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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Disruption Mitigation Solutions for Long Pulse Tokamaks (Final Technical Report)

The project "Disruption Mitigation Solutions for Long Pulse Tokamaks" was a collaborative effort between ORNL, GA, and UCSD. The main goal of the project was to study the injection of shattered cryogenic pellets (SPI) as a method of reducing wall damage from global tokamak plasma instabilities called disruptions. This work was primarily carried out in the JET tokamak in the UK. The team already had extensive experience studying PSI in the medium sized DIII-D tokamak in the US, but adding data from the larger JET tokamak enabled more confident extrapolation to the even larger planned ITER tokamak in France. Each of the collaborating institutions focused on different aspects of SPI disruption mitigation. The UCSD collaboration focused on the volume recombination of post-disruption runaway electron (RE) beams by massive hydrogen injection. Disruptions can sometimes result in the formation of toroidal (ring shaped) beams of highly energetic (relativistic) electron beams, which can cause serious local wall damage, even in present-day tokamaks. A promising method to reduce wall damage from the RE beams is to inject massive amounts of H 2 or D 2 into the beams using SPI. although the precise method by which the injected H 2 reducses RE wall damage is still being investigated, it seems clear that a necessary and required step for the reduction to occur is for the H 2 injection to cause volume recombination (free electron density reduction) of the background cold plasma surrounding the fast electron beam. The UCSD collaboration focused on answering the question of if this recombination could be achievable in the future reactor-size tokamak ITER using realistic levels of H 2 injection. A model capturing the most essential physics was developed and was constrained using both DIII-D and JET data. This model was then used to extrapolate to ITER. It was predicted that RE beam recombination should be achievable in ITER, although in the worst-case scenari (a 10 MA Re beam in ITER), recombination is barely achieved within the maximum allowed levels of H 2 injection.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

The interaction of the ITER first wall with magnetic perturbations

Mitigation of the multiple risks associated with disruptions and runaway electrons in tokamaks involves competing demands. Success requires that each risk be understood sufficiently that appropriate compromises can be made. Here the focus is on the interaction of short timescale magnetic-perturbations with the structure in ITER that is closest to the plasma, blanket modules covered by separated beryllium tiles. The effect of this tiled surface on the perturbations and on the forces on structures is subtle. Indeterminacy can be introduced by tile-to-tile shorting. A determinate subtlety is introduced because electrically separated tiles can act as a conducting surface for magnetic perturbations that have a normal component to the surface. A practical method for including this determinate subtlety into plasma simulations is developed. The shorter the timescales and the greater the localization, particularly in the toroidal direction, the more important the magnetic effects of the tiles become.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

An Iterative Response-Surface-Based Approach for Chance-Constrained AC Optimal Power Flow Considering Dependent Uncertainty

A modern power system is characterized by a stochastic variation of the loads and an increasing penetration of renewable energy generation, which results in large uncertainties in its states. These uncertainties bring formidable challenges to the power system planning and operation process. To address these challenges, we propose a cost-effective, iterative response-surface-based approach for the chance-constrained AC optimal power-flow problem that aims to ensure the secure operation of the power systems considering dependent uncertainties. Starting from a stochastic-sampling-based framework, we first utilize the copula theory to simulate the dependence among multivariate uncertain inputs. Then, to reduce the prohibitive computational time required in the traditional Monte-Carlo method, we propose, instead of using the original complicated power-system model, to rely on a polynomial-chaos-based response surface. This response surface allows us to efficiently evaluate the time-consuming power-system model at arbitrary distributed sampled values with a negligible computational cost. This further enables us to efficiently conduct an online stochastic testing for the system states that not only screens out the statistical active constraints, but also assists in a better design of the tightened bounds without using any Gaussian or symmetric assumption. Finally, an iterative procedure is executed to fine-tune the optimal solution that better satisfies a predefined probability. The simulations conducted in multiple test systems demonstrate the excellent performance of the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Negative fluxes and cell-miss errors in the random ray method

