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"Energy methods"
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Comprehensive evaluation of end-point free energy techniques in carboxylated-pillar6arene host–guest binding: I. Standard procedure
Despite the massive application of end-point free energy methods in protein–ligand and protein–protein interactions, computational understandings about their performance in relatively simple and prototypical host–guest systems are limited. In this work, we present a comprehensive benchmark calculation with standard end-point free energy techniques in a recent host–guest dataset containing 13 host–guest pairs involving the carboxylated-pillar[6]arene host. We first assess the charge schemes for solutes by comparing the charge-produced electrostatics with many ab initio references, in order to obtain a preliminary albeit detailed view of the charge quality. Then, we focus on four modelling details of end-point free energy calculations, including the docking procedure for the generation of initial condition, the charge scheme for host and guest molecules, the water model used in explicit-solvent sampling, and the end-point methods for free energy estimation. The binding thermodynamics obtained with different modelling schemes are compared with experimental references, and some practical guidelines on maximizing the performance of end-point methods in practical host–guest systems are summarized. Further, we compare our simulation outcome with predictions in the grand challenge and discuss further developments to improve the prediction quality of end-point free energy methods. Overall, unlike the widely acknowledged applicability in protein–ligand binding, the standard end-point calculations cannot produce useful outcomes in host–guest binding and thus are not recommended unless alterations are performed.
Journal Article
Design energy simulation for architects : guide to 3D graphics
\"Energy modeling calculations for urban, complex buildings are most effective during the early design phase. And most analysis takes only four to sixteen hours to get results you can use. This software-agnostic book, which is intended for you to use as a professional architect, shows you how to reduce the energy use of all buildings. Written by a practicing architect who specializes in energy modeling, the book includes case studies of net-zero buildings, of Living Building Challenge-certified buildings, as well as of projects with less lofty goals to demonstrate how energy simulation has helped designers make early decisions. Within each case study, author Kjell Anderson mentions the software used and other software that could have been used to get similar results so that you learn general concepts without being tied to particular programs. Each chapter builds on the theory from previous chapters, includes a summary of concept-level hand calculations (if applicable), and gives comprehensive explanations with examples. Topics covered include comfort, design energy simulation, climate analysis, master planning, conceptual design, design development, and existing buildings so that you can create more responsive designs quicker\"-- Provided by publisher.
Book
The use of PINN in analyzing two-dimensional elastoplastic problems
By utilizing characteristics of neural networks, the Physics-Informed Neural Network (PINN) is truly an innovative method in dealing with differential equations. Despite being proposed and developed recently, it still brings an outstanding way to deal with traditional mechanics problems. Regarding PINN as a differential equation solver, the governing equation of a mechanics system can be directly handled using deep learning techniques. As a variation of PINN, the deep energy method (DEM) exhibits some superior properties compared to the traditional PINN, such as the requirement in the order of the derivative field for calculation is less, the implementation is easier, and the convergence rate is higher, etc. In order to examine the applicability of DEM in nonlinear systems, this study applies the DEM model to deal with elastoplastic problems, which possess material-kind nonlinearity. Through the study, problems with the assumption of a bilinear material model are handled and analyzed. The results are validated by the reference results obtained from previous studies. The findings show that DEM is extremely effective in solving nonlinear systems. Also, in comparison to other traditional approaches such as the Finite Element Method (FEM), meshless, etc., PINN is indeed a promising approach in the next stage of research in the computational mechanics field.
Journal Article
Anaerobic digestion : making biogas - making energy : the Earthscan expert guide
\"Hundreds of million tonnes of agricultural and food waste are produced each year around the world, most of which is just that, waste. Anaerobic digestion, biogas and the heat and electricity that can be produced from it is still a nascent industry in many countries, yet the benefits of AD spread throughout the community: - Gives good financial returns to farmers and eco-entrepreneurs. - Helps community leaders meet various policies and legislative targets. - Offers an environmentally sensitive waste disposal option. - Provides a local heat and power supply, & creates employment opportunities - Reduces greenhouse gas emissions, as well as providing an organic fertilizer. Although the process of AD itself is relatively simple there are several system options available to meet the demands of different feedstocks. This book describes, in simple, easy to read language the five common systems of AD; how they work, the impact of scale, the basic requirements, the costs and financial implications, and how to get involved in this rapidly growing green industry\"--Provided by publisher.
