Hybrid Computational Method Research Articles

First-Principles Calculation of Laser Crystal Multiplet Levels via Hybridized Density Functional Theory and Configuration Interaction within the OLCAO Method

Computation of highly-localized multiplet energy levels of transition metal dopants is essential to the design of materials such as laser host crystals. A purely first-principles density functional theory-configuration interaction (DFT-CI) hybrid computational method has been developed to accurately compute multiplet energy levels for single atoms of carbon, nitrogen, oxygen, sodium, aluminum, silicon, titanium, and chromium. The multiplet energy levels have been computed with close experimental agreement in terms of magnitude and degeneracy, and the method does not depend on empirical information (i.e. Racah parameters). The computed multiplet energy level results are distributed according to term symbols, which are then compared to experimentally-observed multiplet energy levels. The hybrid method consists of analytic computation of two-electron integrals via the DFT-based orthogonalized linear combination of atomic orbitals (OLCAO) method, which are subsequently used as input for the CI-based discrete variational multi-electron (DVME) method to obtain the multiplet energy values.Keywords: exchange-correlation; elecron repulsion integral; multiplet; DVME; OLCAO; density functional theory; configuration interaction

Hybrid Computational Intelligence Methods for Power System Optimization

the best economic interest and arranging of electrical network framework has constantly worried an important worry within the power designing. constant improvement inside the petroleum by-product advent, large interconnection of the electrical structures and electricity emergency in the world require the monetary hobby of devoted strength delivering gadgets. subsequently, it is critical to contemplate most gifted streamlining strategies by way of getting basic elements of interest of simple detailing and execution of precise problem. This paper gives a diagram of large half of of and half of of computational knowledge (CI) techniques connected in power framework improvement. one-of-a-type applications and inspirations riding the enhancements of half of and 1/2 CI techniques are accentuated. At final, some impending exploration regulations are proposed for the half of and 1/2 strategies improvement.

(Invited) Structural Characterization of Polymer Electrolyte Fuel Cell Electrodes Using Nano-Scale Synchrotron X-Ray Computed Tomography

With improvements in the intrinsic oxygen reduction reaction activity of both Pt-based and platinum group metal-free (PGM-free) catalysts for polymer electrolyte fuel cells, in recent years more attention is being focused on utilization of the active sites in these catalysts under conditions relevant to the automotive application. Taking full advantage of the performance offered by these high activity catalysts requires comprehensive knowledge of the electrode microstructure and the spatial distribution of the catalyst, support, ionomer, and pores. Nano-scale resolution X-ray computed tomography (nano-CT) has been used as a powerful tool for non-destructive quantification of three-dimensional pore morphology and estimation of ionomer distribution in both Pt-based and PGM-free cathode catalyst layers (CCL) [1, 2]. On the other hand, due to insufficient resolution (20 nm voxel size) this technique cannot resolve the detailed structures of support, ionomer, and catalyst. We developed the “hybrid computational method” [2] combining statistical information from transmission electron microscopy (TEM), ultra-small angle X-ray scattering (USAXS), and BET with nano-CT to reconstruct the features below the nano-CT resolution. In this study, an example will be given of application of this method to a Pt alloy-based electrode. Various shape, size and composition agglomerates are reconstructed to 1 nm voxel resolution using the hybrid methodology. It is shown that ionomer film thickness is spatially non-uniform inside the agglomerates and strongly correlates with ionomer to carbon weight ratio (I/C) while it is independent of the agglomerate size. Compared to experimental observations, the application of capillary condensation theory to the reconstructed agglomerate structure is shown to accurately represent the relative humidity (RH) dependence of the electrochemically-active surface area (ECA). Furthermore, direct numerical simulations are performed to quantify localized transport losses. Effectiveness factor correlations are derived for use in macroscale models. The effects of RH, agglomerate structural properties, and pores in the carbon support on the local oxygen transport resistance are examined. It is demonstrated that low catalyst utilization is responsible for the experimentally-observed high local transport resistance under dry conditions and that agglomerate shape and size affect the local oxygen transport resistance only if the primary pores are poorly accessible.

