Energy Minimization Research Articles

Coupled dynamic modeling of scrap melting and gas phase reactions in the electric arc furnace process

Gas phase reactions have a significant effect on the composition and heat content of the off-gas in the electric arc furnace (EAF) process. In this work, a previously developed dynamic scrap melting and heat transfer model was coupled with a gas phase reaction module based on Gibbs energy minimization. The gas phase reaction module retrieves the necessary thermodynamic data from a previously developed thermochemistry module. The gas phase reaction module has been used to improve the description of the gas burners and the freeboard of the EAF. The implementation of the gas phase reaction module has been found to be in good agreement with commercially available software (HSC Sim) for calculations assuming a gas phase equilibrium.

Aerial STAR-RIS Empowered MEC: A DRL Approach for Energy Minimization

Aerial STAR-RIS Empowered MEC: A DRL Approach for Energy Minimization

Energy Minimization for IRS-Aided Wireless Powered Federated Learning Networks With NOMA

Energy Minimization for IRS-Aided Wireless Powered Federated Learning Networks With NOMA

Modeling, simulations and experiments with generally routed cable-driven continuum robots and their application to three-fingered grippers

Abstract This paper deals with generally routed, pre-bent cable-driven continuum robots (CCR). A CCR consists of a flexible backbone to which multiple disks are attached. Cables are passed through holes in the disk, and when pulled, the flexible backbone and the CCR can attain different shapes based on their routing and backbone configuration. An optimization-based approach, using minimization of strain energy, is shown to give good results for the pose and motion of the CCR and to determine contact with external objects. The pose, motion, and the contact obtained from the model are shown to match very well with experimental results obtained from a 3D-printed CCR. An algorithm is proposed to generate the pre-bent backbone for a CCR which on actuation can attain the desired shape. Using the algorithm, three 3D-printed CCRs with pre-bent backbones are fabricated and these are used to demonstrate a compliant gripper that can grip a spherical object similar to that done by tentacles, and another three-fingered gripper with straight backbone CCRs is used to orient a square object gripped at the end.

QUBO Problem Formulation of Fragment-Based Protein-Ligand Flexible Docking.

Protein-ligand docking plays a significant role in structure-based drug discovery. This methodology aims to estimate the binding mode and binding free energy between the drug-targeted protein and candidate chemical compounds, utilizing protein tertiary structure information. Reformulation of this docking as a quadratic unconstrained binary optimization (QUBO) problem to obtain solutions via quantum annealing has been attempted. However, previous studies did not consider the internal degrees of freedom of the compound that is mandatory and essential. In this study, we formulated fragment-based protein-ligand flexible docking, considering the internal degrees of freedom of the compound by focusing on fragments (rigid chemical substructures of compounds) as a QUBO problem. We introduced four factors essential for fragment-based docking in the Hamiltonian: (1) interaction energy between the target protein and each fragment, (2) clashes between fragments, (3) covalent bonds between fragments, and (4) the constraint that each fragment of the compound is selected for a single placement. We also implemented a proof-of-concept system and conducted redocking for the protein-compound complex structure of Aldose reductase (a drug target protein) using SQBM+, which is a simulated quantum annealer. The predicted binding pose reconstructed from the best solution was near-native (RMSD = 1.26 Å), which can be further improved (RMSD = 0.27 Å) using conventional energy minimization. The results indicate the validity of our QUBO problem formulation.

