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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Surface analysis insight note: X‐ray photoelectron spectroscopy analysis of battery electrodes—Challenges with nickel–manganese–cobalt and Li examples using an Al Kα x‐ray source

X‐ray photoelectron spectroscopy (XPS) has become a highly important tool for the analysis of battery materials and components. However, both anecdotal and detailed analysis of selected parts of the literature indicate that many reports of XPS on battery electrodes have significant analysis or data flaws. In this paper, we highlight several of the common challenges that analysts face when using XPS for battery materials, pointing to recent literature that addresses many of the critical issues associated with sample preparation as well as data collection and analysis. A common error for battery materials (and other materials) involves ignoring peak overlaps and interferences. Specifically, when a “minor” peak associated with a component in relatively high concentration overlaps or contributes to the primary peak (or one recommended for quantitative analysis) from a different element in the material. Overlap issues apply to many battery electrodes composed of many elements with complex photoelectron peak structures, as well as those involving peaks with seemingly simpler spectral envelopes such as Li and F. Examples of issues associated with battery systems are highlighted by a discussion of challenges associated with XPS analysis of Li and nickel–manganese–cobalt (NMC) electrodes in battery systems. Lithium analysis has challenges associated with the preparation and an often‐unrecognized peak overlap with F. In our laboratory and in the literature, NMC electrodes are often examined and new XPS users do not always recognize interference of the Auger signal from F KLL (in or on the electrode) with Ni 2p photoelectron spectrum when generated with Al Kα X‐rays. The use of simulated spectra involving both F and NiO demonstrates the extent of F Auger contributions to the Ni 2p signal strength as a function of the F/Ni atom ratio in the material and suggests spectra information that can be used to identify how significant effects will be on the resultant spectra. Our analysis demonstrates that in many cases overlap issues are significant for real electrode materials.

25 ENERGY STORAGE↗
DOE OSTI2019

Pathway Complexity in the Stacking of Imine-Linked Macrocycles Related to Two-Dimensional Covalent Organic Frameworks

Interlayer interactions play an important role in the formation of two-dimensional covalent organic frameworks (2D COFs), yet the effects of monomer structure on COF formation, crystallinity, and susceptibility to exfoliation are not well understood. Here we probe these effects by studying the stacking behavior of imine-linked macrocycles that represent discrete models of 2D COFs. Specifically, macrocycles based on terephthaldehyde (PDA) or 2,5-dimethoxyterephthaldehyde (DMPDA) stack upon cooling molecularly dissolved solutions. Both macrocycles assemble cooperatively with similar Delta H-e values of -97 kJ/mol and -101 kJ/mol, respectively, although the DMPDA macrocycle assembly process showed a more straightforward temperature dependence. Density functional theory calculations of the stacking of PDA macrocycles suggested two stable configurations that were close in energy. Circular dichroism spectroscopy performed on macrocycles bearing chiral side chains revealed a helix reversion process for the PDA macrocycles that was not observed for the DMPDA macrocycles. Given the structural similarity of these monomers, these findings demonstrate that the stacking processes associated with nanotubes derived from these macrocycles, as well as for the corresponding COFs, are complex and susceptible to kinetic traps, casting doubt on the relevance of thermodynamic arguments for improving materials quality. Rather, a deeper understanding of the mechanism of supramolecular polymerization and its interplay with polymerization and error correction during COF synthesis is needed for improved control of the crystallinity and morphology of these emerging materials.

Wang, Shiwei↗
DOE OSTI2019

Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation

Metal–oxo moieties are important catalytic intermediates in the selective partial oxidation of hydrocarbons and in water splitting. Stable metal–oxo species have reactive properties that vary depending on the spin state of the metal, complicating the development of structure–property relationships. To overcome these challenges, we train machine-learning (ML) models capable of predicting metal–oxo formation energies across a range of first-row metals, oxidation states, and spin states. Using connectivity-only features tailored for inorganic chemistry as inputs to kernel ridge regression or artificial neural network (ANN) ML models, we achieve good mean absolute errors (4–5 kcal/mol) on set-aside test data across a range of ligand orientations. Analysis of feature importance for oxo formation energy prediction reveals the dominance of nonlocal, electronic ligand properties in contrast to other transition metal complex properties (e.g., spin-state or ionization potential). We enumerate the theoretical catalyst space with an ANN, revealing expected trends in oxo formation energetics, such as destabilization of the metal–oxo species with increasing d-filling, as well as exceptions, such as weak correlations with indicators of oxidative stability of the metal in the resting state or unexpected spin-state dependence in reactivity. We carry out uncertainty-aware evolutionary optimization using the ANN to explore a >37 000 candidate catalyst space. New metal and oxidation state combinations are uncovered and validated with density functional theory (DFT), including counterintuitive oxo formation energies for oxidatively stable complexes. This approach doubles the density of confirmed DFT leads in originally sparsely populated regions of property space, highlighting the potential of ML-model-driven discovery to uncover catalyst design rules and exceptions.

