Accident Simulation Research Articles

Fear among patients with Parkinson's disease and repeated falls: Analysis of skin conductance responses during simulated accidents

IntroductionThe fear of falling (FoF) usually increases in patients with Parkinson’s disease (PD); however, it can decrease despite repeated falls for unknown reasons. This phenomenon is similar to the findings that the patients with PD show lower emotional responses in the decision-making during the Iowa gambling task (IGT) than of those without PD. The decision-making during the IGT is reportedly correlated with interoceptive accuracy. This phenomenon could be associated with decreased physiological reaction and interoception deficiency. This study aimed to investigate the factors associated with decreased FoF in patients with PD who fell repeatedly. MethodsWe recorded the skin conductance responses (SCR) during the task delivered by a virtual reality system and heartbeat perception task (HPT) as an interoceptive measure. We measured emotional self-awareness using visual analog scale (VAS) scores. We recruited 28 non-demented patients with PD and 24 age-matched Non-clinical population (NC) group. All patients with PD were grouped by FoF scale score and number of falls within the previous year into repeated falls without FoF, repeated falls with FoF, and no falls groups. ResultsThe SCR during all accident videos experienced by themselves were significantly lower in the repeated falls without FoF group than in the NC group (p < 0.01). The HPT was significantly lower in the repeated falls without FoF group than in the NC group (p = 0.007). ConclusionThe repeated falls without FoF group did not recognize recurrent falls as being problematic, possibly associated with decreased physiological reactions and interoception deficiency.

Numerical simulation and risk assessment of toluene tank leakage in petrochemical industries, China.

Large-capacity toluene storage tank leakage accidents can lead to severe casualties and environmental damage. In this study, numerical simulation of the toluene leakage accidents is performed using PHAST and ALOHA softwares across 16 scenarios. The present research focuses on the dispersion of toluene lethal and toxicity clouds, as well as the associated risks from vapor cloud explosions and pool fires resulting from the large-capacity toluene storage tank leakage. Meanwhile, the effects of influential factors such as leakage height, wind speed, ambient temperature, and atmospheric stability on toluene leakage accidents are also discussed, and then the worst-case scenario of the toluene leakage accident is obtained. The effects of wind speed, ambient temperature, leakage height, and atmospheric stability on outcomes such as toxic clouds, vapor dispersion, explosions, and pool fires are also analyzed. The result reveals that toxic cloud dispersion increases with ambient temperature, while the impact of vapor cloud explosions and pool fires decreases with the increase of leak heights. Wind speed significantly affects pool fire spread. The influential factors related to maximizing hazard range including low leak heights, low wind speeds, high temperatures, and stable conditions are obtained. Risk analysis indicates that vapor cloud explosions significantly impact outdoor potential loss of life (PLLoutdoor) compared with other incidents. Scenario 4 within a directional range from 33.8 to 56.3° shows the highest incident frequencies, highlighting its importance for risk monitoring. These findings are crucial for enhancing emergency response strategies and safety protocols in industrial safety management.

Simulation of a Hazardous Chemical Cascading Accident Using the Graph Neural Network

In the storage of hazardous chemicals, due to space limitations, various hazardous chemicals are usually mixed stored when their chemical properties do not conflict. In a fire or other accidents during storage, the emergency response includes two key steps: first, using fire extinguishers like dry powder and carbon dioxide to extinguish the burning hazardous chemicals. In addition, hazardous chemicals around the accident site are often watered to cool down to prevent the spread of the fire. But both the water and extinguishers may react chemically with hazardous chemicals at the accident site, potentially triggering secondary accidents. However, the existing research about hazardous chemical domino accidents only focuses on the pre-rescue stage and ignores the simulation of rescue-induced accidents that occur after rescue. Aiming at the problem, a quantitative representation algorithm for the spatial correlation of hazardous chemicals is first proposed to enhance the understanding of their spatial relationships. Subsequently, a graph neural network is introduced to simulate the evolution process of hazardous chemical cascade accidents. By aggregating the physical and chemical characteristics, the initial accident information of nodes, and bi-temporal node status information, deep learning models have gained the ability to accurately predict node states, thereby improving the intelligent simulation of hazardous chemical accidents. The experimental results validated the effectiveness of the method.

