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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Climatology of Linear Mesoscale Convective System Morphology in the United States based on Random Forests Method

This study uses machine learning methods, specifically the random forest (RF), on a radar-based mesoscale convective system (MCS) tracking dataset to classify the five types of linear MCS morphology in the contiguous United States during the period 2004-2016. The algorithm is trained using radar- and satellite-derived spatial and morphological parameters, and reanalysis environmental information from 5-26yr manually identified nonlinear and five linear MCS modes. The algorithm is then used to automate the classification of linear MCSs over 8 years with high accuracy, providing a systematic, long-term climatology of linear MCSs. Results reveal that nearly 40% of MCSs are classified as linear MCSs, in which half of the linear events belong to the type of system having a leading convective line. The occurrence of linear MCSs shows large annual and seasonal variations. On average, 113 linear MCSs occur annually during the warm season (through March to October), with most of these events clustered from May through August in the central eastern Great Plains. MCS characteristics, including duration, propagation speed, orientation, and system cloud size, have large variability among the different linear modes. The systems having a trailing convective line and the systems having a back-building area of convection typically move more slowly and have higher precipitation rate, and thus have higher potential in producing extreme rainfall and flash flooding. Analysis of the environmental conditions associated with linear MCSs show that the storm-relative flow is of most importance in determining the organization mode of linear MCSs.

Cui, Wenjun↗
DOE OSTI2023

Design and Development of a Particle Flow Control Mechanism for Particle-Based CSP System Applications

Within particle-based Concentrated Solar Power Systems (CSP), particles are the heat transfer medium, and therefore, the heat transfer effectiveness of these systems is reliant on their ability to effectively control particle mass flow rate. The vast majority of approaches for particle flow control in high temperature CSP is based on a slide-gate approach where a gate is linearly actuated across a tear-drop shaped opening. The slide-gate mechanism has a sensitivity of 0.354 g/s-mm, which is defined as the mass flow rate over the position of the gate. In the present work, we have designed and tested a new particle flow control mechanism with the potential for faster response times and different sensitivities than a slide gate approach enabling more control of particle flow. The new device enables flow control through moving jaws diagonally to change the outlet area. The new devices sensitivity value follows a quadratic curved as compared to the slide-gate’s linear slope over the same jaw movement. A larger sensitivity value is directly related to a quicker response time when utilizing the same actuator.

14 SOLAR ENERGY↗
DOE OSTI2022

Effects of Model Uncertainties in Underground Chemical Explosions on Far-field Results

We used the CTH shock physics code to simulate the explosion of an 18-t chemical explosive at a depth of 250 m. We used the CTH in the two-dimensional axisymmetric (cylindrical) geometry (2DC) and most simulations included fully tamped explosions in wet tuff. Our study focused on parametric studies of three of the traditional strength models available in CTH, namely, geologic-yield, elastic perfectly-plastic von Mises, and Johnson-Cook strength (flow stress) models. We processed CTH results through a code that generates Reduced Displacement Potential (RDP) histories for each simulation. Since RDP is the solution of the linear wave equation in spherical coordinates, it is mainly valid at far-enough distance from the explosion the elastic radius. Among various parameters examined, we found the yield strength to have the greatest effect on the resulting RDP, where the peak RDP reduces almost linearly in log-log space as the yield strength increases. Moreover, an underground chemical explosion results in a cavity whose final diameter is inversely proportional to the material yield strength, i.e., as the material's yield strength increases the resulting final cavity radius decreases. Additionally, we found the choice of explosive material (COMP-C4 versus COMP-B) has minor effects on the peak RDP, where denser COMP-C4 shows higher peak RDP than the less dense COMP-B by a factor of ~1.1. In addition to wet tuff, we studied explosions in dry tuff, salt, and basalt, for a single strength model and yield strength value. We found wet tuff has the highest peak RDP value, followed by dry tuff, salt, and basalt. 2DC simulations of explosions in 11 m radius spherical, hemispherical, and cylindrical cavities showed the RDP signals have much lower magnitude than tamped explosions, where the cavity explosions mimicked nearly decoupled explosions.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2022

Thermodynamic, Kinetic and Electrochemical Studies on Mixed Proton, Oxygen Ion and Electron (Hole) Conductors

