Visual Features Research Articles

How does visual context of urban open spaces drive soundscape pleasantness and eventfulness: a laboratory experiment using a higher order Ambisonics speaker system and immersive virtual reality

Perception of sound environments is influenced by the context we experience them in. A big portion of the contextual information comes from the visual domain, including our understanding of what a place is but also from its visual features. A laboratory study combining questionnaire responses and eye-tracking tools was designed to investigate if the soundscape outcomes and participants' behaviour inside the simulation can be explained by the perceptual outcomes defined by visual information. 360 degree videos and First Order Ambisonics audio recordings of 27 different urban open spaces taken from the International Soundscape Database were used as the stimuli delivered via a Virtual Reality Head-Mounted Display and a Higher Order Ambisonics speaker array, while a neutral grey environment without sounds being reproduced represented the baseline scenario. A questionnaire tool was deployed within the IVR simulation to collect participants' responses describing their perception of the reproduced environments corresponding to the circumplex model featured in the Method A of the ISO/TS 12913-2. The results revealed a good coverage of the two-dimensional perceptual circumplex space and significant differences between perceptual outcomes driven by sound and those driven by visual stimuli.

Enhancing quick-cooking bean quality: The impact of salt concentrations on soaking of fresh and aged Brazilian genotypes

AimThis study aimed to investigate the feasibility of preparing quick-cooking beans from freshly harvested and aged beans with HTC defects from two Brazilian bean genotypes and to evaluate the influence of different salt concentrations in the soaking step. MethodTwo Brazilian bean genotypes (BRS Estilo and BRS Esteio) that were freshly harvested and aged with hard-to-cook (HTC) defects were used to produce quick-cooking beans by testing different treatments with saline solutions (0.5 %, 1.0 %, and 1.5 %) sodium bicarbonate [NaHCO3] and sodium chloride [NaCl]) in the soaking stage. The cooking time, water absorption, damaged grains, colorimetric profile, visual characteristics, and hardness were analyzed. ResultsThe use of salts in the soaking step promoted reductions of up to 95 % in the cooking time of the beans. The shortest cooking times and highest water absorption were observed with NaHCO3 treatments. After processing, browning of the tegument was observed in BRS Estilo, whereas BRS Esteio exhibited the opposite behavior. Beans soaked in NaHCO3 showed higher levels of damage, regardless of the evaluated genotype; however, HTC beans showed less damage and higher hardness than freshly harvested beans. ConclusionDifferent concentrations of NaCl and NaHCO3 enabled the elaboration of beans with greater water absorption capacity, shorter cooking times, and lower hardness. The 1.0 % NaCl treatment produced quick-cooking beans with better characteristics. The use of salts during the soaking step can help in the preparation of quick-cooking beans, providing improvements in technological quality, especially for HTC beans.

Hyperspectral transmittance imaging detection of early decayed oranges caused by Penicillium digitatum using NFINDR-JMSAM algorithm with spectral feature separating

Decay caused by Penicillium spp. is the main cause of postharvest citrus quality loss, moreover, the fungus can quickly infect entire batches of citrus fruit resulting in significant economic losses. However, effective detection of early decay remains a challenge due to the lack of distinct visual features. In this study, a Vis-NIR hyperspectral imaging system was developed to acquire full-transmittance images and an NFINDR-JMSAM algorithm was proposed to segment different image pixels. By extracting pure pixels and separating spectral features, the overall classification accuracy of 99.3 % was obtained for all tested samples. The proposed method can also effectively identify scars on the flavedo, citrus stem-end and navel, thereby eliminating their interference with the detection of decayed orange fruit. This study provided a new idea for accurately detecting the early decayed citrus fruit and visualizing the detection results for different tissues by combining hyperspectral transmittance imaging and NFINDR-JMSAM algorithm.

Beyond Granularity: Enhancing Continuous Sign Language Recognition with Granularity-Aware Feature Fusion and Attention Optimization