Multiscale method based on coupled lattice‐Boltzmann and Langevin‐dynamics for direct simulation of nanoscale particle/polymer suspensions in complex flows

SummaryA hybrid computational method coupling the lattice‐Boltzmann (LB) method and a Langevin‐dynamics (LD) method is developed to simulate nanoscale particle and polymer (NPP) suspensions in the presence of both thermal fluctuation and long‐range many‐body hydrodynamic interactions (HIs). Brownian motion of the NPP is explicitly captured by a stochastic forcing term in the LD method. The LD method is two‐way coupled to the nonfluctuating LB fluid through a discrete LB forcing source distribution to capture the long‐range HI. To ensure intrinsically linear scalability with respect to the number of particles, a Eulerian‐host algorithm for short‐distance particle neighbor search and interaction is developed and embedded to LB‐LD framework. The validity and accuracy of the LB‐LD approach are demonstrated through several sample problems. The simulation results show good agreements with theory and experiment. The LB‐LD approach can be favorably incorporated into complex multiscale computational frameworks for efficiently simulating multiscale multicomponent particulate suspension systems such as complex blood suspensions.

Natural Products Containing the Oxetane and Related Moieties Present Additional Challenges for Structure Elucidation: A DU8+ Computational Case Study.

Analysis of published NMR data for natural products containing the oxetane moiety, with the help of a recently developed parametric/DFT hybrid computational method DU8+, has revealed that oxetanes and related compounds constitute yet another significant challenge in structure elucidation and stereochemistry assignment, as more than 30 structures required revision. The most common pitfalls are discussed, and revised structures are suggested for 26 natural products.

A genetic-ELM neural network computational method for diagnosis of the Parkinson disease gait dataset

ABSTRACTA hybrid computational method based on the extreme learning machine (ELM) neural network for classification and the evolutionary genetic algorithms (GA) for feature selection is presented in this paper. The dimension of the feature space is reduced by the GA in this scheme and only the appointed features are selected. The data is then passed to an ELM neural network for the classification phase. An automated system for the diagnosis of Parkinson’s Disease (PD) based on gait data set is proposed by using the GA-ELM method. PD is a neurodegenerative disease that may cause change in the central nervous causing disturbance to the gait cycle duration. Our hybrid GA-ELM algorithm has produced an optimized diagnosis of PD from healthy subjects given in the gait dataset with accuracy 93.5%, and with five effective features that reduce the original dataset dimension.

Hybrid Modeling Approaches to Study Structures and Dynamics of Biological Systems

Determination of the structure and dynamics of large biological complexes can be performed using experimental techniques ranging from high to low-resolution. With recent progress in experimental techniques, raw data from Cryo Electron Microscopy (cryo-EM) may now comprise millions of two-dimensional (2D) images of single molecules. These may represent distinct conformations of the molecule; therefore, dynamics information might also be embedded within the 2D data. X-ray free electron laser (XFEL) is another exciting new technology that could significantly extend our structural knowledge of biological systems. Strong laser light from XFEL enables the measurement of single molecular complex, without necessity of crystallization. However, for biological systems, due to their low diffraction power, interpretation of the data remains challenging. Given progresses in experimental techniques, new computational methods are needed to process and interpret data obtained from these single particle experiments. We will discuss hybrid computational methods that combine molecular mechanics and image data processing algorithms to derive structural and dynamical information from cryo-EM and XFEL data.

