Discontinuous Front Research Articles

Plastic flow instability in austenitic stainless steels at room temperature: Macroscopic tests and microstructural analysis

AISI 304 steel experiences plastic flow instability during tension at room temperature if appropriate conditions are applied: a low strain rate and a sufficiently long gauge section of the sample. Then, propagation of the strain-localised band is activated. The electron backscattered diffraction (EBSD) research revealed that the reason is not only the difference in the content of the secondary phase – martensite α’ across the front face, but also the change in the volume fraction of austenite grains with Copper (Cu) and Goss-Brass (GB) orientation. Consequently, there is a division between two areas of high and limited deformation capacity. The tendency to maintain the continuity of deformation fields induces a massive rotation of austenite grains to Cu and GB orientations, which then undergo shearing and phase transformation. As a result, momentary strain accumulation leaves behind a stiffer zone. It is shown that the trapping of austenite grains prone to large deformations, inside the matrix with Cu and GB orientations, makes the formation of a plastic strain front possible. These features improve the ductility and strength of the 304 steel over 316L and 316LN at room temperature. The in-situ EBSD tension studies for the considered grades reveal three developing textures, with their comparison showing a gradual decrease in the preferences of the Cu and GB components. Thus, the appearing bands of the accumulated strains in 316L are limited by the Cu and GB areas, while such blockages do not occur in 316LN. The presented strengthening mechanism is confirmed by the digital image correlation (DIC) measurements. The root-mean-square (RMS) function of strains along the tensile direction, characterising the linear surroundings of the considered point, is introduced as a tool for linking the micro and macro scales. The experimental results provide a basis for explaining discontinuous front propagation at a temperature near 0 K.

Mechanisms of phase decomposition in a non-equimolar CrFeMnNi alloy during thermal aging

While high entropy alloys and multi principal element alloys have been largely studied and developed as single solid solutions, fewer investigations have focused on phase transformations in these multicomponent alloys. This study reports on the aging behavior at 500 °C of a non-equimolar CrFeMnNi alloy. The results unveil a complex evolution, leading to complete decomposition into three main phases, namely an L10 NiMn phase, an FCC Fe-rich phase, and a BCC Cr-rich phase. The intricate microstructure is achieved in stages through continuous precipitation of L10 Ni-Mn nanoscale precipitates, which are progressively overtaken by a sweeping discontinuous front involving the co-precipitation of the L10 NiMn and BCC Cr-rich phases in addition to the parent FCC Fe-rich phase. Furthermore, the Cr-rich phase initially forms with metastable compositions leading to further precipitation of Fe-rich precipitates within the Cr-rich region behind the transformation front. Understanding these transformation pathways through metastable states emphasizes the potential for expanding the range of achievable properties through deliberate microstructure tailoring of multi principal element alloys.

Euler’s First-Order Explicit Method–Peridynamic Differential Operator for Solving Two-Dimensional Population Balance Equations in Crystallization

The population balance equations (PBEs) serve as the primary governing equations for simulating the crystallization process. Two-dimensional (2D) PBEs pertain to crystals that exhibit anisotropic growth, which is characterized by changes in two internal coordinates. Because PBEs are the hyperbolic equations, it becomes imperative to establish a high-resolution scheme to reduce numerical diffusion and numerical dispersion, thereby ensuring accurate crystal size distribution. This paper uses Euler’s first-order explicit (EE) method–Peridynamic Differential Operator (PDDO) to solve 2D PBE, namely, the EE method for discretizing the time derivative and the PDDO for discretizing the internal crystal-size derivative. Five examples, including size-independent growth with smooth and non-smooth distributions, size-dependent growth, nucleation, and size-independent/dependent growth for batch crystallization are considered. The results show that the EE–PDDO method is more accurate than the HR method and that it is as good as the fifth-order Weighted Essential Non-Oscillatory (WENO) method in solving 2D PBE. This study extends the EE–PDDO method to the simulation of 2D PBE, and the advantages of the EE-PDDO method in dealing with discontinuous and sharp front problems are demonstrated.

