Variation of the Restoring Force Surface Method to Estimate Nonlinear Stiffness and Damping Parameters.
Abstract not provided.
Roettgen, Daniel Robert↗
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Variation of the Restoring Force Surface Method to Estimate Nonlinear Stiffness and Damping Parameters.
Abstract not provided.
Roettgen, Daniel Robert↗
Optimization of Distribution Feeder Topology: A Differential Programming Learning Approach
This paper presents a gradient based method for optimizing distribution feeder network topology under load un- certainty. We recast the optimal network reconfiguration problem as a learning problem where edge weights of a graph are learned to produce an optimized spanning tree for a distribution network. Using recent methods published on differentiable programming, we provide a data driven method for learning these weights. We test our method on 100 variations of an IEEE 15-bus test system. Our results show that our method outperforms more traditional mathematical programming-based approaches.
differentiable programming↗
In-Flight Gain Monitoring of SPIDER’s Transition-Edge Sensor Arrays
We report experiments deploying large arrays of transition-edge sensors (TESs) often require a robust method to monitor gain variations with minimal loss of observing time. We propose a sensitive and non-intrusive method for monitoring variations in TES responsivity using small square waves applied to the TES bias. We construct an estimator for a TES’s small-signal power response from its electrical response that is exact in the limit of strong electrothermal feedback. We discuss the application and validation of this method using flight data from SPIDER, a balloon-borne telescope that observes the polarization of the cosmic microwave background with more than 2000 TESs. This method may prove useful for future balloon- and space-based instruments, where observing time and ground control bandwidth are limited.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Pore-scale simulation of drying in porous media using a hybrid lattice Boltzmann: pore network model
In this work, a hybrid method coupling a pseudo-potential lattice Boltzmann model (LBM) and a pore network model (PNM) to simulate drying in porous media is proposed. Based on the watershed method, the porous medium is firstly decomposed into pore regions. According to the liquid–vapor phase distribution at a given time, the pore regions are further divided into four pore types, namely two-phase pores where a liquid–vapor interface exists, buffer pores next to the two-phase pores, single-liquid and single-vapor phase pores. The pseudo-potential LBM is used in the two-phase and buffer pores to simulate liquid drying and track the movement of the interfaces, while the single-phase PNM simulations are conducted in the buffer and single-phase pores to simulate vapor or liquid flow. LBM and PNM are coupled in the buffer pores through exchange of boundary information. The hybrid method is applied to simulate liquid drying in a porous medium. The whole-domain LBM simulation is considered as the reference solution to validate the hybrid method. Liquid saturation variation during the drying process and detailed phase and pressure distributions obtained by the two methods match quite well, demonstrating the accuracy of the hybrid method. For the specific case studied, the hybrid method saves more than 60% computational time compared to the whole-domain LBM simulation. In addition, the speedup of the hybrid method becomes more significant for a larger computational domain. In summary, the hybrid method developed in this work combines the accuracy of LBM and the efficiency of PNM to simulate drying in porous media at pore scale and can lead to significant reduction of computation time, thus allowing the pore-scale consideration of drying in larger porous systems.
