Hasil untuk "Dynamic and structural geology"

Kakuta Fujiwara, Lichao Wang

It is an urgent issue for preventing slope failure caused by increasingly severe earthquakes and heavy rain. As a conventional construction method, reinforcing bar insertion work uses the tensile force of the core bar to integrate multiple core bars and pressure plates. Meanwhile, landslide deterrence piles are a construction method in which steel or concrete piles are constructed below the slope, and the rigidity of the piles is used to resist slope failure. In this study, these methods are combined to propose a reinforcing bar insertion work that uses pipes as a construction method. The pipes are not embedded in the immovable layer and are not connected to the reinforcing bar insertion work; therefore, the construction is expected to be simple. Two series of model experiments—a lift-up experiment and a water sprinkling experiment—were performed. Through the lift-up experiment, the effectiveness of the proposed method against static load was confirmed, and the evaluation formula of the load applied to the core bar was proposed. Through the water sprinkling experiment, the effectiveness against rainfall was confirmed, that is, the time until slope failure was extended by the proposed method.

W. Aumer, W. Aumer, I. Hartmeyer et al.

<p>The active layer thickness (ALT) refers to the seasonal thaw depth of a permafrost body and in high alpine environments represents an essential parameter for natural hazard analysis. The aim of this study is to model ALT based on bedrock temperature data measured in four shallow boreholes (SBs, 0.1 m deep) in the summit region of the Kitzsteinhorn (Hohe Tauern Range, Austria, Europe). We set up our heat flow model with temperature data (2016–2021) from a 30 m deep borehole (DB) drilled into bedrock at the Kitzsteinhorn north face. For modeling purposes, we assume one-dimensional conductive heat flow and present an analytical solution of the heat transport equation through sinusoidal temperature waves resulting from seasonal temperature oscillations (damping depth method). The model approach is considered successful: in the validation period (2019–2021), modeled and measured ALT differed by only 0.1 <span class="inline-formula">±</span> 0.1 m, with a root mean square error (RMSE) of 0.13 m. We then applied the DB-calibrated model to four SBs and found that the modeled seasonal ALT maximum ranged between 2.5 m (SB 2) and 10.6 m (SB 1) in the observation period (2013–2021). Due to small differences in altitude (<span class="inline-formula">∼200</span> m) within the study area, slope aspect had the strongest impact on ALT. To project future ALT deepening due to global warming, we integrated IPCC climate scenarios SSP1-2.6 and SSP5-8.5 into our model. By mid-century (<span class="inline-formula">∼2050</span>), ALT is expected to increase by 48 % at SB 2 and by 62 % at DB under scenario SSP1-2.6 (56 % and 128 % under scenario SSP5-8.5), while permafrost will no longer be present at SB 1, SB 3, and SB 4. By the end of the century (<span class="inline-formula">∼2100</span>), permafrost will only remain under scenario SSP1-2.6 with an ALT increase of 51 % at SB 2 and of 69 % at DB.</p>

Lisa Maile, Kai-Steffen Hielscher, Reinhard German

To support reliable and low-latency communication, Time-Sensitive Networking introduced protocols and interfaces for resource allocation in Ethernet. However, the implementation of these allocation algorithms has not yet been covered by the standards. Our work focuses on deadline-guaranteeing resource allocation for networks with static and dynamic traffic. To achieve this, we combine offline network optimization heuristics with online admission control and, thus, allow for new flow registrations while the network is running. We demonstrate our solution on Credit-Based Shaper networks by using the delay analysis framework Network Calculus. We compare our approach with an intuitive and a brute-force algorithm, where we can achieve significant improvements, both, in terms of quality and runtime. Thereby, our results show that we can guarantee maximum end-to-end delays and also increase the flexibility of the network while requiring only minimal user input.

