and LN-229 cells, respectively. A physical model of Au NPs absorption dynamics is proposed, where the diameter and shape of the Au NPs are used as parameters. The obtained theoretical data are consistent with experimental data in 85-98%.

Atmospheric Light Estimation Using Polarization Degree Gradient for Image Dehazing.

A number of image dehazing techniques depend on the estimation of atmospheric light intensity. The majority of dehazing algorithms do not incorporate a physical model to estimate atmospheric light, leading to reduced accuracy and significantly impacting the effectiveness of dehazing. This article presents a novel approach for estimating atmospheric light using the polarization state and polarization degree gradient of the sky. We utilize this approach to enhance the outcomes of image dehazing by applying it to pre-existing dehazing algorithms. Our study and development of a real-time dehazing system has shown that the approach we propose has a clear advantage over previous methods for estimating ambient light. After incorporating the proposed approach into existing defogging methods, a significant improvement in the effectiveness of defogging was noted through the assessment of various criteria such as contrast, PSNR, and SSIM.

Estimating the Intensity of Tropical Cyclones from Spiral Signatures Acquired by Spaceborne SAR

Accurate estimates of tropical cyclone (TC) intensity are important for improving forecasts as well as studying ocean dynamics during such extreme events. Since most cyclone life occurs over the open ocean, remote sensing techniques play an important role in obtaining the necessary data. The possibility of using the configuration of spiral signatures of mature tropical cyclones (TCs) observed in synthetic aperture radar (SAR) images to estimate the maximum wind speed of a TC is considered. This study assessed the intensity of 14 TCs in the Atlantic and Pacific Oceans using radar images obtained by the Radarsat Hurricane Application Project. TC intensity was estimated using the hyperbolic-logarithmic approximation of TC spiral signatures (HLS approximation). Additionally, the edges of the spiral signatures were partially fitted using a logarithmic spiral to improve the reliability of the HLS approximation. For the first time, a physical model of changing the crossing angle of the logarithmic portion of the edges was proposed and tested on SAR images of the TC. HLS maximum wind speed estimates were compared with Best Track estimates. The results showed the closeness of both estimates with a correlation of 0.95 and a standard deviation of 2.9 m s−1. The results indicate the possibility of using the HLS approximation to estimate the intensity of mature TCs from SAR data.

Shear-wave attenuation anisotropy: a new constraint on mantle melt near the Main Ethiopian Rift

The behaviour of fluids in preferentially aligned fractures plays an important role in a range of dynamic processes within the Earth. In the near-surface, understanding systems of fluid-filled fractures is crucial for applications such as geothermal energy production, monitoring CO2 storage sites, and exploration for metalliferous sub-volcanic brines. Mantle melting is a key geodynamic process, exerting control over its composition and dynamic processes. Upper mantle melting weakens the lithosphere, facilitating rifting and other surface expressions of tectonic processes.Aligned fluid-filled fractures are an efficient mechanism for seismic velocity anisotropy, requiring very low volume fractions, but such rock physics models also predict significant shear-wave attenuation anisotropy. In comparison, the attenuation anisotropy expected for crystal preferred orietation mechanisms is negligible or would only operate outside of the seismic frequency band.Here we demonstrate a new method for measuring shear-wave attenuation anisotropy, apply it to synthetic examples, and make the first measurements of SKS attenuation anisotropy using data recorded at the station FURI, in Ethiopia. At FURI we measure attenuation anisotropy where the fast shear-wave has been more attenuated than the slow shear-wave. This can be explained by the presence of aligned fluids, most probably melts, in the upper mantle using a poroelastic squirt flow model. Modelling of this result suggests that a 1% melt fraction, hosted in aligned fractures dipping ca. 40° that strike perpendicular to the Main Ethiopian Rift, is required to explain the observed attenuation anisotropy. This agrees with previous SKS shear-wave splitting analysis which suggested a 1% melt fraction beneath FURI. The interpreted fracture strike and dip, however, disagrees with previous work in the region which interprets sub-vertical melt inclusions aligned parallel to the Main Ethiopian Rift which only produce attenuation anisotropy where the slow shear-wave is more attenuated. These results show that attenuation anisotropy could be a useful tool for detecting mantle melt, and may offer strong constraints on the extent and orientation of melt inclusions which cannot be achieved from seismic velocity anisotropy alone.