Parallel cooperative multiobjective coevolutionary algorithm for constrained multiobjective optimization problems

The existing parallel multiobjective evolutionary computation does not perform well for constrained multiobjective optimization problems with discontinuous Pareto fronts or narrow feasible regions. This study parallelizes the state-of-the-art cooperative multiobjective coevolutionary algorithm and proposes an effective parallel evolutionary algorithm for constrained multiobjective optimization problems that are difficult to optimize. Two parallelization methods are compared: a global parallel model in which solution evaluations are performed in parallel, and a hybrid model that treats the cooperative populations in a distributed manner while performing each solution evaluation in parallel. The first model is a straightforward parallelization, while the second one capitalizes on the characteristics of the coevolutionary framework. To investigate the efficacy of the proposed models, experiments are conducted on constrained multiobjective optimization problems, including complex characteristics, while varying the number of parallel cores up to 64. The experiments compare the two proposed methods from the viewpoint of search performance and execution time. The experimental results reveal that the latter hybrid model shows better computational efficiency and scalability against an increasing number of cores without adversely affecting the search performance compared to the former straightforward parallelization.

A MOEA/D with adaptive weight subspace for regular and irregular multi-objective optimization problems

The performance of the decomposition-based multi-objective optimization algorithm (MOEA/D) is dependent on the consistency of the Pareto front and the distribution of the weight vectors. Uniformly distributed weight vectors have a great advantage in solving Pareto fronts with simplex-like shapes. However, the fronts of real-world problems to be solved are often irregular, and fixed weight vectors are likely to cause deterioration of the solution. To obtain a set of solution sets that are uniformly distributed in the objective space, a MOEA/D-based weight adaptive updating algorithm (called MOEA/D-AWS) is proposed. First, the subproblem evolutionary matrix similarity is used to determine when to adjust the weight vectors. Second, the objective space is divided by creating a subspace of weight vectors to increase population diversity. Third, partial weights are given a second chance to be selected based on the subspace size. Experimental tests are conducted with seven representative algorithms on benchmark functions DTLZ1-7, IDTLZ1-2, MaF1-7, and WFG1-5 (with objective numbers of 3, 5, 8, 10, and 15). The results show that MOEA/D-AWS is more effective for irregular Pareto fronts, especially discontinuous, degenerate, and sharp-tailed Pareto front problems.

Neuroevolutionary diversity policy search for multi-objective reinforcement learning

Sequential decision-making requires balancing multiple conflicting objectives through multi-objective reinforcement learning (MORL). Moreover, decision-makers desire dense solutions that satisfy their requirements and consider the trade-offs between different objectives (Pareto optimal solutions). Most deep reinforcement learning methods focus on single-objective problems or solve multi-objective problems using simple linear combinations, which may oversimplify the underlying problem and lead to suboptimal results. This study proposes a neuroevolutionary diversity policy search approach to address MORL problems. It employs neural networks, each equipped with a buffer for storing recent experiences, representing individuals in a population. The non-dominated sorting method and diversity distance metric are employed in the evolutionary process to select high-quality solutions as teachers. The teachers use gradient-based genetic operators to guide the population to produce high-quality offspring, thereby achieving dense Pareto optimal solutions. Furthermore, we introduce three MORL benchmarks with distinct characteristics: (1) a continuous deep sea treasure with convex and nonconvex Pareto fronts; (2) a multi-objective mountain car with sparse rewards and a discontinuous Pareto front; and (3) a multi-objective HalfCheetah with high-dimensional action-state spaces. The experimental results on the three MORL benchmarks demonstrate the superiority of the proposed algorithm in obtaining dense and high-quality Pareto optimal solutions.

A constrained multi-objective evolutionary algorithm with clustering based weight vector adaptation