Gradient enhanced physics-informed neural network for iterative form-finding of tensile membrane structures by potential energy minimization

Tensile membrane structures (TMS) are overwhelmingly popular as practical design solutions for large roofing structures. The TMS designer, however, has to perform a “form-finding” as the stable configuration of TMS are not known beforehand. Although many form-finding approaches are currently available, most suffer from various implementation complications, particularly with regards to convergence issues stemming from improper hyper-parameter selection, mesh distortion and the choice of initial reference configuration. In this study, a new iterative form-finding methodology is proposed and implemented by the direct minimization of a potential energy functional. A mesh-free approach based on gradient enhanced physics-informed neural network (gPINN) is employed, which expands the applicability to most common TMS types while eliminating the aforementioned issues faced by traditional mesh-based algorithms. Furthermore, a new method for selecting the initial reference configuration using transfinite interpolation is proposed that is significantly better suited to form-finding than conventional approaches. Additionally, approximate distance functions are employed to impose exact boundary conditions. Extensive numerical case studies on frame- and cable-supported TMS, along with multi-dimensional parametric variations, show the efficacy and robustness of the proposed form-finding framework.

Woody biomass waste derivatives in decarbonised blast furnace ironmaking process

Modern ironmaking process relies significantly on fossil-related fuels, which ultimately results in the enormous CO2 emitted into the atmosphere. Biomass of plant origin, as a carbon-neutral energy source, has been considered as an alternative to fossil-based reducing agents such as coke. This study aims to investigate the potential of three woody biomass waste derivatives produced from biomass waste pyrolysis and gasification, namely charcoal, bio-oil, and bio-syngas, as the reducing agents in blast furnace. A model based on heat and mass balance and Gibbs free energy minimisation is proposed to simulate an ironmaking process with assistance of these derivatives. The effects of specific composition of biomass waste derivatives on process operation, CO2 emissions, and coke replacement are explored. Also the effects of H2-rich gas produced from biomass waste gasification on the blast furnace operation are estimated. Results indicate that reactions of woody biomass waste derivatives in blast furnace are complex and greatly dependent on composition. When charcoal has a higher carbon content, lower CO2 concentration is found from the top gas. The higher content of hydrogen in bio-oil will inhibit further reduction in CO2 emissions. Bio-syngas with H2/CO ratio of 1.3 proves to have a remarkable potential to reduce CO2 emissions. From the aspects of available biomass waste resources across the world, woody biomass waste derivatives as reducing agents are more suitable for countries with the limited pig iron production. This study provides a reference on the future of moving forward the decarbonised ironmaking by using woody biomass waste derivatives.

2D MoS2 monolayers integration with metal oxide-based artificial synapses

In this study, we report on a memristive device structure wherein monolayers of two-dimensional (2D) molybdenum disulfide (MoS2) are integrated with an ultrathin yttrium oxide (Y2O3) layer to simulate artificial synapses functionality. The proposed physical simulation methodology is implemented in COMSOL Multiphysics tool and is based on the minimization of free energy of the used materials at the applied input voltage. The simulated device exhibits a stable bipolar resistive switching and the switching voltages is significantly reduced by increasing the number of MoS2 layers, which is key to conventional low-power computing and neuromorphic applications. The device is shown to perform synaptic functionalities under various applied bias conditions. The resulting synaptic weight decreases almost linearly with the increasing number of MoS2 layers due to the increase in the device thickness. The simulation outcomes pave the way for the development of optimised metal oxide-based memristive devices through their integration with semiconducting 2D materials. Also, the 2D MoS2 integration can enable the optoelectronic operation of this memory device.

Polarization Coupling between Ferroelectric Liquids and Ferroelectric Solids: Effects of the Fringing Field Profile

Recent experiments devoted to characterizing the behavior of sessile ferroelectric liquid droplets on ferroelectric solid substrates have shown the existence of a droplet electromechanical Rayleigh-like instability. The instability is induced by the bulk polarization of the ferroelectric fluid, which couples to the polarization of the underlying substrate through its fringing field and solid–fluid interface coupling. With the aim of characterizing this phenomenon, namely the coupling between the polarizations of a fluid and a solid material, we studied the behavior of ferroelectric liquid droplets confined between two solid substrates, arranged in different configurations, realized to generate fringing fields with different profiles. The results show that the features of the droplets instability are indeed affected by the specific fringing field shape in a way dominated by the minimization of the electrostatic energy associated with the bulk polarization of the ferroelectric fluid.

