Influence Of Topography Research Articles

Variation in Root Biomass and Distribution Based on the Topography, Soil Properties, and Tree Influence Index: The Case of Mt. Duryun in Republic of Korea

Root biomass and distribution are influenced by abiotic factors, such as topography and soil physicochemical properties, determining belowground productivity. Hence, we investigated the variation in root biomass and vertical root distribution based on the topography, soil physicochemical properties, and tree influence index, and their relationships, across soil depths (0–10 cm, 10–20 cm, and 20–30 cm) and topographical gradients in a warm-temperate forest in Mt. Duryun, Republic of Korea. Two contrasting research sites were established: a lower slope oriented at ≤3° and an upper slope with a slope of 30°. Each site comprised eleven 400 m2 sampling plots from which root samples from various diameter classes (<2 mm, 2–5 mm, 5–10 mm, and >10 mm) were collected. While the bulk density increased with soil depth in the lower slope, the organic matter, available phosphorus, Ca2+, and Mg2+ showed a reversed pattern. Linear mixed-effects models generally revealed significant negative correlations between root biomass and soil pH, total nitrogen, and cation exchange capacity, particularly in small roots (βstd = −1.03 to −1.51) and coarse roots (βstd = −6.30). Root biomass exhibited a 10–15% increase in the upper slope compared to the lower slope, particularly in fine (median = 52.0 g m2–65.64 g m2) and medium roots (median = 56.04 g m2–69.52 g m2) at a 0–20 cm soil depth. While no significant correlation between root biomass and the tree influence index was found on the lower slope, a different pattern was found on the upper slope. Our results indicate that the variation in root biomass and distribution can also be explained by the differences in the soil environment and topographical positions.

The influence of topography and transport history on the composition of clastic deposits from the Jurassic Tlaxiaco basin, southern Mexico: limitations on the use of mineral and chemical indices as proxies for paleoclimate

ABSTRACT The mineral and chemical composition of clastic deposits is commonly used as a proxy for reconstructing the climatic history of the Earth. A mineralogical and chemical study of clastic deposits from the Jurassic Tlaxiaco basin of southern Mexico illustrates that the entire sedimentary system in which detritus is generated, subaerially transported, and deposited exerts a major control on the composition of sedimentary rocks, placing some constraints on the use of mineral and chemical indices as paleoclimatic proxies. Since clastic deposits of the Tlaxiaco basin were formed under similar humid climatic conditions, but in sedimentary systems with different topography and transport histories, they represent a natural laboratory for testing the control that these parameters exert on the composition of clastic rocks. Our petrographic and chemical results show that, in sedimentary systems associated with low relief and in which detritus had the possibility of being temporarily stored in floodplains and meandering bars, parent rocks and the derivative detritus interacted with weathering fluids over a long time, producing clastic deposits with composition that is representative of the weathering conditions and climate of the region. On the other hand, in environments associated with steep and tectonically active relief, the rates of sediment transport and burial exceed the rate at which weathering can generate detritus, producing sedimentary deposits with composition that largely underestimates potential weathering and provide unreliable information on climate. Mineral and chemical indices should be considered as reliable paleoclimate proxies only when sedimentological data indicate that the sedimentary system allowed sediment interaction with weathering fluids over a long time.

Spatial and temporal change of hydrological connectivity in the Wusuli River Basin

Study regionThe study region of this paper is the Wusuli River basin, which is comprised of nine sub-basins. The basin is located on the border between China and Russia. Study focusWe investigated the hydrological connectivity of the Wusuli River basin, an international boundary river that has received little attention. The spatial and temporal evolution of hydrological connectivity over the last 38 years was characterized using a geostatistical connectivity function combined with the remote sensing technique. In particular, some key sub-basins were identified to help strengthen China-Russian cooperation on ecological protection of the basin. New hydrological insights for the regionThe results show that the hydrological connectivity in the northwestern region of the Wusuli River basin was unstable with several surges from 1984 to 2021. Analyzed from a temporal perspective, the effective range of hydrological connectivity is expanding and connectivity is showing an overall upward trend due to increased fluctuations in rainfall. Analyzed from a spatial perspective, the connectivity of the right bank of the Wusuli River is better than that of the left bank due to the influence of topography. Stable connectivity pathways are a must for efficient hydrological connectivity within a watershed. Another finding is that the capacity of the Wusuli River basin to resist extreme rainfall is weakening.

