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DOE OSTI2023

Anticipating gelation and vitrification with medium amplitude parallel superposition (MAPS) rheology and artificial neural networks

Abstract Anticipating qualitative changes in the rheological response of complex fluids (e.g., a gelation or vitrification transition) is an important capability for processing operations that utilize such materials in real-world environments. One class of complex fluids that exhibits distinct rheological states are soft glassy materials such as colloidal gels and clay dispersions, which can be well characterized by the soft glassy rheology (SGR) model. We first solve the model equations for the time-dependent, weakly nonlinear response of the SGR model. With this analytical solution, we show that the weak nonlinearities measured via medium amplitude parallel superposition (MAPS) rheology can be used to anticipate the rheological aging transitions in the linear response of soft glassy materials. This is a rheological version of a technique called structural health monitoring used widely in civil and aerospace engineering. We design and train artificial neural networks (ANNs) that are capable of quickly inferring the parameters of the SGR model from the results of sequential MAPS experiments. The combination of these data-rich experiments and machine learning tools to provide a surrogate for computationally expensive viscoelastic constitutive equations allows for rapid experimental characterization of the rheological state of soft glassy materials. We apply this technique to an aging dispersion of Laponite ® clay particles approaching the gel point and demonstrate that a trained ANN can provide real-time detection of transitions in the nonlinear response well in advance of incipient changes in the linear viscoelastic response of the system.

Lennon, Kyle R. (ORCID:0000000212515461)↗

DOE OSTI2023

An asynchronous parallel high-throughput model calibration framework for crystal plasticity finite element constitutive models

Crystal plasticity finite element model (CPFEM) is a powerful numerical simulation in the integrated computational materials engineering toolboxes that relates microstructures to homogenized materials properties and establishes the structure–property linkages in computational materials science. However, to establish the predictive capability, one needs to calibrate the underlying constitutive model, verify the solution and validate the model prediction against experimental data. Bayesian optimization (BO) has stood out as a gradient-free efficient global optimization algorithm that is capable of calibrating constitutive models for CPFEM. Here in this paper, we apply a recently developed asynchronous parallel constrained BO algorithm to calibrate phenomenological constitutive models for stainless steel 304 L, Tantalum, and Cantor high-entropy alloy.

304L stainless steel↗

DOE OSTI2016

Modeling the Control Systems of Gas-Turbines to Ensure Their Reliable Parallel Operation in the UPS of Russia

Consideration is given to different approaches to modeling the control systems of gas turbines as a component of CCPP and GTPP to ensure their reliable parallel operation in the UPS of Russia. The disadvantages of the approaches to the modeling of combined-cycle units in studying long-term electromechanical transients accompanied by power imbalance are pointed out. Examples are presented to support the use of more detailed models of gas turbines in electromechanical transient calculations. It is shown that the modern speed control systems of gas turbines in combination with relatively low equivalent inertia have a considerable effect on electromechanical transients, including those caused by disturbances not related to power imbalance.

42 ENGINEERING↗

DOE OSTI2016

Large-scale optimization-based non-negative computational framework for diffusion equations: Parallel implementation and performance studies

It is well-known that the standard Galerkin formulation, which is often the formulation of choice under the finite element method for solving self-adjoint diffusion equations, does not meet maximum principles and the non-negative constraint for anisotropic diffusion equations. Recently, optimization-based methodologies that satisfy maximum principles and the non-negative constraint for steady-state and transient diffusion-type equations have been proposed. To date, these methodologies have been tested only on small-scale academic problems. The purpose of this paper is to systematically study the performance of the non-negative methodology in the context of high performance computing (HPC). PETSc and TAO libraries are, respectively, used for the parallel environment and optimization solvers. For large-scale problems, it is important for computational scientists to understand the computational performance of current algorithms available in these scientific libraries. The numerical experiments are conducted on the state-of-the-art HPC systems, and a single-core performance model is used to better characterize the efficiency of the solvers. Furthermore, our studies indicate that the proposed non-negative computational framework for diffusion-type equations exhibits excellent strong scaling for real-world large-scale problems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Role of Interactions and Correlations on Collective Dynamics of Molecular Motors Along Parallel Filaments

