Simulation Predictions Research Articles

Study on progressive collapse of overall structure based on numerical simulation method and prediction of structural collapse

This study investigated the structural progressive collapse using finite element (FE) and theoretical methods. Firstly, an FE model was established based on the component method and layered shell element method. The reliability of modeling approaches was evaluated by comparing them with the previous experimental tests by the author. The NIST building was taken as an example to investigate the structural collapse behavior and the failure modes after different component failure scenarios including single column, double columns, multiple columns, and horizontal components based on the alternative path method. The results indicated that the failure of gravity columns and joints more easily triggered the progressive collapse of the structure. After the failure of double moment-resisting frame columns, the structure still demonstrated considerable robustness. The impact of floor fragments played a controlling role in the process of collapse propagation. A method for achieving rapid collapse of structures was proposed based on the structural collapse characteristics. The study found that a demolition strategy based on gravity columns can facilitate the rapid collapse of structures. After comparing the different simplified modeling methods, it was revealed that the 2D models underestimated the anti-collapse performance of structures compared to the 3D models. Under the same scenarios, the predicted structural collapse response of the 2D model was several times greater than that of the 3D model (up to 7 times). Meanwhile, the structural collapse occurred earlier in the 2D model. The anti-collapse performance of structures adopting different design codes also was compared. It was revealed that GSA 2016 has put forward requirements for higher collapse resistance of buildings. In this study, a new calculation method was proposed, namely the Collapse Probability Prediction Method Based on the Randomness of Structural Load (CPPM-RSL). In the CPPM-RSL, load reliability theory was employed to ensure that the structural collapse resistance design fell within the realm of structural engineering theory. Meanwhile, the CPPM-RSL offered greater engineering interpretability compared to previous methods. Additionally, a new simplified mechanical model was proposed to predict the collapse resistance of structures. The new simplified model did not depend on the computational results obtained from the finite element method (FEM) unlike the existing model. At the same time, the simplified method also exhibited high reliability as revealed by comparisons with the FEM. To solve highly nonlinear problems, machine learning (ML) algorithms were established to predict the anti-collapse performance and the collapse probability of structures based on the proposed theoretical model. Meanwhile, the applicability and performance of the multiple ML algorithms in estimating the structural collapse behavior were compared and evaluated. The results indicated that the LightGBM model exhibited stronger robustness and predictive ability.

Magnetostrictive Vibration Suppression via Integration of Current Amplification Negative Capacitor and Current Inversion Semi-Active Control Circuit

When attempting to suppress structural vibrations employing a magnetostrictive transducer, owing to the control force is generated from current, a larger amplification of the current entails superior control performance. The semi-active control theory uses the synchronized switching damping (SSD) technique for structural vibration suppression while requiring a minimal energy supply. However, the absence of external energy input limits the current amplification performance. To address this limitation, this study proposes a novel method that combines semi-active vibration suppression method with negative capacitance to improve the current amplification performance. The proposed circuit switches the circuit status between shunt, inductor-capacitor (LC) electrical oscillation, and negative capacitance circuits. A mathematical model was employed to analyze the current operation, suppression performance, and robustness of the proposed circuit. Further, machine learning and kernel regression model were employed to predict the optimal control force gain. Validation experiments conducted on a 10-bay truss structure identical to the simulation model show that the experimental results align with the simulation predictions. The vibration suppression rates of the proposed method under single-mode and multiple-mode vibrations reached 85.8% and 78.3%, respectively, which are 2.64 and 1.89 times higher than those achieved by conventional methods.

Exploration of urease-aided calcium carbonate mineralization by enzyme analyses of Neobacillus mesonae strain NS-6.