The random ray method is a recently developed stochastic method for solving neutral particle transport problems based on the method of characteristics. Perhaps surprisingly for a characteristics-based method using flat sources, we note that the random ray method can produce negative fluxes which may be numerically troublesome in several situations. These occur most severely in fixed source problems where the source is in a region with a small cross section. Additionally, we briefly discuss another source of bias which can occur in similar situations, namely a ray missing a mesh with a strong source and small cross section, resulting in the entirety of the source being unphysically deposited locally. This paper describes the mechanism by which negative fluxes may occur and several different methods to mitigate their effects. These fixes are tested on an eigenvalue problem, a ‘fusion-like’ shielding problem, and a shielding problem featuring an adjoint calculation. Even when extremely coarse random ray quadratures are used such that 20%–30% of cells are missed during a given iteration, use of the preferred fix technique ensures local flux tally errors remain trivial (below 1%). The preferred fix is now the default option in SCONE and OpenMC.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Fast Solution of Fully Implicit Runge--Kutta and Discontinuous Galerkin in Time for Numerical PDEs, Part II: Nonlinearities and DAEs

Fully implicit Runge--Kutta (IRK) methods have many desirable accuracy and stability properties as time integration schemes, but high-order IRK methods are not commonly used in practice with large-scale numerical PDEs because of the difficulty of solving the stage equations. This paper introduces a theoretical and algorithmic framework for solving the nonlinear equations that arise from IRK methods (and discontinuous Galerkin discretizations in time) applied to nonlinear numerical PDEs, including PDEs with algebraic constraints. Several new linearizations of the nonlinear IRK equations are developed, offering faster and more robust convergence than the often-considered simplified Newton, as well as an effective preconditioner for the true Jacobian if exact Newton iterations are desired. Inverting these linearizations requires solving a set of block 2 x 2 systems. Under quite general assumptions, it is proven that the preconditioned 2 x 2 operator's condition number is bounded by a small constant close to one, independent of the spatial discretization, spatial mesh, and time step, and with only weak dependence on the number of stages or integration accuracy. Moreover, the new method is built using the same preconditioners needed for backward Euler-type time stepping schemes, so can be readily added to existing codes. The new methods are applied to several challenging fluid flow problems, including the compressible Euler and Navier--Stokes equations, and the vorticity-streamfunction formulation of the incompressible Euler and Navier--Stokes equations. Up to 10th-order accuracy is demonstrated using Gauss IRK, while in all cases fourth-order Gauss IRK requires roughly half the number of preconditioner applications as required by standard Singly diagonally implicit Runge--Kutta methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Local convergence analysis of an inexact trust-region method for nonsmooth optimization

In Baraldi, we introduced an inexact trust-region algorithm for minimizing the sum of a smooth nonconvex function and a nonsmooth convex function in Hilbert space—a class of problems that is ubiquitous in data science, learning, optimal control, and inverse problems. Furthermore, this algorithm has demonstrated excellent performance and scalability with problem size. In this paper, we enrich the convergence analysis for this algorithm, proving strong convergence of the iterates with guaranteed rates. In particular, we demonstrate that the trust-region algorithm recovers superlinear, even quadratic, convergence rates when using a second-order Taylor approximation of the smooth objective function term.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A novel implicit hybrid machine learning model and its application for reinforcement learning

A novel methodology to develop implicit hybrid models is presented. PyTorch is used to integrate physics-based equations with machine learning models. Automatic differentiation of the hybrid model is leveraged to solve the implicit equations. Iterative solving enables gradient based updates to the machine learning model. The novel methodology is compared to an explicit hybrid approach on a continuously stirred tank reactor (CSTR). The novel method results in a lower modelling error. Both hybrid models effectively train with noisy data. To test the implicit hybrid model, it is employed as a reinforcement learning (RL) training model. The RL algorithm trained on the hybrid model outperforms real time optimization of the CSTR and performs nearly as well as RL trained directly on the CSTR and a traditional gradient based approach. Training RL directly on the CSTR requires over 60,000 system interactions compared to 6000 historical data points for hybrid model development.

42 ENGINEERING↗
DOE OSTI2022

Pseudodiagonalization Method for Accelerating Nonlinear Subspace Diagonalization in Density Functional Theory

In density functional theory, each self-consistent field (SCF) nonlinear step updates the discretized Kohn-Sham orbitals by solving a linear eigenvalue problem. The concept of pseudodiagonalization is to solve this linear eigenvalue problem approximately, and specifically utilizing a method involving a small number of Jacobi rotations that takes advantage of the good initial guess to the solution given by the approximation to the orbitals from the previous SCF iteration. The approximate solution to the linear eigenvalue problem can be very rapid, particularly for those steps near SCF convergence. Here, we adapt pseudodiagonalization to finite-temperature and metallic systems, where partially-occupied orbitals must be individually resolved with some accuracy. We apply pseudodiagonalization to the subspace eigenvalue problem that arises in Chebyshev-filtered subspace iteration. In tests on metallic and other systems for a range of temperatures, we show that pseudodiagonalization achieves similar rates of SCF convergence to exact diagonalization.