36 MATERIALS SCIENCE↗
Leveraging neural control variates for enhanced precision in lattice field theory
Results obtained with stochastic methods have an inherent uncertainty due to the finite number of samples that can be achieved in practice. In lattice QCD this problem is particularly salient in some observables like, for instance, observables involving one or more baryons and it is the main problem preventing the calculation of nuclear forces from first principles. The method of control variables has been used extensively in statistics and it amounts to computing the expectation value of the difference between the observable of interest and another observable whose average is known to be zero but is correlated with the observable of interest. Recently, control variates methods emerged as a promising solution in the context of lattice field theories. In our current study, instead of relying on an educated guess to determine the control variate, we utilize a neural network to parametrize this function. Using 1 + 1 dimensional scalar field theory as a testbed, we demonstrate that this neural network approach yields substantial improvements. Notably, our findings indicate that the neural network ansatz is particularly effective in the strong coupling regime. Published by the American Physical Society 2024
Astronomy & Astrophysics↗
Extrusion‐Spheronization of Energetic Materials
The prevailing method to produce plastic‐bonded explosive (PBX) molding powder, or “prills”, is a complex, multiphase, and bespoke process that was developed by the high explosives (HEs) manufacturing industry several decades ago. This work demonstrates the utility of a simpler, widely‐used mechanical process—extrusion‐spheronization—to produce PBX prills. We begin by detailing precautions taken to enable safe remote operation of extrusion‐spheronization equipment with HE. We then perform a study investigating the effect of lacquer solvent composition on the particle packing, pressed density, and compressive strength properties of a 95 wt.% TATB/5 wt.% polymer binder formulation akin to PBX 9502. It was found that increased composition of low vapor pressure solvents caused prolonged retention of the solvent, resulting in tackier materials that would agglomerate and form larger prills. The larger prills also led to lower poured density and tapped density of HE prills and compressive strength of pressed PBX articles. The samples prepared with a 75% propyl acetate/25% butyl acetate lacquer solvent composition exhibited the highest compressive strength. However, it is believed that the prill packing and compressive strength properties are primarily driven by the prill size rather than the chemical composition of the lacquer itself. Extrusion‐spheronization remains a promising method to reliably and repeatably produce HE prills that is less sensitive to feedstock or process variation than traditional methods.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
The Generality of the GUGA MRCI Approach in COLUMBUS for Treating Complex Quantum Chemistry
The core part of the program system COLUMBUS allows highly efficient calculations using variational multireference (MR) methods in the framework of configuration interaction with single and double excitations (MR-CISD) and averaged quadratic coupled-cluster calculations (MR-AQCC), based on uncontracted sets of configurations and the graphical unitary group approach (GUGA). The availability of analytic MR-CISD and MR-AQCC energy gradients and analytic nonadiabatic couplings for MR-CISD enables exciting applications including, e.g., investigations of π-conjugated biradicaloid compounds, calculations of multitudes of excited states, development of diabatization procedures, and furnishing the electronic structure information for on-the-fly surface nonadiabatic dynamics. With fully variational uncontracted spin-orbit MRCI, COLUMBUS provides a unique possibility of performing high-level calculations on compounds containing heavy atoms up to lanthanides and actinides. Crucial for carrying out all of these calculations effectively is the availability of an efficient parallel code for the CI step. Configuration spaces of several billion in size now can be treated quite routinely on standard parallel computer clusters. Emerging developments in COLUMBUS, including the all configuration mean energy multiconfiguration self-consistent field method and the graphically contracted function method, promise to allow practically unlimited configuration space dimensions. Spin density based on the GUGA approach, analytic spin-orbit energy gradients, possibilities for local electron correlation MR calculations, development of general interfaces for nonadiabatic dynamics, and MRCI linear vibronic coupling models conclude this overview.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Laser pulse driven control of charge and spin order in the two-dimensional Kondo lattice
Fast and dynamical control of quantum phases is highly desired in the application of quantum phenomena in devices. On the experimental side, ultrafast laser pulses provide an ideal platform to induce femtosecond dynamics in a variety of materials. Here we show that a laser pulse driven heavy fermion system can be tuned to dynamically evolve into a phase, which is not present in equilibrium. Using the state-of-the-art time-dependent variational Monte Carlo method, we perform numerical simulations of realistic laser pulses applied to the Kondo lattice model. By tracking spin and charge degrees of freedom we identify a dynamical phase transition, within the partially Kondo screened phase, from a charge ordered into a metallic state, while preserving the spin order of the system. Furthermore, we propose using high-harmonic generation and nonequilibrium optical conductivity to identify the dynamical phase transition in an ultrafast experiment.
36 MATERIALS SCIENCE↗
CoVVVR: Control Variates & Vegas Variance Reduction
This package is a wrapper over the vegas integration package. The control variate variance reduction method is applied to the function when integrated along with the techniques applied in vegas such as importance sampling. To understand control variates, lets first look at how Monte Carlo works.
Scott, JacobL. [Univ. of Kansas, Lawrence, KS (Uni↗
A Characteristics Approach to the Finite Element Method
Herein, we present a new method for solving the linear Boltzmann transport equation. Two commonly used and well-understood methods for solving partial differential equations are the method of characteristics (MOC) and the finite element method (FEM). We propose a new method that combines the fundamental concept of the FEM with the analytic solution from the MOC to obtain coefficients for the FEM basis function expansion. Traditionally, coefficients for the FEM basis function expansion are obtained via matrix inversion. Instead, we solve for the coefficients with the MOC and represent the underlying fields with the basis function expansion using these coefficients. We provide a convergence study for our method with results from two sets of FEM basis functions: Gauss-Legendre and Gauss-Lobatto sets. We also compare two different variations of our method categorized as short characteristics and intermediate characteristics.
