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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

Fully variational incremental CASSCF

We report the complete-active-space self-consistent field (CASSCF) method is a canonical electronic structure theory that holds a central place in conceptualizing and practicing first principles simulations. For application to realistic molecules, however, the CASSCF must be approximated to circumvent its exponentially scaling computational costs. Applying the many-body expansion - also known as the method of increments - to CASSCF (iCASSCF) has been shown to produce a polynomially scaling method that retains much of the accuracy of the parent theory and is capable of treating full valence active spaces. Due to an approximation made in the orbital gradient, the orbital parameters of the original iCASSCF formulation could not be variationally optimized, which limited the accuracy of its nuclear gradient. Herein, a variational iCASSCF is introduced and implemented, where all parameters are fully optimized during energy minimization. This method is able to recover electronic correlations from the full valence space in large systems, produce accurate gradients, and optimize stable geometries as well as transition states. Demonstrations on challenging test cases, such as the oxoMn(salen)Cl complex with 84 electrons in 84 orbitals and the automerization of cyclobutadiene, show that the fully variational iCASSCF is a powerful tool for describing challenging molecular chemistries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Dehydroxylation kinetics of kaolinite and montmorillonite examined using isoconversional methods

The use of calcined clays as supplementary cementitious materials (SCMs) in concrete is a promising strategy towards decarbonizing the cement and concrete industry. This is especially relevant considering the ever-increasing demand for concrete. Comprehensive understanding of the kinetics of calcination is essential towards maximizing the potential reactivity of clay minerals while ensuring energy efficiency. In this study, the kinetics of the dehydroxylation of kaolinite and montmorillonite are investigated under non-isothermal conditions at constant heating rate. Activation energies ( E a ) are determined via Friedman differential and advanced Vyazovkin incremental methods over the isoconversional range; these are devoid of computational approximations, thus allowing kinetic analysis without assuming a specific reaction model. Kinetic equations—in the differential form as well as a combination of differential and integral forms are compared against the experimentally determined reaction models to identify the most probable dehydroxylation mechanism for kaolinite and montmorillonite. A reaction order mechanism is established for dehydroxylation of kaolinite, while montmorillonite is noted to undergo dehydroxylation via a single-step reversible diffusion-controlled process. Kinetic triplet—comprising activation energy, reaction model and pre-exponential factor—is used to predict isothermal calcination conditions, which is further verified using analytical techniques. Heat release rates of clay-portlandite blends from isothermal calorimetry are used within a thermodynamic framework to quantify reactivity of the calcined clays. Here, the study demonstrates a general approach based on isoconversional methods to predict calcination conditions for different clays that can be used in efficient and optimized production of blended cements or SCMs.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Accelerating uncertainty quantification in incremental dynamic analysis using dimension reduction-based surrogate modeling

We propose a surrogate modeling framework based on dimension reduction to facilitate the quantification of seismic risk of structural systems in performance-based earthquake engineering. The framework adopts incremental dynamic analysis (IDA) for addressing hazard variability, and promotes significant computational efficiency improvement for propagating epistemic uncertainties associated with the structural models. It utilizes both linear and nonlinear dimension reduction approaches, equipped with inverse mappings, to learn a functional between the input parameter space (e.g., the epistemic uncertainties of the structure) to the high-dimensional output space created through the IDA implementation across different ground motions and seismic intensity levels. Polynomial chaos expansion is adopted as the surrogate model to learn this functional in the reduced space. A nine-story steel moment-resisting frame with uncertain structural properties is used as a testbed. Furthermore, we select the seismic fragility curves as a measure of the structure’s seismic performance, since it provides an estimate of the probability of entering specified damage states for given levels of ground shaking.

