In the view of the thermal runaway scenario phase change material are not capable to withstand thermal propagation phenomenon, hence it is important to provide a novel structure which can delay thermal runaway trigger point and not allows generated heat to propagate at faster rate from one cell to another. In this work, a series of numerical investigations are performed from cell to pack level configuration under heater assisted triggering method by providing a constant heat supply of 500 W until electrochemical kinetics of cell gets trigger. This setup is used to investigate, time delay and thermal propagation phenomenon by integrating submerged layer of phase change material. Conclusive evidence from investigation suggests that by integrating a submerged layer of phase change material, the thermal runaway trigger point can be effectively delayed. However, because phase change material is highly flammable in nature, heat propagates from one cell to another in a very short period, causing the entire pack to go into catastrophic thermal failure within short span of 10s. To delay this thermal propagation phenomenon and compliance GB 38031 and GB 38032 “national safety regulation of battery pack for Electric Bus and Electric Vehicle” that suggest once cell is trigger, neighbouring cell should not run in thermal runaway for at least 300s, we have optimized battery pack structural design by introducing low thermal conductive pyro liner at the wall of PCM. Results showed, this optimize design represents dual functionality of delaying thermal runaway trigger point of cell along with slowing down thermal propagation for at least 800s. 

Battery thermal runaway propagation time delay strategy using phase change material integrated with pyro block lining: Dual functionality battery thermal design

Time- and rate-dependent material functions in Non-Newtonian fluids in response to different deformation fields pose a challenge in integrating different constitutive models into conventional computational fluid dynamic platforms. Considering their relevance in many industrial and natural settings alike, robust data-driven frameworks that enable accurate modeling of these complex fluids are of great interest. The main goal is to solve the coupled Partial Differential Equations (PDEs) consisting of the constitutive equations that relate the shear stress to the deformation and fully capture the behavior of the fluid under various flow protocols with different boundary conditions. In this work, we present Non-Newtonian Physics-Informed Neural Networks (nn-PINNs) for solving systems of coupled PDEs adopted for complex fluids flow modeling. The proposed nn-PINN method is employed to solve the constitutive models in conjunction with conservation of mass and momentum by benefiting from Automatic Differentiation (AD) in neural networks, hence avoiding the mesh generation step. nn-PINN is tested for a number of different complex fluids with different constitutive models and for several flow protocols. These include a range of Generalized Newtonian Fluids (GNF) empirical constitutive models, as well as some phenomenological models with memory effects and thixotropic timescales. nn-PINN is found to obtain the correct solution of complex fluids in spatiotemporal domains with good accuracy compared to the ground truth solution. We also present applications of nn-PINN for complex fluid modeling problems with unknown boundary conditions on the surface, and show that our approach can successfully recover the velocity and stress fields across the domain, including the boundaries, given some sparse velocity measurements.

nn-PINNs: Non-Newtonian physics-informed neural networks for complex fluid modeling.

Liquid-vapor two-phase flow in centrifugal pump: Cavitation, mass transfer, and impeller structure optimization

The indiscriminate use of fossil fuels has led to several challenges such as greenhouse gas emissions, environmental degradation, and energy security. Establishment of clean fuels is at the forefront of science and innovation in today's society to curb these problems. Dark fermentation (DF) is widely regarded as the most promising clean energy technology of the 21st century due to its desirable properties such as high energy content, its non-polluting features, its ability to use a broad spectrum of feedstocks and inoculum sources, as well as its ability to use mild fermentation conditions. In developing nations, this technology could be instrumental in establishing effective waste disposal systems while boosting the production of clean fuels. However, DF is still hindered by the low yields which stagnate its commercialization. This paper reviews the recent and emerging technologies that are gaining prominence in DF based on information that has been gathered from recent scientific publications. Herein, novel enhancement methods such as cell immobilization, nanotechnology, mathematical optimization tools, and technologies for biogas upgrading using renewable H2 are comprehensively discussed. Furthermore, a section which discusses the potential of bioenergy in Sub-Saharan Africa including South Africa is included. Finally, scientific areas that need further research and development in DF process are also presented.

/pdf/revising-the-dark-fermentative-h2-research-and-development-2qa8xet6uc.pdf

Revising the dark fermentative H2 research and development scenario – An overview of the recent advances and emerging technological approaches

The centrifugal air compressor plays a critical role as a core component in fuel cells, and building an accurate prediction model is essential for evaluating its performance. In this study, three algorithms were used to establish the air compressor performance prediction model based on multiple samples. Through comparing the prediction results, it was found that the backpropagation neural network optimized by genetic algorithm (GA-BP) model fit the real sample data more closely than support vector machine (SVM) and backpropagation neural network (BPNN) models, and could better approximate the performance map of the air compressor. Moreover, a GA-BP-SVM hybrid model was established by combining GA-BP and SVM through the model weighting method, which performed optimally in all performance indexes. This method effectively improves the accuracy of the prediction model and provides a feasible solution for air compressor performance prediction in fuel cell applications. 

