Machine Learning and Artificial Intelligence-Driven Multi-Scale Modeling for High Burnup Accident-Tolerant Fuels for Light Water-Based SMR Applications

The concept of small modular reactor has changed the outlook for tackling future energy crises. This new reactor technology is very promising considering its lower investment requirements, modularity, design simplicity, and enhanced safety features. The application of artificial intelligence-driven multi-scale modeling (neutronics, thermal hydraulics, fuel performance, etc.) incorporating Digital Twin and associated uncertainties in the research of small modular reactors is a recent concept. In this work, a comprehensive study is conducted on the multiscale modeling of accident-tolerant fuels. The application of these fuels in the light water-based small modular reactors is explored. This chapter also focuses on the application of machine learning and artificial intelligence in the design optimization, control, and monitoring of small modular reactors. Finally, a brief assessment of the research gap on the application of artificial intelligence to the development of high burnup composite accident-tolerant fuels is provided. Necessary actions to fulfill these gaps are also discussed

CFD Simulations to Study Parameters Affecting Gas Explosion Venting in Compressor Compartments

In this work, a series of vented explosions in a typical compressor compartment are simulated using FLACS code to analyze the explosion venting characteristics. The effects of relevant parameters on the pressure peaks (i.e., overpressure and negative pressure) are also numerically investigated, including vent area ratio of the compressor compartment, vent activation pressure, mass per unit area of vent panels, and volume blockage ratio of obstacles. In addition, the orthogonal experiment design and improved grey relational analysis are implemented to evaluate the impact degree of these relevant parameters. The results show that the pressure peaks decrease with the increase of vent area ratio. There is an approximately linearly increasing relationship between the pressure peaks and the vent activation pressure. The pressure peaks increase with the mass per unit area of vent panels. The pressure peaks increase with the volume blockage ratio of obstacles. Based on the grey relational grade values, the effects of these relevant parameters on the overpressure peak are ranked as follows: volume blockage ratio of obstacles > vent activation pressure > vent area ratio > mass per unit area of vent panels. These achievements provide effective guidance for the venting safety design of gas compressor compartments

Experimental Validation and Numerical Analysis of a High-Performance Blast Energy-Absorbing System for Building Structures

Funding Information: This research has been funded by NATO Counter Improvised Explosive Device Centre of Excellence under the project Blast Protective Walls Design Optimization (BLADE) and also supported by contract PTDC/ECI-EST/31046/2017 with the Portuguese funding institution FCT - Fundação para a Ciência e Tecnologia. Publisher Copyright: © 2023 by the authors.The paper presents a full-scale blast testing experimental campaign conducted on an energyabsorbing connector comprising thin-walled inversion tubes as kernel elements mounted in a façade protective panel. LS-DYNA finite element predictions of the global and local deformation/inversion of the panel/connectors compared reasonably well with the experimental observations. After validation, the numerical model was used to analyze the response of a simple idealized reinforced concrete structure under three blast-loading scenarios: the first two scenarios produce, approximately, the same impulse but are significantly different in terms of load duration and overpressures, and represent a far-field and a near-field scenario (1600 kg TNT at 20 m (i) and 150 kg TNT at 5 m (ii), respectively); the third scenario is more demanding, and consists in a half standoff distance of the second (150 kg TNT at 2.5 m (iii)). These numerical simulations allow to assess the effect of standoff distance and blast loading on the effectiveness of the protective system. One may conclude that the introduction of EACs strongly limits the forces imparted to the protected structure, reducing significantly the corresponding energy absorption demand. Comparing the energy absorbed by the structure in different scenarios, with and without the protective system (8 × ϕ64 × 2 mm), one can see that these reductions can reach, respectively 67%, 72% and 68% in the far-field, near-field and very near-field explosions.publishersversionpublishe

Forecasting currency exchange rate time series with fireworks-algorithm-based higher order neural network with special attention to training data enrichment