transition metal catalysis↗
DOE OSTI2022

Crystallography of hyperbolic lattices

Hyperbolic lattices are a revolutionary platform for tabletop simulations of holography and quantum physics in curved space and facilitate efficient quantum error correcting codes. Their underlying geometry is non-Euclidean, and the absence of Bloch's theorem precludes the straightforward application of the often indispensable energy band theory to study model Hamiltonians on hyperbolic lattices. Motivated by recent insights into hyperbolic band theory, we initiate a crystallography of hyperbolic lattices. Here we show that many hyperbolic lattices feature a hidden crystal structure characterized by unit cells, hyperbolic Bravais lattices, and associated symmetry groups. Using the mathematical framework of higher-genus Riemann surfaces and Fuchsian groups, we derive a list of example hyperbolic {p,q} lattices and their hyperbolic Bravais lattices, including five infinite families and several graphs relevant for experiments in circuit quantum electrodynamics and topolectrical circuits. This dramatically simplifies the computation of energy spectra of tight-binding Hamiltonians on hyperbolic lattices, from exact diagonalization on the graph to solving a finite set of equations in terms of irreducible representations. The significance of this achievement needs to be compared to the all-important role played by conventional Euclidean crystallography in the study of solids. We exemplify the high potential of this approach by constructing and diagonalizing finite-dimensional Bloch wave Hamiltonians.

2-dimensional systems↗
DOE OSTI2024

Classical and quantum computing of shear viscosity for ( 2 + 1 ) D SU(2) gauge theory

We perform a nonperturbative calculation of the shear viscosity for ( 2 + 1 )-dimensional SU(2) gauge theory by using the lattice Hamiltonian formulation. The retarded Green’s function of the stress-energy tensor is calculated from real time evolution via exact diagonalization of the lattice Hamiltonian with a local Hilbert space truncation, and the shear viscosity is obtained via the Kubo formula. When taking the continuum limit, we account for the renormalization group flow of the coupling but no additional operator renormalization. We find the ratio of the shear viscosity and the entropy density η s is consistent with a well-known holographic result 1 4 π at several temperatures on a 4 × 4 honeycomb lattice with the local electric representation truncated at j max = 1 2 . We also find the ratio of the spectral function and frequency ρ x y ( ω ) ω exhibits a peak structure when the frequency is small. Both the exact diagonalization method and simple matrix product state classical simulation method beyond j max = 1 2 on bigger lattices require exponentially growing resources. So we develop a quantum computing method to calculate the retarded Green’s function and analyze various systematics of the calculation including j max truncation and finite size effects, Trotter errors and the thermal state preparation efficiency. Our thermal state preparation method still requires resources that grow exponentially with the lattice size, but with a very small prefactor at high temperature. We test our quantum circuit on both the Quantinuum emulator and the IBM simulator for a small lattice and obtain results consistent with the classical computing ones. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Common Electric Power Transmission System Model JSON Schema Specification

The Common Electric Power Transmission System Model (CTM) is an intuitive, extensible, language-agnostic, and error-resistant specification of electric power network components parameter names and units, and relation between components, intended for use by the research community developing new computational methods for power systems operations and simulation. Power system datasets following the CTM specification can be read as dictionaries and manipulated in that form in most programming languages (e.g., Python, Julia, C++). This standard data structure in CTM makes it easy to work in multiple power systems domains (e.g., economic operation, reliability assessment, electricity markets, stability assessment, etc.) without requiring conversions between use-case-specific file formats with information loss in the process. This repository specifies CTM as a JSON Schema, provides documentation, derivate (code-generated) implementations of CTM, and example data and usage of the schema for important use cases.