The report contains a summary of work done during the period of performance September 1, 2015 through August 31, 2021. The principal objective of the work was to conduct thermodynamic, kinetic and electrochemical studies on mixed proton, oxygen ion and electron (hole) conductors. The project also involved studies on cation conductors such as Li + and Na + ion conductors of particular relevance to batteries. The work involved theory and experiments. Since any use of ionic conductors necessarily entails two electrodes, electrode reactions are central to the overall transport processes from one electrode, through the electrolyte, to the other electrode. The role of interfaces and electrochemical reactions is thus of central importance. Transport processes in fuel cells, electrolyzers, lithium batteries and sodium batteries were examined using linear non-equilibrium thermodynamics, which is based on the assumption of small departures from thermodynamic equilibrium. The main approach involved expressing transport processes using the Onsager equations which naturally include coupling of thermodynamic flows and thermodynamic forces. The basic tenet of linear nonequilibrium thermodynamics is the existence of local thermodynamic equilibrium which means that all thermodynamic functions are locally defined. Thus, chemical potentials of various species are defined locally as a function of position and also of time. The existence of local thermodynamic equilibrium has fundamental implications concerning transport of electronic species through a predominantly ionic conductor.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Quantifying CTFE content in FK-800 using ATR-FTIR and time to peak crystallization

The chlorotrifluoroethylene (CTFE) content in vinylidene fluoride (VDF)-CTFE copolymers greatly influences the chemical and physical properties and ultimately determines the application. The copolymer known as FK-800 has a VDF:CTFE ratio of ~1:3 and is used almost exclusively as the binder in the insensitive high explosive (HE) formulation PBX 9502. Due to the dangerous nature of HE work and the effect CTFE content variations have, the ability to quickly and easily quantify CTFE content is of interest. Two techniques, attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) and time to peak crystallization measured by heat flow calorimetry, were evaluated here as potential means for quantifying CTFE content. Based on the certificate of analyses (COAs), FK-800 lots with varied CTFE content encompassing the range historically used in HE applications were used as standards for both methodologies. For FTIR, a linear regression was performed on the peak area of the C-Cl IR stretch and the CTFE content; the methodology was then demonstrated on four samples, two lots of unadulterated material and material recovered from two HE samples. The calculated CTFE content and COAs were in good agreement for all four samples. Heat flow calorimetry revealed the relationship between time to peak crystallization and CTFE content was best fit by a power regression; the methodology was then tested on two different FK-800 lots. Only one sample showed good agreement between the calculated CTFE content and the COA. This discrepancy indicates the method developed is not suitable for quantifying CTFE, but provides valuable insight regarding the crystallization behavior of VDF-CTFE copolymers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Aqueous titanium redox flow batteries—State-of-the-art and future potential

Market-driven deployment of inexpensive (but intermittent) renewable energy sources, such as wind and solar, in the electric power grid necessitates grid-stabilization through energy storage systems Redox flow batteries (RFBs), with their rated power and energy decoupled (resulting in a sub-linear scaling of cost), are an inexpensive solution for the efficient electrochemical storage of large amounts of electrical energy. Titanium-based RFBs, first developed by NASA in the 1970s, are an interesting albeit less examined chemistry and are the focus of the present review. Ti, constituting 0.6% of the Earth’s crust and an ingredient in inexpensive white paints, is amongst the few elements (V and Mn being some others) which exhibit multiple soluble oxidation states in aqueous electrolytes. Further, the very high (approaching 10 M) solubility of Ti in low pH solutions suggests the possibility of developing exceptionally high energy density aqueous Redox Flow Batteries systems. With these advantages in mind, we present the state-of the-art in Ti-RFBs with a focus on Ti/Mn, Ti/Fe and Ti/Ce couples and systems that use Ti as an additive (such as Ti/V/Mn). The inherent advantages of inexpensive Ti actives and relatively high energy density is contrasted with potential side-reactions resulting in reduced energy efficiency. Technological pathways are presented with a view to overcoming critical bottlenecks and a vision is presented for the future development of Ti-RFBs.