Performance prediction for a fuel cell air compressor based on the combination of backpropagation neural network optimized by genetic algorithm (GA-BP) and support vector machine (SVM) algorithms

Computational fluid dynamics (CFD) provides numerical approximation to the equations that govern fluid motion. Application of the CFD to analyze a fluid problem requires the following steps. First, the mathematical equations describing the fluid flow are written. These are usually a set of partial differential equations. These equations are then discretized to produce a numerical analogue of the equations. The domain is then divided into small grids or elements. Finally, the initial conditions and the boundary conditions of the specific problem are used to solve these equations. All CFD codes contain three main elements: (1) A pre-processor, which is used to input the problem geometry, generate the grid, and define the flow parameter and the boundary conditions to the code. (2) A flow solver, which is used to solve the governing equations of the flow subject to the conditions provided. There are four different methods used as a flow solver: (i) finite difference method; (ii) finite element method, (iii) finite volume method. (3) A post-processor, which is used to massage the data and show the results in graphical and easy to read format.

A review: Fundamentals of computational fluid dynamics (CFD)

Fluid-structure interactions (FSI) occurs when fluid flow causes deformation of the structure and in turn, the deformation changes the boundary conditions of the fluid flow. FSI is considered essential in the design of many engineering systems. The example of application FSI to engineering system is hydraulic structures. Hydraulic structures are purposely created and built to perform controlled, conveyed and contained water. It will be more useful to consumers if it is functioning satisfactorily. In order to study the effect of the FSI on hydraulic structure, computational fluid dynamic (CFD) is used to analyse the data. The main objective of this paper is to review the studies conducted on FSI within hydraulic structures. The study of FSI on hydraulic structure is devoted to the solution of the equation of fluid flow through the use of computer.Fluid-structure interactions (FSI) occurs when fluid flow causes deformation of the structure and in turn, the deformation changes the boundary conditions of the fluid flow. FSI is considered essential in the design of many engineering systems. The example of application FSI to engineering system is hydraulic structures. Hydraulic structures are purposely created and built to perform controlled, conveyed and contained water. It will be more useful to consumers if it is functioning satisfactorily. In order to study the effect of the FSI on hydraulic structure, computational fluid dynamic (CFD) is used to analyse the data. The main objective of this paper is to review the studies conducted on FSI within hydraulic structures. The study of FSI on hydraulic structure is devoted to the solution of the equation of fluid flow through the use of computer.

Fluid-structure interactions study on hydraulic structures: A review

This paper presents a case study of dam where computational fluid dynamic (CFD) modelling is applied to simulate the three-dimensional (3D) flow hydraulics using computational aided program using ANSYS in order to analyse the existing fluid dynamic from reservoir to spillway of the dam due to long exposure to high pressure of water and high velocity of discharge. The purpose of this study is to ensure efficiency, reliability and safety of the dam generally and specifically on the spillway. Based on the findings, a model that have a worst condition based on both analysis is a model that have bigger area of the opening gate. This is because of the huge amount of the water release from the upstream to the downstream and produce a high value of water velocity and high impact from the water that fall from upstream towards the dam spillway. In order to avoid any sudden huge amount of the water release, the water level of the reservoir need to be check frequently. The water release from the dam need to be control to avoid any disaster to the environment and the dam structure.

Computational fluid dynamic analysis at dam spillway due to different gate openings

The understanding of dam structure condition under the static and dynamic should be aware as to fulfils the criteria of safety to the public. Furthermore, it is crucial to understand the parameter of the condition assessment of hydropower dam. Sliding failure, overturning failure, concrete deterioration, concrete distress, internal failure and foundation failure are the parameter induced to damage the dam stability. However, due to some areas on dam structure are difficult to be assessed such as its penstock and spillway, development condition assessment rating on structural condition for hydropower dam based on fluid-structure interaction (FSI) simulation approach is vital since it is impossible to stop the dam operation as it is needed in generating electricity. The present study aimed is to summarize the collection of various papers that related to dam analysis as to develop the condition assessment rating on structural condition for hydropower dam based on fluid-structure interaction (FSI) simulation approach.

Condition assessment on hydropower dam based on simulation approach: A review

Maximum stress analysis for dam structure due to different magnitude scales of ground movements

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