42 ENGINEERING↗
DOE OSTI2023

A Systematic, Polynomial-Cost Approach to Exact Correlation Energies (Final Technical Report)

The full configuration interaction (FCI) wave function provides the exact solution to the Schrödinger equation in a given basis set. While FCI is intractable due to exponential computational costs, the many-body expansion (or the method of increments) can reduce scaling to a low-order polynomial with system size. This project entailed advances in the incremental FCI (iFCI) approach, designed to allow iFCI to reach larger system sizes and maintain its intrinsic high accuracy. This document describes advances in solvers for iFCI, strategies to treat multiple charge and spin states, and virtual state management methods to reduce memory requirements. Overall, this project allows iFCI to correlate (for the first time) 142 valence electrons in 444 orbitals in a realistic model of a transition metal complex.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Scalable Plug-and-Play ADMM with Convergence Guarantees

Plug-and-play priors (PnP) is a broadly applicable methodology for solving inverse problems by exploiting statistical priors specified as denoisers. Recent work has reported the state-of-the-art performance of PnP algorithms using pre-trained deep neural nets as denoisers in a number of imaging applications. However, current PnP algorithms are impractical in large-scale settings due to their heavy computational and memory requirements. This work addresses this issue by proposing an incremental variant of the widely used PnP-ADMM algorithm, making it scalable to problems involving a large number measurements. Here, we theoretically analyze the convergence of the algorithm under a set of explicit assumptions, extending recent theoretical results in the area. Additionally, we show the effectiveness of our algorithm with nonsmooth data-fidelity terms and deep neural net priors, its fast convergence compared to existing PnP algorithms, and its scalability in terms of speed and memory.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Incremental Threshold Scheme Enabled IoT Group Key Management

Cyber landscape evolves rapidly. Internet of Things (IoT) and Edge Computing (EC) have rapidly become an integral part of the modern computing infrastructure. It is expected that there will be more than 50 billion active and connected IoT devices by 2025 [1]. Pervasive IoT/EC creates unprecedented opportunities bridging the gap between previously segregated cyber and physical spaces. However, this progress also brings along new security challenges. IoT devices typically have limited computation, communication, and storage resources. This leads to security architecture designs such as using symmetric keys for group communication. While secure and efficient in stable network settings, symmetric key solutions are ill-adapted for IoT's highly dynamic device mobility behavior and frequent group membership turnover. Whenever IoT members leave a group, the known symmetric keys cannot be made forgotten, posing a serious vulnerability. This leads to frequent re-groupings that require expensive re-authentication, key regeneration, and key redistribution in order to maintain IoT/EC security. We present a novel symmetric key management framework that integrate an Incremental Threshold Scheme (ITS) cryptographical function into communication protocol's key rotation mechanism to allow for secure and efficient symmetric key communication group member node revocation. This ITS-enabled key management framework alleviates the need of frequent and expensive re-grouping and re-keying needed by today's large and dynamic IoT/EC operations. We further applied this ITS-enabled key management framework to a distributed IoT/EC-integrated publish and subscribe framework for applicability validation.

Li, Mingyan↗
DOE OSTI2023

An Incremental Gradient Method for Optimization Problems With Variational Inequality Constraints

We consider minimizing a sum of agent-specific nondifferentiable merely convex functions over the solution set of a variational inequality (VI) problem in that each agent is associated with a local monotone mapping. This problem finds an application in computation of the best equilibrium in nonlinear complementarity problems arising in transportation networks. We develop an iteratively regularized incremental gradient method where at each iteration, agents communicate over a directed cycle graph to update their solution iterates using their local information about the objective and the mapping. The proposed method is single-timescale in the sense that it does not involve any excessive hard-to-project computation per iteration. We derive nonasymptotic agent-wise convergence rates for the suboptimality of the global objective function and infeasibility of the VI constraints measured by a suitably defined dual gap function. Finally, the proposed method appears to be the first fully iterative scheme equipped with iteration complexity that can address distributed optimization problems with VI constraints over cycle graphs.

convergence↗
DOE OSTI2021

An Orthogonal Recursive Bisection (ORB) Based Time Advancement Algorithm for CFD-DEM Solvers