Guang Shi, Sucheol Shin, D. Thirumalai

Live-cell imaging experiments have shown that the distal dynamics between enhancers and promoters are unexpectedly rapid and incompatible with standard polymer models. The discordance between the compact static chromatin organization and dynamics is a conundrum that violates the expected structure-function relationship. We developed a theory to predict chromatin dynamics by accurately determining three-dimensional (3D) structures from static Hi-C contact maps or fixed-cell imaging data. Using the calculated 3D coordinates, the theory accurately forecasts experimentally observed two-point chromatin dynamics. It predicts rapid enhancer-promoter interactions and uncovers a scaling relationship between two-point relaxation time and genomic separation, closely matching recent measurements. The theory predicts that cohesin depletion accelerates single-locus diffusion while significantly slowing relaxation dynamics within topologically associating domains (TADs). Our results demonstrate that chromatin dynamics can be reliably inferred from static structural data, reinforcing the notion that 3D chromatin structure governs dynamic behavior. This general framework offers powerful tools for exploring chromatin dynamics across diverse biological contexts.

Grisel Jiménez, Abdul Halim Latiff, Katja Schulze

The recent proliferation of Extended Reality (XR) applications in geoscience education and research has opened new avenues for the enhanced visualization and analysis of the Earth’s geodata. This study specifically explores the benefits for teaching when supplementing industry standard software packages, such as Paleoscan, Petrel, and JewelSuite, with 3D visualization in XR. The teaching focuses on but is not limited to an understanding of subsurface seismic and well data. During this study, the undergraduate Petroleum Geoscience students transitioned from 2D computer screen visualizations to immersive XR tools. The dataset selected for teaching focuses on the subsurface carbonate EX field in the South China Sea. The EX-field in Central Luconia is located 100–300 km from Sarawak’s coastline in water depths of 60–140 m. It includes a post-stacked time- 3D seismic cube linked to wells, allowing students to work with seismic data, adjust scales, and conduct preliminary seismic analysis. The findings revealed a significant improvement in respondents’ skills in comprehending and analysing seismic and core data, enhancing the overall learning experience in Petroleum Geoscience. This paper also examines the students’ feedback on their learning experiences during virtual subsurface visualization throughout their university degree in geoscience. For evaluating learning success, we used an approach that merges quantitative and qualitative data, The students’ perceptions were assessed through anonymous quantitative surveys and questions. The analysis of student responses emphasizes the valuable learning experience offered by 3D virtual environments designed for realistic first-person navigation and freedom of movement, like a real field experience. The results highlight the potential of virtual subsurface visualization for imparting essential skills to geosciences.

Nicolas Harrichhausen, Kristin D Morell, Christine Regalla

We employ numerical models to explore the connection between subduction zone coupling or megathrust rupture and upper plate faults in the northern Cascadia forearc. Active forearc faults north of the Olympic Peninsula exhibit similar characteristics: west-northwest strike, oblique right-lateral slip senses, and low slip rates (<1 mm/yr), but a potential to generate large (M ~ 7) earthquakes. Previous hypotheses suggest that stress in the upper plate due to interseismic coupling or coseismic rupture along the subduction zone interface could drive permanent forearc strain. To test these hypotheses, we used a 3D boundary element method model to predict slip on the LRDM if interseismic coupling or coeseismic rupture cause deformation. Our model predicts reverse left-lateral slip if the strain results solely from subduction zone coupling, or normal right-lateral slip if these faults accommodate strain during a megathrust rupture. These results contradict the observed fault kinematics. Additionally, if we use our model to mimic strain partitioning, where only the strain from the strike-slip component of subduction zone coupling is accommodated in the forearc, our results are also inconsistent observed fault kinematics. These models challenge the hypothesis that subduction zone coupling or coseismic rupture are the primary driver of permanent forearc deformation in northern Cascadia.

Kyle A Kilian

As artificial intelligence (AI) becomes increasingly embedded in the core functions of social, political, and economic life, it catalyzes structural transformations with far-reaching societal implications. This paper advances the concept of structural risk by introducing a framework grounded in complex systems research to examine how rapid AI integration can generate emergent, system-level dynamics beyond conventional, proximate threats such as system failures or malicious misuse. It argues that such risks are both influenced by and constitutive of broader sociotechnical structures. We classify structural risks into three interrelated categories: antecedent structural causes, antecedent AI system causes, and deleterious feedback loops. By tracing these interactions, we show how unchecked AI development can destabilize trust, shift power asymmetries, and erode decision-making agency across scales. To anticipate and govern these dynamics, the paper proposes a methodological agenda incorporating scenario mapping, simulation, and exploratory foresight. We conclude with policy recommendations aimed at cultivating institutional resilience and adaptive governance strategies for navigating an increasingly volatile AI risk landscape.