42 ENGINEERING↗
A light weight regularization for wave function parameter gradients in quantum Monte Carlo
The parameter derivative of the expectation value of the energy, ∂E/∂p, is a key ingredient in variational Monte Carlo (VMC) wave function optimization methods. In some cases, a naïve estimate of this derivative suffers from an infinite variance, which inhibits the efficiency of optimization methods that rely on a stable estimate of the derivative. In this work, we derive a simple regularization of the naïve estimator, which is trivial to implement in existing VMC codes, has finite variance, and a negligible bias, which can be extrapolated to zero bias with no extra cost. We use this estimator to construct an unbiased, finite variance estimation of ∂E/∂p for a multi-Slater–Jastrow trial wave function on the LiH molecule and in the optimization of a multi-Slater–Jastrow trial wave function on the CuO molecule. This regularized estimator is a simple and efficient estimator of ∂E/∂p for VMC optimization techniques.
36 MATERIALS SCIENCE↗
Gaussian Process Modeling For Experimental Procedure Uncertainty
Many laboratory experiments generate data that are characterized by a form of uncertainty that differs from noise: experimental procedure uncertainty. The origin of this type of uncertainty is the difficulty in controlling a subset of experimental conditions in such a way that the experiment is perfectly reproducible within noise. In this report, we describe a Gaussian Process modeling-based method that accounts for experimental procedure uncertainty. The method accounts for variations in conditions from experiment to experiment that are independent between experiments, but correlated within each experiment, and incorporates the resulting uncertainty into predictions at future experimental settings. The method is discussed in the context of a specific chemistry application in which Raman scattering spectra are measured from three-component mixtures.
42 ENGINEERING↗
Corrosion Resistance of 309L Stainless Steel Claddings on Carbon Steel Produced with Wire-Fed Directed Energy Deposition
Additive manufacturing (AM) tools are capable of applying overlay austenitic stainless steel (SS) claddings to carbon steel components. The benefits of this approach over arc welding include a smaller heat-affected zone, residual stress reduction, and material savings. In particular, wire-directed energy deposition is a suitable technique because of its low material cost and high rate of production compared to other AM methods. However, metallurgical variations in composition, phase fraction, and microsegregation can potentially influence the corrosion behavior of such claddings. In this work, 309L SS is clad on carbon steel substrates and electrochemical methods are used to measure their general and pitting corrosion resistance in simulated marine environments (3.5 wt% NaCl solutions). Two-layer claddings are fabricated with four laser powers to understand the effects of bulk chemical composition, austenite/δ-ferrite phase fractions, and individual phase compositions on corrosion behavior. The two-layer claddings are compared to a single-layer cladding, wrought 304 SS, and the carbon steel substrate for a comprehensive assessment of corrosion performance. The two-layer claddings are remarkably resistant to general corrosion in the 3.5 wt% NaCl environment because of their high Cr content (21.6 wt% to 23.3 wt% Cr). The single-layer cladding exhibits localized corrosion at unmixed Fe-rich peninsulas that originate at the dissimilar metal boundary and protrude into the first cladding layer. All two-layer claddings possess higher pitting corrosion resistance than wrought 304 SS, demonstrating their effectiveness as a corrosion-resistant barrier. Here, the pitting corrosion resistance is superior for claddings made with lower laser powers, due to low dilution and greater δ-ferrite contents.