In constrained multi-objective optimization, balancing convergence, diversity, and feasibility of solutions in the population are challenging. Most existing approaches focus on convergence and feasibility but fail to enforce the diversity and uniformity of solutions in the approximated Pareto Front (PF). In decomposition-based approaches, the diversity and uniformity of the obtained solution set depend on the set of weight vectors employed, which are generally fixed and initialized uniformly. However, Constrained Multi-objective Optimization Problems (CMOPs) are characterized by discontinuous Pareto-optimal fronts due to the presence of constraints. Therefore, to maintain better diversity and uniformity in the population, the weight vector set employed in the framework should consider the characteristics of PF, which is not known in advance. However, during the evolution, the characteristics of PF can be estimated and the associated weight vectors can be appropriately distributed. Motivated by this issue, we propose a novel Constrained Multi-Objective Evolutionary Algorithm with a Clustering-based Weight vector Adaptation strategy called CMOEA-CWA. The proposed CMOEA-CWA uses the basic framework of CMOEA-DPMS. After exploring the search space, CMOEA-CWA reinitializes the weight vectors if there exist any discontinuities in the approximated pareto front (PF). In CMOEA-CWA, the PF is approximated using the feasible solutions stored in an archive and novel parameter-free clustering techniques. During the clustering process, if there exist any discontinuities in the approximated PF, one population will exploit the feasible regions with a re-initialized set of weight vectors while the other population will explore the feasible regions with uniformly distributed weight vectors. Empirical results obtained on four popular and widely used benchmarks when compared with nine latest state-of-the-art algorithms (ARMOEA, CCMO, TiGE-2, ToP, MOEA/D-DAE, PPS, CTAEA, MOEA/D-2WA and CMOEA-DPMS) suggest the superiority of the proposed algorithm, especially on 47 complicated constrained multi-objective problems. The proposed algorithm performs statistically better than or comparable (in terms of HV) to ARMOEA, CCMO, TiGE-2, ToP, MOEA/D-DAE, PPS, CTAEA, MOEA/D-2WA and CMOEA-DPMS in 91.48 %, 78.72 %, 100 %, 70.21 %, 85.11 %, 78.72 %, 91.49 % and 82.97 % of test instances, respectively. The performance of the proposed algorithm is also demonstrated on a real-world Economic Environmental Dispatch (EED) problem.

New insights into reservoir on chip: Numerical investigation and experimental validation

The present study reports a numerical investigation of oil extraction from a pore-scale perspective using water as the injection fluid. The pore network is constructed from the statistical realization of the pore space of the reservoir rock. Conceptually, the pore network model used in the study miniaturizes the porous reservoir containing oil/gas onto a microfluidic platform, capturing the actual pore-level length scale and it complex features. The validity of the numerical model is established through an experimental investigation of single-phase flow. The experimentally calculated absolute permeability based on the Darcy law shows an excellent agreement with the numerically attained value. The two-phase numerical model uses the phase field technique to track the development of the interface between the two immiscible phases, i.e., oil and water. The numerical model shows a piston-like displacement and captures interesting pore-level phenomena like snap-off and trapping. Implementing the complete network for simulation reveals the unstable nature of the flooding, which is persistent with invasion percolation. The complete network simulation reveals the discontinuous flood front with a segregated flow configuration. Two-phase experiments conducted on a polydimethylsiloxane test chip with an equivalent pore level network also showed similar flow features, thereby establishing the credibility of our two-phase simulations. The recovery factor obtained from the simulation was found to be 0.78, which is in close agreement with experimental data reported in the literature. The modified Darcy law applied to the numerical model generates relative permeability plots similar to the experimental core flooding plots reported in the literature. The numerical model presented here provides valuable insight into the oil recovery process and its implications at field scale. To the best of our knowledge, this is the first instance involving numerical analysis of the full-scale system of Reservoir on a Chip system detailing the pore-level flow dynamics.

Asymptotic behavior of Riemann solutions for the inhomogeneous Aw-Rascle-Zhang traffic model with the logarithmic equation of state

The exact Riemann solutions are solved constructively for the inhomogeneous Aw-Rascle-Zhang traffic model with the logarithmic equation of state under the Coulomb-like friction term, where all the emerged waves are bent into the parabola curves with the same curvature grade under the influence of this friction term. On the one side, the 1-shock front tends to the stationary 2-contact discontinuity front and eventually coincides to form a curved delta shock front when the traffic pressure vanishes, where the accompanied concentration phenomenon can be observed and explored. On the other side, the head of the 1-rarefaction wave tends to the stationary 2-contact discontinuity front as well as the tail of the 1-rarefaction wave is changed into the 1-contact discontinuity front when the traffic pressure vanishes, where the associated cavitation phenomenon can also be inspected and discussed.