Urease containing nickel cofactor is crucial for urea-hydrolytic induced calcium carbonate (CaCO3) precipitation (UICP). However, limited information exists regarding the influence of amino acid residues interacting with nickel ions in its structure on induced CaCO3 mineralization. Herein, RT-qPCR was used to demonstrate that the addition of NiCl2 dramatically upregulated the expression of urease structural gene ureC that was correlated with nickel binding in Neobacillus mesonae strain NS-6. Homology modeling and molecular docking were employed to construct the three-dimensional structure of urease and seek the key residues involved in nickel binding process, and virtual mutation technology was adopted to inform three key residues coordinated with nickel ions and urea, His249, His275, and Asp363. Four metrics, including root mean square deviation values for mutations of those key residues in urease-urea complexes severally and wild-type, were calculated by molecular dynamics simulations when they were mutated into alanine, respectively. Subsequently, the mutations of H249A, H275A, and D363A were characterized using western blotting to reveal a decrease in the relative expression and activity of urease, along with a corresponding reduction in CaCO3 precipitation. Ultimately, the mutations also exhibited that they had lower substrate affinity and catalytic efficiency for urea through enzymatic properties analysis. The findings suggested that those residues played a pivotal role in UICP of strain NS-6, which would expand the theoretical basis for modulating urease activity.IMPORTANCEUrease-producing bacterium is of great importance in diverse application fields, such as environmental remediation, due to its key driving characteristics in catalyzing urea hydrolysis via urea-hydrolytic induced CaCO3 precipitation (UICP). As essential cofactors of urease, nickel ions play a crucial role in regulating urease catalysis and maintaining structural stability. Numerous investigations have emphasized the impact of nickel ions on urease activity in recent years, to our best knowledge, only a few literatures have studied the molecular-level regulation of nickel-ligand residues. This study focused on the highly urease-producing bacterial Neobacillus mesonae NS-6 to explore the effects of specific nickel-ligand residues on the urease-aided CaCO3 mineralization process using molecular simulation predictions and targeted mutation experiments. The aim was to provide a molecular-level understanding of the interactive effects between urea and critical residues associated with the urease active center, as well as propose an effective modification strategy to enhance the application of UICP in future environmental areas.

Dielectric high gradient insulator – Progress towards multilayer insulating structures

High gradient insulators (HGI) consisting of ceramic and metallic alternating layer structure, have been shown to reduce surface breakdown occurrence in high voltage devices. Recently, the HGI's metal layers were replaced with high dielectric constant ceramics, creating dielectric high gradient insulators (DHGI) that were shown to outperform pure alumina analog. A 2-layer DHGI prototype manufactured by spark plasma sintering (SPS) demonstrated an increased surface breakdown field and fewer surface breakdowns during conditioning, compared to plain alumina. However, weak breakdowns at the opposite polarity were observed in the 2-layer structure. This study focuses on overcoming this issue by introducing a 3-layer design, with two high dielectric layers capping a plain alumina layer. Breakdown tests confirmed the elimination of weak breakdowns and improved dielectric strength, consistent with simulations predictions. Additionally, post-SPS air annealing was shown to be essential for removing adsorbed gases and recovering the high dielectric layers composition that changed during SPS. The annealed DHGIs were shown to reduce significantly the breakdown pulses during high-voltage conditioning. The 3-layer DHGI exhibited a 33.5 % higher breakdown field than plain alumina and a 13.5 % improvement over the 2-layer DHGI reported earlier.

Molecular dynamics insight of interaction between Artemisinin and its derivatives and the cancer cell membrane

In the present study, a molecular dynamics simulation approach has been utilized to investigate the effectiveness of four molecules, including Artemisinin, a natural product, and its derivatives Dihydroartemisinin, Artesunate, and Artemisone, on a membrane of a cancerous cell. Performed simulations predicted that Dihydroartemisinin and Artemisone form stronger hydrogen bonds with the cancer membrane, exhibit higher mobility, and have a longer lifetime at the water-membrane interface. Artemisone molecules could penetrate the hydrophobic part of the lipid’s tail, leading to higher fluidity of the cancer membrane. These two compounds exerted the greatest effect on the properties and characteristics of the membrane model while showing stronger anti-cancer effects than the other two compounds. The simulation outcomes and predictions were found to agree with the results of experimental studies. It is noticeable that Dihydroartemisinin and Artemisone enter the cancer cell membrane from their functional group side, while Artemisinin and Artesunate enter from their peroxide ring side.

On the pillow-plate heat exchanger with wavy walls and filled with a hybrid nanofluid: Numerical simulation and data-assisted prediction

Pillow-plate heat exchangers have garnered significant attention due to their enhanced heat transfer capabilities compared to traditional flat-plate designs. Considering previous studies, the effects of incorporating wavy plates and hybrid nanofluids have not been widely investigated. This study aims to address this gap by numerically simulating the flow and heat transfer characteristics. After validation by the experiments, the numerical investigation examines the influence of varying wave amplitude and wavelength as well as the Reynolds number and the volume fraction of the nanofluid on the hydrodynamic and thermal field. Furthermore, the irreversibility analysis due to heat transfer and friction is also performed. An artificial neural network model is applied to provide a data-assisted prediction of the heat exchanger performance, reducing the need for costly simulations. Findings: The results show that using wavy plates and the hybrid nanofluid boosted the Nusselt number in the range of 12–42 %, while it has a modest impact on the pressure drop by a value of almost 10 %. The proposed geometries demonstrate consistent thermo-hydrodynamic advantages, with performance evaluation criteria (PEC) values exceeding unity. Decreasing the wavelength and increasing the wave amplitude can further improve the PEC, with a maximum value of nearly 1.8.