Chengling Li, Matthias Merkel, Daniel M. Sussman

Understanding the structural dynamics of many-particle glassy systems remains a key challenge in statistical physics. Over the last decade, glassy dynamics has also been reported in biological tissues, but is far from being understood. It was recently shown that vertex models of dense biological tissue exhibit very atypical, sub-Arrhenius dynamics, and here we ask whether such atypical structural dynamics of vertex models are related to unusual elastic properties. It is known that at zero temperature these models have an elasticity controlled by their under-constrained or isostatic nature, but little is known about how their elasticity varies with temperature. To address this question we investigate the 2D Voronoi model and measure the temperature dependence of the intermediate-time plateau shear modulus and the bulk modulus. We find that unlike in conventional glassformers, these moduli increase monotonically with temperature until the system fluidizes. We further show that the structural relaxation time can be quantitatively linked to the plateau shear modulus $G_p$, i.e.\ $G_p$ modulates the typical energy barrier scale for cell rearrangements. This suggests that the anomalous, structural dynamics of the 2D Voronoi model originates in its unusual elastic properties. Based on our results, we hypothesize that under-constrained systems might more generally give rise to a new class of "ultra-strong" glassformers.

Eirini Katsidoniotaki, Biao Su, Eleni Kelasidi et al.

As the global population grows and climate change intensifies, sustainable food production is critical. Marine aquaculture offers a viable solution, providing a sustainable protein source. However, the industry's expansion requires novel technologies for remote management and autonomous operations. Digital twin technology can advance the aquaculture industry, but its adoption has been limited. Fish net cages, which are flexible floating structures, are critical yet vulnerable components of aquaculture farms. Exposed to harsh and dynamic marine environments, the cages experience significant loads and risk damage, leading to fish escapes, environmental impacts, and financial losses. We propose a multifidelity surrogate modeling framework for integration into a digital twin for real-time monitoring of aquaculture net cage structural dynamics under stochastic marine conditions. Central to this framework is the nonlinear autoregressive Gaussian process method, which learns complex, nonlinear cross-correlations between models of varying fidelity. It combines low-fidelity simulation data with a small set of high-fidelity field sensor measurements, which offer the real dynamics but are costly and spatially sparse. Validated at the SINTEF ACE fish farm in Norway, our digital twin receives online metocean data and accurately predicts net cage displacements and mooring line loads, aligning closely with field measurements. The proposed framework is beneficial where application-specific data are scarce, offering rapid predictions and real-time system representation. The developed digital twin prevents potential damages by assessing structural integrity and facilitates remote operations with unmanned underwater vehicles. Our work also compares GP and GCNs for predicting net cage deformation, highlighting the latter's effectiveness in complex structural applications.

H. Shiogama, H. Tatebe, M. Hayashi et al.

<p>Single model initial-condition large ensembles (LEs) are a useful approach to understand the roles of forced responses and internal variability in historical and future climate change. Here, we produce one of the largest ensembles thus far using the MIROC6 coupled atmosphere–ocean global climate model (MIROC6-LE). The total experimental period of MIROC6-LE is longer than 76 000 years. MIROC6-LE consists of a long preindustrial control run, 50-member historical simulations, 8 single forcing historical experiments with 10 or 50 members, 5 future scenario experiments with 50 members and 3 single forcing future experiments with 50 members. Here, we describe the experimental design. The output data of most of the experiments are freely available to the public. This dataset would be useful to a wide range of research communities.</p> <p>We also demonstrate some examples of initial analyses. Specifically, we confirm that the linear additivity of the forcing-response relationship holds for the 1850–2020 trends of the annual mean values and extreme indices of surface air temperature and precipitation by analyzing historical fully forced runs and the sum of single forced historical runs. To isolate historical anthropogenic signals of annual mean and extreme temperature for 2000–2020 relative to 1850–1900, ensemble sizes of 4 and 15, respectively, are sufficient in most of the world. Historical anthropogenic signals of annual mean and extreme precipitation are significant with the 50-member ensembles in 76 % and 69 % of the world, respectively. Fourteen members are sufficient to examine differences in changes in annual mean values and extreme indices of temperature and precipitation between the shared socioeconomic pathways (ssp), ssp585 and ssp126, in most of the world. Ensembles larger than 50 members are desirable for investigations of differences in annual mean and extreme precipitation changes between ssp126 and ssp119.</p> <p>Historical and future changes in internal variability, represented by departures from the ensemble mean, are analyzed with a focus on the El Niño/Southern Oscillation (ENSO) and global annual mean temperature and precipitation. An ensemble size of 31 is large enough to detect ENSO intensification from preindustrial conditions to 1951–2000, from 1951–2000 to 2051–2100 in all future experiments, and from low- to high-emission future scenario experiments. The single forcing historical experiments with 27 members can isolate ENSO intensification due to anthropogenic greenhouse gas and aerosol forcings. Future changes in the global mean temperature variability are discernible with 23 members under all future experiments, while 50 members are not sufficient for detecting changes in the global mean precipitation variability in ssp119 and ssp126. We also confirm that these temperature and precipitation variabilities are not precisely analyzed when detrended anomalies from the long-term averages are used due to interannual climate responses to the historical natural forcing, which highlights the importance of large ensembles for assessing internal variability.</p>