Aravena Solis, Ignacio↗
DOE OSTI2024

Spatially Local Surrogate Modeling of Subgrid-Scale Effects in Idealized Atmospheric Flows: A Deep Learned Approach Using High-Resolution Simulation Data

Abstract We introduce a machine learned surrogate model from high-resolution simulation data to capture the subgrid-scale effects in dry, stratified atmospheric flows. We use deep neural networks (NNs) to model the spatially local state differences between a coarse-resolution simulation and a high-resolution simulation. The setup enables the capture of both dissipative and antidissipative effects in the state differences. The NN model is able to accurately capture the state differences in offline tests outside the training regime. In online tests intended for production use, the NN-coupled coarse simulation has higher accuracy over a significant period of time compared to the coarse-resolution simulation without any correction. We provide evidence of the capability of the NN model to accurately capture high-gradient regions in the flow field. With the accumulation of the errors, the NN-coupled simulation becomes computationally unstable after approximately 90 coarse simulation time steps. Insights gained from these surrogate models further pave the way for formulating stable, complex, physics-based spatially local NN models which are driven by traditional subgrid-scale turbulence closure models. Significance Statement Flows in the atmosphere are highly chaotic and turbulent, comprising flow structures of broad scales. For effective computational modeling of atmospheric flows, the effects of the small- and large-scale structures need to be captured by the simulations. Capturing the small-scale structures requires fine-resolution simulations. Even with the current state-of-the-art supercomputers, it can be prohibitively expensive to simulate these flows when computed for the entire earth over climate time scales. Thus, it is necessary to focus on the larger-scale structures using a coarse-resolution simulation while capturing the effects of the smaller-scale structures using some parameterization (approximation) scheme and incorporating it into the coarse-resolution simulation. We use machine learning to model the effects of the small-scale structures (subgrid-scale effects) in atmospheric flows. Data from a fine-resolution simulation is used to compute the missing subgrid-scale effects in coarse-resolution simulations. We then use machine learning models to approximate these differences between the coarse- and fine-resolution simulations. We see improved accuracy for the coarse-resolution simulations when corrected using these machine learned models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Nanometer flat blazed x-ray gratings using ion beam figure correction

With the development of nanometer accuracy stitching interferometry, ion beam figuring (IBF) of x-ray mirrors can now be achieved with unprecedented performance. However, the process of producing x-ray diffraction gratings on these surfaces may degrade the figure quality due to process errors introduced during the ruling of the grating grooves. To address this challenge, we have investigated the post-production correction of gratings using IBF, where stitching interferometry is used to provide in-process feedback. A concern with ion beam correction in this case is that ions will induce enough surface mobility of atoms to cause smoothing of the grating structure and degradation of diffraction efficiency. In this study we found however that it is possible to achieve a nanometer-level planarity of the global grating surface with IBF, while preserving the grating structure. The preservation was so good, that we could not detect a change in the diffraction efficiency after ion beam correction. This is of major importance in achieving ultra-high spectral resolution, and the preservation of brightness for coherent x-ray beams.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Commercial integration of advanced nuclear energy with Artificial Intelligence (AI): Possible implications

The integration of advanced nuclear technologies (both fission and fusion) with artificial intelligence (AI) presents unprecedented national security challenges and opportunities. As fusion energy approaches commercial viability alongside advanced Small Modular Reactors (SMRs), their integration with AI and Artificial General Intelligence (AGI) systems could fundamentally transform the global energy and AI landscapes — two pillars of national security. This document briefly examines how AI could accelerate nuclear energy development and deployment while altering existing power structures, a lot could be done to deepen the discussions. Simultaneously, it observes how nuclear-powered AI may expedite advances toward AGI and beyond. These issues are deeply interconnected and thus need to be examined as a whole and more comprehensively than what’s being summarized here. For instance, AI-powered autonomous operation of nuclear facilities could reduce human error but introduce new cybersecurity vulnerabilities and uncertainties. Further investigation would also address how AI-enhanced nuclear technologies might complicate proliferation concerns through advanced fuel cycle management, nuclear materials production and safeguard. The strategic advantage gained by first entities achieving successful AI-nuclear integration could reshape global and national security framework. Timely analysis of these implications may be crucial for policymakers seeking to harness these technologies' benefits while effectively mitigating their potential risks.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Characterization of Soft Error Rate Against Memory Elements Spacing and Clock Skew in a Logic with Triple Modular Redundancy in a 65nm Process

Single Event Effects introduce soft errors in ASICs. Design methodologies like Triple ModularRedundancy (TMR) with clock skew insertion, a system level redundancy technique is a commonpractice by designers to mitigate soft errors. However, the optimal spacing between memoryelements in a TMR in 65nm process hasn't been addressed so far. RD53SEU is a mini ASICdevelopment under the framework of the CERN RD53 collaboration to characterize the soft errorrates against the separation spacing and clock skew between memory elements in a TMR. Thisarticle describes the architecture and design aspects of the various test structures on the RD53SEUtest chip.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Automated electrical demand peak leveling in a manufacturing facility with short term energy storage for smart grid participation

