Simulative Tests Research Articles

Identification of Dynamic Recrystallization Model Parameters for 40CrMnMoA Alloy Steel Using the Inverse Optimization Method

The microstructure of 40CrMnMoA during hot forging determines its macroscopic mechanical properties. Dynamic recrystallization (DRX) behavior is commonly used to refine grains and improve the microstructure of materials; therefore, it is important to be able to predict mechanical behavior during hot forging and the microstructure evolution during dynamic recrystallization. In order to accurately determine the DRX model parameters of 40CrMnMoA steel, an inverse optimization method is proposed in this work. The uniaxial isothermal compression experiment of 40CrMnMoA steel was carried out on a Gleeble-1500D thermal simulation tester (Dynamic Systems Inc. (DSI), Poestenkill, NY, USA) under the temperature range of 900~1200 °C and the strain rate range of 0.005 to 5 s−1. Based on the true stress–strain data obtained by a compression test, the DRX model of 40CrMnMoA was initially established using the traditional averaging method. Subsequently, the DRX model parameters calculated by the conventional averaging method were used as the initial values, the mean-square error between the experimental and calculated values of the DRX volume fraction was set as the objective function, and the DRX model parameters were optimized by the adaptive simulated annealing (ASA) algorithm. By comparing the correlation coefficient R, average absolute relative error (AARE), and the root mean square error (RMSE) of the predicted DRX percentage with the experimental values before and after optimization, it was found that the optimized model achieved an R-value of 0.992, with AARE and RMSE decreased by 34% and 2%, respectively, which verified the accuracy of the optimized DRX model. Through the program’s secondary development, the optimized DRX model of 40CrMnMoA was integrated into finite element software Forge® 3.2 to simulate the isothermal compression process. The comparison between grain size from the central region of simulation results and actual samples revealed that the relative error is less than 3%. This result demonstrated that the inverse optimization method can accurately identify the DRX model parameters of 40CrMnMoA alloy steel.

MoWe: Motion Observation for Wind Estimation of Sailing Robots

ABSTRACTToward sustained mobility in complex marine environment, there is an urgent need for sailing robots to operate robustly when wind measurements are unavailable (e.g., wind sensors are damaged). This study proposes an effective motion observation‐based wind estimation (MoWe) scheme, which enables the robotic sailboat to consistently acquire wind information from its own maneuvers. MoWe incorporates motion analysis (MA) and data‐driven (DD) methods. In the MA method, dead zone constraints of the robotic sailboat are identified as crucial references in deriving wind direction. For the DD approach, the sailing robot as shown in Figure 1 is employed to collect a data set, which serves as the basis for regressing an estimator. We conducted extensive validation tests in both simulation and experiments. Results indicate favorable performance for both methods in simulated scenarios. Notably, the DD method exhibited higher estimation accuracy in all experiments. The mean absolute error (MAE) of estimated wind direction was 4.25°, with the range of confidence interval spanning from 25.13° to 33.56°, demonstrating the robustness of the DD method. Furthermore, the estimation of wind direction has been successfully applied in straight‐line sailing tests, and the wind magnitude has been estimated. The MAE of wind magnitude estimation was 1.13m/s.

Some Aspects of Interactive Simulation of Software Development with Integration of Ontological Models

Modern simulation technologies are of great importance, especially in those areas where errors in real processes can lead to significant costs and risks caused by the consequences of errors. The purpose of the article is to research of the interactive simulators in the field of software development with the integration of ontological models. The research methods are the main methodological approaches and technological tools for the development of interactive simulators in the field of software development with the integration of ontological models. Such methods, in particular, are: system and comparative analyzes – to identify the features of the creation and further use of interactive simulators in the field of software development with the integration of ontological models; the method of expert assessments, which involves the analysis of literary sources and information resources, conducting interviews and surveys of experts, the processes of development and testing of scalable and high-performance interactive simulators in the field of software development with the integration of ontological models. The novelty of the conducted research is the research of the possibility of integrating ontological models directly into the process of developing software simulated in interactive environments. The conclusion of the research carried out in the article is that the work was: the research was conducted on the use of simulation technologies in various subject areas; the role of interactive simulators is determined, which help the participants practice various scenarios in a virtual environment; development of critical thinking; improving decision-making skills; it is determined that interactive simulators are an important learning tool in the field of software development, where the integration of ontological models allows automating part of the processes and ensuring the standardization of procedures. Difficulties in implementing simulation training were identified: high initial costs and technical difficulties. For the successful integration of simulations, it is necessary to: provide a clear definition of educational goals (in particular, for the training of IT specialists); adapt simulations to the specific needs of the organization; integrate simulations into the general training program of IT specialists. It was determined that: ontological models play a key role in the improvement of interactive simulators, helping to structure and formalize knowledge in various subject areas; the use of ontologies contributes to the process of integration of various software components, thereby ensuring flexibility and scalability of systems.