The time integration of the granular phase in coupled computational fluid dynamics (CFD) – discrete element method (DEM) simulations presents a unique computational challenge brought about by the large variations in particle collisional time scales. Particles in the dilute regions of the computational domain can be advanced with large time steps while dense regions require much smaller time increments. However, the time step size in most solvers is globally set as the limit for accuracy and stability imposed by the collisions and is typically orders of magnitude less than that required away from collisions. This work addresses this precise issue and provides a strategy to avoid the use of a global conservative small time step size for the entire set of particles.A novel time stepping algorithm for CFD-DEM solvers using a partitioning approach using orthogonal recursive bisection (ORB) that allows for variable time steps among particles is described and its computational performance is compared against baseline explicit methods, typically used in several CFD-DEM solvers. ORB has advantages of being relatively quick and easy to update incrementally and has the required heuristic behavior (i.e., it will split the region in half with a cluster on each side) when groups of particles are well separated (clustered). The algorithm presented in this work uses a local time stepping approach to resolve collisional time scales for subsets of particles that are present at the leaves of the ORB, thereby resulting in substantial reduction of computational cost. The parallel implementation of this method where a ``knapsack” algorithm is used in tandem with ORB for effective load-balancing is also presented, where a best possible partitioning is obtained based on number of particles and local time-stepping costs. The algorithm is tested against benchmark problems with varying particle distributions that include fluidized bed and riser flow scenarios. Preliminary results indicate that the approach is 2-3X faster than traditional explicit methods for problems that involve both dense and dilute regions, while maintaining the same level of accuracy.

adaptive timestepping↗
DOE OSTI2020

Fracture Shearing in the Eau Claire Formation

This data set consists of Computed Tomographic (CT) data for five sheared Eau Claire Formation core samples with complex and heterogeneous lithology. Each sample was prefractured, housed in a specially adapted core holder, and sheared in incremental fashion. CT scans were taken before and after each shearing event.

Computed Tomography↗
DOE OSTI2024

A simple solution to the Rietveld refinement recipe problem

Rietveld refinements are widely used for many purposes in the physical sciences. Conducting a Rietveld refinement typically requires expert input because correct results may require that parameters be added to the fit in the proper order. This order will depend on the nature of the data and the initial parameter values. A mechanism for computing the next parameter to add to the refinement is shown. The fitting function is evaluated with the current parameter value set and each parameter incremented and decremented by a small offset. This provides the partial derivatives with respect to each parameter, along with information to discriminate meaningful values from numerical computational errors. The implementation of this mechanism in the open-source GSAS-II program is discussed. This new method is discussed as an important step towards the development of automated Rietveld refinement technology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A detailed study of pre-heating effects in electron beam melting powder bed fusion process

Metal-based additive manufacturing processes, such as powder bed fusion with electron beam (PBF-EB) process, also referred to as electron beam melting (EBM), can produce high-density parts with minimal residual stresses due to the uniform and coherent preheating of the powder bed. However, understanding and controlling the multiple stages of preheating is required to enable the production of high-quality, consistent parts of various materials. This work presents a large-scale, multi-layer, three-dimensional numerical analysis focused on studying the preheating stages for predicting thermal history during the PBF-EB process. The model follows a continuous multi-stage cyclic process, that incorporates all the main stages of the PBF-EB process for 316 L stainless steel. This includes the gradual deposition of a new powder layer, the first and second preheating levels of the powder bed, and the energy deposition during melting (excluding the actual melt-pool behavior simulation). The model employs an adaptive time-scaling approach that automatically adjusts the energy deposition for each solution time-increment. This allows for localized changes in time-resolution over an otherwise computationally expensive multi-layer procedure. The material property variations are also taken into account, with an emphasis on the subtle irreversible changes in powder effective thermal conductivity after the two requisite preheating stages of the powder bed. This effect is studied using simplified conductivity models from the literature for partially sintered powder, validated by a dedicated experiment and numerical simulation. The large-scale model is then used to estimate the actual temperatures during first and second preheating levels for 316 L steel, which is not yet fully supported commercially for PBF-EB. Model predictions are corroborated by experiments, using and analyzing IR images, taken at the completion of each layer by the machine’s built-in infrared camera. The current model also incorporates a qualitative assessment for the effects of conductivity change during pre-heating, as well as evaluates the applicability of the time-scaling approach.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Active Thermochemical Tables: the thermophysical and thermochemical properties of methyl, CH 3 , and methylene, CH 2 , corrected for nonrigid rotor and anharmonic oscillator effects