Demand side management of energy is a vital function in the smart grid that allows for greater integration of renewable energy resources, and is facilitated with economic incentives in energy and demand pricing schedules. Manufacturing systems can take advantage of these incentives to reduce their energy costs through active energy management. This paper is a simulation analysis on the implementation of a novel algorithm that levelizes the maximum power load of an industrial bakery that operates under an on-peak demand price structure. The algorithm takes advantage of an untapped thermal energy storage resource in the facility, a chilled glycol buffer tank, to level the short intra-day power oscillations that the facility experiences. One of the key advantages of the algorithm is that it does not require precise demand forecasting or complex control algorithms, such as model predictive control. Even with substantial error in peak power estimates (5%), the algorithm is expected to result in at least a 2% peak reduction. Under ideal prediction, the algorithm reduces the on-peak facility power maximum by 7.3% of the possible 9.0% reduction the storage can provide. Further, the algorithm uses very few facility specific inputs, and can readily be adapted for any facility with short intra-day oscillatory power profiles and untapped storage capacity. The key insight of the paper is the employment of a novel algorithm to leverage untapped energy storage in manufacturing facilities to transform them into smart grid participants with no major capital investment.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Characterizing leaf-scale fluorescence with spectral invariants

Sun-induced chlorophyll fluorescence (SIF) is increasingly recognized as a non-destructive probe for tracking terrestrial photosynthesis. Emerging developments in spectral invariants theory provide an innovative and efficient approach for representing SIF radiative transfer processes at the canopy scale. However, modeling leaf-scale fluorescence based on the spectral invariants properties (SIP) remains underexplored. In this study, the spectral invariants theory is employed for the first time to model the leaf-scale total, backward and forward fluorescence (leaf-SIP SIF). The leaf-SIP SIF model separates the leaf-scale radiative transfer process into two distinct components: the wavelength-dependent one associated with leaf biochemical properties, and the wavelength-independent component linked to leaf structural characteristics. The leaf structure-related effects are characterized by two spectrally invariant parameters: the photon recollision probability (p) and the scattering asymmetry parameter (q), which are parameterized using the directly measurable leaf dry matter. Evaluation against field measurements shows that the proposed leaf-SIP SIF model has a good performance, with coefficient of determination (R 2 ) of 0.89, 0.89, 0.90 and root mean squared errors (RMSE) of 1.28, 0.69, 0.74 Wm -2 µm -1 sr -1 , respectively for the total, backward, and forward fluorescence (660–800 nm). The leaf-SIP SIF model with a more concise formulation demonstrates comparable performance with the widely used Fluspect model. Further, the leaf-SIP SIF model provides a simple and efficient approach for simulating leaf-scale fluorescence, with the potential to be integrated into a unified SIP-based model framework for simulating the radiative transfer processes across the soil-leaf-canopy-atmosphere continuum.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

van der Waals Correction to the Physisorption of Graphene on Metal Surfaces

Adsorption is a scientifically and technologically important interfacial phenomenon, which however presents challenges to conventional density functional theory (DFT) due to the long-range van der Waals (vdW) interactions. We have developed a model of long-range vdW correction for physisorption of graphene (G) on metals with the Lifshitz–Zaremba–Kohn second-order perturbation theory, by incorporating dipole- and quadrupole-surface interactions and screening effects. The physisorption energies calculated by the model between graphene and eight metal surfaces (Al, Ni, Co, Pd, Pt, Cu, Ag, and Au), and the adsorption energies for the same G/metal structures from self-consistent DFT PBE (Perdew–Burke–Ernzerhof) calculations, are obtained in a range of distances between G and the metal surfaces. The sum of these two parts is the total adsorption energy as a function of the distance, from which the equilibrium distance and the binding energy are determined simultaneously. The results show high accuracy, with the mean absolute error (MAE) of binding energy of 7 meV and the MAE of equilibrium distance of 0.2 Å, significantly improving upon other vdW methods. Here, the PBE + vdW binding energy curves give better fits to the random phase approximation curves around the equilibrium distances than do the curves of other methods considered here. The higher-order quadrupole-surface correction is important and accounts for about 30% of the total vdW correction.