Dune behavior in the Source Area of the Yellow River under climate changes observed from various remote sensing datasets

Sand dunes are a landscape feature with a quick response time to climate change and human influences (e.g. grazing, greening projects, and fixation structures). Their migration rates and their development can help to gather information about changing environmental conditions over time. The Source Area of the Yellow River (SAYR), located upon the Tibetan Plateau, is highly complex with topographical, hydrological, and climatological influences on active dunes, making it a good study area for these interactions. Based on remote sensing datasets, spanning the last 54 years, 415 dunes were mapped for migration rate calculations. Further, climate data from ERA-5 reanalysis and a local climate station was used to assess their changes within a changing climate. Generally, dune migration rates are rather slow with an average of 3.62 m y-1. In accordance, the averaged resultant drift potential (RDP) values are lower than 10 m3/s−3(−|-). Further, we assessed the density development of the main active barchan dune field in direct premise of the Yellow River. Throughout the past 54 years, we observed the emergence of more than 5 new barchans per square kilometer. This increase is likely attributed to higher sand flux from the Yellow River, which has resulted from increased discharge due to declining snowfall and rising precipitation levels.

Open‐source stand‐alone version of atmospheric model Aeolus 2.0 software

AbstractIn this discourse, we present the unveiling of an open‐source software package designed to facilitate engagement with the atmospheric model, Aeolus 2.0. This particular iteration stands as a self‐contained model of intermediate complexity. The model's dynamical core is underpinned by a multi‐layer pseudo‐spectral moist‐convective Thermal Rotating Shallow Water (mcTRSW) model. The pseudo‐spectral problem‐solving tasks are handled by the Dedalus algorithm, acknowledged for its spin‐weighted spherical harmonics. The model captures the temporal and spatial evolution of vertically integrated potential temperature, thickness, water vapour, precipitation, and the intricate influence of bottom topography. It comprehensively characterizes velocity fields in both the lower and upper troposphere, employing resolutions spanning a spectrum from the smooth to the coarse, enabling the exploration of a wide range of dynamic phenomena with varying levels of detail and precision.

Revealing forest structural "fingerprints": An integration of LiDAR and deep learning uncovers topographical influences on Central Amazon forests

Amazon forests are characterized by rich structural diversity. However, the influence of factors such as topography, soil attributes, and external disturbances on structural variability is not always well characterized, and traditional structural metrics may be inadequate to capture this type of complexity. While LiDAR offers expanded structural insights, traditional parameters used in LiDAR analysis, such as mean or maximum canopy height, are not always well directly linked to environmental variables like topography. Emerging approaches merge LiDAR with machine learning to uncover deeper structural complexities. However, work to date may fail to fully utilize the potential of fine-scale LiDAR information. Here we introduce a novel approach, leveraging 2D point cloud images derived from a profiling canopy LiDAR (PCL). The technique targets intricate details within LiDAR point clouds by using deep learning algorithms. With a dataset from the Central Amazon comprising 18 multitemporal transects of 450 m in length, our objective was to detect structural "fingerprints" of varied topographical types along a hillslope, comprising: Riparian, White-sand, and Plateau, and to detect any gradient of structural shifts based on terrain variations here represented by the height above the nearest drainage (HAND). The dataset was trained and tested using a leave-one-group-out approach (LOGO) in which, for each iteration, a complete 450 m multitemporal transect was excluded from training and tested after each iteration. The fast.ai platform and a ResNet-34 architecture, coupled with transfer learning, were used to perform a classification to distinguish between three topographical types. Furthermore, a hybrid model combining a Convolutional Autoencoder, and Partial Least Square (PLS) regression was designed to detect forest structural gradient correlations with HAND variation. Cross-validation achieved a promising high weighted F1 score of 0.83 to classify forests based on the topographical types. Additionally, a combined Convolutional Autoencoder and PLS regression revealed a strong correlation (R2 = 0.76) between actual and predicted HAND. Innovatively combining deep learning with ground-based PCL LiDAR, our study revealed unique Amazon Forest structures connected to topographic variation. Our findings underscore the transformative potential of such integrative approaches for investigating forest dynamics and promise a powerful new tool for understanding climate-related forest structure change.