An improved algorithm for detecting mesoscale ocean fronts from satellite observations: Detailed mapping of persistent fronts around the China Seas and their long-term trends

Mesoscale fronts are highly energetic ocean processes that dominate local biogeochemical processes and air-sea interactions and exert significant effects on ocean dynamics, ecology, and climate. An automatic front detection algorithm applied to satellite-based observations is a fundamental approach to acquiring frontal occurrence information. Histogram-based algorithms have been widely used in previous studies, and, despite being the most popular front detection algorithm, continuous improvements have been needed to address imperfect detection issues (undetected coastal fronts, discontinuities of fronts and repeated detection of fronts). In this study, a new algorithm is proposed to address the issues by coupling inverse distance weighting and mathematical morphology operators. Using 14,610 images acquired from 1982 to 2021, a series of comparisons between the new and existing algorithms have been implemented to validate the improvements. Statistical results suggest that the number of frontal pixels detected by our algorithm increased by 58.45% in the coastal domain when compared to previous methods. Statistical analysis also shows most of these increased fronts can be supported by a classic gradient-based method which is superior in detecting coastal fronts, which demonstrates the improvement of new algorithm in detecting coastal fronts. Furthermore, visual analyses suggest that our algorithm can better connect discontinuous fronts and reduce repeated detections, and statistical comparisons in the open seas present a 34.50% improvement in the average length of frontal segments. Moreover, based on an automated objective delineation method, an analysis of 40 years of seasonal frontal occurrences (as detected by our algorithm) produced a dataset that shows a detailed map of 59 persistent fronts around the China Seas. This map updates previously hand-delineated frontal positions and identifies some previously unreported persistent fronts. Further analyses show that the frontal occurrence or intensity of a majority of persistent fronts around the China Seas presents significant (P < 0.05) long-term variability over the last 40 years. The improved algorithm, together with its application in identifying persistent fronts around the China Seas, is expected to be extended to other waters to better investigate the response of marine ecosystems to front dynamics and predict the distribution of marine organisms within a changing climate.

On the Theory of Shock Waves in Isotropic Hardening Plastic Media

Based on the thermomechanical model of plastic deformation of an elastically compressible isotropic hardening medium, the system of relations for describing plastic shock waves of finite amplitude is obtained, which satisfies the maximum entropy production principle at the front of strong discontinuity. A classification of admissible shock-wave transitions is performed within the framework of the model of isotropic hardening under the von Mises plasticity condition.

Shallow gas in Holocene sediments of the Pearl River Estuary and the implication for anthropogenic effects on its release

High-resolution seismic and side-scan surveys were conducted to investigate shallow gas accumulation and seepage in the Holocene sediment of the Pearl River Estuary. Extensive shallow gas accumulation and active seepage were detected in the Holocene sediments of the estuary and adjacent shelf regions. Laterally continuous and extremely shallow gas fronts were observed in areas with relatively higher sedimentation rates, whereas shallow gas was absent or discontinuous in regions with the sediment with lower sedimentation rates. Gas accumulation was present at extremely shallow depths in the West Shoal, which is the recent depocentre location. The co-occurrence of shallow gas distributions and variations in the sedimentation rate was related to the role of sediment accumulation in organic carbon burial and preservation, both of which are required for gas generation and accumulation in sediment. In addition, the consistency between the distribution of high water column methane concentrations in the West Shoal, the super shallow gas accumulation pattern in the sediment profile, and active seepage indicate that shallow gas in the Holocene sediment is a methane source in the Pearl River Estuary. Further, considerable evidence of recent or relic seepage is present in areas that have experienced active anthropogenic disturbances, including sand mining, dredging, and bridge building. The disrupted and discontinuous gas fronts at these sites suggest that active shallow gas releases were influenced by anthropogenic activity.

A Weight Vector Adjustment Method for Decomposition-Based Multi-Objective Evolutionary Algorithms

Multi-objective evolutionary algorithm based on decomposition (MOEA/D) is effective to solve most multi-objective optimization problems (MOPs) in the past 20 years. However, the algorithm MOEA/D with constant weight vectors has bad performance in solving several MOPs with discontinuous Pareto front (PF). This paper analyses the limitations of the constant weight vectors in MOEA/D and explains the necessity of adjusting the weight vectors in the processing. This paper proposes a weight vector adjustment method for MOEA/D (MOEA/D-WVA). It deletes the weight vectors which have bad search direction, and adds some new weight vectors in the processing. Experimental studies are conducted on several MOPs with discontinuous PF to compare the MOEA/D-WVA with other state-of-the-art multi-objective optimization algorithms in solving those MOPs with complex PF. The results show MOEA/D-WVA performs better than other algorithms on those MOPs with discontinuous PF.