Pinnaduwa H. S. W. Kulatilake, Mawuko Luke Yaw Ankah

This paper provides a review of the present status of the topic dealt with. The contact and non-contact methods used for rock joint roughness measurement are summarized including their salient features, advantages, and disadvantages. A critical review is given of the empirical, statistical, and fractal-based methods used for rock joint roughness quantification identifying their salient features, shortcomings, and strong attributes. The surface topography of rough rock joints is highly erratic. Fractional geometry is better suited than Euclidean geometry in representing highly erratic rock joint surfaces. The influence of non-stationarity on accurate quantification of roughness is discussed. The existence of heterogeneity of natural rock joint roughness and its effect on computed roughness parameters are well illustrated. The controversial findings that have been appearing in the literature on roughness scale effects during the last 40 years have resulted from neglecting the effect of roughness heterogeneity on scale effects. The roughness heterogeneity controls the rock joint roughness scale effect, and it can be either negative, positive, or no scale effect depending on the type and level of the roughness heterogeneity of the rock joint surface. The importance of consideration of the existence of possible anisotropy in the quantification of roughness is well illustrated. The indices available to quantify the level of anisotropy are given. Effects of sampling interval and measurement resolution on the accurate quantification of roughness are discussed. A comparison of results obtained by using different quantification methods is discussed. A few recommendations are given for future research to address the shortcomings that exist on the topic dealt with in the paper.

Irina Piyanzina, Kirill Evseev, Andrey Kamashev et al.

Magneto-electric coupling is a desirable property for a material used in modern electronic devices to possess due to the favorable possibilities of tuning the electronic properties using a magnetic field and vice versa. However, such materials are rare in nature. That is why the so-called superlattice approach to creating such materials is receiving so much attention. In the superlattice approach, the functionality of a combined heterostructure depends on the interacting components and can be adjusted depending on the desired property. In the present paper, we present supercells of ferromagnetic thin films of Fe and Co deposited on ferroelectric and piezoelectric substrates of BaTiO₃ and SrTiO₃ that exhibit magnetism, ferroelectric polarization and piezoelectric effects. Within the structures under investigation, magnetic moments can be tuned by an external electric field via the ferroelectric dipoles. We investigate the effect of magnetoelectric coupling by means of ab initio spin-polarized and spin–orbit calculations. We study the structural, electronic and magnetic properties of heterostructures, and show that electrostriction can reduce the magnitude of the magnetization vector of a ferromagnet. This approach can become the basis for controlling the properties of one of the ferromagnetic layers of a superconducting spin valve, and thus the superconducting properties of the valve.

Sean Fancher, Prashant Purohit, Eleni Katifori

Truss structures at macro-scale are common in a number of engineering applications and are now being increasingly used at the micro-scale to construct metamaterials. In analyzing the properties of a given truss structure, it is often necessary to understand how stress waves propagate through the system and/or its dynamic modes under time dependent loading so as to allow for maximally efficient use of space and material. This can be a computationally challenging task for particularly large or complex structures, with current methods requiring fine spatial discretization or evaluations of sizable matrices. Here we present a spectral method to compute the dynamics of trusses inspired by results from fluid flow networks. Our model accounts for the full dynamics of linearly elastic truss elements via a network Laplacian; a matrix object which couples the motions of the structure joints. We show that this method is equivalent to the continuum limit of linear finite element methods as well as capable of reproducing natural frequencies and modes determined by more complex and computationally costlier methods.