Convergence tests of self-interacting dark matter simulations

Convergence tests of self-interacting dark matter simulations

Effects of Elliptical and Circular Nozzles on Diesel Spray Characteristics Under High Ambient Density

In this paper, the macroscopic and microscopic characteristics of diesel spray with elliptical and circular nozzles were investigated under an ambient density of 65.6 kg/m3 by combining an optical test and numerical simulation method of VOF-Spray One-Way Coupling and Large Eddy Simulation. Two elliptical nozzles with varying aspect ratios (1.25 and 1.5) and a circular nozzle were employed for comparison, with the same cross-sectional area. The results demonstrated that the spray tip penetration (STP) of elliptical nozzles was significantly diminished in comparison to that of the circular nozzle and that STP for the elliptical nozzle with a larger aspect ratio was observed to be smaller, primarily due to the elevated aerodynamic drag and accelerated kinetic energy dissipation. The spray cone angle (SCA) of elliptical nozzles was greater than that of the circular nozzle. The average SCA of the elliptical nozzle with a larger aspect ratio was the greatest in both planes. The spray asymmetry with elliptical nozzles resulted in the instability of the spray boundary, leading to the earlier fragmentation and atomization of the spray and faster radial diffusion. For the same STP, the elliptical nozzle with a larger aspect ratio exhibited the greatest spray area in both planes. Elliptical nozzles are subject to a greater degree of inhomogeneous shear than circular nozzles, which results in an accelerated rate of droplet breakage and a concomitant decrease in Sauter Mean Diameter.

2D Polyamides Enable Self-Healing and Recyclable Elastomers with High Robustness, Toughness, and Crack Resistance via Supramolecular Interactions.

High-performance elastomers with exceptional mechanical properties and self-healing capabilities have garnered significant attention due to their wide range of potential applications. However, designing elastomers that strike a balance between self-healing capabilities and mechanical properties remains a considerable challenge. Inspired by biological cartilage, a highly robust, tough, and crack-resistant self-healing elastomer is presented by incorporating hydrogen-bond-rich 2D polyamide (2DPA) into a poly(urethane-urea) matrix. This integration enhances supramolecular interactions driven by multiple hydrogen bonds. The resulting elastomer exhibits impressive strength (54.6 MPa), remarkable elongation at break (705.4%), exceptional toughness (116.7 MJ m-3), outstanding crack resistance (fracture energy up to 187.2 kJ m-2), high self-healing efficiency (98.9% at 50°C for 9 h, 97.9% at room temperature for 48 h), and excellent recyclability, capable of lifting ≈40000 times its own weight. Furthermore, a damage-tolerant, fatigue-resistant anticorrosive coating from this elastomer, showcasing its potential for protective skin applications in underwater robotics is developed. The underlying enhancement mechanism is validated through testing of various elastomers and molecular dynamics simulations, confirming the potential of engineering 2DPA for high-performance elastomers by leveraging supramolecular interactions.

Development of Immersive 360° Virtual Reality Videos for Intervention with Test Anxiety

This study explores the impact of 360-degree virtual reality (VR) videos on simulating test anxiety in students who already experience it. Using a phenomenological design, participants answered questions about their immersion in the virtual environment, including the realism of visuals, emotional responses, and adaptation. Seven students (four girls and three boys) were selected based on specific criteria, and the collected data underwent content analysis. Findings indicate that 360-degree VR videos effectively simulated test environments, creating a strong sense of realism and immersion. Participants reported that VR exposure elicited test anxiety symptoms similar to those experienced in real tests, with emotional distress intensifying during VR sessions. Test-anxious individuals noted that VR exposure triggered stronger emotional responses compared to imagination-based scenarios. While VR exposure proved effective in evoking realistic experiences and emotions, it was not deemed superior to real-life exposure techniques. This suggests that 360-degree VR has potential as a tool for anxiety interventions.