Discovering de novo peptide substrates for enzymes using machine learning

The discovery of peptide substrates for enzymes with exclusive, selective activities is a central goal in chemical biology. In this paper, we develop a hybrid computational and biochemical method to rapidly optimize peptides for specific, orthogonal biochemical functions. The method is an iterative machine learning process by which experimental data is deposited into a mathematical algorithm that selects potential peptide substrates to be tested experimentally. Once tested, the algorithm uses the experimental data to refine future selections. This process is repeated until a suitable set of de novo peptide substrates are discovered. We employed this technology to discover orthogonal peptide substrates for 4’-phosphopantetheinyl transferase, an enzyme class that covalently modifies proteins. In this manner, we have demonstrated that machine learning can be leveraged to guide peptide optimization for specific biochemical functions not immediately accessible by biological screening techniques, such as phage display and random mutagenesis.

Unmanned Aerial Vehicle Propagation Datalink Tool Based on a Hybrid Multiscale Modeling

This article presents a hybrid computational method to address the datalink characteristics of an Unmanned Aerial Vehicle (UAV) relay. It uses the 3D far-field gain data from the antennas (UAV and ground station), separately computed or based on available measurements as inputs, and then applies the 2-Ray propagation model, taking into consideration the ground permittivity and the respective antenna heights. By doing so, different numerical approaches cover the two different physical scales. Since the installed antennas (i.e. antennas considered placed on the UAV fuselage) performances are considered, a higher reliability can be achieved with the covered procedure, at lower costs when compared to field measurements. Results for different example scenarios are presented and discussed.

Hybrid method of plane-wave and cylindrical-wave expansions for distributed Bragg-reflector pillars: formalism and its application to topological photonics

A hybrid computational method of plane-wave and cylindrical-wave expansions for distributed Bragg-reflector (DBR) pillars is proposed. The plane-wave expansion is employed to represent the one-dimensional periodic structure of the DBR. The cylindrical-wave expansion is employed to describe the scattering by circular pillars with the DBR structure inside. This formalism enables us to calculate the radiation fields, $t$-matrices, scattering cross sections, photonic band structures, and quality factors of the DBR pillars. Furthermore, optical properties of arrayed DBR pillars are also investigated with the aid of the multiple-scattering method. Using this formalism, we demonstrate explicitly that high $Q$ photonic band modes including the so-called bound states in continuum are obtained both in isolated and arrayed DBR pillars. We also present a novel formation of gapless Dirac-cone surface states in a three-dimensional photonic crystal composed of a two-dimensional periodic arrangement of core-shell DBR pillars.

X-Ray Absorption Spectroscopy, Scattering, and Tomography Characterization of Platinum Group Metal-Free Oxygen Reduction Reaction Catalysts and Electrodes