The advancement of deep learning techniques has significantly propelled the development of the continuous sign language recognition (cSLR) task. However, the spatial feature extraction of sign language videos in the RGB space tends to focus on the overall image information while neglecting the perception of traits at different granularities, such as eye gaze and lip shape, which are more detailed, or posture and gestures, which are more macroscopic. Exploring the efficient fusion of visual information of different granularities is crucial for accurate sign language recognition. In addition, applying a vanilla Transformer to sequence modeling in cSLR exhibits weak performance because specific video frames could interfere with the attention mechanism. These limitations constrain the capability to understand potential semantic characteristics. We introduce a feature fusion method for integrating visual features of disparate granularities and refine the metric of attention to enhance the Transformer’s comprehension of video content. Specifically, we extract CNN feature maps with varying receptive fields and employ a self-attention mechanism to fuse feature maps of different granularities, thereby obtaining multi-scale spatial features of the sign language framework. As for video modeling, we first analyze why the vanilla Transformer failed in cSLR and observe that the magnitude of the feature vectors of video frames could interfere with the distribution of attention weights. Therefore, we utilize the Euclidean distance among vectors to measure the attention weights instead of scaled-dot to enhance dynamic temporal modeling capabilities. Finally, we integrate the two components to construct the model MSF-ET (Multi-Scaled feature Fusion–Euclidean Transformer) for cSLR and train the model end-to-end. We perform experiments on large-scale cSLR benchmarks—PHOENIX-2014 and Chinese Sign Language (CSL)—to validate the effectiveness.

A chromatic feature detector in the retina signals visual context changes.

The retina transforms patterns of light into visual feature representations supporting behaviour. These representations are distributed across various types of retinal ganglion cells (RGCs), whose spatial and temporal tuning properties have been studied extensively in many model organisms, including the mouse. However, it has been difficult to link the potentially nonlinear retinal transformations of natural visual inputs to specific ethological purposes. Here, we discover a nonlinear selectivity to chromatic contrast in an RGC type that allows the detection of changes in visual context. We trained a convolutional neural network (CNN) model on large-scale functional recordings of RGC responses to natural mouse movies, and then used this model to search in silico for stimuli that maximally excite distinct types of RGCs. This procedure predicted centre colour opponency in transient suppressed-by-contrast (tSbC) RGCs, a cell type whose function is being debated. We confirmed experimentally that these cells indeed responded very selectively to Green-OFF, UV-ON contrasts. This type of chromatic contrast was characteristic of transitions from ground to sky in the visual scene, as might be elicited by head or eye movements across the horizon. Because tSbC cells performed best among all RGC types at reliably detecting these transitions, we suggest a role for this RGC type in providing contextual information (i.e. sky or ground) necessary for the selection of appropriate behavioural responses to other stimuli, such as looming objects. Our work showcases how a combination of experiments with natural stimuli and computational modelling allows discovering novel types of stimulus selectivity and identifying their potential ethological relevance.

A human visual attention analysis model for remote interaction interface of unmanned agricultural vehicles

When unmanned agricultural vehicles (UAVs) encounter unexpected failures or incidents during autonomous operations, a highly efficient remote human–machine interaction (HMI) interface is needed to assist in the manual joint control process. The highly complex cognitive mechanisms and visual characteristics of humans make it difficult to design efficient human–machine interfaces. Therefore, a human visual attention analysis model was proposed based on fully convolutional networks for the remote interaction interface of unmanned agricultural vehicles. First, visual attention experimental software combined with gaze-contingent real-time simulation methods was developed to build the dataset. Subsequently, the human visual attention analysis model (HVAAM) is constructed, and the corresponding training strategy is proposed, which is trained on the ILSVRC2012 standard and self-constructed datasets. The accuracy of the HVAAM analysis on the test dataset was validated. Ultimately, a human–machine interaction test of random failure warning for remote monitoring interface of unmanned plowing operation is performed with a self-developed unmanned electric tractor as the carrier. In the test dataset, the average analysis accuracy of HVAAM was 79.2 %, and the proposed training strategy led to a 20.7 % improvement in the model accuracy. In the unmanned electric tractor human–machine interaction test, HVAAM achieved 77.9 % accuracy in the visual attention analysis of the monitoring interface. This study can effectively facilitate the analysis, design, and optimization of remote monitoring interfaces for unmanned agricultural vehicles.

Optimal visual control of tendon-sheath-driven continuum robots with robust Jacobian estimation in confined environments

Accurate control of continuum robots in confined environments presents a significant challenge due to the need for a precise kinematic model, which is susceptible to external interference. This paper introduces a model-less optimal visual control (MLOVC) method that enables a tendon-sheath-driven continuum robot (TSDCR) to effectively track visual targets in a confined environment while ensuring stability. The method allows for intraluminal navigation of TSDCRs along narrow lumens. To account for the presence of external outliers, a robust Jacobian estimation method is proposed, wherein improved iterative reweighted least squares with sliding windows are used to online calculate the robot’s Jacobian matrix from sensing data. The estimated Jacobian establishes the motion relationship between the visual feature and the actuation. Furthermore, an optimal visual control method based on quadratic programming (QP) is designed for visual target tracking, while considering the robot’s physical constraint and control constraints. The MLOVC method for visual tracking provides a reliable alternative that does not rely on the precise kinematics of TSDCRs and takes into consideration the impact of outliers. The control stability of the proposed approach is demonstrated through Lyapunov analysis. Simulations and experiments are conducted to evaluate the effectiveness of the MLOVC method, and the results demonstrate that it enhances tracking performance in terms of accuracy and stability.