36 MATERIALS SCIENCE↗
Primal interface debonding formulation for finite strain isotropic plasticity
In this work, a framework is developed for modeling ductile damage of nonlinear materials whose plastic deformation is characterized using rate independent classical plasticity. This method relies on the assumption that the free energy can be decomposed into elastic, plastic and damage parts. A thermodynamically consistent method is derived which satisfies the second law of thermodynamics in the Clausius–Duhem inequality form. The dissipation associated with plasticity takes place in the domain only, while damage dissipation is localized to the interface. The method is developed using Variational Multiscale ideas to obtain definitions of the interface fluxes within a primal formulation analogous to the Discontinuous Galerkin method, which ensures weakly vanishing interface gap prior to reaching a damage initiation criterion. The local nonlinear problem to calculate both plastic deformation gradient and damage variable follows an incremental approach similar to classical plasticity return mapping algorithm. This elastoplastic damage formulation is developed for material undergoing finite strain, and it naturally accommodates a trapezoidal traction separation law (TSL) whose shape can be varied to model either ductile interface behavior or brittle interface behavior. The formulation's performance is assessed through modeling a patch test and a compact tension specimen.
42 ENGINEERING↗
Investigating the crust of neutron stars with neural-network quantum states
An accurate description of low-density nuclear matter is crucial for explaining the physics of neutron star crusts. In the density range between approximately 0.01 fm −3 and 0.1 fm −3 , matter transitions from neutron-rich nuclei to various higher-density pasta shapes, before ultimately reaching a uniform liquid. In this work, we introduce a variational Monte Carlo method based on a neural Pfaffian-Jastrow quantum state, which allows us to model the transition from the liquid phase to neutron-rich nuclei microscopically. At low densities, nuclear clusters dynamically emerge from the microscopic interactions among protons and neutrons, which we model based on pionless effective field theory. Our variational Monte Carlo approach represents a significant improvement over the state-of-the-art auxiliary-field diffusion Monte Carlo method, which is severely hindered by the fermion-sign problem in this low-density regime and cannot capture the onset of clusters. In addition to computing the energy per particle of symmetric nuclear matter and pure neutron matter, we analyze an intermediate isospin-asymmetry configuration to elucidate the formation of nuclear clusters. We also provide evidence that the presence of such nuclear clusters influences the amount of protons in the crust compared to protons in beta-equilibrated, neutrino-transparent matter.
Nuclear astrophysics↗
Dual-cone variational calculation of the two-electron reduced density matrix
The computation of strongly correlated quantum systems is challenging because of its potentially exponential scaling in the number of electron configurations. Variational calculation of the two-electron reduced density matrix (2-RDM) without the many-electron wave function exploits the pairwise nature of the electronic Coulomb interaction to compute a lower bound on the ground-state energy with polynomial computational scaling. Recently, a dual-cone formulation of the variational 2-RDM calculation was shown to generate the ground-state energy, albeit not the 2-RDM, at a substantially reduced computational cost, especially for higher $\textit{N}$-representability conditions such as the T2 constraint. Here we generalize the dual-cone variational 2-RDM method to compute not only the ground-state energy but also the 2-RDM. The central result is that we can compute the 2-RDM from a generalization of the Hellmann-Feynman theorem. Specifically, we prove that in the Lagrangian formulation of the dual-cone optimization the 2-RDM is the Lagrange multiplier. Further, we apply the method to computing the energies and properties of strongly correlated electrons—including atomic charges, electron densities, dipole moments, and orbital occupations—in an illustrative hydrogen chain and the nitrogen-fixation catalyst FeMoco. The dual variational computation of the 2-RDM with T2 or higher N -representability conditions provides a polynomially scaling approach to strongly correlated molecules and materials with significant applications in atomic and molecular and condensed-matter chemistry and physics.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Floating Wind Farm Layout Optimization Considering Moorings and Seabed Variations
This paper presents a method for optimizing the layout of floating wind farms that accounts for realistic seabed variations and the consequent adjustments to the mooring systems required for different turbine positions. The mooring lines of floating wind farms create large spatial constraints that are depth-dependent, since mooring designs must adapt to variations in seabed conditions over the array area. We develop a layout optimization methodology that addresses this, adjusting mooring system designs based on the local seabed characteristics as the layout changes and using steady-state models for the wake effects and mooring lines. The approach includes design algorithms that adjust the anchor positions and line length to achieve the desired mooring line profile for different water depths, and a layout optimization framework that implements spatial constraints between the turbines, mooring lines, and lease area boundaries. Demonstrating the method on several cases shows the effect of the seabed and spatial-constraint factors, as well as their interactions, on the optimal array layout. This demonstration paves the way for scaling up the method, using more powerful optimization algorithms to handle larger farm sizes and situations with more intensely varied seabed conditions.
17 WIND ENERGY↗