The activity and stability of platinum group metal-free (PGM-free) oxygen reduction electrocatalysts and the performance of the electrodes based on these catalysts depends on the atomic species present, their stability in the fuel cell environment, and the nano- and micro-structure of the catalyst-ionomer composite electrode. As part of the U.S. Department of Energy Fuel Cell Technologies Office’s Electrocatalysis Consortium (ElectroCat),1 a suite of in situ and ex situ synchrotron X-ray techniques is being used to characterize these properties of PGM-free electrocatalysts and electrodes. X-ray absorption spectroscopy (XAFS) is used to determine the coordination environment and oxidation state of the transition metals (e.g., Fe, Co, and Mn) in the as-prepared catalyst powders, precursors during the pyrolysis step, and catalyst-ionomer-solvent inks as a function of potential in an aqueous electrolyte and during adsorption and stripping of probe molecules, such as nitric oxide. Ultra-small angle and small angle X-ray scattering (USAXS and SAXS) are used to determine the aggregate and agglomerate size of the catalysts in the as-prepared catalyst, catalyst-ionomer-solvent inks during sonication, and electrodes. Nano-X-ray tomography (XCT) is used to determine the three-dimensional distribution of catalyst, pores, and ionomer. This X-ray characterization methodology has thus far been applied to a series of catalysts, inks, and electrodes prepared through pyrolysis of iron salts and carbon-nitrogen precursors. The XAFS studies are being used to correlate the ORR activity of the catalysts synthesized by the core national laboratory, university, and industrial partners, including those prepared using high-throughput techniques, with the content of various iron species. The studies thus far have shown that the activity is correlated with the content of Fe-N4-like species and declines with the formation of iron carbide at higher pyrolysis temperatures and higher iron contents (Fig. 1). The in situ XAFS experiments have shown that the Fe centers are highly oxidized at open circuit and gradually reduced as potential is decreased and as ORR current increases. The electrode structural information from XCT is being combined with characterization results obtained via other techniques, such as porosimetry and ultra-small angle X-ray scattering (USAXS), to build a structural and transport model of the electrode using a hybrid computational method. The XCT data, when correlated with fuel cell polarization curve data, show that electrode performance is limited by oxygen and proton transport through the thick electrode layers (~70-95 mm thick). The application of the full suite of X-ray techniques to an iron zeolitic imidazolate framework-derived catalyst2 will be presented. AcknowledgementsThis work was supported by the U.S. Department of Energy, Energy Efficiency and Renewable Energy, Fuel Cell Technologies Office under the auspices of the Electrocatalysis Consortium (ElectroCat). This research used the resources of the Advanced Photon Source (APS), a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357. Argonne National Laboratory is managed for the U.S Department of Energy by the University of Chicago Argonne, LLC, also under contract DE-AC-02-06CH11357. References T. Thompson, A.R. Wilson, P. Zelenay, D.J. Myers, K.L. More, K.C. Neyerlin, and D. Papageorgopoulos, Solid State Ionics, 319 (2018) 68-76.Zelenay, D.J. Myers, H. Dinh, and K.L. More, “ElectroCat (Electrocatalysis Consortium)”, 2017 Department of Energy Hydrogen and Fuel Cells Program 2017 Annual Merit Review and Peer Evaluation Meeting, Washington DC, June, 2017. (https://www.hydrogen.energy.gov/pdfs/review17/fc160_zelenay_2017_o.pdf) Figure 1. Non-phase-corrected Fourier transform of the Fe K-edge XAFS spectra of pyrolyzed iron sulfate, zeolitic imidazolates, an Fe-N4 standard (Fe3+ phthalocyanine), and iron carbide. Figure 1

Optimization and profile calculation of ODE models using second order adjoint sensitivity analysis.

MotivationParameter estimation methods for ordinary differential equation (ODE) models of biological processes can exploit gradients and Hessians of objective functions to achieve convergence and computational efficiency. However, the computational complexity of established methods to evaluate the Hessian scales linearly with the number of state variables and quadratically with the number of parameters. This limits their application to low-dimensional problems.ResultsWe introduce second order adjoint sensitivity analysis for the computation of Hessians and a hybrid optimization-integration-based approach for profile likelihood computation. Second order adjoint sensitivity analysis scales linearly with the number of parameters and state variables. The Hessians are effectively exploited by the proposed profile likelihood computation approach. We evaluate our approaches on published biological models with real measurement data. Our study reveals an improved computational efficiency and robustness of optimization compared to established approaches, when using Hessians computed with adjoint sensitivity analysis. The hybrid computation method was more than 2-fold faster than the best competitor. Thus, the proposed methods and implemented algorithms allow for the improvement of parameter estimation for medium and large scale ODE models.Availability and implementationThe algorithms for second order adjoint sensitivity analysis are implemented in the Advanced MATLAB Interface to CVODES and IDAS (AMICI, https://github.com/ICB-DCM/AMICI/). The algorithm for hybrid profile likelihood computation is implemented in the parameter estimation toolbox (PESTO, https://github.com/ICB-DCM/PESTO/). Both toolboxes are freely available under the BSD license.Supplementary information Supplementary data are available at Bioinformatics online.

Addressing the Challenges of Structure Elucidation in Natural Products Possessing the Oxirane Moiety.