The thermophysical properties (isobaric heat capacity, entropy, enthalpy increment) of two prominent radicals, methyl, CH 3 , and methylene, CH 2 , were computed using the Nonrigid Rotor Anharmonic Oscillator (NRRAO) approach and compared to their RRHO counterparts, demonstrating significant differences between the results from the two approaches. Methylene presents a typical case in which the NRRAO thermophysical properties have significantly higher values than their RRHO counterparts at higher temperatures. In the case of methyl, the positive anharmonicity of the umbrella motion causes an opposite effect, and the NRRAO corrected thermophysical properties have lower values than their RRHO counterparts. The NRRAO corrected thermophysical properties, in turn, affect the resulting thermochemical properties. Here, two reactions important in combustion modelling were tested: the recombination of methyl radical with hydrogen atoms to form methane, and the recombination of two methyl radicals to form ethane. The related NRRAO equilibrium constants differ significantly from their RRHO analogs, and the consequences for chemical modelling are discussed. Also reported are the most current ATcT enthalpies of formation for CH n (n = 4-0) species and for C 2 H 6 , together with the tightly related sequential bond dissociation enthalpies along with the CHn series.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Performance and Emissions of an SI Engine Fueled With DME-Propane Blends

Dimethyl Ether (DME) is an alternative fuel that can be produced renewably and has the potential for lower CO and NOx emissions than conventional petroleum-based fuels. Blending DME with another gaseous fuel such as propane, which has a lower knock tendency than gasoline, can allow this fuel to be leveraged on SI engines. In this study, the use of DME-propane blends on a spark ignition (SI) engine was studied via computer simulations in order to understand the impact on engine performance and emissions and to identify the knock limitations of using such fuel blends. A 2L Hyundai SI engine was modeled in GT Power and the model was validated by comparing it with computational fluid dynamics (CFD) simulation results. Starting from pure propane, DME was added incrementally until knock was observed in the engine. Results indicate that it is feasible to run propane with DME percentages up to 35% before severe knock impacts were observed. The BTE was higher, but the BSFC also increased for DME-propane blends as compared to gasoline. Here, an increase in NOx emissions was detected along with a significant decrease in CO emissions. CO 2 emissions declined for propane as compared to gasoline but increased with the addition of DME.

alternative fuels↗
DOE OSTI2023

A Real-Time Degradation Estimation Approach for Batteries in PV and Battery Hybrid Plant Operation

Conventional cyclic degradation assessment for batteries is often limited in its ability to provide real-time degradation information during operation, because the estimation of degradation is commonly performed after a specific evaluation period. Modified methods can provide real-time evaluation but at the cost of high computation complexity. This paper proposes a novel estimation method that addresses these limitations. It efficiently determines the incremental degradation of any two consecutive state-of-charge (SOC) samples during battery operation, thus enabling real-time estimation. To validate the proposed method, measurement data from lithium-ion phosphate (LFP) battery testing was adopted, and the results were compared with the conventional model and the measured capacity. The results demonstrate accuracy over the evaluation period in terms of cumulative degradation, and a reduced computation cost compared with other methods. This approach has benefits in various applications, especially in cases where batteries experience irregular charging and discharging stress cycles such as PV and battery hybrid plants.

Wang, Shumeng↗
DOE OSTI2026

An Algorithm for Atom-Centered Lossy Compression of the Atomic Orbital Basis in Density Functional Theory Calculations