Optimizing 3D Laser Foil Printing Parameters for AA 6061: Numerical and Experimental Analysis

Abstract This study utilizes a technology known as 3D laser foil printing (LFP) to create precise structures by layering metal foils using laser welding. Metal foils have the advantages of rapid cooling and efficient heat conduction, allowing for the formation of fine-grained structures. However, when dealing with materials like aluminum alloys in laser processes, defects can arise as a result of their high reflectivity. To address this challenge, laser circular oscillation welding (LCOW) is applied to the LFP process. LCOW's circular motions with higher scanning frequencies widen the keyholes and reduce some defects such as spattering, bubble formation, and microcracks. Simulation predictions with an error margin of approximately 10% in comparison to experimental results demonstrate the reliability of the model. Furthermore, the study integrates circular packing design with artificial neural networks to create comprehensive processing maps tailored to different criteria for extracting optimal welding parameters. As a result, for the optimized processing parameters screened using the above systematic process, no cracks were observed on the upper surface of the 3D LFP parts produced with a laser power of 800 W and a scanning speed of 550 mm/s, and only 0.12% porosity was observed from the cross section of the sample. Future research will focus on incorporating simulation results to model microstructures more precisely and continually refining LCOW parameters as new materials and technologies emerge, ensuring the ongoing enhancement of weld quality in the 3D LFP process.

Impact resistance characteristics of pipeline system covered with W-shape elastic-porous metallic damper

As a novel elastic-porous damping material fabricated through entangled wire mesh, W-shape elastic-porous metallic damper (W-EPMD) is considered an ideal damping element for coated pipeline system due to the micro dry friction between metal wires, which induces energy dissipation. The complex interwoven cellular formations of metallic wire mesh pose challenges in characterizing its dynamic characteristics. In this work, the dynamic properties of the pipeline system covered with W-EPMD under various impact conditions, including the acceleration response and impact isolation coefficient, were investigated by numerical simulations and experimental analysis. Constitutive models used to characterize the hysteresis behavior of W-EPMD were introduced, comprising Yeoh and Bergström-Boyce models, and parameter identification were conducted through quasi-static experiments. The reliability of the established numerical model was confirmed through drop impact experiments. The results demonstrate that there is a maximum discrepancy of 9.1 % between the simulation predictions and experimental results of the stress-strain curve. The impact isolation coefficient of the pipeline system covered with W-EPMD exhibits a fluctuating trend with the rise of the pulse peak, while the maximum compression of W-EPMD steadily increases. During the pipeline impact process, the increased density of W-EPMD reduces the impact resistance of the pipeline system, while excessively low density leads to over-compression and structural damage to W-EPMD. Furthermore, the discrepancy of the acceleration response between experimental and numerical results under various excitation signals remain within 6 %, demonstrating that the hysteresis model effectively characterizes the impact resistance characteristics of the pipeline system covered the W-EPMD.

Analysis and Prediction of Chymotrypsin Substrate Preferences through Large Data Acquisition with Target-Free mRNA Display.

Oral delivery of peptide therapeutics is limited by degradation by gut proteases like chymotrypsin. Existing databases of peptidases are limited in size and do not enable systematic analyses of protease substrate preferences, especially for non-natural amino acids. Thus, stability optimization of hit compounds is time and resource intensive. To accelerate the stability optimization of peptide ligands, we generated large datasets of chymotrypsin-resistant peptides via mRNA display to create a predictive model for chymotrypsin-resistant sequences. Through analysis of enriched motifs, we recapitulate known chymotrypsin cleavage sites, reveal positionally dependent effects of monomers on peptide cleavage, and report previously unidentified protective and destabilizing residues. We then developed a machine-learning-based modelpredicting peptide resistance to chymotrypsin cleavage and validated both model performance and the NGS experimental data by measuring chymotrypsin half-lives for a subset of peptides. Finally, we simulated stability predictions on non-natural amino acids through a leucine hold-out model and observed robust performance. Overall, we demonstrate the utility of mRNA display as a tool for big data generation and show that pairing mRNA display with machine learning yields valuable predictions for chymotrypsin cleavage. Expansion of this workflow to additional proteases could provide complementary predictive models that focus future peptide drug discovery efforts.