25 ENERGY STORAGE↗
DOE OSTI2023

Data-Driven RANS Turbulence Closures for Forced Convection Flow in Reactor Downcomer Geometry

Recent progress in data-driven turbulence modeling has shown its potential to enhance or replace traditional equation-based Reynolds-averaged Navier-Stokes (RANS) turbulence models. Here, this work utilizes invariant neural network (NN) architectures to model Reynolds stresses and turbulent heat fluxes in forced convection flows (when the models can be decoupled). As the considered flow is statistically one dimensional, the invariant NN architecture for the Reynolds stress model reduces to the linear eddy viscosity model. To develop the data-driven models, direct numerical and RANS simulations in vertical planar channel geometry mimicking a part of the reactor downcomer are performed. Different conditions and fluids relevant to advanced reactors (sodium, lead, unitary-Prandtl-number fluid, and molten salt) constitute the training database. The models enabled accurate predictions of velocity and temperature, and compared to the baseline k–τ turbulence model with the simple gradient diffusion hypothesis, do not require tuning of the turbulent Prandtl number. The data-driven framework is implemented in the open-source graphics processing unit–accelerated spectral element solver nekRS and has shown the potential for future developments and consideration of more complex mixed convection flows.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Predicting Flow in Fracture Networks With Quantum Algorithms

Uncertainty quantification plays a crucial role in the modeling of subsurface flow. For instance, uncertainties in the properties of geologic fracture networks significantly impact flow, requiring numerous simulations to accurately estimate quantities of interest. However, each simulation is computationally expensive because it requires solving a large linear system to capture features that involve both small and large fractures. An example is in percolation, where the interaction of many small fractures (which cumulatively can have a large surface area) with the rock matrix must be modeled precisely. Quantum computing is an emerging tool with the potential to address this issue. Quantum algorithms offer a significant speedup in solving linear systems, achieving efficiencies that are challenging to match with classical approaches. These classical approaches include direct solvers, such as LU decomposition, and iterative methods, notably preconditioned conjugate gradient, commonly used in subsurface modeling to solve large sparse systems. However, applying quantum algorithms to geologic fracture flow requires careful attention to algorithmic and problem-specific constraints to fully realize this quantum advantage. In this work we describe a quantum algorithm for generalized Monte Carlo applications with a quadratic speedup over the classical approaches which can be combined with the quantum speedup, currently under investigation, for solving quantum linear systems for subsurface flow. We show that for quantum algorithms the computational cost of estimating a quantity of interest for a statistical ensemble of networks is roughly the same as that of a single realization, essentially implying that one can get uncertainty quantification for free.

58 GEOSCIENCES↗
DOE OSTI2024

Polymer Macrocycles: A novel topology to control dynamics of rubbery materials (Final Report)

One of the current challenges in polymer physics and engineering is to understand the behavior of circular macromolecules. Though such molecules have been made by dilute solution ring closure methods in the past, such procedures have been limited to relatively small, unentangled rings as well as to small sample quantities. Initial studies were also hampered by issues surrounding purity of the rings, though this problem has been somewhat overcome by the use of liquid chromatography at critical conditions (LCCC) methods that separate rings and linear chains. Furthermore, in spite of the continued progress a full understanding of ring dynamics remains elusive. The present work has addressed multiple aspects of the behavior of ring molecules by using a novel synthetic route: reversible radical recombination redox polymerization (R3P) to produce poly(3,6-dioxa-1,8-octanedithiol) (PolyDODT) molecules. The synthesis conditions were chosen to produce linear (LDODT), cyclic (RDODT) and linear-cyclic mixtures (LRDODT) in order to investigate dilute solution and rheological responses of the materials. Importantly, the R3P synthesis makes larger quantities and higher molecular weights than do the dilute solution ring closure methods and also has the potential to be scaled to industrial quantities, thus becoming commercializable. We emphasize two important outcomes of the work. The first is that the rheological investigation shows that very large molecular weight the PolyDODT rings permitted the investigation of ring dynamics from glassy towards terminal flow for molecular sizes corresponding to approximately 300 entanglements of the linear counterpart. At the same time, these very large sized rings could be diluted in a solvent in order to reduce the molecular entanglement density to less than unity. The results demonstrate that ring viscosity follows a Rouse-like linear dependence on molecular weight or entanglement number to approximately 15 entanglements. This range of "Rouse-like" response is greater than what was achieved in all prior works where synthesis conditions have limited the molecular sizes achievable. After the entanglement number $Z_w$ increases beyond approximately 15, we find that the viscosity-molecular weight scaling undergoes a rapid change from the Rouse-like $η\sim{Z}^1_w$ to a very strong power-law scaling that is approximately $η\sim{Z}^{5.8}_w$. This aspect of the work strongly suggests that prior investigations on highly pure rings have invariably been performed on unentangled systems simply because ring molecules have very high entanglement thresholds. The results are further supported by plateau modulus measurements that show that in the "Rouse-like" viscosity regime, for the RDODT samples there is a corresponding lack of a rubbery entanglement plateau and once entanglement coupling is seen, the rubbery plateau also appears in the dynamic modulus data. Furthermore, upon appropriate scaling, we show that the unentangled polyDODT rings (i.e., the diluted high molecular weight rings) show virtually identical dynamics (upon appropriate scaling to account for the chain dilutions) to the dynamics of similarly unentangled LCCC fractionated polystyrene rings.