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

Towards Automated Reasoning Chains for Verification of LLM-Generated Scientific Code

With the rise of Large Language Model (LLM) generated code, including in domains like scientific computing, ensuring not only syntactical, but also mathematical correctness, has become a critical task. Traditional formal methods approaches often struggle with the ambiguity of floating-point code, and full symbolic execution is extremely costly and limited. We propose a chain-of-reasoning approach that iteratively lifts basic semantics from code into the SPIRAL system and then establishes numerical equivalency to the desired mathematical operation. Here, we leverage the ample mathematical knowledge already formalized in SPIRAL to enable the system to recognize not just different implementations of the same algorithm but fully separate approaches to solving the given problem. The chain establishes tight error bounds on the output of given code with respect to the true continuous solution it approximates, quantifying all sources of error. We demonstrate this approach by establishing the correctness of a pseudospectral solver for a simple 1-dimensional Poisson problem.

Oschatz, Quentin [Carnegie Mellon University,Pitts↗
DOE OSTI2025

Leveraging data mining, active learning, and domain adaptation for efficient discovery of advanced oxygen evolution electrocatalysts

Developing advanced catalysts for acidic oxygen evolution reaction (OER) is crucial for sustainable hydrogen production. This study presents a multistage machine learning (ML) approach to streamline the discovery and optimization of complex multimetallic catalysts. Our method integrates data mining, active learning, and domain adaptation throughout the materials discovery process. Unlike traditional trial-and-error methods, this approach systematically narrows the exploration space using domain knowledge with minimized reliance on subjective intuition. Then, the active learning module efficiently refines element composition and synthesis conditions through iterative experimental feedback. The process culminated in the discovery of a promising Ru-Mn-Ca-Pr oxide catalyst. Our workflow also enhances theoretical simulations with domain adaptation strategy, providing deeper mechanistic insights aligned with experimental findings. By leveraging diverse data sources and multiple ML strategies, we demonstrate an efficient pathway for electrocatalyst discovery and optimization. This comprehensive, data-driven approach represents a paradigm shift and potentially benchmark in electrocatalysts research.

Science & Technology - Other Topics↗
DOE OSTI2023

IDAES-PSE 2.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. IDAES-PSE 2.0.0 Release Highlights Removal of deprecated features from IDAES v1 Update to Pyomo v6.5 – this required a number of updates to support the new NL solver writer and to address some changes in Pyomo Creation of new testing suite for backward compatibility, model robustness and verification More general implementation of the Helmholtz EoS. This brings some new features like standard property diagrams, choice of mass or mole basis, and new state variable options Standardizing names in Heat Exchanger models (breaking change from v2.0.0a2): Control Volumes named hot_side and cold_side Ports names hot_side_inlet, hot_side_outlet, cold_side_inlet and cold_side_outlet Config Blocks names hot_side_config and cold_side_config Config arguments for user provided names for each side: hot_side_name and cold_side_name. Updating Keras surrogate tool to use v1.1 of OMLT New prototype API for model initialization (idaes.core.initialization) The new API uses "Model Initializer" objects instead of class methods, allowing for the definition of multiple initialization routines for a single model A number of common, model agnostic initialization routines have also been defined, including initialization from data, block-decomposition and a general hierarchical approach equivalent to the existing method for common unit models New metadata for thermophysical properties – valid_range This can be used to record the range of values over which a property value can be trusted, such as the range of experimental data used to regress parameters A number of new utility functions have been added to check for properties with values outside the valid range and to set bounds based on this metadata Updated construction of balance expressions in Control Volumes to remove unneeded terms In the past, unneeded terms were added as a constant 0 term, however they will now be dropped entirely from the expression This was necessary due to more strict unit checking in the new Pyomo solver writer which no longer ignores 0 terms Updates to metadata for thermophysical properties to better define known properties and units of measurement This results in more strict enforcement of standard naming for thermophysical and reaction properties Users can still define custom properties, but these must be done explicitly using the define_custom_properties() method instead of being implicitly created by add_property() Updated convergence tester utility tool to support definition of benchmark files (JSON format) and comparison of performance to benchmarks Set default iteration limit for IPOPT in IDAES config to 200 iterations Update scaling of example models to work with new Pyomo NL solver writer Improve testing of extensions and examples infrastructure to avoid need for downloading files Updated distillation column to centralize common functionality and remove a number of Pyomo warnings