Shape optimization of tall buildings cross‐section: Balancing profit and aeroelastic performance

SummaryShape optimization is an effective tool to improve the aerodynamic performance of tall buildings by introducing minor modifications to the original project. Nevertheless, economic criteria demand efficient cross sections aiming at maximizing the building's profitability. These two contradictory criteria are commonly handled by adopting multi‐objective optimization approaches seeking the definition of Pareto fronts. However, the aerodynamic nonlinear features of low‐aspect‐ratio cross sections typically adopted in architectural practice can cause wind‐induced acceleration response surfaces over the considered design domain with multiple local minima that eventually lead to discontinuous Pareto fronts with non‐convex regions. This study delves into this problem and proposes a design framework that effectively combines the reduced basis method with multi‐objective optimization techniques to carry out the aerodynamic shape optimization using surrogates trained with CFD simulations. The ability of the optimization strategy to properly define the non‐convex regions of discontinuous Pareto fronts is successfully leveraged by adopting the weighted min–max method.

Response of the Jovian Magnetosphere‐Ionosphere System to the Interplanetary Magnetic Field Discontinuity: A Simulation Study

AbstractThe response of the magnetosphere‐ionosphere (MI) system at Jupiter to the interplanetary magnetic field (IMF) tangential discontinuity is studied via global Magnetohydrodynamics (MHD) simulations. Our results show that the IMF discontinuity, which has a transition front perpendicular to the Sun‐Jupiter line, has strong effects on the Jovian MI system depending on local times and radial distances to Jupiter. The magnetospheric significant response to the discontinuity is delayed by ∼2–6 hr after the encounter of the discontinuity front with the bow shock, while the delay time for the ionospheric field‐aligned currents (FACs) is ∼3–8 hr. The outer magnetosphere, particularly at dayside, tends to respond slightly quicker than the inner regions. At the ionosphere, the response of downward FACs is found to occur earlier than that of the upward ones by a time ranging from less than 1 hr to several hours, which is also more prominent at dayside sectors. Using a new MI mapping method with Jupiter's JRM09 magnetic field model, we reproduce for the first time the ionospheric FACs consistent with the morphology of the observed ultraviolet (UV) main auroral emissions. This indicates a potential application of the simulated upward FACs on the morphology of the UV main auroral ovals.

Microstructural evolution for a single-crystal superalloy after high-temperature treatments with different holding times

In the present work, the thermal heating and cooling behaviour of a single-crystal superalloy containing 2wt-% Re was tested to investigate the γ′ dissolution process, and the surface recrystallisation behaviour of the grit-blasted superalloy was investigated near its ultimate service temperature of 1100°C by EBSD and TEM. Particularly, the aged samples were vertically polished to reveal the progress of the discontinuous precipitation reaction front. The microstructure characteristics, including grain orientation, grain size, texture and TCP phase precipitation, were revealed for a detailed understanding of the surface crystallisation behaviour of the material.

Clustering Analysis for the Pareto Optimal Front in Multi-Objective Optimization

Bio-inspired algorithms are a suitable alternative for solving multi-objective optimization problems. Among different proposals, a widely used approach is based on the Pareto front. In this document, a proposal is made for the analysis of the optimal front for multi-objective optimization problems using clustering techniques. With this approach, an alternative is sought for further use and improvement of multi-objective optimization algorithms considering solutions and clusters found. To carry out the clustering, the methods k-means and fuzzy c-means are employed, in such a way that there are two alternatives to generate the possible clusters. Regarding the results, it is observed that both clustering algorithms perform an adequate separation of the optimal Pareto continuous fronts; for discontinuous fronts, k-means and fuzzy c-means obtain results that complement each other (there is no superior algorithm). In terms of processing time, k-means presents less execution time than fuzzy c-means.

Redistribution of Energy during Interaction of a Shock Wave with a Temperature Layered Plasma Region at Hypersonic Speeds

The paper is devoted to the problem of the interaction between a shock wave and a thermally stratified energy source for the purpose of supersonic/hypersonic flow control realization. The effect of the thermally stratified energy source on a shock wave with the Mach number in the range of 6–12 is researched numerically based on the Navier-Stokes system of equations. Redistribution of specific internal energy and volume density of kinetic energy behind the wave front is investigated. Multiple manifestations of the Richtmyer-Meshkov instability has been obtained which has caused the blurring and disappearance of shock wave and contact discontinuity fronts in density fields. A study of the efficiency of using a stratified energy source instead of a homogeneous one with the same value of the full energy is carried out. The agreement with the available experimental data for the shock wave Mach number 6 has been obtained.