Cytoskeletal motors known as motor proteins are molecules that drive cellular transport along several parallel cytoskeletal filaments and support many biological processes. Experimental evidences suggest that they interact with the nearest molecules of their filament while performing any mechanical work. These interactions modify the microscopic level properties of motor proteins. In this work, a new version of two-channel totally asymmetric simple exclusion process, that incorporates the intra-channel interactions in a thermodynamically consistent way, is proposed. As the existing approaches for multi-channel systems deviate from analyzing the combined effect of inter and intra-channel interactions, a new approach known as modified vertical cluster mean field is developed. The approach along with Monte Carlo simulations successfully encounters some correlations and computes the complex dynamic properties of the system. Role of symmetry of interactions and inter-channel coupling is observed on the phase diagrams, maximal particle current and its corresponding optimal interaction strength. Surprisingly, for all values of coupling rate and most of the interaction splittings, the optimal interaction strength corresponding to maximal current belongs to the case of weak repulsive interactions. Moreover, for weak interaction splittings and with an increase in the coupling rate, the optimal interaction strength tends towards the known experimental results. The effect of coupling as well as interaction energy is also measured for correlations. They are found to be short-range and weaker for repulsive and weak attractive interactions while they are long-range and stronger for large attractions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2019

Two-Dimensional Problem of Thermoelasticity for a Half Space in the Presence of Heat Release in a Ribbon-Shaped Domain Parallel to its Boundary

Using a logarithmic single-layer potential, thermoelastic displacement potential, Airy stress function, and Boussinesq function, we solve two-dimensional problems of stationary heat conduction and thermoelasticity under the conditions of plane deformation of a semiinfinite body in the presence of heat release in a ribbon-shaped domain parallel to its boundary (in this domain, either a temperature or a heat flux is given). The boundary of the body is kept at temperature equal to zero. We study the distribution of stresses in the domain of heat release with given heat sources of constant intensity at constant temperature.

42 ENGINEERING↗

DOE OSTI2015

Experimental and numerical analysis of parallel reactant flow and transverse mixing with mineral precipitation in homogeneous and heterogeneous porous media

Formation of mineral precipitates in the mixing interface between two reactant solutions flowing in parallel in porous media is governed by reactant mixing by diffusion and dispersion and is coupled to changes in porosity/permeability due to precipitation. The spatial and temporal distribution of mixing-dependent precipitation of barium sulfate in porous media was investigated with side-by-side injection of barium chloride and sodium sulfate solutions in thin rectangular flow cells packed with quartz sand. The results for homogeneous sand beds were compared to beds with higher or lower permeability inclusions positioned in the path of the mixing zone. In the homogeneous and high permeability inclusion experiments, BaSO 4 precipitate (barite) formed in a narrow deposit along the length and in the center of the solution–solution mixing zone even though dispersion was enhanced within, and downstream of, the high permeability inclusion. In the low permeability inclusion experiment, the deflected BaSO 4 precipitation zone broadened around one side and downstream of the inclusion and was observed to migrate laterally toward the sulfate solution. A continuum-scale fully coupled reactive transport model that simultaneously solves the nonlinear governing equations for fluid flow, transport of reactants and geochemical reactions was used to simulate the experiments and provide insight into mechanisms underlying the experimental observations. Lastly, migration of the precipitation zone in the low permeability inclusion experiment could be explained by the coupling effects among fluid flow, reactant transport and localized mineral precipitation reaction.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

SCIP-Jack—a solver for STP and variants with parallelization extensions

The Steiner tree problem in graphs is a classical problem that commonly arises in practical applications as one of many variants. While often a strong relationship between different Steiner tree problem variants can be observed, solution approaches employed so far have been prevalently problem-specific. In contrast, this paper introduces a general-purpose solver that can be used to solve both the classical Steiner tree problem and many of its variants without modification. This versatility is achieved by transforming various problem variants into a general form and solving them by using a state-of-the-art MIP-framework. The result is a high-performance solver that can be employed in massively parallel environments and is capable of solving previously unsolved instances.

90C10↗

DOE OSTI2019

Ion-Ion Proton Transfer and Parallel Ion Parking for the Analysis of Mixtures of Intact Proteins on a Modified Orbitrap Mass Analyzer

We have enabled parallel ion parking on a modified Orbitrap Elite™ as a way to control ion-ion proton transfer reactions via selective activation of a range of ions. The result is the concentration of the majority of ion current from multiple charge states of each precursor proteoform into a single charge state, maximizing signal intensity and increasing effective sensitivity compared to conventional MS1 spectra. These techniques were applied in an on-line HPLC, data-dependent MS/MS analysis of intact E. coli ribosomal proteins with HCD fragmentation. With one injection, all but two ribosomal proteins were selected for fragmentation and subsequently identified. The techniques described facilitate rapid identification of intact proteins in complex mixtures and an enhanced ability to observe proteins of low abundance. .