Improving forecasts of precipitation extremes over northern and central Italy using machine learning

AbstractThe accurate prediction of intense precipitation events is one of the main objectives of operational weather services. This task is even more relevant nowadays, with the rapid progression of global warming which intensifies these events. Numerical weather prediction models have improved continuously over time, providing uncertainty estimation with dynamical ensembles. However, direct precipitation forecasting is still challenging. Greater availability of machine‐learning tools paves the way to a hybrid forecasting approach, with the optimal combination of physical models, event statistics, and user‐oriented postprocessing. Here we describe a specific chain, based on a random‐forest (RF) pipeline, specialised in recognising favourable synoptic conditions leading to precipitation extremes and subsequently classifying extremes into predefined types. The application focuses on northern and central Italy, taken as a testbed region, but is seamlessly extensible to other regions and time‐scales. The system is called MaLCoX (Machine Learning model predicting Conditions for eXtreme precipitation) and is running daily at the Italian regional weather service of ARPAE Emilia‐Romagna. MalCoX has been trained with the ARCIS gridded high‐resolution precipitation dataset as the target truth, using the last 20 years of the European Centre for Medium‐Range Weather Forecasts (ECMWF) reforecast dataset as input predictors. We show that, with a long enough training period, the optimal blend of larger‐scale information with direct model output improves the probabilistic forecast accuracy of extremes in the medium range. In addition, with specific methods, we provide a useful diagnostic to convey to forecasters the underlying physical storyline which makes a meteorological event extreme.

Battery Electrolyte Design for Electric Vertical Takeoff and Landing (eVTOL) Platforms

AbstractThe development of robust and high‐performance battery systems is crucial for the advancement of Electric Vertical Takeoff and Landing (eVTOL) vehicles for urban air mobility. This study evaluates the performance of different lithium‐ion battery chemistries under Electric Vertical Takeoff and Landing (eVTOL) load profiles. The actual flight data coupled with physical models is used to create discharge profiles for testing on developed lithium‐ion cells for eVTOLs. The performance of a standard liquid electrolyte (1.2 M LiPF6 in EC:EMC), labeled Gen‐2, is benchmarked and compared with a fast‐charging electrolyte (1.2 M LiFSI in EC:EMC), labeled XFC. Cell analysis involves the use of various techniques, such as impedance spectroscopy, polarization curves, and capacity retention measurements. Capacity retention is stable for both systems over 500 cycles, but unique discharge capacity trends are observed for different mission segments. During the initial takeoff hover stages, Gen‐2 electrolytes experience substantial voltage fade, while XFC electrolytes maintain consistent behavior. In general, the Gen‐2 electrolyte demonstrated lower discharge overpotentials and higher decay during cycling compared to the XFC electrolyte. This work highlights the complexity of eVTOL battery behavior and provides insights into battery system design, contributing to the advancement of battery energy storage solutions for urban air mobility.

PRODIGE - Planet-forming disks in Taurus with NOEMA. II. Modeling the CO (2-1) isotopologue emission of the Class II T Tauri disks in Taurus

To understand how planets form in protoplanetary disks, it is necessary to characterize their gas and dust distribution and masses. This requires a combination of high-resolution dust continuum and molecular line interferometric observations, coupled with advanced theoretical models of protoplanetary disk physics, chemical composition, and radiative transfer. We aim to constrain the gas density and temperature distributions as well as gas masses in several T Tauri protoplanetary disks located in Taurus. We use the 12CO, 13CO, and C18O (2-1) isotopologue emission observed at $0.9 with the IRAM NOrthern Extended Millimeter Array (NOEMA) as part of the MPG-IRAM Observatory Program PRODIGE (PROtostars and DIsks: Global Evolution \, PIs: P. Caselli Th. Henning). Our sample consists of Class II disks with no evidence of strong radial substructures. We use these data to constrain the thermal and chemical structure of these disks through theoretical models for gas emission. To fit the combined optically thick and thin CO line data in Fourier space, we developed the DiskCheF code, which includes the parameterized disk physical structure, machine-learning (ML) accelerated chemistry, and the RADMC-3D line radiative transfer module. A key novelty of DiskCheF is the fast and feasible ML-based chemistry trained on the extended grid of the disk physical-chemical models precomputed with the ANDES2 code. This ML approach allows complex chemical kinetics models to be included in a time-consuming disk fitting without the need to run a chemical code. We present a novel approach to incorporate chemistry into disk modeling without the need to explicitly calculate a chemical network every time. Using this new disk modeling tool, we successfully fit the 12CO, 13CO, and C$ $O (2-1) data from the CI, CY, DL, DM, DN, and IQ Tau disks. The combination of optically thin and optically thick CO lines allows us to simultaneously constrain the disk temperature and mass distribution, and derive the CO-based gas masses. The best-fit disk gas masses range between 0.005 and $0.04 M_ sun $. These values are in reasonable agreement with the disk dust masses rescaled by a factor of 100 as well as with other indirect gas measurements via, for example, modeling of the wavelength dependence of the dust continuum emission radii, and HD and CO isotopologue emission.