Biobjective Optimization of Well Placement: Algorithm, Validation, and Field Testing

Summary We describe the development and validation of a novel algorithm for field-development optimization problems and document field-testing results. Our algorithm is founded on recent developments in bound-constrained multiobjective optimization of nonsmooth functions for problems in which the structure of the objective functions either cannot be exploited or are nonexistent. Such situations typically arise when the functions are computed as the result of numerical modeling, such as reservoir-flow simulation within the context of field-development planning and reservoir management. We propose an efficient implementation of a novel parallel algorithm, namely BiMADS++, for the biobjective optimization problem. Biobjective optimization is a special case of multiobjective optimization with the property that Pareto points may be ordered, which is extensively exploited by the BiMADS++ algorithm. The optimization algorithm generates an approximation of the Pareto front by solving a series of single-objective formulations of the biobjective optimization problem. These single-objective problems are solved using a new and more efficient implementation of the mesh adaptive direct search (MADS) algorithm, developed for nonsmooth optimization problems that arise within reservoir-simulation-based optimization workflows. The MADS algorithm is extensively benchmarked against alternative single-objective optimization techniques before the BiMADS++ implementation. Both the MADS optimization engine and the master BiMADS++ algorithm are implemented from the ground up by resorting to a distributed parallel computing paradigm using message passing interface (MPI) for efficiency in industrial-scale problems. BiMADS++ is validated and field tested on well-location optimization (WLO) problems. We first validate and benchmark the accuracy and computational performance of the MADS implementation against a number of alternative parallel optimizers [e.g., particle-swarm optimization (PSO), genetic algorithm (GA), and simultaneous perturbation and multivariate interpolation (SPMI)] within the context of single-objective optimization. We also validate the BiMADS++ implementation using a challenging analytical problem that gives rise to a discontinuous Pareto front. We then present BiMADS++ WLO applications on two simple, intuitive, and yet realistic problems, and a model for a real problem with known Pareto front. Finally, we discuss the results of the field-testing work on three real-field deepwater models. The BiMADS++ implementation enables the user to identify various compromise solutions of the WLO problem with a single optimization run without resorting to ad hoc adjustments of penalty weights in the objective function. Elimination of this “trial-and-error” procedure and distributed parallel implementation renders BiMADS++ easy to use and significantly more efficient in terms of computational speed needed to determine alternative compromise solutions of a given WLO problem at hand. In a field-testing example, BiMADS++ delivered a workflow speedup of greater than fourfold with a single biobjective optimization run over the weighted-sums objective-function approach, which requires multiple single-objective-function optimization runs. NOTE: This paper is also published as part of the 2021 SPE Reservoir Simulation Conference Special Issue.

Fingerprinting local controls on the Neoproterozoic carbon cycle with the isotopic record of Cryogenian carbonates in the Panamint Range, California

Neoproterozoic carbon isotope excursions are commonly attributed to changes in the global fraction of organic carbon burial associated with climate instability and/or oxygenation. Here we show that carbonate sediment deposited during the ca. 661 – <651 Ma Cryogenian non-glacial interlude between the Sturtian and Marinoan glaciations exhibit lateral offsets in carbonate-carbon isotope values from coeval units by as much as 10‰. Within the Thorndike submember of the Cryogenian succession in the Panamint Range, California, USA, carbonate-carbon isotope values can be linked to a laterally discontinuous dolomitization front: limestones exhibit δ13Ccarb values of ∼+4 to +9‰, whereas values of stratigraphically equivalent dolostones are consistently lower, between ∼-4 and +4‰. Field observations and analyses of clasts from the overlying Marinoan glacial diamictite show that the offset in δ13Ccarb values resulted from pre- to syn-Marinoan dolomitization. Further, δ44/40Ca and δ26Mg data indicate that this isotopic variability resulted from sediment-buffered diagenesis. We propose that extremely positive δ13Ccarb values record local primary productivity within restricted platform surface waters and/or oxygenated pore fluids and negative values reflect anaerobic remineralization of organic carbon within sediment pore waters. In this scenario, neither the original calcite/aragonite nor subsequent dolomite precipitates of the Thorndike submember record δ13Ccarb values that are representative of global Cryogenian seawater, and instead they archive the evolution of local dissolved inorganic carbon pools.