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2018

Parallel iterative methods for a finite family of sequences of nearly nonexpansive mappings in Hilbert spaces

In this paper, we propose some new parallel iterative methods by the hybrid and shrinking projection to find a common fixed point of a finite family of sequences of nearly nonexpansive mappings in Hilbert spaces when the domain C satisfies diam(C) < ∞. We also give some applications of our main results for the problem of finding a common fixed point of nonexpansive mappings, nonexpansive semi-groups, the problem of finding a common zero point of monotone operators, the system of generalized mixed equilibrium problems and the system of variational inequalities without the condition diam(C) < ∞. Three numerical examples also are given to illustrate the effectiveness of the proposed algorithms.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Parallel hybrid quantum-classical machine learning for kernelized time-series classification

Supervised time-series classification garners widespread interest because of its applicability throughout a broad application domain including finance, astronomy, biosensors, and many others. Here, in this work, we tackle this problem with hybrid quantum-classical machine learning, deducing pairwise temporal relationships between time-series instances using a timeseries Hamiltonian kernel (TSHK). A TSHK is constructed with a sum of inner products generated by quantum states evolved using a parameterized time evolution operator. This sum is then optimally weighted using techniques derived from multiple kernel learning. Because we treat the kernel weighting step as a differentiable convex optimization problem, our method can be regarded as an end-to-end learnable hybrid quantum-classical-convex neural network, or QCC-net, whose output is a data set-generalized kernel function suitable for use in any kernelized machine learning technique such as the support vector machine (SVM). Using our TSHK as input to a SVM, we classify univariate and multivariate time-series using quantum circuit simulators and demonstrate the efficient parallel deployment of the algorithm to 127-qubit superconducting quantum processors using quantum multi-programming.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

An efficient parallel sampling technique for Multivariate Poisson-Lognormal model: Analysis with two crash count datasets

Our study investigates the Multivariate Poisson-lognormal (MVPLN) model that jointly models crash frequency and severity accounting for correlations. The ordinary univariate count models analyze crashes of different severity level separately ignoring the correlations among severity levels. The MVPLN model is capable to incorporate the general correlation structure and takes account of the over dispersion in the data that leads to a superior data fitting. But, the traditional estimation approach for MVPLN model is computationally expensive, which often limits the use of MVPLN model in practice. In this work, a parallel sampling scheme is introduced to improve the original Markov Chain Monte Carlo (MCMC) estimation approach of the MVPLN model, which significantly reduces the model estimation time. Two MVPLN models are developed using the pedestrian vehicle crash data collected in New York City from 2002 to 2006, and the highway-injury data from Washington State (5-year data from 1990 to 1994) The Deviance Information Criteria (DIC) is used to evaluate the model fitting. The estimation results show that the MVPLN models provide a superior fit over univariate Poisson-lognormal (PLN), univariate Poisson, and Negative Binomial models. Moreover, the correlations among the latent effects of different severity levels are found significant in both datasets that justifies the importance of jointly modeling crash frequency and severity accounting for correlations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

Coherent quantum states of a relativistic particle in an electromagnetic plane wave and a parallel magnetic field

We analyze the solutions of the Klein–Gordon and Dirac equations describing a charged particle in an electromagnetic plane wave combined with a magnetic field parallel to the direction of propagation of the wave. It is shown that the Klein–Gordon equation admits coherent states as solutions, while the corresponding solutions of the Dirac equation are superpositions of coherent and displaced-number states. Particular attention is paid to the resonant case in which the motion of the particle is unbounded. -- Highlights: •We study a relativistic electron in a particular electromagnetic field configuration. •New exact solutions of the Klein–Gordon and Dirac equations are obtained. •Coherent and displaced number states can describe a relativistic particle.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

MAPPRAISER: A massively parallel map-making framework for multi-kilo pixel CMB experiments

Forthcoming cosmic microwave background (CMB) polarized anisotropy experiments have the potential to revolutionize our understanding of the Universe and fundamental physics. The sought-after, tale-telling signatures will be however distributed over voluminous data sets which these experiments will collect. These data sets will need to be efficiently processed and unwanted contributions due to astrophysical, environmental, and instrumental effects characterized and efficiently mitigated in order to uncover the signatures. This poses a significant challenge to data analysis methods, techniques, and software tools which will not only have to be able to cope with huge volumes of data but to do so with unprecedented precision driven by the demanding science goals posed for the new experiments. A keystone of efficient CMB data analysis is solvers of very large linear systems of equations. Such systems appear in very diverse contexts throughout CMB data analysis pipelines, however they typically display similar algebraic structures and can therefore be solved using similar numerical techniques. Linear systems arising in the so-called map-making problem are one of the most prominent and common ones. In this work we present a massively parallel, flexible and extensible framework, comprised of a numerical library, MIDAPACK, and a high level code, MAPPRAISER, which provide tools for solving efficiently such systems. Here, the framework implements iterative solvers based on conjugate gradient techniques: enlarged and preconditioned using different preconditioners. We demonstrate the framework on simulated examples reflecting basic characteristics of the forthcoming data sets issued by ground-based and satellite-borne instruments, executing it on as many as 16,384 compute cores. The software is developed as an open source project freely available to the community at: https://github.com/B3Dcmb/midapack.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Dynamic analysis of fully constrained Cable-Driven Parallel Robots for automated prefabricated component installation