Characteristics of Carbon Monoxide and Ethylene Generation in Mine’s Closed Fire Zone and Their Influence on Methane Explosion Limits

Methane explosions often occur during the closure process of mine fire zones, during which the concentration of combustible gases such as monoxide and ethylene produced by coal combustion dynamically changes, which changes the risk of methane explosion. Therefore, studying the gas concentration distribution and methane explosion limits during the process of mine closure is of great significance for disaster prevention and control. In this paper, a three-dimensional physical model of gob was built, and the distribution of monoxide and ethylene in the process of fire zone closure was investigated. Further, the explosion limits of methane enriched with CO and C2H4 in the closed fire zone of gob were analyzed. The results indicate that CO and C2H4 would form a small-scale accumulation phenomenon near the fire zone after the closure of the fire zone, and when the fire zone is closed for more than 15 min, the mixed combustible gases in the environment lose their explosiveness.

Image recovery method for underwater targets with complex polarization characteristics

Polarization imaging techniques have been effective in improving the clarity of turbid underwater images affected by water scattering. These techniques offer valuable additional information compared to traditional methods. However, previous descattering methods have mostly been designed for targets with uniform distribution of polarimetric characteristics. Therefore, targets with complex polarization characteristics have non-uniform distribution of polarization characteristics when dealing with such problems, additional parameter estimation errors can limit the results of image recovery. This paper proposes what we believe is a novel approach to address this issue. The method involves obtaining a new set of images using the polarization images vector space transformation method. The angle of polarization (AOP) of the target reflected light is estimated using the additivity law of the Stokes vector. This information is then combined with the physical model of underwater imaging to recover the underwater images affected by scattering. Experimental results conducted under varying levels of water turbidity validate the effectiveness of the proposed method. Moreover, the proposed method significantly broadens the range of application scenarios.

Application of Bayesian networks based on Sequential Monte Carlo simulation and physical model in fault diagnosis of horizontal three-phase separator system

In offshore production platforms, horizontal three-phase separator is common process equipment. Its main function is to complete the dehydration and degassing of crude oil. Separator system is more complex, and its failure may cause significant economic losses and disastrous consequences. Therefore, it is critical to accurately and quickly identify where and why faults occur in the separator system. In this study, separator fault diagnosis model based on Bayesian networks is developed. Moreover, Sequential Monte Carlo simulation and physical model are introduced to overcome field problems such as missing separator failure data and the inability of experts to provide accurate empirical knowledge. Using this model, 13 faults in a separator in an offshore crude oil processing system are successfully diagnosed. Meanwhile, the proposed model is compared with deep neural network, convolutional neural network, and deep residual network, with accuracy rates of 100%, 91.34%, 87.99%, and 94.62%, respectively. Then, the diagnostic accuracy of each model for different faults is also compared in this paper under various signal-to-noise ratios. The results show that the method proposed in this paper has better noise immunity compared to the other three models. Therefore, the accuracy and robustness of the proposed model is further demonstrated. Finally, to analyze the fault-tolerance of the proposed model, 2–3 error evidence is randomly entered. The results show that the proposed model has better fault tolerance compared to data-driven Bayesian networks.

Lie Symmetry Analysis, Power Series Solutions and Conservation Laws of (2+1)-Dimensional Time Fractional Modified Bogoyavlenskii–Schiff Equations

In this paper, Lie symmetry analysis method is applied to the (2+1)-dimensional time fractional modified Bogoyavlenskii–Schiff equations, which is an important model in physics. The one-dimensional optimal system composed by the obtained Lie symmetries is utilized to reduce the system of (2+1)-dimensional fractional partial differential equations with Riemann–Liouville fractional derivative to the system of (1+1)-dimensional fractional partial differential equations with Erdélyi–Kober fractional derivative. Then the power series method is applied to derive explicit power series solutions for the reduced system. In addition, the new conservation theorem and the generalization of Noether operators are developed to construct the conservation laws for the equations studied.

Robust chiral spin transport in the antiferromagnetic iron oxide/heavy metal bilayers

We have observed robust chiral spin torques and non-reciprocal charge transport behaviors in the α-Fe2O3/Pt bilayers through a combination of magnetic field and current-dependent second longitudinal harmonic resistance measurements. The interfacial Dzyaloshinskii–Moriya interaction induced magnetic chirality has been predicted to account for the sign reversal characteristic of the second longitudinal harmonic resistance with increasing the current amplitude. A physical model that considers the chirality dependence of both the asymmetric scattering and the giant Rashba spin–orbit coupling has been set up to uncover the microscopic interactions between charge, spin, and magnetic chirality. Our comprehensive approach leverages the semiclassical Boltzmann theory to validate the consistency between this model and our experimental findings. Through our investigation, we have established the pivotal role of interfacial magnetic chirality in determining both charge and spin transport behaviors within antiferromagnetic insulator/heavy metal bilayer systems. Our work not only enhances the comprehension of spin–orbit torques and non-reciprocal charge transport but also contributes to the broader understanding of these phenomena. The outcomes of this study have broader implications for the advancement of spintronics and related fields.