Predicting the entire pyrolysis process of representative charring material infiltrated with kerosene using an improved artificial neural network

It is crucial to predict the entire pyrolysis process of charring materials infiltrated with flammable liquids to provide guidance for reducing the fire hazards and implementing numerical simulations of fire accidents caused by the leakage of flammable liquids. In the present study, pyrolysis data obtained from the thermogravimetric experiments were preprocessed using linear interpolation and data normalization methods. The processed data set was then used to train the artificial neural network (ANN) framework to predict the pyrolysis behaviors of typical charring material (Mongolian Scots pine) infiltrated with typical flammable liquid (kerosene) under three different conditions: (1) pyrolysis at a constant mass fraction of kerosene with multiple heating rates; (2) pyrolysis at multiple mass fractions of kerosene with a constant heating rate; and (3) pyrolysis at multiple mass fractions of kerosene and multiple heating rates. The ANN, with the double hidden layer structure (6−3), was capable to well predict the entire pyrolysis behaviors of Mongolian Scots pine under all the three scenarios. Besides, five data transformation function (multiplication, exponential function with base e, logarithmic function with base e, square root, and exponentiation function) were applied to the present three input variables (temperature, mass fraction of kerosene, and heating rate) to generate new input variables in order to extract more characteristics among the pyrolysis data. Therein, the ANN utilizing the data transformation functions of multiplication and exponential function with base e exhibited the best predictive ability. Among the three input variables in the ANN (temperature, mass fraction of kerosene, and heating rate), temperature was identified as the most sensitive one to the prediction performance, followed by mass fraction of kerosene, with heating rate being the least sensitive.

Investigation and CFD simulation of coal dust explosion accident in confined space: A case study of Gaohe Coal Mine Ventilation Air Methane oxidation power plant

The study investigates a coal dust explosion accident within a confined space in Shanxi. The accident was caused by coal dust coming into contact with a high-temperature heat source under the influence of the stack effect. Based on the investigation of the coal dust explosion accident, the study uses computational fluid dynamics (CFD) simulation to reconstruct the accident scenario. The simulation results provide a detailed depiction of coal dust movement within subsurface semi-enclosed confined spaces during the ventilation diffusion stage, influenced by the stack effect, and confirm the formation of a coal dust layer at the heat source due to coal dust deposition. The simulation results of the two explosion processes indicate that excessively high coal dust concentrations within confined spaces have a negative correlation with the development of explosion flames and the peak overpressure. It resulted in a secondary explosion with a relatively low concentration of coal dust, where the peak overpressure was five times that of the primary explosion. Comparative analysis between simulation results and actual investigations the accuracy of the CFD simulation in capturing the accident process and its characteristics, providing valuable insights for preventing and controlling coal dust explosion accidents.

Mesh morphing-based pre-processing and numerical simulation of blockage accident in lead–bismuth fast reactor fuel assembly

This paper presents a CFD modeling approach for a 19-rod bundle fuel assembly with spiral semi-cylindrical ribs, which potentially can be used for liquid metal cooled fast reactor, as well as a numerical simulation of flow and thermal behavior under partially blocked conditions. The ribs on fuel rod surface are integrally formed with the cladding to enhance heat transfer while preventing detachment. However, the complex channel geometry presents challenges for grid generation, as using a large number of tetrahedral or polyhedral elements would consume significant computational resources. Therefore, this paper proposes a mesh morphing-based pre-processing method to establish a structured hexahedral mesh for such complex geometry. Using this approach, the 19-rods assembly is analyzed, and the result has been verified by conventional mesh scheme as well as experiment.