Study on the gas pressure impact test of gun propellant and fluid-solid coupling dynamics simulation

Abstract In order to reveal the mechanical response characteristics of high-energy propellant under high pressure gas impact, the gas pressure impact test and fluid-solid coupling dynamics simulation of propellant are carried out. The propellant gas pressure impact test device is established. The propellant gas pressure impact tests are conducted under different quality of depressant and thickness of iron pressure relief fragments. The gas pressure-time curve, gas pressure rise and decreasing rate are obtained. It is found that longitudinal and transverse cracks occur locally in propellant under high pressure impact. In order to reveal that mechanical property change of propellant under gas impact, the simulation study on fluid-solid coupling dynamics of gas pressure impact test is carried out using ANSYS Workbench multi-field coupling platform. The flow field pressure in the device and the change process of fixed propellant stress and deformation are obtained. The error of monitoring point simulation and test pressure is 5.3%. The maximum peak value of internal flow field pressure reaches 71.6MPa. The average value of maximum stress and maximum deformation of fixed propellant reaches 220MPa and 1.77mm respectively. It reveals the mechanical response characteristics of propellant under high pressure gas impact.

Numerical simulation on the three-dimension mesoscopic model of aerated ceramsite concrete based on CT scan

As a kind of composite construction material integrated with relatively high strength and excellent acoustic and thermal insulation, aerated ceramsite concrete (ACC) characterizes with multilevel pore structures with different features in aerated cement slurry base, ceramsite aggregate and the interfacial transition zone (ITZ) respectively. It was found that pores distribution in ceramsite aggregates was relatively regular with pore walls being smooth and compact. In contrast, pore sizes and distribution in aerated cement slurry base were obviously discrete and pore walls were loose. Prominently, the ITZ was denser than both of aerated cement slurry base and ceramsite with less pores inside. To reproduce reasonable 3D mesoscopic model of ACC for effective simulation, the ACC specimen was first remodeled using MIMICS by CT scanning. The proposed simulation method well represented the real ceramsite aggregates and distribution in ACC and the result coincided with the test based on the related defining of crushable foam material model of the aerated mortar cement slurry and ceramsite aggregates. The uniaxial compressive test of ACC and simulation indicate that, the porous structure and strength matching between the aerated cement slurry base and ceramsite aggregates were the key factors to affect the mechanism and failure of ACC. The internal cracks mostly originated from the aerated cement slurry base or in the ceramsite aggregates being more porous with lower strength.

Inferring the distribution of the ionising photon escape fraction

Context. The escape fraction of ionising photons from galaxies (fesc) is a key parameter for understanding how intergalactic hydrogen became reionised, but it remains mostly unconstrained. Measurements have been limited to the average value in galaxy ensembles and to handfuls of individual detections. Aims. To help understand which mechanisms govern ionising photon escape, here we infer the distribution of fesc. Methods. We developed a hierarchical Bayesian inference technique to estimate the population distribution of fesc from the ratio of Lyman continuum to non-ionising UV flux measured from broadband photometry. We applied it to a sample of 148 z ≃ 3.5 star-forming galaxies from the VANDELS spectroscopic survey. Results. We explored four physically motivated distributions: constant, log-normal, exponential, and bimodal, and recovered ⟨fesc⟩≈5% for most models. We find the observations are best described by an exponential fesc distribution with scale factor μ =0.05−0.02+0.01. This indicates most galaxies in our sample exhibit very low escape fractions, while predicting substantial ionising photon leakage for only a few galaxies, implying a range of optical depths in the interstellar medium and/or time variability in ionising photon escape. We rule out a bimodal distribution at high significance, indicating that a purely bimodal model of ionising photon escape (due to very strong sightline and/or time variability) is not favoured. We compare our recovered exponential distribution with the SPHINX simulations and find that, while the simulation also predicts an exponential distribution, it significantly underpredicts our inferred mean. The distribution of fesc can be a vital test for simulations in understanding ionising photon leakage, and is important to consider to gain a complete picture of reionisation.

ISOM 1.0: a fully mesoscale-resolving idealized Southern Ocean model and the diversity of multiscale eddy interactions