Vertical structures and microphysical characteristics of summer precipitation in North China detected by GPM-DPR

The vertical structure and microphysical characteristics of stratiform precipitation (SP) and convective precipitation (CP) in North China are revealed based on the GPM-DPR product during boreal summer of 2014–2021 in this study. Additionally, the differences in precipitation features between the mountain and the plain are investigated. Under the combined influence of climatic factors and local topography, the precipitation amount is larger in the plain than in the mountain while precipitation frequency exhibits an opposite pattern. The proportions of the two precipitation types are similar in the mountain and the plain, with CP contributing to approximately a quarter of total precipitation frequency. In terms of mean intensity, both SP and CP are roughly 20 % more intense in the plain than in the mountain. The greater number of light SPs is a major contributor to higher precipitation frequency in the mountain, while more intense CPs result in larger precipitation amount in the plain. Compared to the mountain, the precipitation system is deeper in the plain, where higher storm top altitudes (STAs) and larger freezing level heights contribute to more intense CPs. Besides, it is observed that for the STA, more intense CPs occur in the plain compared to the mountain. In both the mountain and the plain, the coalescence process is dominant in the low-level layers for heavy (8–20 mm/h) to storm-level (>20 mm/h) CPs. Compared to the mountain, the low-level growth of hydrometeor size and radar reflectivity is more significant in the plain. These findings are important to quantitative precipitation estimation and precipitation prediction in the mountainous region, and can help understand the influence of local topography on precipitation.

Influence of chemistry and topography on the wettability of copper

To understand the complex interplay of topography and surface chemistry in wetting, fundamental studies investigating both parameters are needed. Due to the sensitivity of wetting to miniscule changes in one of the parameters it is imperative to precisely control the experimental approach. A profound understanding of their influence on wetting facilitates a tailored design of surfaces with unique functionality.We present a multi-step study: The influence of surface chemistry is analyzed by determining the adsorption of volatile carbonous species (A) and by sputter deposition of metallic copper and copper oxides on flat copper substrates (B). A precise surface topography is created by laser processing. Isotropic topography is created by ps laser processing (C), and hierarchical anisotropic line patterns are produced by direct laser interference patterning (DLIP) with different pulse durations (D).Our results reveal that the long-term wetting response of polished copper surfaces stabilizes with time despite ongoing accumulation of hydrocarbons and is dominated by this adsorption layer over the oxide state of the substrate (Cu, CuO, Cu2O). The surfaces’ wetting response can be precisely tuned by tailoring the topography via laser processing. The sub-pattern morphology of primary line-like patterns showed great impact on the static contact angle, wetting anisotropy, and water adhesion. An increased roughness inside the pattern valleys combined with a minor roughness on pattern peaks favors air-inclusions, isotropic hydrophobicity, and low water adhesion. Increasing depth of the primary topography can also induce air-inclusions despite increasing peak roughness while time dependent wetting transitions were observed.

Integrating spatial patterns and driving factors of cultural ecosystem services into territorial spatial governance: A case study of the Horqin Sandy Land with multi-ethnic settlements