Research on fence protection for liquid hydrogen leakage in the storage tank area

Hydrogen energy is an important carrier for energy terminals to achieve green and low-carbon transformation, but hydrogen safety remains a bottleneck for its large-scale commercial development. This study conducts numerical simulation of hydrogen leakage accidents in liquid hydrogen refueling stations, analyzes the shortcomings of protective walls in the safety aspect, and proposes the use of fences for protection. The results show that although the protective walls can prevent the cold hydrogen gas from diffusing outside the storage tank area, it greatly increases the duration and final dissipation time. Then gas cloud is prone to accumulate in corners. The fences can alter the turbulent field around the storage tank area, accelerate the heat exchange between cloud and air, and promote buoyancy. The promoting effect of fences on turbulence is most evident under windy conditions, especially outside the fence. Results indicate that the 3 cm and 4 cm fences have the best effect. Compared with the protective wall case, under windy conditions, the dissipation time of flammable clouds in the corner of the 3 cm fence is reduced by 8 s, while the 4 cm fence is reduced by 7 s. It should be noted that under wind conditions, the dilution time outside the 3 cm fence decreases by 21 s, while the 4 cm fence decreases by 35 s. Under windy conditions, there is a uniformly concentrated strong turbulent flow field near the 3 cm fence.

On using machine learning algorithms for motorcycle collision detection

Globally, motorcycles attract vast and varied users. However, since the rate of severe injury and fatality in motorcycle accidents far exceeds that of passenger car accidents, efforts have been directed towards increasing passive safety systems. Impact simulations show that the risk of severe injury or death in the event of a motorcycle-to-car impact can be greatly reduced if the motorcycle is equipped with passive safety measures such as airbags and seat belts. For the passive safety systems to be activated, a collision must be detected within milliseconds for a wide variety of impact configurations, but under no circumstances may it be falsely triggered. For the challenge of reliably detecting impending collisions, this paper presents an investigation towards the applicability of machine learning algorithms. First, a series of simulations of accidents and driving operation is introduced to collect data to train machine learning classification models. Their performance is henceforth assessed and compared via multiple representative and application-oriented criteria.

Application of reinforcement learning to deduce nuclear power plant severe accident scenario

Severe accident scenarios for nuclear power plants are determined through probabilistic safety analysis (PSA). In this process, it is possible to identify the failure sequence of specific components, but assessing the impact of component failure time on the severity of accident remains a challenge. In this study, a novel approach is presented that utilizes machine learning methodologies such as reinforcement learning (RL) to complement traditional PSA. The proposed process was validated by comparing whether the most severe accident scenarios obtained through critical accident simulations can be reproduced by a RL, since this is a novel use of machine learning techniques. The comparison shows feasibility of exploring critical accident scenarios using RL. To implement the reinforcement learning methodology based on the existing system code, supervised learning model that can predict the remaining time of reactor vessel failure was implemented in this study. Based on this prediction model and data from existing catastrophic accident simulation, RL was implemented. The results obtained from RL were subsequently validated with the results of severe accident code simulation. In summary, new methodology for applying machine learning techniques to the nuclear accident analysis process was presented, and the feasibility and potential of the proposed methodology were discussed.

Assessment of radiological consequences to a hypothetical accident of the 3-MW TRIGA Mark-II nuclear research reactor of Bangladesh

The radiological consequences to a hypothetical accident are assessed for the 3-MW TRIGA Mark-II Nuclear Research Reactor of Bangladesh for both wet and dry seasons. Accident simulations are crucial for assessing the potentialrisks in the event of a nuclear accident. ORIGEN code was used to carry out the facility's radionuclide inventory. In the event of a hypothetical accident, the Total Effective Dose Equivalent (TEDE) is assessed using HotSpots. The findings indicate that for stability class A during the rainy season, the selected radionuclides arrived at a downwind distance of 0.03 km with an arrival time of less than one minute, and the highest TEDE, respiratory time-integrated air concentration, and ground deposition activity, respectively, were marked at 2.8E-12 Sv, 9.4E-01 Bq-sec/m3, and 2.4E-05 kBq/m2. In the case of I-131 at 0.03 km from the source, the thyroid gland is the most sensitive human organ, followed by the lung, esophagus, thymus, surface bone, skin, and breast. The estimated Committed Effective Dose Equivalent (CEDE) from I-131 is 9.1E-11 Sv. In contrast, the adrenal was the most sensitive organ with a maximum CEDE of 1.3E-13 Sv in the case of Cs-137 at a downwind distance of 0.03 km. The highest TEDE values for the general public and the vicinity around the TRIGA Mark II reactor facility are lower than the annual dose limits.