Chemical looping reforming of the micromolecular component from biomass pyrolysis via Fe2O3@SBA-16

AbstractTo solve the problems of low gasification efficiency and high tar content caused by solid–solid contact between biomass and oxygen carrier in traditional biomass chemical looping gasification process. The decoupling strategy was adopted to decouple the biomass gasification process, and the composite oxygen carrier was prepared by embedding Fe2O3 in molecular sieve SBA-16 for the chemical looping reforming process of pyrolysis micromolecular model compound methane, which was expected to realize the directional reforming of pyrolysis volatiles to prepare hydrogen-rich syngas. Thermodynamic analysis of the reaction system was carried out based on the Gibbs free energy minimization method, and the reforming performance was evaluated by a fixed bed reactor, and the kinetic parameters were solved based on the gas–solid reaction model. Thermodynamic analysis verified the feasibility of the reaction and provided theoretical guidance for experimental design. The experimental results showed that the reaction performance of Fe2O3@SBA-16 was compared with that of pure Fe2O3 and Fe2O3@SBA-15, and the syngas yield was increased by 55.3% and 20.7% respectively, and it had good cycle stability. Kinetic analysis showed that the kinetic model changed from three-dimensional diffusion to first-order reaction with the increase of temperature. The activation energy was 192.79 kJ/mol by fitting. This paper provides basic data for the directional preparation of hydrogen-rich syngas from biomass and the design of oxygen carriers for pyrolysis of all-component chemical looping reforming.

Modeling remote-sensing and geophysical data to delineate the favorable mineralization localities at Semna area, Central Eastern Desert, Egypt

Semna area is located within the Golden Triangle district in the Central Eastern Desert of Egypt. The study maintains using multisource datasets of remote sensing, aero-spectrometry, aero-magnetic, and field investigations for unraveling the ambiguities associated with the alteration zones at the targeted area. Three remote sensing mapping techniques including; constrained energy minimization, linear spectral unmixing, and mineral indices, were adopted to delineate the alteration zones. The γ-ray spectrometry data of K (%), K/eTh, K/eU, and F-parameter enabled mapping the potassium-enriched localities as indication on possible potassic alteration zones. The surface and subsurface linear structural features were delineated from the digital elevation model and aero-magnetic data, respectively. The outcomes of all the implemented datasets were integrated using GIS overlay modeling, producing an integrated mineralization favorability/potentiality map. Eighteen localities with high potential for mineralization were highlighted. A field study was conducted to the investigated area during which, several alteration indicators were observed, including quartz veins, iron oxides staining, kaolinite, malachite, carbonate, and muscovite (sericite) alteration indicator minerals.

Efficient Generation of Conformer Ensembles Using Internal Coordinates and a Generative Directional Graph Convolution Neural Network.

We present a neural-network-based high-throughput molecular conformer-generation algorithm. A chemical graph-convolutional network is trained to predict low-energy conformers in internal coordinate representation (bond lengths, bond, and torsion angles), starting from two-dimensional (2D) chemical topology. Generative neural network (NN) architecture performs denoising from torsion space, producing conformer ensembles with populations that are well correlated with torsion energy profiles. Short force-field-based energy minimization is applied to refine final conformers. All computation-intensive stages of the algorithm are GPU-optimized. The procedure (termed GINGER) is benchmarked on a commonly used test set of bioactive three-dimensional (3D) conformers from the PDB. We demonstrate highly competitive results in conformer recovery and throughput rates suitable for giga-scale compound library processing. A web server that allows interactive conformer ensemble generation by GINGER and their viewing is made freely available at https://www.molsoft.com/gingerdemo.html.

Quantum trapping and rotational self-alignment in triangular Casimir microcavities.