Tips versus Holes: ×10 Higher Scattering in FIB-made Plasmonic Nanoscale Arrays for Spectral Imaging.

Plasmonic nanostructure arrays, designed for performance as pixels in an advanced SERS imaging device, were fabricated by gallium focused ion beam (FIB). Though the FIB is best suited for etching holes and negative structures, our previously reported simulations favor protrusions. Herein, we report on the FIB methodology to "sculpt" positive structures by "ion-blasting" away the surrounding material. Nanoprotrusions and nanoholes with different aspect ratios are compared experimentally with depth and height controlled by the dwell time. The amplitude and spectra of optical absorption and scattering from the two species are compared as a function of structure height. Measurements were performed using ASI's model Rainbow hyperspectral camera, demonstrating the utility of hyperspectral microscopy for plasmonic imaging applications. Both the scattered and absorbed radiation display the broad peak qualitatively similar to the localized surface plasmon (LSP) scattering spectrum of gold nanospheroids. The intensity of the scattered light from the protrusions-measured in dark-field-was observed to be an order of magnitude higher than that from the nanoholes, consistent with simulation predictions. Poor contrast against bright background specular reflection is inherent in bright-field reflection mode; image-processing succeeded in detecting patterns indiscernible to the eye. To extract the absorption the full hyperspectral image field was exploited, allowing the sample to serve as its own reference. The resulting spectra display a plasmonic resonance which grows stronger and increasingly red-shifted at increasing heights, corroborating visual observations of changes in sample hue.

Optimization method for predicting coal reservoir fractures in the Laochang area of Eastern Yunnan using paleotectonic stress inversion

IntroductionCoal reservoir fractures serve as critical storage spaces and migration pathways for coalbed methane (CBM), significantly influencing CBM enrichment. The characteristics of coal reservoir fracture development can be obtained using traditional simulation methods, but these still have shortcomings. This work presents an optimization approach for the traditional method.MethodsThis study introduces an optimization approach for traditional methods with two novel contributions. This study integrates the simulation of tectonic stress fields with fracture prediction, using surface sandstone fractures as constraints to reconstruct the paleostress field of the coal seam, while also accounting for the influence of coal thickness on fracture development to calculate fracture density.ResultsThe predicted fracture density results were validated against measured values from the Bailongshan mine and Xiongdong coal mine with a relative error of approximately 12%, suggesting a reasonable degree of reliability.DiscussionBased on the results of the fracture simulation predictions, it is believed that the coal seam fracture density in the study area is mostly 10–20 lines/m and that the sweet spot for CBM development is located in the Yuwang block.

A Software-Defined Radio Platform for Teaching Beamforming Principles

This paper presents the development and validation of a hybrid beamforming system based on software-defined radio (SDR), designed for telecommunications engineering education. The system provides an agile and user-friendly platform that allows students to observe, test, and evaluate beamforming techniques in real time. The platform integrates a multichannel SDR device (USRP N310) with traditional radiofrequency equipment and open-source software, facilitating hands-on learning experiences. The paper details the proposed hardware and software architecture and documents the calibration and validation phases. The testing and validation processes were conducted using a 3.5 GHz antenna array in both indoor and outdoor environments. The results demonstrated the system’s effectiveness in achieving the desired beam orientations, with experimental results aligning closely with simulation and theoretical predictions. Significant differences in the radiation patterns observed between the indoor and outdoor measurements were documented, highlighting the impact of environmental factors on beamforming performance. The insights gained from this research provide valuable contributions to the education of future telecommunications engineers, enhancing their understanding of practical beamforming applications and the integration of modern SDR technology.