36 MATERIALS SCIENCE↗
DOE OSTI2023

On the convergence of an IEQ-based first-order semi-discrete scheme for the Beris-Edwards system

We present a convergence analysis of an unconditionally energy-stable first-order semi-discrete numerical scheme designed for a hydrodynamic Q-tensor model, the so-called Beris-Edwards system, based on the Invariant Energy Quadratization Method (IEQ). The model consists of the Navier–Stokes equations for the fluid flow, coupled to the Q-tensor gradient flow describing the liquid crystal molecule alignment. By using the Invariant Energy Quadratization Method, we obtain a linearly implicit scheme, accelerating the computational speed. However, this introduces an auxiliary variable to replace the bulk potential energy and it is a priori unclear whether the reformulated system is equivalent to the Beris-Edward system. In this work, we prove stability properties of the scheme and show its convergence to a weak solution of the coupled liquid crystal system. We also demonstrate the equivalence of the reformulated and original systems in the weak sense.

Weber, Franziska↗
DOE OSTI2023

Large eddy simulation of atmospheric boundary layer flow over complex terrain in comparison with RANS simulation and on-site measurements under neutral stability condition

Large eddy simulation (LES) of the atmospheric boundary layer (ABL) flow over complex terrain is presented with a validation using meteorological tower (met-tower) data through an improved neutral stability sampling approach. The proposed stability sampling procedure includes a condition based on the most-likely occurrence time-periods of the neutral ABL and reduces the variabilities of the conditional wind statistics calculated at the met-towers in comparison to our previous work. The ABL flow simulations are carried out over a potential wind site with a prominent hill based using the OpenFOAM-based simulator for on/off-shore wind farm applications by applying the Lagrangian-averaged scale-invariant dynamic sub-grid scale turbulence model. A low-dissipative scale-selective discretization scheme for the non-linear convection term in the LES governing equation is adopted implicitly to ensure both the second-order accuracy and bounded solution. The LES inflow is generated through a precursor method with a “tiling” approach based on the flow driving parameters obtained from a corresponding Reynolds-averaged Navier–Stokes (RANS) simulation. Overall, the averaged wind velocity profiles predicted by the LES approach at all met-tower locations show a similar tendency as the RANS results, which are also in reasonable agreement with the met-tower data. An obvious difference in wind speed standard deviation profiles is seen between LES and RANS, especially at regions downstream of the hill edge, where the LES shows under-predicted results at the highest measurement levels in comparison to the tower data. The computational costs of the LES are found to be about 20 times higher than the RANS simulations.

Energy & Fuels↗
DOE OSTI2025

An experimental study of the existence regions and non-linear interactions of drift wave and Kelvin–Helmholtz instabilities in a linear magnetized plasma

Experimental observations of the intrinsic excitation and non-linear interactions of drift wave (DW) and Kelvin–Helmholtz (KH) instabilities in a linear magnetized plasma column are presented. The experiments are carried out in the inverse mirror plasma experimental device (IMPED)—a cylindrical, magnetized, linear plasma machine designed to study low-frequency waves and instabilities in plasma. A novel feature of IMPED is the ability to control plasma profiles, such as the density n(r)⁠, electron temperature T e (r)⁠, and plasma potential V p (r) by varying the ratio Rm of the magnetic field in the main chamber to that in the source chamber. At high values of Rm, higher-density gradient scale length promotes the drift wave (DW) instability while lower Rm value results in a higher radial electric field, inducing a sheared poloidal flow that enhances the dominance of the Kelvin–Helmholtz (KH) mode. The background and fluctuating plasma parameters are characterized using various configurations of multiple in situ electric probes at different spatial locations to quantify the local gradients that excite the low-frequency primary instabilities. Statistical, spectral, and bispectral analysis of the density and potential signals help identify these modes in terms of wave number, frequency, phase, and amplitude and also delineate the nature of their non-linear interactions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Magnetohydrodynamics in free surface liquid metal flow relevant to plasma-facing components

