Numerical Simulation of Hydraulic Fracturing in Enhanced Geothermal Systems Considering Thermal Stress Cracks

With the increasing attention to clean and economical energy resources, geothermal energy and enhanced geothermal systems (EGS) have gained much importance in recent years. For the efficient development of deep geothermal reservoirs, it is crucial to understand the mechanical behavior of reservoir rock and its interaction with injected fluid under high-temperature and high confining pressure environments for employing hydraulic stimulation technologies. In the present study, we develop a novel numerical scheme based on the distinct element method (DEM) to simulate the failure behavior of rock by considering the influence of thermal stress cracks and high confining pressure for EGS. The proposed methodology is validated by comparing uniaxial compression tests at various temperatures and biaxial compression tests at different confining pressures with laboratory experimental results. The numerical results indicate a good agreement in terms of failure models and stress-strain curves with those of laboratory experiments. We then apply the developed scheme to the hydraulic fracturing simulations under various temperatures, confining pressures, and injection fluid conditions. Based on our numerical results, the number of hydraulic cracks is proportional to the temperature. At a high-temperature and low confining pressure environment, a complex crack network with large crack width can be observed, whereas the generation of the micro-cracks is suppressed in high confining pressure conditions. In addition, high-viscosity injection fluid tends to induce more hydraulic cracks. Since the crack network in the geothermal reservoir is an essential factor for the efficient production of geothermal energy, the combination of the above factors should be considered in hydraulic fracturing treatment in EGS

Label-Free Multivariate Time Series Anomaly Detection

Anomaly detection in multivariate time series (MTS) has been widely studied in one-class classification (OCC) setting. The training samples in OCC are assumed to be normal, which is difficult to guarantee in practical situations. Such a case may degrade the performance of OCC-based anomaly detection methods which fit the training distribution as the normal distribution. In this paper, we propose MTGFlow, an unsupervised anomaly detection approach for MTS anomaly detection via dynamic Graph and entity-aware normalizing Flow. MTGFlow first estimates the density of the entire training samples and then identifies anomalous instances based on the density of the test samples within the fitted distribution. This relies on a widely accepted assumption that anomalous instances exhibit more sparse densities than normal ones, with no reliance on the clean training dataset. However, it is intractable to directly estimate the density due to complex dependencies among entities and their diverse inherent characteristics. To mitigate this, we utilize the graph structure learning model to learn interdependent and evolving relations among entities, which effectively captures complex and accurate distribution patterns of MTS. In addition, our approach incorporates the unique characteristics of individual entities by employing an entity-aware normalizing flow. This enables us to represent each entity as a parameterized normal distribution. Furthermore, considering that some entities present similar characteristics, we propose a cluster strategy that capitalizes on the commonalities of entities with similar characteristics, resulting in more precise and detailed density estimation. We refer to this cluster-aware extension as MTGFlow_cluster. Extensive experiments are conducted on six widely used benchmark datasets, in which MTGFlow and MTGFlow cluster demonstrate their superior detection performance.Comment: arXiv admin note: substantial text overlap with arXiv:2208.0210

Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detection

Self-supervised methods have gained prominence in time series anomaly detection due to the scarcity of available annotations. Nevertheless, they typically demand extensive training data to acquire a generalizable representation map, which conflicts with scenarios of a few available samples, thereby limiting their performance. To overcome the limitation, we propose \textbf{AnomalyLLM}, a knowledge distillation-based time series anomaly detection approach where the student network is trained to mimic the features of the large language model (LLM)-based teacher network that is pretrained on large-scale datasets. During the testing phase, anomalies are detected when the discrepancy between the features of the teacher and student networks is large. To circumvent the student network from learning the teacher network's feature of anomalous samples, we devise two key strategies. 1) Prototypical signals are incorporated into the student network to consolidate the normal feature extraction. 2) We use synthetic anomalies to enlarge the representation gap between the two networks. AnomalyLLM demonstrates state-of-the-art performance on 15 datasets, improving accuracy by at least 14.5\% in the UCR dataset.Comment: 12 pages, 5 figure