Abstract. We describe an idealized Southern Ocean model (ISOM 1.0) that contains simplified iconic topographic features in the Southern Ocean and conduct a fully mesoscale-resolving simulation with the horizontal resolution of 2 km, based on the Massachusetts Institute of Technology general circulation model. The model obtains a fully developed and vigorous mesoscale eddying field with a k−3 eddy kinetic energy spectrum and captures the topographic effect on stratification and large-scale flow. To make a natural introduction of large eddy simulation (LES) methods to ocean mesoscale parameterization, we propose the concept of mesoscale ocean direct numerical simulation (MODNS). A qualified MODNS dataset should resolve the first baroclinic deformation radius and ensure that the affected scales by the dissipation schemes are sufficiently smaller than the radius. Such datasets can serve as the benchmark for a priori and a posteriori tests of LES schemes or mesoscale ocean large eddy simulation (MOLES) methods in ocean general circulation models. The 2 km simulation can meet the requirement of MODNS and also capture submesoscale effects. Therefore, its output can be a type of MODNS and provide reliable data support for relevant a priori and a posteriori tests. We demonstrate the diversity of multiscale eddy interactions, validate the crucial role of mesoscale-related strain in submesoscale processes, and uncover the bridge effect of submesoscale processes between mesoscale entities and in the eddy–jet interaction. In addition, we use the model to conduct multipassive tracer experiments and reveal guidelines for the initial settings of passive tracers to delay the homogenization process and ensure the mutual independence of tracers over a long period.

Unraveling Potential Causative Components for the Deleterious Effect of Atmospheric Fine Particulate Matter on Red Blood Cells.

Atmospheric fine particulate matter (PM2.5) poses threats to the cardiovascular system. Red blood cells (RBCs) are the most abundant cells in blood, which are actively involved in multiple hematological diseases, such as blood clot formation and thrombosis. Exploring how PM2.5 with spatiotemporal heterogeneity influences the hematological system by targeting RBCs would help gain insights into the deleterious effects of PM2.5 and provide clues for finding the causative components therein. Herein, the PM2.5 samples collected from 3 urban sites in Beijing (i.e., Chaoyang, Shunyi, and Yanqing districts) during 4 seasons of 2022 were studied for their toxicities to mouse RBCs, and the main contributing components were further explored through chemical analysis and correlation measure. The results showed that exposure to PM2.5 samples decreased adenosine triphosphate (ATP) levels and increased phosphatidylserine (PS) externalization of RBCs, causing cell morphological deformity. The Pearson correlation analysis showed that the aromaticity of the dissolved organic matter (DOM) in PM2.5 samples was positively correlated with PS exposure of RBCs, showing that the lignin-like compounds were the potential contributors. The negative correlation of zeta potentials of PM2.5 samples with PS exposure of RBCs showed the particle-derived bioactivities of this airborne pollutant. The simulative test based on artificial nanomaterials of carbon black (CB) and oxidized CB (OCB) confirmed the crucial role of particulate carbon in PM2.5-induced effects on RBCs, and soot with a certain oxidation degree was, thus, recognized as another contributor, given its ubiquitous existence in PM2.5 samples. This study, for the first time, revealed PM2.5-induced PS exposure of RBCs, and the causative components of DOM and soot were unraveled. Considering the inevitable contact of airborne PM2.5 with RBCs in the blood circulatory system, the findings obtained herein would help bridge the gap between PM2.5 exposure and the risk of cardiovascular diseases, like thrombogenesis.

GPS-free autonomous navigation in cluttered tree rows with deep semantic segmentation

Segmentation-based autonomous navigation has recently been presented as an appealing approach to guiding robotic platforms through crop rows without requiring perfect GPS localization. Nevertheless, current techniques are restricted to situations where the distinct separation between the plants and the sky allows for the identification of the row’s center. However, tall, dense vegetation, such as high tree rows and orchards, is the primary cause of GPS signal blockage. In this study, we increase the overall robustness and adaptability of the control algorithm by extending the segmentation-based robotic guiding to those cases where canopies and branches occlude the sky and prevent the utilization of GPS and earlier approaches. An efficient Deep Neural Network architecture has been used to address semantic segmentation, performing the training with synthetic data only. Numerous vineyards and tree fields have undergone extensive testing in both simulation and real world to show the solution’s competitive benefits. The system achieved unseen results in orchards, with a Mean Average Error smaller than 9% of the maximum width of each row, improving state-of-the-art algorithms by disclosing new scenarios such as close canopy crops. The official code can be found at: https://github.com/PIC4SeR/SegMinNavigation.git.