Lagrangian and Eulerian approach to predict the movement of radionuclides in selected potential sites in Malaysia during the monsoon period

In computing the movement of particles through the fluid, three types of dispersion models are used which are Gaussian plume models, Lagrangian puff models, and small-scale numerical models. Lagrangian particle dispersion models are increasingly used for nuclear applications. In this study, the usage of the Lagrangian model is implemented in a Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) which combines with the Eulerian model to predict the movement of radionuclides from the simulated nuclear power plant accident in selected locations in Malaysia. These include nuclear risk studies in these areas, emergency response systems, and source term analyses. This study aims to compare the risk from the dispersion of 137Cs from a simulated Nuclear Power Plant (NPP) at Mukah and Tasik Kenyir during the Northeast monsoon (8th October 2022) and Southeast monsoon (16th May 2022). The HYSPLIT model was set up based on the meteorological data during both monsoon periods and was simulated for 5 days. The outcomes revealed that the concentration of 137Cs is higher at Tasik Kenyir compared to Mukah for both during the Northeast monsoon period (3.5 x 107 Bq/m2) while for Southwest monsoon, the result is vice versa where the concentration of 137Cs at Mukah is (2.2 x 107Bq/m2). The activity concentration is then converted into rate effective dose measurement and compared with the limit suggested by ICRP which is 1 Sv per year. It is concluded that Tasik Kenyir is a more suitable location to build NPP compared to Mukah as the movement of radionuclides was dispersed to the location that contained less population.

Multi-physics analysis of SBLOCA by coupled Neutronics/Thermal-Hydraulics full plant model

With the development of computational tools, the accident analysis would be performed by best estimate approaches. The employment of multi-physics codes would be considered as new calculation procedure to obtain high valuable results by improving calculations with specialized codes. The small break loss of coolant accident (SBLOCA) is an accident that has typically been analyzed using system codes with a limited number of Thermal-Hydraulics (TH) channels in the core region, neglecting the use of multi-physics calculations. This approach results in insufficient accuracy and resolution in both neutronics and TH calculations within the core zone. To address this issue, this study proposes the implementation of a Neutronics/Thermal-Hydraulics coupling with one-to-one mapping in the core zone, achieved through the development of a comprehensive plant TH model. This approach aims to enhance the accuracy and resolution of calculations, enabling the identification of local phenomena, parameters, and feedback during accident simulations, and the incorporation of these effects on the macroscopic cross-sections at each time step. The results of SBLOCA simulation reveals the effects of conservative/best estimate approach on the sequence of events, the initiation time of the safety systems, the critical controlled parameters of the power plant, and the precise understanding of the accident. Also, it was found that the radial power distribution becomes disrupted and loses its symmetry immediately after the SCRAM leading to different characteristics of steam generators (SGs) and natural circulation flow rate in each loop of primary side.

Simulated safety analysis of a total loss of feedwater accident in the steam generator of CPR1000 nuclear power plant

As M310 series units, CPR1000 Nuclear Power Plant in China is also the first batch of demonstration nuclear power plants in China to try out SOP(the state-oriented procedure) accident procedures. The establishment of SOP accident regulations is an important work to mitigate the consequences of radioactive accidents in the design of nuclear power plants, and it is also the embodiment of the defense-in-depth requirements in the implementation of nuclear safety regulations. It can handle superimposed accidents, human errors in program implementation and events not considered in the primary cause sequence. In order to study the efficiency of its upgraded SOP procedure and its ability to deal with severe accidents, the typical primary cause event of severe accidents in nuclear power plants–such as the total loss of feedwater accident of steam generators has been adopted for research. The initial state of the simulation is the full power operation of the nuclear power plant, the simulation process is from the loss of all the water supply accidents to the whole accident processing. A total of 30 simulation experiments were carried out. The temperature and pressure of the key parameters obtained are the average values of 30 simulation experiments. The stability and efficiency of the updated SOP are evaluated by the treatment time of the water supply loss accident and the changes of reactor temperature and pressure after the accident. The results show that the upgraded SOP procedure is more stable and efficient. The upgraded SOP procedure, when dealing with other types of accidents, has improved the treatment of residual heat conduction in the core, increased the efficiency of accident treatment, and enhanced the safety of the reactor. At the same time, based on the weaknesses of the upgraded SOP procedure, constructive suggestions on changing the setting value of the steam generator bypass valve have also been put forward accordingly. Furthermore, it can be concluded from many simulated accidents that this change will win about 5% more of the disposal time for nuclear power plants under accidents, which provides references for the subsequent upgrading of M310 series power plant procedures.