Large atomic-orbital (AO) basis sets of at least triple and preferably quadruple-ζ (QZ) size are required to adequately converge Kohn–Sham density functional theory (DFT) calculations toward the complete basis set limit. However, incrementing the cardinal number by one nearly doubles the AO basis dimension, and the computational cost scales as the cube of the AO dimension, so this is very computationally demanding. Here, in this work, we develop and test a threshold-based natural atomic orbital (NAO) scheme in which ϵ-NAOs are obtained as eigenfunctions of atomic blocks of the density matrix in a one-center orthogonalized representation. This enables compression of the AO basis that is optimal for a given threshold, 10 –ϵ , by discarding NAOs with occupation numbers below that threshold. Extensive pilot test calculations using the Hartree–Fock functional and taking the converged density matrix as input suggest that a threshold of 10 –5 can yield a compression factor (ratio of AO to compressed ϵ-NAO dimension) between 2.5 and 4.5 for the QZ pc-3 basis. The errors in relative energies are typically less than 0.1 kcal/mol when the compressed basis is used instead of the uncompressed basis. Between 10 and 100 times smaller errors (i.e., usually less than 0.01 kcal/mol) can be obtained with a threshold 10 –7 , while the compression factor is typically between 2 and 2.5.

basis sets↗
DOE OSTI2025

Interactions of Polar and Nonpolar Groups of Alcohols in Zeolite Pores

Understanding the quantitative interactions among zeolite pore walls, Bro̷nsted acid sites, and molecules with both polar and nonpolar regions is essential for scoping out the potential of zeolites as sorbents and catalysts. Purely siliceous zeolites (MFI and Beta in the present study) are hydrophobic, whereas those containing aluminum are considered hydrophilic, preferentially adsorbing organic molecules even in aqueous environments. To characterize these interactions, we use primary alcohols of increasing molecular weight, quantifying their specific interactions in the confined pore space of the alkyl (CH x ) and OH groups. Three types of interactions were identified: (i) alkyl CH x groups interacting with the zeolite pore walls (approximately 10 kJ mol −1 per carbon), (ii) alcohol OH groups interacting with the pore walls (30−35 kJ mol −1 ), and (iii) alcohol OH groups interacting with Bro̷nsted acid sites (37 kJ mol −1 ). All three interactions were well mirrored by computational simulations. The contribution of the alkyl CH x groups was inferred from the incremental increase in sorption enthalpy with increasing molecular weight; the interaction strength of the OH groups was determined by extrapolating the global adsorption enthalpy of the alcohols to a hypothetical OH group without an alkyl group. This value was identical to the adsorption enthalpy of water. The experiments demonstrated that only water has an adsorption enthalpy on zeolite pore walls lower than its condensation enthalpy (30−35 kJ mol −1 vs 45 kJ mol −1 ), limiting the concentration of water that can be adsorbed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

XFEM Development for Modeling Crack Growth in Prototypical Welded Components

Nuclear power plant components are subjected to harsh operating environments that can lead to multiple degradation mechanisms in which fracture can play a prominent role. Predicting crack growth is important for assessing the integrity of welded components. The extended finite element method (XFEM) is an important tool for modeling such crack growth, and XFEM capabilities have been developed within the MOOSE framework. This report documents work in the MOOSE XFEM module to model fractures in three-dimensional representations of components using a topologically two-dimensional mesh to define cutting planes. Crack growth algorithms have been implemented to evolve the cutting mesh based on equations for stress corrosion cracking. Additionally, several usability and robustness improvements have been developed to enable three-dimensional fracture simulations. The cutting algorithms were demonstrated on a three-dimensional model of a prototypical reactor component undergoing stress corrosion cracking driven by idealized weld residual stresses. This is an incremental step toward using this capability to model more complex components with residual stresses computed through welding process simulations.

42 - ENGINEERING↗
DOE OSTI2021

Applying Incremental, Inductive Model Checking to the Modal Mu Calculus

This work aims to demonstrate that an incremental inductive model checking algorithm built on top of Boolean satisfiability (SAT) solvers can be extended to support modal mu calculus (MMC) formulas. The resulting algorithm, called modal mu calculus model checking using myopic constraints (MC3), solves MMC model checking problems over Boolean labeled transition systems (LTSs). MMC subsumes simple invariance/reachability (as solved by the IC3 algorithm), linear temporal logic (LTL, as solved by the fair algorithm), computation tree logic (CTL, as solved by IICTL), and CTL* (which in turn subsumes LTL and CTL, but was not previously supported by any incremental inductive algorithm). The algorithm is implemented in a prototype solver, mc3.

97 MATHEMATICS AND COMPUTING↗