MTGFlow: Unsupervised Multivariate Time Series Anomaly Detection via Dynamic Graph and Entity-aware Normalizing Flow

Multivariate time series anomaly detection has been extensively studied under the semi-supervised setting, where a training dataset with all normal instances is required. However, preparing such a dataset is very laborious since each single data instance should be fully guaranteed to be normal. It is, therefore, desired to explore multivariate time series anomaly detection methods based on the dataset without any label knowledge. In this paper, we propose MTGFlow, an unsupervised anomaly detection approach for Multivariate Time series anomaly detection via dynamic Graph and entity-aware normalizing Flow, leaning only on a widely accepted hypothesis that abnormal instances exhibit sparse densities than the normal. However, the complex interdependencies among entities and the diverse inherent characteristics of each entity pose significant challenges on the density estimation, let alone to detect anomalies based on the estimated possibility distribution. To tackle these problems, we propose to learn the mutual and dynamic relations among entities via a graph structure learning model, which helps to model accurate distribution of multivariate time series. Moreover, taking account of distinct characteristics of the individual entities, an entity-aware normalizing flow is developed to describe each entity into a parameterized normal distribution, thereby producing fine-grained density estimation. Incorporating these two strategies, MTGFlowachieves superior anomaly detection performance. Experiments on the real-world datasets are conducted, demonstrating that MTGFlow outperforms the state-of-the-art (SOTA) by 5.0% and 1.6% AUROC for SWaT and WADI datasets respectively. Also, through the anomaly scores contributed by individual entities, MTGFlow can provide explanation information for the detection results

Temporal-Coded Deep Spiking Neural Network with Easy Training and Robust Performance

Spiking neural network (SNN) is interesting both theoretically and practically because of its strong bio-inspiration nature and potentially outstanding energy efficiency. Unfortunately, its development has fallen far behind the conventional deep neural network (DNN), mainly because of difficult training and lack of widely accepted hardware experiment platforms. In this paper, we show that a deep temporal-coded SNN can be trained easily and directly over the benchmark datasets CIFAR10 and ImageNet, with testing accuracy within 1% of the DNN of equivalent size and architecture. Training becomes similar to DNN thanks to the closed-form solution to the spiking waveform dynamics. Considering that SNNs should be implemented in practical neuromorphic hardwares, we train the deep SNN with weights quantized to 8, 4, 2 bits and with weights perturbed by random noise to demonstrate its robustness in practical applications. In addition, we develop a phase-domain signal processing circuit schematic to implement our spiking neuron with 90% gain of energy efficiency over existing work. This paper demonstrates that the temporal-coded deep SNN is feasible for applications with high performance and high energy efficient

Long-chain hydrocarbons by CO2 electroreduction using polarized nickel catalysts

The electroreduction of CO2, driven by renewable electricity, can be used to sustainably generate synthetic fuels. So far, only copper-based materials have been used to catalyse the formation of multicarbon products, albeit limited to C2 or C3 molecules. Herein, we disclose that inorganic nickel oxygenate-derived electrocatalysts can generate linear and branched C3 to C6 hydrocarbons with sustained Faradaic efficiencies of up to 6.5%, contrasting with metallic nickel, which is practically inactive. Operando X-ray absorption spectroscopy, electrochemical CO stripping and density functional theory pinpoint the presence of stable, polarized Niδ+ active sites associated with Ni–O bonds, which bind CO moderately. The reduction of selected C1 molecules and density functional theory simulations suggest that the Niδ+ sites promote a mechanism reminiscent of the Fischer–Tropsch synthesis: COOH + CHx coupling followed by successive CHx insertions. Our results disclose atom polarization to be the key that prevents the CO poisoning of nickel and enables CO2 reduction to a wider pool of valuable products