Intrinsic functional networks for distinct sources of error in visual working memory.

Visual working memory (VWM) is a core cognitive function wherein visual information is stored and manipulated over short periods. Response errors in VWM tasks arise from the imprecise memory of target items, swaps between targets and nontargets, and random guesses. However, it remains unclear whether these types of errors are underpinned by distinct neural networks. To answer this question, we recruited 80 healthy adults to perform delayed estimation tasks and acquired their resting-state functional magnetic resonance imaging scans. The tasks required participants to reproduce the memorized visual feature along continuous scales, which, combined with mixture distribution modeling, allowed us to estimate the measures of memory precision, swap errors, and random guesses. Intrinsic functional connectivity within and between different networks, identified using a hierarchical clustering approach, was estimated for each participant. Our analyses revealed that higher memory precision was associated with increased connectivity within a frontal-opercular network, as well as between the dorsal attention network and an angular-gyrus-cerebellar network. We also found that coupling between the frontoparietal control network and the cingulo-opercular network contributes to both memory precision and random guesses. Our findings demonstrate that distinct sources of variability in VWM performance are underpinned by different yet partially overlapping intrinsic functional networks.

Probing the link between vision and language in material perception using psychophysics and unsupervised learning.

We can visually discriminate and recognize a wide range of materials. Meanwhile, we use language to describe what we see and communicate relevant information about the materials. Here, we investigate the relationship between visual judgment and language expression to understand how visual features relate to semantic representations in human cognition. We use deep generative models to generate images of realistic materials. Interpolating between the generative models enables us to systematically create material appearances in both well-defined and ambiguous categories. Using these stimuli, we compared the representations of materials from two behavioral tasks: visual material similarity judgments and free-form verbal descriptions. Our findings reveal a moderate but significant correlation between vision and language on a categorical level. However, analyzing the representations with an unsupervised alignment method, we discover structural differences that arise at the image-to-image level, especially among ambiguous materials morphed between known categories. Moreover, visual judgments exhibit more individual differences compared to verbal descriptions. Our results show that while verbal descriptions capture material qualities on the coarse level, they may not fully convey the visual nuances of material appearances. Analyzing the image representation of materials obtained from various pre-trained deep neural networks, we find that similarity structures in human visual judgments align more closely with those of the vision-language models than purely vision-based models. Our work illustrates the need to consider the vision-language relationship in building a comprehensive model for material perception. Moreover, we propose a novel framework for evaluating the alignment and misalignment between representations from different modalities, leveraging information from human behaviors and computational models.

Connectomic reconstruction predicts visual features used for navigation

Many animals use visual information to navigate1–4, but how such information is encoded and integrated by the navigation system remains incompletely understood. In Drosophila melanogaster, EPG neurons in the central complex compute the heading direction5 by integrating visual input from ER neurons6–12, which are part of the anterior visual pathway (AVP)10,13–16. Here we densely reconstruct all neurons in the AVP using electron-microscopy data17. The AVP comprises four neuropils, sequentially linked by three major classes of neurons: MeTu neurons10,14,15, which connect the medulla in the optic lobe to the small unit of the anterior optic tubercle (AOTUsu) in the central brain; TuBu neurons9,16, which connect the AOTUsu to the bulb neuropil; and ER neurons6–12, which connect the bulb to the EPG neurons. On the basis of morphologies, connectivity between neural classes and the locations of synapses, we identify distinct information channels that originate from four types of MeTu neurons, and we further divide these into ten subtypes according to the presynaptic connections in the medulla and the postsynaptic connections in the AOTUsu. Using the connectivity of the entire AVP and the dendritic fields of the MeTu neurons in the optic lobes, we infer potential visual features and the visual area from which any ER neuron receives input. We confirm some of these predictions physiologically. These results provide a strong foundation for understanding how distinct sensory features can be extracted and transformed across multiple processing stages to construct higher-order cognitive representations.

Microstate Analyses to Study face Processing in Healthy Individuals and Psychiatric Disorders: A Review of ERP Findings.