Abstract While flowing Liquid Metal (LM) Plasma-Facing Components (PFCs) represent a potentially transformative technology to enable long-pulse operation with high-power exhaust for fusion reactors, Magnetohydrodynamic (MHD) drag in the conducting LM will reduce the flow speed. Experiments have been completed in the linear open-channel LMX-U device [Hvasta et al 2018 Nucl. Fusion 58 01602] for validation of MHD drag calculations with either insulating or conducting walls, with codes similar to those used to design flowing LM PFCs for a Fusion Nuclear Science Facility [Kessel et al 2019 Fusion Sci. Technol . 75 886]. We observe that the average channel flow speed decreased with the use of conducting walls and the strength of the applied transverse magnetic field. The MHD drag from the retarding Lorentz force resulted in an increase of the LM depth in the channel that ‘piled up’ near the inlet, but not the outlet. As reproduced by OpenFOAM and ANSYS CFX calculations, the magnitude and characteristics of the pileup in the flow direction increased with the applied traverse magnetic field by up to 120%, as compared to the case without an applied magnetic field, corresponding to an average velocity reduction of ∼45%. Particle tracking measurements confirmed a predicted shear in the flow speed, with the surface velocity increasing by 300%, despite the 45% drop in the average bulk speed. The MHD effect makes the bulk flow laminarized but keeps surface waves aligned along the magnetic field lines due to the anisotropy of MHD drag. The 3D fringe field and high surface velocity generate ripples around the outlet region. It was also confirmed that the MHD drag strongly depends on the conductivity of the channel walls, magnetic field, and volumetric flow rate, in agreement with the simulations and a developed analytical model. These validated models are now available to begin to determine the conditions under which the ideal LM channel design of a constant flow speed and fluid depth could be attained.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Early Exploration of a Flexible Framework for Efficient Quantum Linear Solvers in Power Systems

The rapid integration of renewable energy resources presents formidable challenges in managing power grids. While advanced computing and machine learning techniques offer some solutions for accelerating grid modeling and simulation, there remain complex problems that classical computers cannot effectively address. Quantum computing, a promising technology, has the potential to fundamentally transform how we manage power systems, especially in scenarios with a higher proportion of renewable energy sources. One critical aspect is solving linear systems of equations, crucial for power system applications like power flow analysis, for which the Harrow-Hassidim-Lloyd (HHL) algorithm is a well-known quantum solution. However, HHL quantum circuits often exhibit excessive depth, making them impractical for current Noisy-Intermediate-Scale-Quantum (NISQ) devices. In this paper, we introduce a versatile framework, powered by NWQSim, that bridges the gap between power system applications and quantum linear solvers available in Qiskit. This framework empowers researchers to efficiently explore power system applications using quantum linear solvers. Through innovative gate fusion strategies, reduced circuit depth, and GPU acceleration, our simulator significantly enhances resource efficiency. Power flow case studies have demonstrated up to a eight-fold speedup compared to Qiskit Aer, all while maintaining comparable levels of accuracy.

quantum computing, Harrow-Hassidim-Lloyd, high-per↗
DOE OSTI2022

Multi-messenger heavy-ion collision physics

We report that this work studies the production of direct photons in relativistic nuclear collisions, along with the production of hadrons. Radiation from the very first instants to the final moments of the evolution is included. The hybrid model used here describes all stages of relativistic heavy-ion collisions. Chronologically, those are an initial state reflecting the collision of nuclei described within the Color Glass Condensate effective theory; a pre-equilibrium phase based on non-equilibrium linear response; relativistic viscous hydrodynamics, and a hadronic afterburner. The effect of the preequilibrium phase on both photonic and hadronic observables is highlighted for the first time. The potential of photon observables - spectrum, differential elliptic and triangular flow - to reveal the chemical equilibration time is studied. Finally, we consider "small collision systems", including proton+nucleus collisions and collisions of light nuclei, as probed by hadronic and electromagnetic observables. We demonstrate how photon production can signal the formation of quark-gluon plasma in such small systems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Quantum algorithms for geologic fracture networks