Driver Behavior Analysis in Simulated Jaywalking and Accident Prediction Using Machine Learning Algorithms

Driver Behavior Analysis in Simulated Jaywalking and Accident Prediction Using Machine Learning Algorithms

Pearson Correlation and Chi-Squared Methods Applied to the Sensitivity and Uncertainty Analysis of Hydrogen Generation During a Boiling Water Reactor STSBO Using MAAP5 and AZTUSIA

Abstract After the nuclear accident at the Fukushima Daiichi nuclear power plant, hydrogen generation has resurfaced as a key issue, with respect to its quantification in the simulation of severe accidents (SAs), because of the potential risk of deflagration or detonation, which would compromise the integrity of the plant facilities. Severe accident simulation codes may have different models to predict cladding oxidation rates and the consequent hydrogen generation. Thus, sensitivity and uncertainty analysis can be applied to determine which input variables of the code have a significant impact in the implemented models, and consequently on the overall predicted values of key figures of merit. In this work, the generation of hydrogen in the reactor core during a short-term station blackout (STSBO) is studied as a figure of merit for a boiling water reactor (BWR). The scope of the analysis is until the failure of the reactor pressure vessel (RPV). This study shows a sensitivity and uncertainty analysis through the complementary calculations of two methods: Chi-squared parameter and Pearson simple correlation coefficient (PSCC). The severe accident simulation code was MAAP 5.0.3 (Modular Accident Analysis Program, EPRI, USA) and the AZTUSIA code (AZtlan Tool for Uncertainty and SensItivity Analysis, Mexico) was used as the statistical tool for the sensitivity and uncertainty analysis. Ten uncertain parameters were chosen, of which two are MAAP models options, and the values were generated via the simple random Monte Carlo technique. The results show that the Chi-squared parameter in conjunction with correlation coefficients provides a more comprehensive understanding of the impact of both quantitative and qualitative input variables on the figures of merit.

Android Accident Detection and Alert System

Increasing concern for road safety has led to the search for new technological methods to reduce the incidence of traffic accidents. This article describes the design, development, and evaluation of an Android-based incident detection and notifi- cation system that aims to improve emergency response capabili- ties and reduce incident severity. The system leverages ubiquitous smartphones and their built-in sensors by combining accelerom- eters, GPS, and image processing technology to instantly capture and share events. When detected, the system triggers an alarm to notify emergency services and first responders of the incident’s location and important information. The system also has features such as automatic emergency call initiation to provide immediate assistance to victims. The effectiveness of the system has been evaluated through various tests conducted in various simulated accident situations, demonstrating its accuracy, reliability and ability to shorten time in emergency situations. This research contributes to the advancement of smart transportation by using mobile technology to improve road safety and reduce the risk of accidents. Index Terms—Android-based system, accident detection, alert- ing, real-time monitoring, integrated sensor, accelerometer, GPS, imaging, emergency response, machine learning, automatic emer- gency calls, security, smart transportation, ubiquitous computing, smart mobile applications.

Development of an AI-based image/ultrasonic convergence camera system for accurate gas leak detection in petrochemical plants

Outdoor pipeline leaks are difficult to accurately measure using existing concentration measurement systems installed in petrochemical plants owing to external air currents. Besides, leak detection is only possible for a specific gas. The purpose of this study was to develop an image/ultrasonic convergence camera system that incorporates artificial intelligence (AI) to improve pipe leak detection and establish a real-time monitoring system. Our system includes an advanced ultrasonic camera coupled with a deep learning-based object-detection algorithm trained on pipe image data from petrochemical plants. The collected data improved the accuracy of detected gas leak localization through deep learning. Our detection model achieves an mAP50 (Mean average precision calculated at an intersection over union (IoU) threshold of 0.50)score of 0.45 on our data and is able to detect the majority of leak points within a system. The petrochemical plant environment was simulated by visiting petrochemical plants and reviewing drawings, and an outdoor experimental demonstration site was established. Scenarios such as flange connection failure were set under medium-/low-pressure conditions, and the developed product was experimented under gas leak conditions that simulated leakage accidents. These experiments enabled the removal of potentially confounding surrounding noise sources, which led to the false detection of actual gas leaks using the AI piping detection technique.