Although the spatial patterns of cultural ecosystem services (CES) are often considered in land use management, there is a noticeable disparity in research coverage compared to biophysical ecosystem services (BES). Moreover, there is a lack of research focusing on human perceptions of CES. To address this gap, this study proposed a CES-BES framework that integrates spatial patterns, driving factors of CES, and their interactions with BES, based on the general framework of social and ecological systems. To alleviate the dual pressures of ecological degradation and cultural preservation, this study utilized the constructed CES-BES framework to offer land use recommendations aimed at promoting sustainable development in the Horqin Sandy Land. CES and BES were quantified through a combination of questionnaire surveys, SolVES model and process modeling, including aesthetic, biodiversity, cultural, economic, future, and historical values, as well as net primary productivity, crop production, wind protection, and sand fixation. The results revealed that CES in the Horqin Sandy Land exhibited a discernible pattern of "high in the west and low in the east." Notably, the factors perceived by respondents exerted a more substantial influence on CES than non-perceptual factors. Female respondents showed relatively lower sensitivity to CES overall, but higher sensitivity to biodiversity. Han respondents tended to prioritize biodiversity, while Mongolian respondents were more sensitive to cultural and historical values. Herders exhibited higher sensitivity to biodiversity, while respondents from other occupations showed higher sensitivity to cultural and future values. The influence of land use, topography, and accessibility on different CES was generally similar. All CES showed a positive correlation with elevation, slope, and distance to water, while they showed a negative correlation with distance to roads. Based on the results, the Horqin Sandy Land has been divided into five regions, with development recommendations provided for each region to ensure sustainable development. This study provides a novel perspective and methodological framework for CES supporting territorial spatial governance and land use management, contributing to a more comprehensive understanding of CES.

On the data assimilation of initial distribution for 2-dimensionalshallow-water equation model

Abstract The utilization of data assimilation (DA) techniques is prevalent in marine meteorology for the purpose of estimating the complete state of the system. This is done to address the practical limitations associated with measurement data, which can only be specified at finite number of discrete points within a limited domain. We develop an efficient DA algorithm to reconstruct the initial state of the shallow-water equations (SWE) within a 2-dimensional rectangular domain using sparse spatial measurement data. Our algorithm takes into account both the complete Coriolis force and the ocean bottom topography in the SWE model, resulting in accurate recovery of the initial status. After establishing the uniqueness of the solution to the nonlinear SWE with appropriate boundary conditions, we proceed to establish the conservation laws for the suitably defined energy quantity for this traveling wave system. This generalization of the known conservation laws for the simplified SWE system, which ignores the Coriolis force and topography, allows us to reveal the influence of nonconstant sea floor topography on wave propagation. In order to restore the initial state through the minimization of a cost functional using DA techniques, we proceed by deriving the adjoint problem for our iteration process. Additionally, we establish a discrete scheme for the governing equations in the Arakawa C-grid framework, from which we rigorously derive the error associated with energy conservation in discrete form. The numerical implementations are also provided to validate our proposed scheme through the verification of energy conservation and the reconstruction effect of the initial state for various configurations.

A network approach for multiscale catchment classification using traits

Abstract. The classification of river catchments into groups with similar biophysical characteristics is useful to understand and predict their hydrological behavior. The increasing availability of remote sensing and other large-scale geospatial datasets has enabled the use of advanced data-driven approaches to classify catchments using traits such as topography, geology, climate, land cover, land use, and human influence. Unsupervised clustering algorithms based on the Euclidean distance are commonly used for trait-based classification but are not suitable for highly dimensional data. In this study we present a new network-based method for multi-scale catchment classification, which can be applied to large datasets and used to determine the traits associated with different catchment groups. In this framework, two networks are analyzed in parallel: the first being where the nodes are traits and the second being where the nodes are catchments. In both cases, edges represent pairwise similarity, and a network cluster detection algorithm is used for the classification. The trait network is used to investigate redundancy in the trait data and to condense this information into a small number of interpretable categories. The catchments network is used to classify the catchments into clusters and to identify representative catchments for the different groups using the degree centrality metric. We apply this method to classify 9067 river catchments across the contiguous United States at both regional and continental scales using 274 non-categorical traits. At the continental scale, we identify 25 interpretable trait categories and 34 catchment clusters of sizes greater than 50. We find that catchments with similar trait categories are typically located in the same region, with different spatial patterns emerging among clusters dominated by natural and anthropogenic traits. We also find that the catchment clusters exhibit distinct hydrological behavior based on an analysis of streamflow indices. This network approach provides several advantages over traditional means of classification, including better separation of clusters, the use of alternate similarity metrics that are more suitable for highly dimensional data, and reducing redundancy in the trait information. The paired catchment–trait networks enable analysis of hydrological behavior using the dominant trait categories for each catchment cluster. The approach can be used at multiple spatial scales since the network topologies adjust automatically to reflect the trait patterns at the scale of investigation. Finally, the representative catchments identified as hub nodes in the network can be used to guide transferable observational and modeling strategies. The method is broadly applicable beyond hydrology for classification of other complex systems that utilize different types of trait datasets.