Casimir torque, a rotational motion driven by zero-point energy minimization, is a problem that attracts notable research interest. Recently, it has been realized using liquid crystal phases and natural anisotropic substrates. However, for natural materials, substantial torque occurs only at van der Waals distances of ~10 nm. Here, we use Casimir self-assembly with triangular gold nanostructures for rotational self-alignment at truly Casimir distances (100 to 200 nm separation). The interplay of repulsive electrostatic and attractive Casimir potentials forms a stable quantum trap, giving rise to a tunable Fabry-Pérot microcavity. This cavity self-aligns both laterally and rotationally to maximize area overlap between templated and floating flakes. The rotational self-alignment is sensitive to the equilibrium distance between the two triangles and their area, offering possibilities for active control via electrostatic screening manipulation. Our self-assembled Casimir microcavities present a versatile and tunable platform for nanophotonic, polaritonic, and optomechanical applications.

Effects of Miscut on Step Instabilities in Homo-Epitaxially Grown GaN.

The rough morphology at the growth surface results in the non-uniform distribution of indium composition, intentionally or unintentionally doped impurity, and thus impacts the performance of GaN-based optoelectronic and vertical power electronic devices. We observed the morphologies of unintentionally doped GaN homo-epitaxially grown via MOCVD and identified the relations between rough surfaces and the miscut angle and direction of the substrate. The growth kinetics under the effect of the Ehrlich-Schwoebel barrier were studied, and it was found that asymmetric step motions in samples with a large miscut angle or those grown at high temperature were the causes of step-bunching. Meandering steps were believed to be caused by surface free energy minimization for steps with wide terraces or deviating from the [11¯00] m-direction.

Challenges in determining individual chemical potentials of system elements by Gibbs energy minimization

Accounting for the elusive gas phase in thermodynamic equilibrium calculations is demonstrated to be important for achieving accurate and unique determination of constituent element chemical potentials, when Gibbs energy minimization (GEM) encounter indeterminacy due to rank deficiency in mass-balance equations. The molar GEM of the equilibrium vapor phase offers a robust solution for unambiguously and accurately determining element chemical potentials. To enable this, a novel optimization method for mass-balance equation is presented, paving the way for reliable thermodynamic calculations.

Phase field modeling of hyperelastic material interfaces –Theory, implementation and application to phase transformations

Interface mechanics can significantly govern the evolution of multiple phases on smaller scales, e.g., determining the properties of TWIP- and TRIP-steels, geopolymers or Li-ion batteries. The present contribution is specifically centered around the influence of interface elasticity on mechanically induced phase transformations. A geometrically exact finite element framework is developed for this purpose based on incremental energy minimization. It is set up in three-dimensional space and comprises an Allen-Cahn type phase field formulation based on a Modica-Mortola double-well functional, as well as a Ginzburg-Landau type viscosity formulation. The phase field approximation of the interface deformation gradient is derived by means of homogenization theory. A monolithic solver is employed for the resulting set of non-linear coupled equations. The example of coalescing spheres shows that larger interface energies can amplify and accelerate coalescence. Lower interface energies, in contrast, allow for distinct spreading of the energetically favorable bulk phase. Deformation-dependent interface energies particularly add stress peaks to this process that are localized at the phase transition zone. The relaxation of a stepped interface shape showed that deformation-dependent interface energies favor a plane partition of the bulk phases. Yet, they also delay breakup events compared to simple constant interface energies. This peculiarity can be beneficial for calibration purposes. Strain dependence moreover causes an additional elastic stiffness in the direction of the interface tangent plane and constitutes thus another origin for anisotropy of the overall structure.