NMR data for natural products containing the epoxy moiety have been revisited and reanalyzed with the help of a recently developed parametric/DFT hybrid computational method, DU8+. More than 20 structures needed revision, which points to challenges in NMR solution structure assignment for molecules possessing this structural feature. Among the revised structures are achicretin 2, acremine P, aromaticane I, artanomalide B, botryosphaerihydrofuran, chloroklotzchin, crithmifolide, crotodichogamoin A, emervaridone C, 9α,15-epoxyafricanane, fischambiguine B, grandilobalide B, guaianolide A, guatterfriesols A and B, juncenolide G, roscotane D, secoafricane 7, taccalonolides AJ and AF, and related compounds.

Numerical Investigation of Wavy Leading Edges on Rod–Airfoil Interaction Noise

A hybrid computational aeroacoustics method is applied to investigate the effect of wavy leading edges on rod–airfoil interaction noise. The flow field is solved by an incompressible large-eddy simulation. The acoustic far-field is predicted using the Ffowcs Williams and Hawkings acoustic analogy. NACA 0012 airfoils with straight and wavy leading edges (denoted by SLE and WLE, respectively) are located one chord downstream of a circular rod. The free-stream Mach number is 0.2 and the Reynolds number based on the rod diameter is 48,000. The present numerical method is first validated by existing experimental and numerical results for the SLE case. Then, both the sound pressure level and sound power level of the SLE and WLE cases are computed to assess the noise reduction effect. A considerable noise reduction level is obtained in the mid- to high-frequency range. The main noise reduction mechanisms of the WLE are investigated in detail. The surface pressure fluctuations along the leading edge of the WLE airfoil exhibit a significant attenuation compared with the SLE counterpart, which leads to reduced sound source levels. It is also found that there exist spanwise decorrelation and decoherence effects along the leading edge of the WLE case, which means a noise reduction effect according to Amiet’s theory. Phase interference effect is also found along the WLE geometry and this effect is conjectured to be a more essential noise reduction mechanism, because the trends of the phase-shift spectrum are almost consistent with the noise-reduction spectrum.

Optimal experimental conditions for Welan gum production by support vector regression and adaptive genetic algorithm.

Welan gum is a kind of novel microbial polysaccharide, which is widely produced during the process of microbial growth and metabolism in different external conditions. Welan gum can be used as the thickener, suspending agent, emulsifier, stabilizer, lubricant, film-forming agent and adhesive usage in agriculture. In recent years, finding optimal experimental conditions to maximize the production is paid growing attentions. In this work, a hybrid computational method is proposed to optimize experimental conditions for producing Welan gum with data collected from experiments records. Support Vector Regression (SVR) is used to model the relationship between Welan gum production and experimental conditions, and then adaptive Genetic Algorithm (AGA, for short) is applied to search optimized experimental conditions. As results, a mathematic model of predicting production of Welan gum from experimental conditions is obtained, which achieves accuracy rate 88.36%. As well, a class of optimized experimental conditions is predicted for producing Welan gum 31.65g/L. Comparing the best result in chemical experiment 30.63g/L, the predicted production improves it by 3.3%. The results provide potential optimal experimental conditions to improve the production of Welan gum.

Triquinanes and Related Sesquiterpenes Revisited Computationally: Structure Corrections of Hirsutanols B and D, Hirsutenol E, Cucumin B, Antrodins C-E, Chondroterpenes A and H, Chondrosterins C and E, Dichrocephone A, and Pethybrene.

NMR data for 90+ natural sesquiterpenes possessing triquinane cores were examined with the help of a relatively fast parametric/DFT hybrid computational method, DU8+. Thirteen of these compounds, i.e., approximately 14% of the sample, required structure correction. This rate of misassignment is similar to the percentage of misassigned halogenated sesquiterpenes reported previously.