Lacustrine Wetlands Landscape Simulation and Multi-Scenario Prediction Based on the Patch-Generating Land-Use Simulation Model: A Case Study on Shengjin Lake Reserve, China

Landscape simulation and prediction are crucial for understanding the dynamic evolution and future trends of wetlands. However, only a few existing studies have focused on the applicability and limitations of commonly used land-use/cover change (LUCC) simulation models in lake wetland landscapes. Taking Shengjin Lake Reserve in China as the study area, we firstly analyzed landscape variations during 2010–2020 using multisource remote sensing images. Then, the patch-generating land-use simulation (PLUS) model was employed to simulate wetland landscapes in 2020, the accuracy and limitation of which in simulating lacustrine wetlands were also explored. Lastly, the changing trends of wetland landscapes in 2030 under different development scenarios were predicted. The results show that the landscape of Shengjin Lake Reserve has changed significantly during 2010–2020, with increases in mudflats, reservoirs/ponds, woodlands, and built-up land, and there has been decreases in lakes, grass beaches, and croplands. The PLUS model demonstrated an ideal simulation accuracy for Shengjin Lake Reserve, with the overall accuracy exceeding 80%, kappa coefficient greater than 0.75, and figure of merit (FOM) coefficient of 0.35, indicating that the model can capture the dynamic changes in wetland landscapes accurately. The simulation accuracy can be effectively improved with the adjacent initial year, shorter time interval, and the primary driver factors. Under the natural development scenario, the number of patches in the Shengjin Lake Reserve increased sharply, and landscape fragmentation intensified. Under the urban development scenario, the expansion of built-up land increased, and the average patch area increased. In the ecological protection scenario, the Shannon diversity index and Shannon evenness index of the landscape improved significantly, and the natural wetlands such as grass beaches and lakes can be protected effectively. Our study confirms the applicability of the PLUS model in simulating and predicting lacustrine wetlands landscapes, and the conclusions provide a scientific basis for formulating reasonable development strategies to realize wetland resource conservation and management.

Developing the optimized control scheme for digital light processing 3D printing by combining numerical simulation and machine learning-guided temperature prediction

Digital light processing (DLP) 3D printing has attracted significant attention for its rapid printing speed, high accuracy, and diverse applications. However, the continuous DLP printing process releases substantial heat, resulting in a swift temperature rise in the curing area, which may lead to printing failures. Due to the lack of effective means to measure real-time temperature changes of the curing surface during continuous DLP 3D printing, the prevailing approach is to predict temperature variations during printing via numerical simulation. Nevertheless, temperature prediction methods relying solely on numerical simulation tend to be slow and overlook heat exchange dynamics during printing, potentially resulting in prediction inaccuracies, particularly for complex models. To address these issues, this paper proposes a method to combine numerical simulation and a machine learning approach for temperature prediction in the DLP 3D printing process, along with a printing control scheme generation method. Firstly, the m+nth order autocatalytic kinetic model considering the light intensity and the Beer–Lambert law are employed to formulate the heat calculation equation for the photopolymer resin curing reaction. Subsequently, a heat exchange calculation equation is established based on Fourier heat conduction law and Newton’s cooling equation. A numerical simulation model for temperature changes during the printing process is then developed by integrating the heat calculation equation, heat exchange calculation equation, and measurement data from Photo-DSC. Furthermore, a temperature measurement device for the printing process is designed to validate the accuracy of the numerical simulation. Following this, an improved Long Short-term Memory (LSTM) network is proposed, using temperature change data generated by the numerical simulation model to train the network for rapid (2×10−4s/layer) prediction of temperature changes during printing. Finally, aiming for the shortest printing time, an optimized control scheme planning algorithm and a target function are designed based on the model’s temperature change data and the monomer’s flash point to ensure the temperature remains below this threshold. This algorithm can automatically generate the optimal printing control scheme for any model. Experimental results demonstrate that the proposed temperature prediction method can predict temperature variation accurately. Based on this, the generated printing control scheme can guarantee efficient and high-quality manufacturing for anymodel.

Land-Use-Change-Driven Erosion and Sediment Transport in the Yaqui River Sub-Basin (Mexico): Insights from Satellite Imagery and Hydraulic Simulations

Soil erosion and sediment transport are significant concerns in the Yaqui River sub-basin in northwest Mexico, driven by land use changes and environmental degradation. This study aims to evaluate erosion processes between 2000 and 2020 using a combination of satellite imagery and numerical simulations with Iber software (Version 2.5.2). The primary objective is to assess the impacts of land use changes, particularly the conversion of forest to grassland, on erosion rates and sediment transport. Satellite images from 2000 and 2020 were analyzed to detect land cover changes, while Iber’s sediment transport module was used to simulate erosion patterns based on the Meyer–Peter and Müller equation for bedload transport. Hydrological and topographical data were incorporated to provide accurate simulations of flow velocity, depth, and erosion potential. The results reveal a 35.3% reduction in forest cover, leading to increased erosion and sediment transport in steep areas. Simulation predictions highlighted areas with high future erosion potential, which are at risk of further soil loss if current trends continue. Flow velocity increased, contributing to riverbank destabilization and higher sediment yield, posing a risk to infrastructure such as the Álvaro Obregón Dam. This study underscores the need for targeted erosion control measures and sustainable land management practices to mitigate future risks and protect vital infrastructure in the Yaqui River Basin.