IDAES↗
DOE OSTI2024

Support of the Third Summer School and Workshop Focused on Theory and Applications of Time-Dependent Density Functional Theory, Electronic Excited States, and Excited State Dynamics

To create a community of scholars with a deep understanding of TDDFT and excited state methods, both their capabilities and limitations, and how they can be used to model the excited state properties and charge transfer in materials and molecules, we are planning the third iteration of a summer school and workshop focusing on theory and applications of TDDFT, Excited States, and Excited State Dynamics to be offered in the summer of 2023. The two iterations of the US-based school that we previously organized have been very successful with overwhelmingly positive feedback. There is a high level of engagement, enthusiasm, and active participation and networking amongst the students of the school and the scientists of the workshop.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Ensemble Manufacturing Techniques for Steam Turbine Components Across Length Scales

Faster design to manufacturing requirements were sought for steam turbine components to meet the changing demands of today’s power grid of improved efficiency through operating temperature increases and enhanced operational flexibility from baseload to cyclic operations. Advances in multiple advanced manufacturing (AM) process enabled redesign of turbine components for extreme environments with the potential to reduce cost. AM is of particular interest to improve component functionality, higher temperature capability, and superior durability in large scale steam turbine applications. AM methods have an immense potential to open-up the design space by working directly with the 3D model to produce near-net shapes, enable fast design-manufacturing iterations, thereby significantly reducing product cost and lead-time up to 25 % from current baseline. However, these benefits cannot be fully realized due to the potential for unknown AM processing defects and their resultant effect upon component performance in service. Siemens is partnering with Oak Ridge National Laboratory (ORNL), Electric Power Research Institute (EPRI), and Connecticut Center for Advanced Technology (CCAT) to advance the knowledge of complex process-material interactions for desired microstructures and properties that are closely interlinked to component geometries across different length scales. The proposed program utilized an ensemble of multidisciplinary technologies to accelerate the development of materials, high-throughput experiments for their qualification, and design flexibility/topology optimization for repair/redesign of components to address critical failure mechanisms for improved performance and increased reliability of existing power plant components. The proposed activities, if successfully demonstrated for identified components, will enable paradigm shift in customized manufacturing and accelerated qualification/certification towards increased steam turbine component durability, increased turbine efficiency, and reduced CO 2 emissions in load-following environments compared to today’s technology. Technology maturation is built into the project as successful research will include customized process-component down-selection enabling AM methodologies to be incorporated directly into the existing supply chain. The specific activities of the proposed effort are: 1. Topology optimization of down-selected steam turbine parts that are amenable to additive and hybrid manufacturing for cost/performance improvement. 2. Process-structure-property relationships for five AM processes for steam turbine materials of interest to compare with conventional materials. 3. Perform part/assembly build process using advanced additive/hybrid machine tools followed by quality inspection of the built components for insight into qualification for production scale-up. Steam turbine rig testing of printed components under targeted, well monitored and characterized environmental conditions of for performance comparison of baseline and redesigned components.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Salt Vessel-Sample Generator Interface