Book
A study on the energy dissipation mechanism of dynamic mechanical systems with particle dampers by using the novel energy method
Adding particles to mechanical elements can reduce their vibrations. Both the particles and the mechanical elements interrelate in a highly complex manner, thereby influencing the energy dissipation of the mechanical elements. The particle damping is extremely nonlinear, and the energy dissipation mechanism in such a granule–structure interaction system has scarcely been examined. This study aims to investigate the dynamic behavior and energy dissipation mechanism for a mass–spring–damper–slider system with a particle damper. A simple but robust energy method was first proposed to explore the energy dissipation mechanism, and a two-way coupled model of the discrete element method (DEM) and multi-body dynamics (MBD) was employed to analyze the complex interaction system. Three numerical benchmark tests and free vibration experiments for the system with a particle damper were conducted to validate the proposed energy method and the adopted coupled DEM–MBD model. Results show that the coupled DEM–MBD simulations reasonably agree with the corresponding experiments. The validated coupled model was subsequently employed to calculate the distribution of system energy, and to explore the effect of contact properties on the energy dissipation of the system during the free vibration process. In the mass–spring–damper–slider system with a particle damper, the damping effect resulting from particles is essentially caused by the contact forces generated when the particles make contact with the hollow box. The induced contact forces act as resistance forces to the hollow box, always do negative work, and suppress the motion of the hollow box. The energy loss of the particles primarily occurs through contact friction and contact damping when the particles are hit by the hollow box. Contact properties, such as friction and restitution coefficients, exhibit a negligible effect on the dynamic behavior of the hollow box, but substantially affect the distribution of energy dissipation in the particular system.
Journal Article
Unconditional Convergence of a Fast Two-Level Linearized Algorithm for Semilinear Subdiffusion Equations
A fast two-level linearized scheme with nonuniform time-steps is constructed and analyzed for an initial-boundary-value problem of semilinear subdiffusion equations. The two-level fast L1 formula of the Caputo derivative is derived based on the sum-of-exponentials technique. The resulting fast algorithm is computationally efficient in long-time simulations or small time-steps because it significantly reduces the computational cost
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Journal Article
Li–He’s modified homotopy perturbation method coupled with the energy method for the dropping shock response of a tangent nonlinear packaging system
This paper couples Li–He’s homotopy perturbation method with the energy method to obtain an approximate solution of a tangent nonlinear packaging system. A higher order homotopy equation is constructed by adopting the basic idea of the Li–He’s homotopy perturbation method. The energy method is used to improve the maximal displacement and the frequency of the system to an ever higher accuracy. Comparison with the numerical solution obtained by the Runge–Kutta method shows that the shock responses of the system solved by the new method are more effective with a relative error of 0.15%.
Journal Article
Form-finding of grid-shells using the ground structure and potential energy methods: a comparative study and assessment
The structural performance of a grid-shell depends directly on the geometry of the design. Form-finding methods, which are typically based on the search for bending-free configurations, aid in achieving structurally efficient geometries. This manuscript proposes two form-finding methods for grid-shells: one method is the potential energy method, which finds the form in equilibrium by minimizing the total potential energy in the system; the second method is based on an augmented version of the ground structure method, in which the load application points become variables of the topology optimization problem. The proposed methods, together with the well-known force density method, are evaluated and compared using numerical examples. The advantages and drawbacks of the methods are reviewed, compared and highlighted.
Journal Article
Physics-informed neural network based topology optimization through continuous adjoint
In this paper, we introduce a Physics-Informed Neural Networks (PINNs)-based Topology optimization method that is free from the usual finite element analysis and is applicable for both self-adjoint and non-self-adjoint problems. This approach leverages the continuous formulation of TO along with the continuous adjoint method to obtain sensitivity. Within this approach, the Deep Energy Method (DEM)—a variant of PINN-completely supersedes traditional PDE solution procedures such as a finite-element method (FEM) based solution process. We demonstrate the efficacy of the DEM-based TO framework through three benchmark TO problems: the design of a conduction-based heat sink, a compliant displacement inverter, and a compliant gripper. The results indicate that the DEM-based TO can generate optimal designs comparable to those produced by traditional FEM-based TO methods. Notably, our DEM-based TO process does not rely on FEM discretization for either state solution or sensitivity analysis. During DEM training, we obtain spatial derivatives based on Automatic Differentiation (AD) and dynamic sampling of collocation points, as opposed to the interpolated spatial derivatives from finite element shape functions or a static collocation point set. We demonstrate that, for the DEM method, when using AD to obtain spatial derivatives, an integration point set of fixed positions causes the energy loss function to be not lower-bounded. However, using a dynamically changing integration point set can resolve this issue. Additionally, we explore the impact of incorporating Fourier Feature input embedding to enhance the accuracy of DEM-based state analysis within the TO context. The source codes related to this study are available in the GitHub repository:
https://github.com/xzhao399/DEM_TO.git
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Journal Article