A high-precision data-driven radar modeling method for autonomous driving simulations testing

Autonomous driving represents the future of transportation, and simulation testing is a critical technology for ensuring the safety and reliability of autonomous driving. In simulation systems, a real-time and accurate radar model is crucial for enhancing the confidence of autonomous driving simulation systems. Existing radar models are typically simplistic and cannot accurately reflect the detection results of real radars. This paper introduces a data-driven radar modeling method that analyzes radar detection mechanisms to identify the model’s input parameters. Furthermore, this method decouples the model’s output parameters based on the differing mechanisms of radar output parameters and tailors model structures to align with the mechanistic characteristics of each parameter. Subsequently, considering the varied influences among input parameters, a segmented model structure is proposed that reduces input dimensionality while retaining critical information. Lastly, the model outputs are optimized using Kalman filtering. This study collects radar data from real vehicles to train the radar model. Testing has demonstrated that the radar model developed by this paper’s method yields outputs very close to actual radar predictions, with a significant improvement in prediction accuracy compared to traditional radar models. The model operates in approximately 0.8 ms, markedly faster than the real radar’s 72-millisecond detection cycle. This demonstrates that the radar model developed using this modeling method can predict radar detection results accurately and in real-time.

Thermal Processing Map Study of the GH99 Nickel-Based Superalloy Based on Different Instability Criteria

The thermal compression experiments of the GH99 alloy were carried out at different strains from 1020 °C to 1170 °C and 0.001 s−1–1 s−1 conditions using a Gleeble-3800 thermal compression simulation tester. Construction of thermal processing maps with four instability criteria were superimposed on Murty, Prasad, Gegel, and Malas at different strains based on stress-strain data. Based on the theoretical basis, prediction results, and EBSD microstructure characterization method of four instability criteria, the suitable forming processing region and rheological instability region of the alloy were predicted. It was found that the Prasad instability criterion had the most accurate prediction results. The instability range predicted by Murty was accurate under minor strains, but as the strain increased, the expected instability range slightly increased compared to the actual range. However, the Gegel and Malas criteria have biases in predicting alloys under low-rate conditions at different strains. A scientific and rational optimization was carried out to select hot working process parameters for GH99 alloy in response to the influence of strain on its hot deformation behavior.

Effect of cooling temperature on microstructure and mechanical properties of Nb microalloyed hot-rolled rebar

In this study, the Gleeble-3800 thermal simulation tester was used to simulate various cooling temperature processes for two groups of rebar. Then, the microstructure, precipitation, and mechanical properties of the rebars were characterized using SEM (scanning electron microscopy), EBSD (electron backscatter diffraction), HRTEM (high resolution transmission electron microscopy), and MST810 (universal tensile testing machine). The effect of cooling temperature on the microstructure and mechanical properties was systematically investigated. The results showed that reducing the cooling temperature from 750 °C to 600 °C decreased the ferrite grain size in 0.032 wt % Nb rebar to 7.03 ± 1.5 μm, compared to the 0 wt% Nb rebar. In addition, the pearlite lamellar spacing was refined to 0.103 ± 0.5 μm, and the average particle size of elliptical Nb-rich composite carbides was 9 nm. The tensile strength and yield strength reached 795 ± 9 MPa and 638 ± 8 MPa, respectively, with an elongation after fracture of 17.78 ± 1.0 %. The fracture surface exhibited a dimple morphology.

INVESTIGATION INTO THE THERMAL DEFORMATION BEHAVIOR AND THE ESTABLISHMENT OF A CONSTITUTIVE RELATIONSHIP FOR Mn-Cr-Ni-Mo STEEL

Isothermal compression tests were conducted on Mn-Cr-Ni-Mo steel at temperatures ranging from 1173 K to 1473 K and strain rates from 0.01 s–1 to 10 s–1 using a Gleeble-3800 thermal simulation tester. Four constitutive models for Mn-Cr-Ni-Mo steel, namely the Arrhenius model, Fields-Backofen model (F-B), original Johnson-Cook model (J-C), and the improved Johnson-Cook model (mJ-C) were established. A correlation coefficient (R) and the average absolute relative error (AARE) were employed to evaluate the predictive capability of these four models. Among them, the Arrhenius model exhibited superior accuracy in predicting the behavior of Mn-Cr-Ni-Mo steel. It and the isothermal thermal compression finite-element model were imported into Deform-3D software for a numerical simulation, aiming to analyze the distribution law of the equivalent stress field. A comparison was made between the time-stress data obtained from numerical simulation under different conditions and that from the isothermal compression tests. The results demonstrate good agreement between the time-stress curves of the numerical simulation and the experimental measurements, indicating that the established Arrhenius model can effectively simulate the thermal deformation of Mn-Cr-Ni-Mo steel. These research findings provide valuable fundamental data for simulating the plastic-deformation process of Mn-Cr-Ni-Mo steel.