The goal of the salt sampling program at Argonne is to develop and deploy automated molten salt sampling approaches for interfacing relevant unit operations with salt analysis to improve the timeliness and accuracy of sampling-based accountancy measurements. One technology under development is a vacuum sampler loop module. In this system, molten salt is drawn from a process vessel through a heated sampling line and into a charge vessel. Next, one or more samples are split from the volume in the charge vessel and the remaining salt is returned to the process. The vacuum sampler loop module is being developed to interface with other sample processing and analysis modules to enable rapid at-line sample characterization. The main purpose of the vacuum sampling loop module is to enable the collection of samples that are more representative of the bulk salt by replacing traditional point samplers (i.e., dip probes) with a sampling approach that captures a larger cross section of salt. Additionally, the vacuum sampling approach eliminates the risk of dross contamination of samples and avoids the use of moving parts in the salt. Two methods of interfacing the vacuum sampling loop with a precision pneumatic sample generator were investigated in FY21. This report covers the testing of fluidic coupling between the two modules. Two iterations of the fluidically coupled modules were tested. The first iteration system coupled the two modules using a freeze valve to seal the vacuum sampler during filling and to control flow into the pneumatic sample generator. While this integrated system functioned as intended, some changes were implemented to make the system more robust and better suited to remote deployment. Specifically, the system was made to be more modular and active control of the vacuum filling operation was replaced with a passive control mechanism. For passive filling, the salt charge vessel was vented to a small gas tank that was at negative pressure, causing salt to be drawn into the vessel until the force of the fluid head was in equilibrium with the gas pressure. The passive control system performed well and will be used in future iterations. Another change in the second system was a newly configured pneumatic sample generator in which sample ejection occurred through a hole in the reservoir’s stainless-steel side wall instead of through a non-wetted sapphire orifice on the bottom. This alternate configuration may be better suited for near-process deployment because it enables on-line orifice maintenance and an orifice bypass drain back to the process. A third change in the second iteration system was the transition to a two-chamber charge vessel which split off a fraction of the sampled salt as a liquid aliquot. The goal was to create a buffer mechanism that would allow reproducible aliquoting of samples, independently of variability in the charge vessel fill height. While the two-chamber design was functional, the sample size reproducibility was below target values. To improve reproducibility and overcome many of the impediments to remote deployment of the vacuum sampling loop module, separate work was conducted to replace the two-chamber liquid aliquoting mechanism with aliquoting into single-use sample tubes. Solid salt transfer in the sample tubes will replace fluidic coupling for integrating the vacuum sampler with downstream modules. Because the proposed operations can all be executed with simple overhead actuation mechanisms or other existing hot-cell technology, there will be no need for large investments in novel hot cell sample handling technologies using this alternate approach. As such, near-term deployment of the vacuum sampling technology will be achievable. This new approach for automated coupling of sample tubes with down-stream modules is covered in a separate FY21 report, and a remotely operated version of the vacuum sampler loop module with the tube aliquoting feature is planned for FY22.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Revisiting Dropout: Escaping Pressure for Training Neural Networks with Multiple Costs

A common approach to jointly learn multiple tasks with a shared structure is to optimize the model with a combined landscape of multiple sub-costs. However, gradients derived from each sub-cost often conflicts in cost plateaus, resulting in a subpar optimum. In this work, we shed light on such gradient conflict challenges and suggest a solution named Cost-Out, which randomly drops the sub-costs for each iteration. We provide the theoretical and empirical evidence of the existence of escaping pressure induced by the Cost-Out mechanism. While simple, the empirical results indicate that the proposed method can enhance the performance of multi-task learning problems, including two-digit image classification sampled from MNIST dataset and machine translation tasks for English from and to French, Spanish, and German WMT14 datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Preconditioned least‐squares Petrov–Galerkin reduced order models

Abstract In this article, we introduce a methodology for improving the accuracy and efficiency of reduced order models (ROMs) constructed using the least‐squares Petrov–Galerkin (LSPG) projection method through the introduction of preconditioning. Unlike prior related work, which focuses on preconditioning the linear systems arising within the ROM numerical solution procedure to improve linear solver performance, our approach leverages a preconditioning matrix directly within the minimization problem underlying the LSPG formulation. Applying preconditioning in this way has the potential to improve ROM accuracy for several reasons. First, preconditioning the LSPG formulation changes the norm defining the residual minimization, which can improve the residual‐based stability constant bounding the ROM solution's error. The incorporation of a preconditioner into the LSPG formulation can have the additional effect of scaling the components of the residual being minimized to make them roughly of the same magnitude, which can be beneficial when applying the LSPG method to problems with disparate scales (e.g., dimensional equations, multi‐physics problems). Importantly, we demonstrate that an “ideal preconditioned” LSPG ROM (a ROM in which the preconditioner is the inverse of the Jacobian of its corresponding full order model) emulates projection of the full order model solution increment onto the reduced basis. This quantity defines a lower bound on the error of a ROM solution for a given reduced basis. By designing preconditioners that approximate the Jacobian inverse—as is common in designing preconditioners for solving linear systems—it is possible to obtain a ROM whose error approaches this lower bound. The proposed approach is evaluated on several mechanical and thermo‐mechanical problems implemented within the Albany HPC code and run in the predictive regime, with prediction across material parameter space. We demonstrate numerically that the introduction of simple Jacobi, Gauss‐Seidel, and ILU preconditioners into the proper orthogonal decomposition/LSPG formulation reduces significantly the ROM solution error, the reduced Jacobian condition number, the number of nonlinear iterations required to reach convergence, and the wall time (thereby improving efficiency). Moreover, our numerical results reveal that the introduction of preconditioning can deliver a robust and accurate solution for test cases in which the unpreconditioned LSPG method fails to converge.