Moment preserving tomographic image reconstruction model

Shape descriptors provide valuable prior information in many tomographic image reconstruction methods. Such descriptors include, among others, centroid, circularity, orientation, and elongation. Shape descriptor measures are often analytically expressed as a composition of certain geometric moments. Building upon this fact, this paper suggests preserving the values of a specific geometric moment in the reconstruction process, instead of preserving entire descriptors, as it has been suggested so far. Reconstructions from two natural projection directions (vertical and horizontal) are considered with special attention. The provided theoretical analysis demonstrates that preserving the value of a specific geometric moment, provided as prior information for the reconstruction process, simultaneously ensures the preservation of the true measures of all four abovementioned descriptors. Based on this result, a novel regularized energy minimization reconstruction model is proposed. The minimization task of the new model is solved using gradient-based optimization algorithm. Performance evaluation of the proposed method is supported by experimental results obtained through comparisons with other well-known reconstruction methods.

Calculation of Littlewood predominance diagrams for metal electrodeposition from molten chlorides using CALPHAD software and databases

Littlewood predominance diagrams are maps of phase stability with a potential in the ordinate and a pO2− on the abscissa. pO2− is the negative logarithm of the activity of O2− ions.In the current work, a new method for calculation of Littlewood predominance diagrams is presented. The method is particularly suitable for software and databases constructed according to the CALPHAD (CALculation of Phase Diagrams) methodology. This enhances the consistency of the calculations and enables inclusion of elaborate descriptions of non-stoichiometric phases. The calculation is based on Gibbs energy minimization so that identification of relevant reaction equations is not needed as input.A thermodynamic database with all the relevant phases, including metals, chlorides, oxides and oxychlorides is a perquisite for the calculation. The calculation results depend on the thermodynamic modeling and data. However, the algorithm does not depend on the details of the thermodynamic models used. That is, the same calculation method is applied irrespective of the thermodynamic model chosen. Sample calculations are given for the simple Mg-Cl-O system and for the more-complex Ca-Ti-Cl-O system. We use thermodynamic datasets published in the literature, as well as a small CALPHAD database for the Ca-Ti-Cl-O system constructed in the course of the present work.

Elastic energy driven multivariant selection in martensites via quantum annealing

We demonstrate the use of quantum annealing for the selection of multiple martensite variants in a microstructure with long-range coherency stresses and external mechanical load. The general approach is illustrated for martensites with four different variants based on the minimization of the linear elastic energy. The equilibrium variant distribution is then analyzed under application of tensile and shear strains and for different values of the considered shear and tetragonal contributions of the different martensite variants. The interface orientations between different domains of variants can be explained using the perspective of the elastic energy anisotropy for regular stripe patterns. For random grain orientations, the response to an external elastic strain is weaker and variant changes can be interpreted based on the rotated eigenstrain tensor. Published by the American Physical Society 2024

Self-shaping of triglyceride and alkane drops: Similarities and differences

Small emulsion drops typically exhibit spherical shape at positive interfacial tension due to the energy minimization principle. However, in a series of studies (Denkov et al., Nature, 2015, 528, 392–395; Cholakova et al., Nature Phys., 2021, 17, 1050–1055) we showed that alkane droplets stabilized by appropriate saturated long-chain surfactants may spontaneously change their shape upon cooling, morphing into various polyhedra; hexagonal, tetragonal and triangular platelets; rod-like particles and even synthetic swimmers. These deformations are governed by the formation of thin plastic rotator phases adjacent to the drop surface. Although alkanes have numerous industrial applications, they cannot be used in food and pharma related products, in which most often triglyceride molecules are employed. The possibility for self-shaping of triglyceride drops has been demonstrated, but the detailed understanding of the process is currently missing. In the present study, we performed model experiments aimed to reveal the conditions under which the triglyceride emulsion drops may change their shape upon cooling. We show that most of the various non-spherical shapes known for alkanes can be reproduced with triglyceride droplets providing that the surfactant adsorption layer freezes before the nucleation of the oily molecules inside the drops. By comparing the behavior of triglyceride and alkane droplets, we draw unified picture and provide guiding principles which can be used for selection of appropriate surfactants enabling the spontaneous shape deformations upon cooling of oily drops of different chemical compositions.