A three-stage stochastic programming method for LNG supply system infrastructure development and inventory routing in demanding countries

Despite of the high demand for liquefied natural gas (LNG) in demanding countries, an ideal means for establishing the LNG supply system has not yet been found in many regions. In this paper, a three-stage stochastic programming method has been proposed for LNG supply system infrastructure development and inventory routing in demanding countries. The minimum daily cost is set as the objective function; and the cost consists of the delivery cost, liquefaction cost, purchase cost, and construction cost. Under the constraints of delivery mode, volume, vehicle, time, and infrastructure construction, a multi-scenario MILP model was established and solved by a hybrid computational method (ACO-MILP), and the optimal infrastructure development and inventory routing were presented as the result. Finally, the method was successfully applied to the LNG supply system along the Yangtze River in China. Furthermore, compared with the other methods, the superiority of the method was verified.

A fully coupled hybrid computational aeroacoustics method on hierarchical Cartesian meshes

Abstract Hybrid computational fluid dynamics (CFD) – computational aeroacoustics (CAA) schemes are the standard method for aeroacoustics simulations. This approach requires the exchange of information between the CFD and the CAA step, which is usually accomplished by storing acoustic source data. This data exchange procedure, however, poses two problems when such hybrid methods are used for large-scale problems with O ( 10 9 ) degrees of freedom: On the one hand, the required disk space becomes large and reaches hundreds of terabytes for a single simulation. On the other hand, the parallel scalability of the overall numerical scheme is limited by the available I/O bandwidth, which typically peaks between 5,000 and 10,000 cores. To avoid these problems, a highly scalable direct-hybrid scheme is presented, in which both the flow and the acoustics simulations run simultaneously. That is, all data between the two solvers is transferred in-memory, avoiding the restrictions of the I/O subsystem. Both solvers operate on a joint hierarchical Cartesian grid, which enables efficient parallelization and dynamic load balancing and inherently supports local mesh refinement. To demonstrate the capabilities of the new scheme, the aeroacoustic field of a co-rotating vortex pair is computed. The results show that the direct-hybrid method is able to efficiently predict the acoustic pressure field and that it is suitable for highly parallel simulations. Furthermore, in comparison to the hybrid method with data exchange via disk I/O, the novel approach shows superior performance when scaling to thousands of cores.

Bio-inspired computational heuristics to study Lane-Emden systems arising in astrophysics model.

This study reports novel hybrid computational methods for the solutions of nonlinear singular Lane–Emden type differential equation arising in astrophysics models by exploiting the strength of unsupervised neural network models and stochastic optimization techniques. In the scheme the neural network, sub-part of large field called soft computing, is exploited for modelling of the equation in an unsupervised manner. The proposed approximated solutions of higher order ordinary differential equation are calculated with the weights of neural networks trained with genetic algorithm, and pattern search hybrid with sequential quadratic programming for rapid local convergence. The results of proposed solvers for solving the nonlinear singular systems are in good agreements with the standard solutions. Accuracy and convergence the design schemes are demonstrated by the results of statistical performance measures based on the sufficient large number of independent runs.

Coupling heterogeneous continuum-particle fields to simulate non-isothermal microscale gas flows

This paper extends the hybrid computational method proposed by Docherty et al. (2014) for simulating non-isothermal rarefied gas flows at the microscale. Coupling a continuum fluid description to a direct simulation Monte Carlo (DSMC) solver, the original methodology considered the transfer of heat only, with validation performed on 1D micro Fourier flow. Here, the coupling strategy is extended to consider the transport of mass, momentum, and heat, and validation in 1D is performed on the high-speed micro Couette flow problem. Sufficient micro resolution in the hybrid method enables good agreement with an equivalent pure DSMC simulation, but the method offers no computational speed-up for this 1D problem. However, considerable speed-up is achieved for a 2D problem: gas flowing through a microscale crack is modelled as a microchannel with a high-aspect-ratio cross-section. With a temperature difference imposed between the walls of the cross-section, the hybrid method predicts the velocity and temperature variation over the cross-section very accurately; an accurate mass flow rate prediction is also obtained.