Abstract Solving large systems of equations is a challenge for modeling natural phenomena, such as simulating subsurface flow. To avoid systems that are intractable on current computers, it is often necessary to neglect information at small scales, an approach known as coarse-graining. For many practical applications, such as flow in porous, homogenous materials, coarse-graining offers a sufficiently-accurate approximation of the solution. Unfortunately, fractured systems cannot be accurately coarse-grained, as critical network topology exists at the smallest scales, including topology that can push the network across a percolation threshold. Therefore, new techniques are necessary to accurately model important fracture systems. Quantum algorithms for solving linear systems offer a theoretically-exponential improvement over their classical counterparts, and in this work we introduce two quantum algorithms for fractured flow. The first algorithm, designed for future quantum computers which operate without error, has enormous potential, but we demonstrate that current hardware is too noisy for adequate performance. The second algorithm, designed to be noise resilient, already performs well for problems of small to medium size (order 10–1000 nodes), which we demonstrate experimentally and explain theoretically. We expect further improvements by leveraging quantum error mitigation and preconditioning.

58 GEOSCIENCES↗
DOE OSTI2024

A Vehicle-to-Grid planning framework incorporating electric vehicle user equilibrium and distribution network flexibility enhancement

The rapid surge in electric vehicle (EV) adoption, coupled with advancements in charging technologies, emphasizes the critical necessity for expanding EV recharging infrastructure. Simultaneously, the Distribution Network (DN) encounters escalating challenges in meeting charging demand during peak traffic periods. Consequently, there is a mounting demand for the deployment of innovative Vehicle-to-Grid (V2G) technologies to augment the DN’s flexibility in power dispatch and alleviate travel costs for EV users. Hence, this paper proposes an EV-user-equilibrium-(UE)-constrained V2G planning framework that enhances flexibility in the DN. The framework aims to ascertain the optimal placement and capacity of EV charging stations (EVCSs) and V2G charging piles within the Transportation Network (TN). It takes into account the equilibrium condition stemming from competitive EV charging and routing behaviors alongside the optimal expansion of DN energy resources to accommodate the electricity supplied by the V2G piles. This study commences by analyzing EV drivers’ travel decisions, considering the influence of charging and V2G pile locations and sizes. Subsequently, we tackle the Traffic Assignment Problem with User Equilibrium (TAP-UE) model to characterize the steady-state traffic flow distribution of EVs. Following this, we formulate the optimization model for the Coordinated Power and Transportation Network (CPTN), which encompasses the optimal expansion of DN facilities and traffic flow regulation under UE conditions. To mitigate the computational complexity associated with the V2G planning model, we introduce a series of linearization methods to obtain a manageable Mixed-Integer Linear Programming (MILP) solution. Finally, to validate the efficacy of our proposed planning framework, we apply it to two test systems, including a real-world case study. Through these case studies, we explore the necessity and potential benefits of V2G technologies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Wave function analysis with a maximum flow algorithm

An efficient algorithm for computing the maximum-flow path in a network is applied to the identification of the dominant configuration state functions (CSFs) in a graphically contracted function (GCF), configuration interaction, wave function. The flow network is a space of spin-adapted CSFs represented by a Shavitt graph, wherein the nodes correspond to orbital occupations and spin quantum numbers. The graph nodes are connected by arcs, and an arc density is defined as sums of the associated squared CSF coefficients. A max-min approach determines an upper bound to the maximum possible incoming flow for each graph node. A backtracking step generates a candidate walk and is followed by a limited search of alternative branching paths for the dominant CSF. The arc density contributions are removed from the graph, and the algorithm is reapplied to the updated graph. This list of generated walks can be partitioned in order to guarantee that the dominant CSFs have been identified. All of the steps in this algorithm are computationally efficient and do not depend on the potentially large dimension of the underlying linear CSF expansion space. An analysis of low-lying valence states of C-2 illustrates the method.

74 ATOMIC AND MOLECULAR PHYSICS↗