Timothy LaRock, Renaud Lambiotte

Hypergraphs have emerged as a powerful modeling framework to represent systems with multiway interactions, that is systems where interactions may involve an arbitrary number of agents. Here we explore the properties of real-world hypergraphs, focusing on the encapsulation of their hyperedges, which is the extent that smaller hyperedges are subsets of larger hyperedges. Building on the concept of line graphs, our measures quantify the relations existing between hyperedges of different sizes and, as a byproduct, the compatibility of the data with a simplicial complex representation -- whose encapsulation would be maximum. We then turn to the impact of the observed structural patterns on diffusive dynamics, focusing on a variant of threshold models, called encapsulation dynamics, and demonstrate that non-random patterns can accelerate the spreading in the system.

John Keith Magali, Thomas Bodin

Geodynamic tomography, an imaging technique that incorporates constraints from geodynamics and mineral physics to restrict the potential number of candidate seismic models down to a subset consistent with geodynamic predictions, is applied to a thermal subduction model. The goal is to test the ability of geodynamic tomography to recover structures harbouring complex deformation patterns. The subduction zone is parameterised in terms of four unknown parameters namely the slab length L, thickness R, temperature Tc, and dip angle θ that define its thermal structure. A temperature-dependent viscosity is then prescribed with an activation coefficient E controlling the sensitivity. Using the full forward approach to geodynamic tomography, we generate anisotropic surface wave dispersion measurements as synthetic data. We then attempt to retrieve the five unknown parameters by inverting the synthetics corrupted with random uncorrelated noise. The final output is an ensemble of models of L, R, θ, Tc and E cast in terms of a posterior probability distribution and their uncertainty limits. Results show that the parameters are tightly constrained with the apparent existence of a single misfit minima in each of them, which implies the implicit retrieval of the complete patterns of upper mantle deformation, and correspondingly, the 21-independent coefficients defining elastic anisotropy. Each model realisation however fails to swarm around its true value. Such results are attributed to the inability of the surrogate model to accurately replicate the correct forward model for computing anisotropy due to the inherent complexity of the deformation patterns considered. Nevertheless, this proof of concept shows a self-consistent method that incorporates mantle flow modeling in a seismic inversion scheme.

E. Pujades, L. Scheiber, M. Teixidó et al.

<p>Urban aquifers are a valuable resource of freshwater for cities, however, their quality is degraded due to the presence of organic contaminants of emerging concern (CECs). The effects of organic CECs are largely unknown, but there is evidence that they pose a risk for human health, soil, plants and animals. Organic CECs are naturally degraded in aquifers and their degradation rates depend on the physico-chemical properties, i.e., redox conditions and groundwater temperature. Some anthropogenic activities, like low-enthalpy geothermal energy (LEGE), may modify subsurface physico-chemical conditions altering the behaviour of organic CECs. LEGE is a renewable and carbon-free energy that allows obtaining cooling and heating energy. The utilization of LEGE is currently growing and it is expected that in a near future the density of LEGE systems will increase. LEGE modifies the groundwater temperature and in some situations the redox state (i.e., if the dissolved oxygen increases when groundwater is returned to the aquifer as a result of a poorly design), thus, it is of paramount importance to determine the impact of LEGE related activities on the behaviour of organic CECs. The behaviour of organic CECs under the influence of LEGE is investigated by means of thermo-hydro-chemical numerical modelling. Simulation output shows that LEGE activities have the potential to modify the degradation rates of organic CECs, and thus, their concentrations in aquifers. In the simulated scenario, the concentration of the chosen CEC decreases by the 77 % at the downgradient boundary of the model. The results of this study have significant implications for predicting the behaviour of organic CECs in urban aquifers and suggest specific changes in the design of LEGE facilities aiming to improve the quality of urban groundwater by boosting in-situ attenuation mechanisms.</p>