Microstates represent brief periods of quasi-stable electroencephalography (EEG) scalp topography, offering insights into dynamic fluctuations in event-related potential (ERP) topographies. Despite this, there is a lack of a comprehensive systematic overview of microstate findings concerning cognitive face processing. This review aims to summarize ERP findings on face processing using microstate analyses and assess their effectiveness in characterizing face-related neural representations. A literature search was conducted for microstate ERP studies involving healthy individuals and psychiatric populations, utilizing PubMed, Google Scholar, Web of Science, PsychInfo, and Scopus databases. Twenty-two studies were identified, primarily focusing on healthy individuals (n = 16), with a smaller subset examining psychiatric populations (n = 6). The evidence reviewed in this study suggests that various microstates are consistently associated with distinct ERP stages involved in face processing, encompassing the processing of basic visual facial features to more complex functions such as analytical processing, facial recognition, and semantic representations. Furthermore, these studies shed light on atypical attentional neural mechanisms in Autism Spectrum Disorder (ASD), facial recognition deficits among emotional dysregulation disorders, and encoding and semantic dysfunctions in Post-Traumatic Stress Disorder (PTSD). In conclusion, this review underscores the practical utility of ERP microstate analyses in investigating face processing. Methodologies have evolved towards greater automation and data-driven approaches over time. Future research should aim to forecast clinical outcomes and conduct validation studies to directly demonstrate the efficacy of such analyses in inverse space.

Rapid and noninvasive estimation of human arsenic exposure based on 4-photo-set of the hand and foot photos through artificial intelligence

Chronic exposure to arsenic is linked to the development of cancers in the skin, lungs, and bladder. Arsenic exposure manifests as variegated pigmentation and characteristic pitted keratosis on the hands and feet, which often precede the onset of internal cancers. Traditionally, human arsenic exposure is estimated through arsenic levels in biological tissues; however, these methods are invasive and time-consuming. This study aims to develop a noninvasive approach to predict arsenic exposure using artificial intelligence (AI) to analyze photographs of hands and feet. By incorporating well water consumption data and arsenic concentration levels, we developed an AI algorithm trained on 9988 hand and foot photographs from 2497 subjects. This algorithm correlates visual features of palmoplantar hyperkeratosis with arsenic exposure levels. Four pictures per patient, capturing both ventral and dorsal aspects of hands and feet, were analyzed. The AI model utilized existing arsenic exposure data, including arsenic concentration (AC) and cumulative arsenic exposure (CAE), to make binary predictions of high and low arsenic exposure. The AI model achieved an optimal area under the curve (AUC) values of 0.813 for AC and 0.779 for CAE. Recall and precision metrics were 0.729 and 0.705 for CAE, and 0.750 and 0.763 for AC, respectively. While biomarkers have traditionally been used to assess arsenic exposure, efficient noninvasive methods are lacking. To our knowledge, this is the first study to leverage deep learning for noninvasive arsenic exposure assessment. Despite challenges with binary classification due to imbalanced and sparse data, this approach demonstrates the potential for noninvasive estimation of arsenic concentration. Future studies should focus on increasing data volume and categorizing arsenic concentration statistics to enhance model accuracy. This rapid estimation method could significantly contribute to epidemiological studies and aid physicians in diagnosis.

Towards fine-grained adaptive video captioning via Quality-Aware Recurrent Feedback Network

To achieve accurate video captioning, most existing solutions focus on employing auxiliary features to provide complementary generation clues, such as detected target regions, audio speech signals, and in-plane text. However, beyond introducing external information, the potential of using the generated captions has been overlooked. We argue that the intrinsic value lies in the caption text, which can faithfully reflect the video content. To this end, we propose to reuse the captions via a Quality-Aware Recurrent Feedback Network (QARFNet), a model that progressively exploits the supportive power between video features and the generated captions. Specifically, inspired by the backtracking AdaBoost algorithm, we propose to build a recurrent loop structure that recycles the obtained linguistic predictions to refine the input visual features. Referring to the greedy selective nature of the AdaBoost algorithm, we set a quality-aware gate to assess the necessity of forwarding the next loop, lessening the complexity. After the refinement loop, if the confidence score remains below the predefined threshold, we select the output with the highest confidence score to advance the subsequent transformer decoder. To facilitate feedback in each loop, a Multi-level Update Module is constructed before fusing linguistic predictions with video features. This involves extracting nouns and verbs from linguistic predictions to lighten relevant video tokens, achieving self-interactions across recurrent loops. By merging the coarse-grained and gradually refined linguistic prediction features, the video tokens are semantically closer to the desired textual representation. To alleviate the increased calculation caused by the loops, we employ a flat transformer encoder to decrease the complexity. The experimental results on several benchmark datasets confirm the effectiveness of our approach, with superior performance compared to the state-of-the-art.