Diffusion mechanism of quick-setting slurry in water-rich fractured rock mass based on circle-outburst diffusion model

Grouting is the most commonly used methods in dealing with water inrush issue in mine and tunnel engineering. In order to better predict the grouting effect, research on slurry diffusion mechanism became a hotspot for scholars. At present, the mainstream theoretical model used to study the slurry diffusion mechanism are the circle diffusion model and the modified ones in plane plate fracture. However, these models cannot explain the “contradiction” between the rapid setting characteristics of quick-setting slurry and the continuous long-term injection of slurry in grouting engineering, which cannot accurately predict the grouting pressure or grouting diffusion range. Therefore, a “circle-outburst diffusion model” which can explain the above “contradiction” was proposed in this paper. Based on the new model, stepwise algorithms were developed to predict the grouting pressure and slurry diffusion range. By means of conducting fracture grouting simulation test and collecting grouting pressure data in an actual grouting project, the new model was verified and the slurry diffusion mechanism was studied. Comparison results indicate that the new model can reveal the intrinsic reason for the irregular diffusion phenomenon of the slurry and forecast the outburst moment accurately. The circle diffusion stage and outburst diffusion stage elaborated in the new “circle-outburst diffusion model” were consistent with the staged characteristics of the grouting process presented in the grouting simulating test. Differences between theoretical and experimental pressure value were within 10 %, indicating a high degree of consistency. The number and distribution of outburst diffusion points determine slurry diffusion form and significantly influence the diffusion range. The grouting pressure and the length of slurry flow path increase nonlinearly with time in each diffusion stage. It is hoped that the new model can provide a theoretical basis for the study of diffusion mechanism of quick-setting slurry.

Investigation of constitutive models and microstructure evolution during hot deformation and solution treatment of Al–Zn–Mg–Cu alloy

In this paper, isothermal thermal compression tests were carried out on Al–Zn–Mg–Cu alloys at varying deformation temperatures (360°C∼440°C) and strain rates (0.001s−1∼10s−1) using a Gleeble-3500C thermal simulation tester. Based on the true strain stress data obtained from the tests, the flow behavior of Al–Zn–Mg–Cu alloys during thermal deformation was investigated. Through a comprehensive consideration of deformation temperature, strain rate, and strain compensation, a high-precision constitutive model was established. Additionally, the specimens subjected to hot compression were further treated with solution annealing for different durations (0 h–2 h) to investigate the static recrystallization (SRX) behavior and the associated microstructural evolution of the alloy during the solution treatment process. Electron Backscatter Diffraction (EBSD) was employed to characterize the microstructure evolution of Al–Zn–Mg–Cu alloy under various deformation and solution treatment conditions. The EBSD data were further processed and analyzed using the MTEX toolbox. The results revealed that dynamic recovery (DRV) is the predominant softening mechanism during the deformation process, accompanied by a small amount of dynamic recrystallization (DRX). The dynamically recrystallized grains exhibit a random crystallographic orientation. Further investigation showed that low temperature and high strain rate conditions are unfavorable for the occurrence of dynamic recovery and dynamic recrystallization during the thermomechanical processing. However, dislocations accumulated during deformation provided sufficient stored energy for the growth of static recrystallized grains during subsequent solution treatment, with static recrystallized grains predominantly forming at grain boundaries.

Multispectral pathogens detection in food using multiplex hyperbranched saltatory rolling circle amplification

Foodborne illnesses caused by Salmonella and Staphylococcus aureus are a significant public health concern, leading to societal and economic repercussions. It is important to develop a simple and straightforward bacteria detection and identification method. A triple-probe multiplex rolling circle amplification technique has been developed to simultaneously detect Salmonella Typhimurium and S. aureus. This method utilizes fluorophore-labeled long padlock probes targeting S. Typhimurium invA and S. aureus glnA specific genes, along with a pH-based detection approach for direct visual identification. The multiplex hyperbranched saltatory rolling circle amplification assay at 30 °C has showed promising results with synthetic targets within 30 min and real bacteria within 2 h after establishing the detection settings. The assay is specific for S. aureus and S. Typhimurium, with a limit of detection of 39 μM for fluorescence and 78 μM for colorimetric. In the simulative test of this method for the detection of S. Typhimurium and S. aureus in milk, the limit of detection for the fluorescence signal after 2 h of amplification was 10 CFU/mL and 5 CFU/mL, respectively. The detection method was evaluated to be stable enough to detect pathogen for 3.29 months. Consequently, this triple-probe-multiplex rolling circle amplification method displays notable specificity, sensitivity, as well as ease of interpretation when testing food samples for harmful pathogens.