Exchange rates are highly fluctuating by nature, thus difficult to forecast. Artificial neural networks (ANN) have proved to be better than statistical methods. Inadequate training data may lead the model to reach suboptimal solution resulting, poor accuracy as ANN-based forecasts are data driven. To enhance forecasting accuracy, we suggests a method of enriching training dataset through exploring and incorporating of virtual data points (VDPs) by an evolutionary method called as fireworks algorithm trained functional link artificial neural network (FWA-FLN). The model maintains the correlation between the current and past data, especially at the oscillation point on the time series. The exploring of a VDP and forecast of the succeeding term go consecutively by the FWA-FLN. Real exchange rate time series are used to train and validate the proposed model. The efficiency of the proposed technique is related to other models trained similarly and produces far better prediction accuracy

Gasoline price spikes and regional gasoline context regulations : a structural approach

Since 1999, gasoline prices in California, Illinois and Wisconsin have spiked occasionally well above gasoline prices in nearby states. In May and June 2000, for example, gasoline prices in Chicago rose twenty eight cents per gallon to $2.13, while prices nationally rose only nine to$1.73. Several qualitative studies identify unique gasoline formulations in California, Illinois and Wisconsin as crucial factors related to regional price spikes. This paper provides the first quantitative estimates of two distinct effects of state-level gasoline content regulations in California, Illinois and Wisconsin: (i) the effect of increased production costs associated with additional refining necessary to meet content criteria, and (ii) the effect of incompatibility between these blends and gasoline meeting federal reformulated gasoline (RFG) standards. Using a structural model based on the production optimization problem of refiners, I simulate wholesale prices for jet fuel, diesel and four blends of gasoline in each geographic market. I then specify a counterfactual in which gasoline in the three states only met federal RFG requirements. Using a constructed dataset of refinery outages, I am able to separately identify each effect. Using a similar methodology, I also estimate the effect of two other factors thought to increase gasoline prices, (i) changes in refinery ownership and (ii) limited expansion of domestic refining capacity. Point estimates for the effect of increased refining costs are 4.5, 3.0 and 2.9 cents per gallon in California, Illinois and Wisconsin.(cont.) The effect of incompatibility with federal RFG criteria, conditional on an in-state refinery outage, is 4.8, 6.6 and 7.1 cents per gallon in California, Illinois and Wisconsin. Controlling for the magnitude of local outages in these areas, I estimate that 72, 92 and 91 percent of price spikes created by local refinery outages could be mitigated by compatibility with federal RFG standards. I find that changes in refinery ownership in the late 1990₂s increase prices by 1.4 to 1.5 cpg in Illinois and Wisconsin and by 0.73 cents per gallon in California. A five-percent increase in domestic refining capacity reduces prices 3.7 to 3.8 cents per gallon in Illinois and Wisconsin and 4.3 cents per gallon in California

Numerical study on scaling effects and decoupled network-based simulation of gaseous explosion

This research seeks to improve the prediction efficiency of gaseous explosions realized by numerical simulations in a full-scale underground network using a decoupled method. To provide quick predictions of overpressure distribution of methane explosions in underground airway networks, a two-section theory is employed. The explosion space is divided into a driver section and a blast-wave section. Governing equations including conservation of mass, momentum, and energy, together with chemical reaction and turbulence models are solved for the driver and the blast-wave sections using computational fluid dynamics (CFD) solver ANSYS Fluent (3D-based) and Flowmaster (1D-based) respectively. The three dimensional (3D) and one dimensional (1D) numerical analyses are preceded separately (decoupled). In the driver section, the numerical calculation results with three variables (FLSF, HDSF, and concentration) considering the size of explosion space and methane concentration level for the driver section are stored in a database tool Microsoft SQL Server Express aims to generate a methane explosion source database. To validate the selected combustion and turbulent models, a series of lab-scale methane explosion experiments were conducted. In the blast-wave section, the influences of geometric changes are quantified by using 2D Euler equations, whereas the simulation results are used to adjust the 1D network-based modeling. The decoupled method is applied in two case studies and proved capable to predict the pressure distribution of methane explosions that occurs in a complex airway network. --Abstract, page iii