Temperature Dependence of the Dielectric Constant on the Lunar Surface Based on Mini-RF and Diviner Observations

Radar observation is an effective way to understand subsurface structures in terms of the dielectric constant, whose controlling factors include chemical composition, packing density, and water/ice content. Recently, laboratory measurements have shown that the dielectric constant of lunar regolith simulants also depends on the temperature, which has never been evaluated from remote sensing data. In this study, we estimated the dielectric constant from the Miniature Radio Frequency (Mini-RF) data on a lunar crater floor in the north polar region at two different local times (i.e., different surface temperatures). We calculated the dielectric constant using the inversion method and obtained the bolometric surface temperature from the Diviner Lunar Radiometer Experiment (Diviner) data. The histograms of the estimated dielectric constant values are different between the two local times. This could be interpreted as a result of the temperature dependence of the dielectric constant, while further evaluation of the influence of topography on the incidence angle and small surface roughness is needed. Nevertheless, our result suggests that the temperature dependence of the dielectric constant should be considered when interpreting S-band radar observations of the Moon and other celestial bodies with large surface temperature differences.

The Influence of Topography on the Diurnal Rainfall Propagation in the Bay of Bengal

Abstract A significant diurnal offshore propagation of rainfall is observed extending from the eastern coast of India to the central Bay of Bengal. This study focuses on understanding the influence of topography over the Indian subcontinent on this rainfall propagation through a series of semi-idealized mesoscale numerical simulations. These simulations with varying topography highlight the crucial role of inertia-gravity waves, driven by diurnal mountain-land-sea thermal contrast between India and the Bay of Bengal, in initiating and promoting the offshore propagation of convective systems in the Bay. These waves’ phase speed of around 14.8 m s−1 aligns well with the speed of diurnal rainfall propagation. Even after eliminating the impact of Indian topography, the offshore propagating signal persists, suggesting a secondary rather than dominant effect of terrain on offshore rainfall propagation. Furthermore, the topography affects the depth of diurnal heating within the land’s boundary layer, which thus influences the amplitude, phase, and speed of the inertia-gravity waves. Specifically, the presence of higher mountains along the coastal area drives faster waves by increasing heating depth, resulting in faster rainfall propagation.

Modelling runoff components and hydrological processes in glaciated catchments of the inner Tien-Shan, Kyrgyzstan

Accurately quantifying runoff sources and understanding hydrological processes in glaciated mountain basins is essential for effective water resource management in the face of climate change. This study aimed to determine the contributions from various runoff sources in glaciated basins within the inner Tien-Shan mountains of Kyrgyzstan by utilizing integrated land surface, glacier energy-mass balance, and river routing models. To account for local topographic influences on solar radiation and cloud transmissivity processes, gridded meteorological forcing data were downscaled. The integrated models were then evaluated against observed discharge, glacier mass balance, and snow water equivalent, with a focus on the Kara-Batkak glacier reference site. Shortwave radiation correction was particularly important for improving the accuracy of model simulations. The results indicate that the peak glacier melt contribution occurs in July and August, with some basins reaching up to 54%. On an annual basis, the average contribution from glaciers across the basins was found to be 19%, while the ratios of snowmelt and rainfall were 58% and 23%, respectively. This study highlights the utility of integrated modelling approaches in understanding and quantifying runoff components in data-scarce high mountain regions. The incorporation of observed glacier data proved to be crucial for accurately representing hydrological processes under current climatic conditions. These findings underscore the significance of considering glacier dynamics and their influence on water resources to inform effective water management strategies in glaciated mountain basins.