Lindsay, Payton↗
DOE OSTI2022

Procedure Parsing: A Method for Parsing Handwritten Documents into Computer-Based Procedures

The nuclear industry is heavily procedure driven, where almost everything has a step-by-step instruction that is expected to be followed in detail. Historically, these procedures were printed on paper copies. Recently, the industry transitioned towards electronic copies (i.e., PDFs on tablets). One major drive for this transition is the introduction of human error and loss of situation awareness when using paper copies. However, electronic copies of documents inherently have the same error traps as their paper cousins. Therefore, there is an increased interest in a way to utilize the information in the step-by-step guidance, but to present it in a dynamic manner that guides the user and adapts to any encountered conditions. Researchers at Idaho National Laboratory propose a flexible, automated method based on document parsing and augmented by natural language processing (NLP) techniques, to address these shortcomings and capitalize on these recent advancements in machine learning. The proposed method provides a cost-effective solution for computer-assisted procedure parsing of hand-written control room procedures, originally authored in Word or PDF formats, into instructions that can be displayed as computer-based procedures (CBP) in a modern graphical user interface. The researchers devised, implemented and demonstrated the Operating Procedure Extender for Novel Systems (OPENS) method in 2020. The key to OPENS is to map the original procedure text into a context-free grammar, tying content to equipment, locations, and other steps, actions, etc. This formal grammar is then used to isolate and define keywords and actions verbs, such as “measure” or “evaluate” and tie them to specific equipment referenced within that step or located in other steps, substeps, actions, subactions and tables throughout the procedure. OPENS generates an abstract syntax tree from the document which it uses to store a copy of this information in the open-standard, machine-readable and human-readable file formats XML and JSON. The XML is useful to preserve the relational aspects of the procedure for referencing tables and branching information so the user can be directed to the next appropriate active step based on the values entered for that step and previous steps. The JSON is useful for storing and exchanging data objects used to track responses to previous steps and state changes in simulated environments. In future iterations, these formats can also be used for storing more detailed information about input during plant operation or simulation. The techniques the researcher developed could further be improved by integration of recent advancements in machine learning. NLP methods could standardize documents, correct for grammatical error, and provide automated semantic validation. The researcher expects that self-supervised techniques applied to collections of natural language instructions could strengthen the model with broader context. All these methods together give us a practical way to automatically extract protocols from documents and user interactions, empowering researchers, procedure writers and nuclear operators while moving the industry forward.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Generalized spin σ -SCF method

We introduce a generalization of the σ-SCF method to approximate noncollinear spin ground and excited single-reference electronic states by minimizing the Hamiltonian variance. The new method is based on the σ-SCF method, originally proposed by Ye et al. [J. Chem. Phys. 147, 214104 (2017)], and provides a prescription to determine ground and excited noncollinear spin states on an equal footing. Our implementation was carried out utilizing an initial simulated annealing stage followed by a mean-field iterative self-consistent approach to simplify the cumbersome search introduced by generalizing the spin degrees of freedom. The simulated annealing stage ensures a broad exploration of the Hilbert space spanned by the generalized spin single-reference states with random complex element-wise rotations of the generalized density matrix elements in the simulated annealing stage. The mean-field iterative self-consistent stage employs an effective Fockian derived from the variance, which is utilized to converge tightly to the solutions. This process helps us to easily find complex spin structures, avoiding manipulating the initial guess. As proof-of-concept tests, we present results for Hn (n = 3–7) planar rings and polyhedral clusters with geometrical spin frustration. We show that most of these systems have noncollinear spin excited states that can be interpreted in terms of geometric spin frustration. These states are not directly targeted by energy minimization methods, which are meant to converge to the ground state. This stresses the capability of the σ-SCF methodology to find approximate noncollinear spin structures as mean-field excited states.

Chemistry↗