Neural Encoding of Bodies for Primate Social Perception.

Primates, as social beings, have evolved complex brain mechanisms to navigate intricate social environments. This review explores the neural bases of body perception in both human and nonhuman primates, emphasizing the processing of social signals conveyed by body postures, movements, and interactions. Early studies identified selective neural responses to body stimuli in macaques, particularly within and ventral to the superior temporal sulcus (STS). These regions, known as body patches, represent visual features that are present in bodies but do not appear to be semantic body detectors. They provide information about posture and viewpoint of the body. Recent research using dynamic stimuli has expanded the understanding of the body-selective network, highlighting its complexity and the interplay between static and dynamic processing. In humans, body-selective areas such as the extrastriate body area (EBA) and fusiform body area (FBA) have been implicated in the perception of bodies and their interactions. Moreover, studies on social interactions reveal that regions in the human STS are also tuned to the perception of dyadic interactions, suggesting a specialized social lateral pathway. Computational work developed models of body recognition and social interaction, providing insights into the underlying neural mechanisms. Despite advances, significant gaps remain in understanding the neural mechanisms of body perception and social interaction. Overall, this review underscores the importance of integrating findings across species to comprehensively understand the neural foundations of body perception and the interaction between computational modeling and neural recording.

Visual-guided hierarchical iterative fusion for multi-modal video action recognition

Vision-Language models(VLMs) have shown promising improvements on various visual tasks. Most existing VLMs employ two separate transformer-based encoders, each dedicated to modeling visual and language features independently. Because the visual features and language features are unaligned in the feature space, it is challenging for the multi-modal encoder to learn vision-language interactions. In this paper, we propose a V,isual-guided Hierarchical Iterative Fusion (VgHIF) method for VLMs in video action recognition, which acquires more discriminative vision and language representation. VgHIF leverages visual features from different levels in visual encoder to interact with language representation. The interaction is processed by the attention mechanism to calculate the correlation between visual features and language representation. VgHIF learns grounded video-text representation and supports many different pre-trained VLMs in a flexible and efficient manner with a tiny computational cost. We conducted experiments on the Kinetics-400 Mini Kinetics 200 HMDB51, and UCF101 using VLMs: CLIP, X-CLIP, and ViFi-CLIP. The experiments were conducted under full supervision and few shot settings, and compared with the baseline multi-modal model without VgHIF, the Top-1 accuracy of the proposed method has been improved to varying degrees, and several groups of results have achieved comparable results with state-of-the-art performance, which strongly verified the effectiveness of the proposed method.

Cross-Subject EEG Feedback for Implicit Image Generation.

Generative models are powerful tools for producing novel information by learning from example data. However, the current approaches require explicit manual input to steer generative models to match human goals. Furthermore, how these models would integrate implicit, diverse feedback and goals of multiple users remains largely unexplored. Here, we present a first-of-its-kind system that produces novel images of faces by inferring human goals directly from cross-subject brain signals while study subjects are looking at example images. We report on an experiment where brain responses to images of faces were recorded using electroencephalography in 30 subjects, focusing on specific salient visual features (VFs). Preferences toward VFs were decoded from subjects' brain responses and used as implicit feedback for a generative adversarial network (GAN), which generated new images of faces. The results from a follow-up user study evaluating the presence of the target salient VFs show that the images generated from brain feedback represent the goal of the study subjects and are comparable to images generated with manual feedback. The methodology provides a stepping stone toward humans-in-the-loop image generation.

Preserving text space integrity for robust compositional zero-shot learning via mixture of pretrained experts