Tomilola M. Obadiya, Daniel M. Sussman

Data-driven approaches to inferring the local structures responsible for plasticity in amorphous materials have made substantial contributions to our understanding of the failure, flow, and rearrangement dynamics of supercooled fluids. Some of these methods, such as the ``softness'' approach based on linear support vector machines, have identified combinations of local structural features of a supercooled particle's environment that predict energy barriers associated with particle rearrangements. This approach also predicts the onset temperature, often characterized as the temperature below which the system's dynamics becomes non-Arrhenius and above which local structures are no longer predictive of dynamical activity. We implement a transfer-learning approach in which we first show that classifiers can be trained to predict dynamical activity even far above the onset temperature. We then show that applying these classifiers to data from the supercooled phase recovers essentially the same physical information about the relationship between local structures and energy barriers that softness does.

D. J. Furbish, S. G. W. Williams, T. H. Doane et al.

<p>Theoretical and experimental work (Furbish et al., 2021a, b) indicates that the travel distances of rarefied particle motions on rough hillslope surfaces are described by a generalized Pareto distribution. The form of this distribution varies with the balance between gravitational heating, due to conversion of potential to kinetic energy, and frictional cooling, due to particle–surface collisions; it varies from a bounded form associated with rapid thermal collapse to an exponential form representing isothermal conditions to a heavy-tailed form associated with net heating of particles. The generalized Pareto distribution in this problem is a maximum entropy distribution constrained by a fixed energetic “cost” – the total cumulative energy extracted by collisional friction per unit kinetic energy available during particle motions. That is, among all possible accessible microstates – the many different ways to arrange a great number of particles into distance states where each arrangement satisfies the same fixed total energetic cost – the generalized Pareto distribution represents the most probable arrangement. Because this idea applies equally to the accessible microstates associated with net cooling, isothermal conditions and net heating, the fixed energetic cost provides a unifying interpretation for these distinctive behaviors, including the abrupt transition in the form of the generalized Pareto distribution in crossing isothermal conditions. The analysis therefore represents a novel generalization of an energy-based constraint in using the maximum entropy method to infer non-exponential distributions of particle motions. Moreover, the energetic costs of individual particle motions follow an extreme-value distribution that is heavy-tailed for net cooling and light-tailed for net heating. The relative contribution of different travel distances to the total energetic cost is reflected by the product of the travel distance distribution and the cost of individual particle motions – effectively a frequency–magnitude product.</p>

U. Sperberg

<p>At the beginning of the 19th century, meteor observations were not well established. One of its pioneers, who observed meteors on a regular basis, was Eduard Heis in Münster, Germany. We summarise the life of this scientist. Besides his main task of teaching mathematics in Aachen and Münster, he observed atmospheric phenomena and variable stars with exceptional perseverance. He was an editor of <i>Wochenschrift für Astronomie</i> and contributed to the circulation of astronomical reports and knowledge. We focus on his contributions to meteor astronomy, in which he predated the work of Schiaparelli by 30 years.</p>

C. Otten, B. Dassler, S. Teitz et al.

<p>Application of the environmentally friendly scaling inhibitor NC47.1 B in geothermal systems was studied in laboratory and field-scale experiments. Biodegradation was investigated under anaerobic, in situ-like conditions and a mass balance confirmed the almost complete conversion of the polycarboxylate to e.g. acetate, formate, methane and CO<span class="inline-formula">₂</span>. Much higher concentrations of inhibitor were chosen than applied in situ and rapid degradation was observed in biofilm-inoculated setups: A concentration of 100 mg/L of the inhibitor was degraded below detection limit within 8 d of incubation. Furthermore, the inhibitor was applied at the geothermal plant in Unterhaching, Germany. Monitoring of the microbial community in situ showed an increase in the abundance of <i>Bacteria</i>. Particularly, relatives of the fermenting <i>Caldicellulosiruptor</i> dominated the biocenosis after about six months of continuous inhibitor dosage (5–10 mg/L). However, in long-term laboratory experiments representatives of <i>Caldicellulosiruptor</i> were only detected in traces and the microbial community comprised a broader spectrum of fermentative bacteria. The different composition of the biocenosis in situ and in laboratory experiments is probably caused by the different inhibitor concentrations, temperatures as well as nutrient availability in situ compared to the closed system of the batch experiments.</p>