Android Accident Detection and Alert System

Increasing concern for road safety has led to the search for new technological methods to reduce the incidence of traffic accidents. This article describes the design, development, and evaluation of an Android-based incident detection and notifi- cation system that aims to improve emergency response capabili- ties and reduce incident severity. The system leverages ubiquitous smartphones and their built-in sensors by combining accelerom- eters, GPS, and image processing technology to instantly capture and share events. When detected, the system triggers an alarm to notify emergency services and first responders of the incident’s location and important information. The system also has features such as automatic emergency call initiation to provide immediate assistance to victims. The effectiveness of the system has been evaluated through various tests conducted in various simulated accident situations, demonstrating its accuracy, reliability and ability to shorten time in emergency situations. This research contributes to the advancement of smart transportation by using mobile technology to improve road safety and reduce the risk of accidents. Index Terms—Android-based system, accident detection, alert- ing, real-time monitoring, integrated sensor, accelerometer, GPS, imaging, emergency response, machine learning, automatic emer- gency calls, security, smart transportation, ubiquitous computing, smart mobile applications.

Release Behavior of Gaseous Ruthenium Tetroxide During Heating of High-Level Liquid Waste Simulant During Simulated Accident Conditions

A malfunction of the cooling system of high-level liquid waste (HLLW) and the failure of countermeasures may lead to the evaporation to dryness due to the loss of cooling functions (EDLCF) of the HLLW. In the EDLCF, ruthenium (Ru) can be released at a greater fraction to the initial amount than the other elements in HLLW by forming gaseous Ru. It is important to identify the chemical form of the released gaseous Ru to achieve a comprehensive understanding of the events impacting the source term assessment of Ru in this accident, such as particle formation, gas absorption, and deposition on migration pathways. In this study, we observed the ultraviolet/visible spectroscopy of the off-gas generated during the heating of a HLLW simulant. Employing a program that allows for the separation and quantification of known components within the spectrum [ruthenium tetroxide (RuO4), nitrogen dioxide, and nitric acid], we attempted to analyze the composition of gaseous Ru within the generated off-gas. Our findings revealed RuO4 as the main component of the gaseous Ru in the off-gas after comparing the total amount of released Ru and the RuO4 released amount obtained via spectroscopic analysis.

Applications of digital twin technology in construction safety risk management: a literature review

PurposeWith increasing complexity of construction projects and new construction processes and methods are adopted, more safety hazards are emerging at construction sites, requiring the application of the modern risk management methods. As an emerging technology, digital twin has already made valuable contributions to safety risk management in many fields. Therefore, exploring the application of digital twin technology in construction safety risk management is of great significance. The purpose of this study is to explore the current research status and application potential of digital twin technology in construction safety risk management.Design/methodology/approachThis study followed a four-stage literature processing approach as outlined in the systematic literature review procedure guidelines. It then combined the quantitative analysis tools and qualitative analysis methods to organize and summarize the current research status of digital twin technology in the field of construction safety risk management, analyze the application of digital twin technology in construction safety risk management and identify future research trends.FindingsThe research findings indicate that the application of digital twin technology in the field of construction safety risk management is still in its early stages. Based on the results of the literature analysis, this paper summarizes five aspects of digital twin technology's application in construction safety risk management: real-time monitoring and early warning, safety risk prediction and assessment, accident simulation and emergency response, safety risk management decision support and safety training and education. It also proposes future research trends based on the current research challenges.Originality/valueThis study provides valuable references for the extended application of digital twin technology and offers a new perspective and approach for modern construction safety risk management. It contributes to the enhancement of the theoretical framework for construction safety risk management and the improvement of on-site construction safety.