Soil pore characteristics and the fate of new switchgrass-derived carbon in switchgrass and prairie bioenergy cropping systems

Monoculture switchgrass and restored prairie are promising perennial feedstock sources for bioenergy production on the lands unsuitable for conventional agriculture. Such lands often display contrasting topography that influences soil characteristics and interactions between plant growth and soil C gains. This study aimed at elucidating the influences of topography and plant systems on the fate of C originated from switchgrass plants and on its relationships with soil pore characteristics. For that, switchgrass plants were grown in intact soil cores collected from two contrasting topographies, namely steep slopes and topographical depressions, in the fields in multi-year monoculture switchgrass and restored prairie vegetation. The 13C pulse labeling allowed tracing the C of switchgrass origin, which X-ray computed micro-tomography enabled in-detail characterization of soil pore structure. In eroded slopes, the differences between the monoculture switchgrass and prairie in terms of total and microbial biomass C were greater than those in topographical depressions. While new switchgrass increased the CO2 emission in depressions, it did not significantly affect the CO2 emission in slopes. Pores of 18–90 µm Ø facilitated the accumulation of new C in soil, while > 150 µm Ø pores enhanced the mineralization of the new C. These findings suggest that polyculture prairie located in slopes can be particularly beneficial in facilitating soil C accrual and reduce C losses as CO2.

Patterns, drivers, and implications of postfire delayed tree mortality in temperate conifer forests of the western United States

AbstractConifer forest resilience may be threatened by increasing wildfire activity and compound disturbances in western North America. Fire refugia enhance forest resilience, yet may decline over time due to delayed mortality—a process that remains poorly understood at landscape and regional scales. To address this uncertainty, we used high‐resolution satellite imagery (5‐m pixel) to map and quantify delayed mortality of conifer tree cover between 1 and 5 years postfire, across 30 large wildfires that burned within three montane ecoregions in the western United States. We used statistical models to explore the influence of burn severity, topography, soils, and climate moisture deficit on delayed mortality. We estimate that delayed mortality reduced live conifer tree cover by 5%–25% at the fire perimeter scale and 12%–15% at the ecoregion scale. Remotely sensed burn severity (1‐year postfire) was the strongest predictor of delayed mortality, indicating patch‐level fire effects are a strong proxy for fire injury severity among surviving trees that eventually perish. Delayed mortality rates were further influenced by long‐term average and short‐term postfire climate moisture deficits, illustrating the impact of drought on fire‐injured tree survival. Our work demonstrates that delayed mortality in conifer forests of the western United States can be remotely quantified at a fine grain and landscape scale, is a spatially extensive phenomenon, is driven by fire–climate–environment interactions, and has important ecological implications.

Geoelectric model of the exposure of basement rocks to the surface in the Uimon depression of Gorny Altay based on three-dimensional numerical modeling

In the western part of the Uimon depression of the Altai Mountains, during the 2023 field season, additional measurements were carried out using electrical tomography along three profiles to clarify the structure of the area with basement rocks exposed to the surface. During the study, the results of three-dimensional inversion were obtained taking into account new electrical tomography data, and a refined geoelectric model of the study area was constructed. To verify the resulting model, 2D and 3D numerical modeling was performed with the help of which the geometric dimensions and electrical resistivity of bedrock outcrops were justified. The influence of the daytime surface topography on geoelectric sections in mountainous regions with significant differences in elevation on the earth's surface has been shown. Determining the structure of the depressions is necessary to understand the stages of neotectonic formation of the Uimon depression and the entire region, as well as to assess the seismic hazard of the Altai Mountains. In the course of further research, it is possible to trace the object to a greater depth (~100 m) using a three-electrode electrical tomography installation or alternative equipment with an increased size of the measuring-generator cable.