In the current landscape of Compositional Zero-Shot Learning (CZSL) methods that leverage CLIP, the predominant approach is based on prompt learning paradigms. These methods encounter significant computational complexity when dealing with a large number of categories. Additionally, when confronted with new classification tasks, there is a necessity to learn the prompts again, which can be both time-consuming and resource-intensive. To address these challenges, We present a new methodology, named the Mixture of Pretrained Expert (MoPE), for enhancing Compositional Zero-shot Learning through Logit-Level Fusion with Mutile Expert Fusion Module. The MoPE skillfully blends the benefits of extensive pre-trained models like CLIP, Bert, GPT-3 and Word2Vec for effectively tackling Compositional Zero-shot Learning. Firstly, we extract the text label space for each language model individually, then map the visual feature vectors to their respective text spaces. This maintains the integrity and structure of the original text space. During this process, the pre-trained expert parameters are kept frozen. The mapping of visual features to the corresponding text spaces is subject to learning and could be considered as multiple learnable visual experts. In the model fusion phase, we propose a new fusion strategy that features a gating mechanism that adjusts the contributions of various models dynamically. This enables our approach to adapt more effectively to a range of tasks and data sets. The method’s robustness is demonstrated by the fact that the language model is not tailored to specific downstream task datasets or losses. This preserves the larger model’s topology and expands the potential for application. Preliminary experiments conducted on the UT-Zappos, AO-Clever, and C-GQA datasets indicate that MoPE performs competitively when compared to existing techniques.

Shunting at Arbitrary Feature Levels via Spatial Disentanglement: Toward Selective Image Translation.

The past few years have witnessed considerable efforts devoted to translating images from one domain to another, mainly aiming at editing global style. Here, we focus on a more general case, selective image translation (SLIT), under an unsupervised setting. SLIT essentially operates through a shunt mechanism that involves learning gates to manipulate only the contents of interest (CoIs), which can be either local or global, while leaving the irrelevant parts unchanged. Existing methods typically rely on a flawed implicit assumption that CoIs are separable at arbitrary levels, ignoring the entangled nature of DNN representations. This leads to unwanted changes and learning inefficiency. In this work, we revisit SLIT from an information-theoretical perspective and introduce a novel framework, which equips two opposite forces to disentangle the visual features. One force encourages independence between spatial locations on the features, while the other force unites multiple locations to form a "block" that jointly characterizes an instance or attribute that a single location may not independently characterize. Importantly, this disentanglement paradigm can be applied to visual features of any layer, enabling shunting at arbitrary feature levels, which is a significant advantage not explored in existing works. Our approach has undergone extensive evaluation and analysis, confirming its effectiveness in significantly outperforming the state-of-the-art baselines.

HADT: Image super-resolution restoration Using Hybrid Attention-Dense Connected Transformer Networks

Image super-resolution (SR) plays a vital role in vision tasks, in which Transformer-based methods outperform conventional convolutional neural networks. Existing work usually uses residual linking to improve the performance, but this type of linking provides limited information transfer within the block. Also, existing work usually restricts the self-attention computation to a single window to improve feature extraction. This means transformer-based networks can only use feature information within a limited spatial range. To handle the challenge, this paper proposes a novel Hybrid Attention-Dense Connected Transformer Network (HADT) to utilize the potential feature information better. HADT is constructed by stacking an attentional transformer block (ATB), which contains an Effective Dense Transformer Block (EDTB) and a Hybrid Attention Block (HAB). EDTB combines dense connectivity and swin-transformer to enhance feature transfer and improve model representation, and meanwhile, HAB is used for cross-window information interaction and joint modeling of features for better visualization. Based on the experiments, our method is effective on SR tasks with magnification factors of 2, 3, and 4. For example, using the Urban100 dataset in an experiment with an amplification factor of 4 our method has a PSNR value that is 0.15 dB higher than the previous method and reconstructs a more detailed texture.

Research on Internal Flooding Analysis of Small Modular Reactors Based on Particle Method

Abstract The analysis of internal water flooding in nuclear power plants is a necessary part of safety review in the approval process of nuclear power plant construction projects. According to relevant regulations, it is necessary to conduct water flooding spread analysis on pipelines, water tanks, or pools filled with liquid in nuclear power plant buildings, analyze the spread path and water flooding height. This article analyzes and calculates the flow characteristics of water in a nuclear power plant building using a fluid calculation method based on the Lagrangian method (particle method). Taking the small modular reactor JR building as a case study, it analyzes the spread and height of four water flooding sources inside the building. According to the simulation time of 1800S, the results show that the flooding caused by the No. 2 water source is the most severe, with a flooding height of 1.367m. Drainage or preventive measures need to be taken to prevent the pipeline from breaking accidents; The flooding sources 1, 2, and 4 have a relatively mild impact, resulting in a lower flooding height and will not have a significant impact on important safety equipment, with little impact on the safety of nuclear power plants. The CFD analysis method was used to conduct water flooding analysis for nuclear power plants, which has the characteristics of strong adaptability and good visualization. It can dynamically simulate the entire process of water flooding analysis and better guide the design of water flooding protection for nuclear power plants.