This paper presents a dynamic analysis and validation framework to assess a fully constrained six-anchor Cable-Driven Parallel Robot (CDPR) for automated installation of prefabricated facade components. Compared with conventional eight-anchor systems, the six-anchor configuration simplifies setup and reduces cost, but it also reduces control authority, shrinks the wrench-feasible workspace, and tightens orientation limits. Consequently, it is unclear a priori whether dynamically feasible trajectories exist to move the end effector from pickup to the facade. A constrained trajectory optimization is formulated to enforce the system dynamics, cable-tension bounds, and pose/velocity limits, and the framework is evaluated in simulation at three levels: (i) an idealized reference model, (ii) a lab-scale prototype model incorporating measured anchor misalignments and identified damping, and (iii) a full-scale three-story building model with load decomposition for structural feasibility checks. Across these scenarios, the analysis shows that optimal, constraint-satisfying trajectories exist that move the end effector from pickup to installation while maintaining a near-plumb, level orientation at the final pose. Collectively, this multi-scale dynamic analysis and validation framework supports the deployment readiness of the six-anchor CDPR and provides a prototype-based sensitivity case study of how measured anchor placement deviations affect feasibility.

CDPR↗

DOE OSTI2026

A novel xylosylated fucoglucuronan in Penium reveals structural parallels to rhamnogalacturonan-I and its broad evolutionary footprint in lower plants

Green algae inhabit aquatic environments across the planet and play a crucial role in sustaining the global ecosystem. Ancestors of some Charophytes adapted to terrestrial conditions and eventually evolved into land plants. Extant green algae have inherited traits from their ancestors and evolved into their current morphological and chemical forms, as reflected by their cell walls with distinct shapes and compositions. To illuminate the evolution of plant cell walls and bridge the gap between green algae and land plants, we investigated the charophyte Penium margaritaceum, a close relative of terrestrial plants. We discovered a previously unknown polysaccharide in both its culture medium and cell wall. This polysaccharide, termed xylosylated fucoglucuronan (XFG), possesses a rhamnogalacturonan-I (RG-I)-like backbone composed of repeating [-3-α-Fucp-(1,4)-α-GlcpA-] disaccharides that are extensively xylosylated and acetylated. Surveying approximately 20 non-vascular plants revealed that XFG and RG-I (or related structures) first emerge in certain Chlorophyceae and subsequently co-occur throughout lineages along the evolutionary trajectory to bryophytes, thereby bridging aquatic green algae to early land plants. The striking structural parallels between XFG, RG-I, and ulvan suggest a shared evolutionary origin, offering new insight into how plant cell walls adapted during the transition from marine to freshwater environments and ultimately to land.

Algae↗

DOE OSTI2021

Parallels between enzyme catalysis, electrocatalysis, and photoelectrosynthesis

Catalysts are central to accelerating chemistry in biology and technology. In biochemistry, the relationship between the velocity of an enzymatic reaction and the concentration of chemical substrates is described via the Michaelis-Menten model. Additionally, the modeling and benchmarking of synthetic molecular electrocatalysts are also well developed. However, such efforts have not been as rigorously extended to photoelectrosynthetic reactions, where, in addition to chemical substrates and charge carriers, light is a required reagent. In this perspective, we draw parallels between concepts involving enzyme catalytic efficiency, the benchmarking of molecular electrocatalysts, and the performance of photoelectrosynthetic assemblies, while highlighting key differences, assumptions, and limitations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

Algorithms and analyses for stochastic optimization for turbofan noise reduction using parallel reduced-order modeling

Simulation-based optimization of acoustic liner design in a turbofan engine nacelle for noise reduction purposes can dramatically reduce the cost and time needed for experimental designs. Because uncertainties are inevitable in the design process, a stochastic optimization algorithm is posed based on the conditional value-at-risk measure so that an ideal acoustic liner impedance is determined that is robust in the presence of uncertainties. A parallel reduced-order modeling framework is developed that dramatically improves the computational efficiency of the stochastic optimization solver for a realistic nacelle geometry. The reduced stochastic optimization solver takes less than 500 s to execute. Additionally, well-posedness and finite element error analyses of the state system and optimization problem are provided.

97 MATHEMATICS AND COMPUTING↗