Rock Glacier Inventory of the Southwestern Pamirs Supported by InSAR Kinematics

Although rock glaciers (RGs) are prevalent in the southwestern Pamirs, systematic studies on them are scarce. This article introduces the first inventory of RGs in the southwestern Pamirs, situated at the western edge of the High Mountain Asia region. The inventory, established through a combination of Google Earth optical imagery and Interferometric Synthetic Aperture Radar (InSAR) techniques, encompasses details on the locations, geomorphological parameters, and kinematic attributes of RGs. A total of 275 RGs were cataloged in an area of 55.52 km2 from 3620 to 5210 m in altitude. Our inventory shows that most RGs in this region are talus-connected (213 landforms), with the highest frequency facing northeast (23%). The distribution of RGs thins from west to east and is more abundant in higher altitudes. The Shakhdara range to the south hosts a denser and more active population of RGs than the Shughnon range to the north, highlighting the influence of topography and precipitation. Overall, RGs in the southwestern Pamirs exhibit high activity levels, with active RGs predominating (58%). A comparison between active and transitional RGs showed no significant differences in elevation, temperature, and slope. Glacier-connected and glacier forefield-connected RGs demonstrated higher line-of-sight (LOS) velocities than talus-connected and debris-mantled slope-connected RGs, underscoring the significant impact of precipitation and meltwater on their activity.

Size effect characteristics and influences on fatigue behavior of laser powder bed fusion of thin wall GRCop-42 copper alloy

Size effects, influencing a material's strength, elongation, fatigue limit, and longevity, depend on the operative and dominant deformation and failure mechanisms. This study explores the size effects in additive manufactured (AM) GRCop-42 (Cu-4at%Cr-2at%Nb) thin wall structures fabricated via laser-powder bed fusion (L-PBF) and their impact on fatigue life. The influence of internal defects and surface topography on the fatigue life of specimens in both as-built and hot isostatic pressed (HIP) conditions across different thicknesses is investigated. Where micro-computed tomography (μCT) was used to quantify the internal porosity of as-built, pristine HIP'd, and fatigued HIP'd specimens, and laser microscopy was employed to quantify the surface topography of specimens prior to fatigue. Additionally, quasi-static tests were used to establish baseline mechanical properties (i.e. yield strength (YS), ultimate tensile strength (UTS), and elongation) to frame fatigue testing conditions. Results indicate a significant enhancement in fatigue life for HIP'd specimens for both thicknesses, with internal defects depicting a greater impact than surface topography. Furthermore, fractographic analysis suggests that thicker specimens exhibit higher resistance to crack propagation during fatigue testing in the absence of substantial porosity. Thus, the size effects observed on the fatigue life of L-PBF GRCop-42 appears to be dominated by internal defects.

The Distribution Law of Ground Stress Field in Yingcheng Coal Mine Based on Rhino Surface Modeling

The distribution law of the ground stress field is of great significance in guiding the design of coal mine roadway alignment, determining the parameters of roadway support, and preventing and controlling the impact of ground pressure in coal mines. A geostress inversion method combining Rhino surface modeling and FLAC3D 6.0 numerical simulation software is proposed. Based on the geological data of the coal mine and the results of on-site measurements, a three-dimensional geological model of Yingcheng Coal Mine is established for the geostress inversion, and the distribution law of the geostress field in Yingcheng Coal Mine is obtained. Research shows the following: (1) The horizontal maximum principal stress values of the Yingcheng Mine are between 33.9 and 35.3 MPa, the horizontal minimum principal stress values are between 23.6 and 25.4 MPa, and the direction of the horizontal maximum principal stress is roughly in the southwest to west direction; (2) the three-way principal stress magnitude relationship is σH > σv > σh, indicating that the horizontal stress dominates in the study area, which belongs to the slip-type stress state; (3) The maximum principal stress of No. 3 coal seam is 33.1–34.8 MPa, the middle principal stress is 27.5–29.2 MPa, and the minimum principal stress is 17.3–22.9 MPa. Due to the influence of topography and burial depth, there is a phenomenon of stress concentration in some areas. By comparing the inversion values with the measured values, the accuracy of the geostress inversion is high, and the initial geostress inversion method based on Rhino surface modeling accurately inverts the geostress distribution pattern of the Yingcheng coal mine.