Optimizing laser-induced phase transformations in Sb2S3 thin films: Simulation framework and experiments

We present a novel simulation approach combined with pulsed laser experiments, spectroscopic ellipsometry, and Raman spectroscopy to comprehensively analyze phase transformation dynamics in thin films. The simulations apply to any thin film stack and incorporate critical factors, such as thin film interference, heat transfer, and temperature-dependent optical properties during heating and melting. As a case study, we investigate the picosecond laser-induced amorphization of antimony sulfide (Sb2S3) thin films, a promising alternative to traditional phase-change materials in photonic applications to validate the simulation model. The computational efficiency of our simulations enables not only the investigation of the laser-induced phase transformation but also the optimization of key process parameters and parameter fitting. The simulations identified optimal film thickness and laser fluence parameters that maximize energy efficiency, melting effectiveness, and quenching rate while ensuring high reflectivity contrast between the amorphous and crystalline states. By constructing a wide-ranging, high-resolution parameter map of the laser fluence and film thickness dependence of the melting process, we demonstrate how this model guides the understanding of phase transformation dynamics. Raman spectroscopy confirms the polycrystalline to amorphous transition of Sb2S3 and provides a semiquantitative estimate of the amorphous fraction as a function of laser fluence, which is qualitatively consistent with the simulation predictions of the model. The open-source simulation framework, experimentally validated, provides valuable insights into laser-induced amorphization dynamics in Sb2S3 and related phase-change material thin films, enabling rapid optimization of photonic devices.

Effect of substrate rotation on the growth behavior and topography of the [formula omitted] film deposited over a large area using DC magnetron sputtering with a rectangular target: Simulation approach and experiment

It is often challenging to achieve a uniform film with the necessary surface properties when using deposition techniques. If the film is not uniformly deposited or lacks suitable surface roughness, it may not meet performance standards. To understand and control the factors controlling the deposition process, this paper presents a simulation approach to predict the growth behavior of a Ti-film deposited on substrates during the bombardment of the target with argon ions at low pressure. The simulation of the deposition process consists of several Monte Carlo simulations, which include predicting the source of the atoms flow from the target surface and tracing its trajectories through the vacuum chamber until it falls or reaches the surface of the substrates, then predicting the thickness distribution and topography of film. After conducting a comparison between simulation predictions and experimental data, it was found that the measurements obtained using the Profilometer, Scanning Electron Microscope, and Atomic Force Microscopy were relatively close. The results indicate that substrate rotation during deposition leads to a thinner film that is more uniform and less rough, it was also found that the coefficient of variation in the film thickness distribution was improved from 3.96% in the case of a non-rotating substrate to 0.33% in the case of a rotating substrate. The proposed approach can be used to improve the design of magnetron sputtering systems and to provide theoretical guidance for optimizing the thickness distribution and topographical characteristics of the performance specifications of high-quality film without economic constraints.

The SRG/eROSITA All-Sky Survey

Context. Galaxy groups lying between galaxies and galaxy clusters in the mass spectrum of dark matter halos play a crucial role in the evolution and formation of the large-scale structure. Their shallower potential wells compared to clusters of galaxies make them excellent sources to constrain non-gravitational processes such as feedback from the central active galactic nuclei (AGN). Aims. We investigate the impact of feedback, particularly from AGN, on the entropy and characteristic temperature measurements of galaxy groups detected in the SRG/eROSITA’s first All-Sky Survey (eRASS1) to shed light on the characteristics of the feedback mechanisms and help guide future AGN feedback implementations in numerical simulations. Methods. We analyzed the deeper eROSITA observations of 1178 galaxy groups detected in the eRASS1. We divided the sample into 271 subsamples based on their physical and statistical properties and extracted average thermodynamic properties, including the electron number density, temperature, and entropy, at three characteristic radii from cores to outskirts along with the integrated temperature by jointly analyzing X-ray images and spectra following a Bayesian approach. Results. We present the tightest constraints with unprecedented statistical precision on the impact of AGN feedback through our average entropy and characteristic temperature measurements of the largest group sample used in X-ray studies, incorporating major systematics in our analysis. We find that entropy shows an increasing trend with temperature in the form of a power-law-like relation at the higher intra-group medium (IGrM) temperatures, while for the low-mass groups with cooler (T < 1.44 keV) IGrM temperatures, a slight flattening is observed on the average entropy. Overall, the observed entropy measurements agree well with the earlier measurements in the literature. Additionally, comparisons with the state-of-the-art cosmological hydrodynamic simulations (MillenniumTNG, Magneticum, OWL) after applying the selection function calibrated for our galaxy groups reveal that observed entropy profiles in the cores are below the predictions of simulations. At the mid-region, the entropy measurements agree well with the Magneticum simulations, whereas the predictions of MillenniumTNG and OWL simulations fall below observations. At the outskirts, the overall agreement between the observations and simulations improves, with Magneticum simulations reproducing the observations the best. Conclusions. These measurements will pave the way for achieving more realistic AGN feedback implementations in numerical simulations. The future eROSITA Surveys will enable the extension of the entropy measurements in even cooler IGrM temperatures below 0. 5 keV, allowing for the testing of the AGN feedback models in this regime.

Fast Outflow in the Host Galaxy of the Luminous z = 7.5 Quasar J1007+2115

The James Webb Space Telescope opens a new window to directly probe luminous quasars powered by billion solar mass black holes in the Epoch of Reionization and their coevolution with massive galaxies with unprecedented details. In this paper, we report the first results from a deep NIRSpec integral field unit spectroscopic study of a quasar at z = 7.5. We obtain a bolometric luminosity of ∼1.8 × 1047 erg s−1 and a black hole mass of ∼0.7–2.5 × 109 M ⊙ based on the Hβ emission line in the quasar spectrum. We discover ∼2 kpc scale, highly blueshifted (∼−870 km s−1) and broad (∼1400 km s−1) [O iii] line emission after the quasar point-spread function has been subtracted. Such line emission most likely originates from a fast, quasar-driven outflow, the earliest one at galactic scales known so far. The dynamical properties of this outflow fall within the typical ranges of quasar-driven outflows at lower redshift, and the outflow may be fast enough to reach the circumgalactic medium. Combining both the extended and nuclear outflow together, the mass outflow rate, ∼300 M ⊙ yr−1, is ∼60%–380% of the star formation rate of the quasar host galaxy, suggesting that the outflow may expel a significant amount of gas from the inner region of the galaxy. The kinetic energy outflow rate, ∼3.6 × 1044 erg s−1, is ∼0.2% of the quasar bolometric luminosity, which is comparable to the minimum value required for negative feedback based on simulation predictions. The dynamical timescale of the extended outflow is ∼1.7 Myr, consistent with the typical quasar lifetime in this era.

Prediction of residual stresses in additively manufactured parts using lumped capacitance and classical lamination theory

Several industries are interested in Laser Powder Bed Fusion (L-PBF) Additively Manufactured (AM) metal parts because their designs can be made arbitrarily complex while retaining bulk-type material properties. However, the residual stresses (RS) and distortions caused by the heat gradients inherent to L-PBF processes are detrimental to the structural integrity of the parts and must be taken into consideration during the part design cycle. Predicting the state of stresses in as-built 3D printed parts is a difficult problem that is typically approached with the use of transient thermomechanical Finite Element Models (FEMs). However, the nonlinearities associated with AM processes are difficult to capture in these FEMs without increasing the computational cost of the simulation, limiting their ability to be incorporated into practical design cycles. This work presents a novel analytical framework that combines lumped capacitance nonlinear heat transfer with time dependent classical lamination theory to efficiently and accurately predict RS in as-built L-PBF parts without the need of FEMs. The simulation was compared to Neutron Diffraction (ND) residual strain measurements taken at Oak Ridge National Laboratories (ORNL) as well as Synchrotron X-ray Diffraction (XRD) strain data published by the National Institute of Standards and Technology (NIST). The simulation predictions and the experimental data showed excellent agreement for the in-plane strain directions, and general agreement for the out of plane strain component, highlighting an area where further development can be implemented.