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

The northeast materials database for magnetic materials

The discovery of magnetic materials with high operating temperature ranges and optimized performance is essential for advanced applications. Current data-driven approaches are limited by the lack of accurate, comprehensive, and feature-rich databases. This study aims to address this challenge by using Large Language Models (LLMs) to create a comprehensive, experiment-based, magnetic materials database named the Northeast Materials Database (NEMAD), which consists of 67,573 magnetic materials entries (www.nemad.org). The database incorporates chemical composition, magnetic phase transition temperatures, structural details, and magnetic properties. Enabled by NEMAD, we trained machine learning models to classify materials and predict transition temperatures. Our classification model achieved an accuracy of 90% in categorizing materials as ferromagnetic (FM), antiferromagnetic (AFM), and non-magnetic (NM). The regression models predict Curie (Néel) temperature with a coefficient of determination (R 2 ) of 0.87 (0.83) and a mean absolute error (MAE) of 56K (38K). These models identified 25 (13) FM (AFM) candidates with a predicted Curie (Néel) temperature above 500K (100K) from the Materials Project. This work shows the feasibility of combining LLMs for automated data extraction and machine learning models to accelerate the discovery of magnetic materials.

Ferromagnetism↗
DOE OSTI2019

Quantifying the hydration structure of sodium and potassium ions: taking additional steps on Jacob's Ladder

The ability to reproduce the experimental structure of water around the sodium and potassium ions is a key test of the quality of interaction potentials due to the central importance of these ions in a wide range of important phenomena. In this study, we simulate the Na + and K + ions in bulk water using three density functional theory functionals: (1) the generalized gradient approximation (GGA) based dispersion corrected revised Perdew, Burke, and Ernzerhof functional (revPBE-D3) (2) the recently developed strongly constrained and appropriately normed (SCAN) functional (3) the random phase approximation (RPA) functional for potassium. We compare with experimental X-ray diffraction (XRD) and X-ray absorption fine structure (EXAFS) measurements to demonstrate that SCAN accurately reproduces key structural details of the hydration structure around the sodium and potassium cations, whereas revPBE-D3 fails to do so. However, we show that SCAN provides a worse description of pure water in comparison with revPBE-D3. RPA also shows an improvement for K + , but slow convergence prevents rigorous comparison. Finally, we analyse cluster energetics to show SCAN and RPA have smaller fluctuations of the mean error of ion–water cluster binding energies compared with revPBE-D3.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

EMISSA (Exploring Millimeter Indicators of Solar-Stellar Activity): I. The initial millimeter–centimeter main-sequence star sample

Due to their wide wavelength coverage across the millimeter to centimeter (mm–cm) range and their increased sensitivity, modern interferometric arrays facilitate observations of the thermal and non-thermal radiation that is emitted from different layers in the outer atmospheres of stars. We study the spectral energy distribution (S obs (ν)) of main-sequence stars based on archival observations in the mm–cm range with the aim to study their atmospheric stratification as a function of stellar type. The main-sequence stars with significant detection in mm bands were identified in the ALMA Science Archive. These data were then complemented with spectral flux data in the extreme ultraviolet to cm range as compiled from various catalogues and observatory archives. We compared the resultant S obs (ν) of each star with a photospheric emission model (S mod (ν)) calculated with the PHOENIX code. The departures of S obs (ν) from S mod (ν) were quantified in terms of a spectral flux excess parameter (ΔS/S mod ) and studied as a function of stellar type. The initial sample consists of 12 main-sequence stars across a broad range of spectral types from A1 to M3.5 and the Sun-as-a-star as reference. The stars with T eff = 3000–7000 K (F–M type) showed a systematically higher S obs (ν) than S mod (ν) in the mm–cm range. Their ΔS/S mod exhibits a monotonic rise with decreasing frequency. The steepness of this rise is higher for cooler stars in the T eff = 3000–7000 K range, although the single fully convective star (T eff ~ 3000 K) in the sample deviates from this trend. Meanwhile, Sobs(ν) of the A-type stars agrees with S mod (ν) within errors. The systematically high ΔS/S mod in F–M stars indicates hotter upper atmospheric layers, that is, a chromosphere and corona in these stars, like for the Sun. The mm–cm ΔS/S mod spectrum offers a way to estimate the efficiency of the heating mechanisms across various outer atmospheric layers in main-sequence stars, and thereby to understand their structure and activity. We emphasise the need for dedicated surveys of main-sequence stars in the mm–cm range.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Orchestrated trios: compiling for efficient communication in Quantum programs with 3-Qubit gates

Current quantum computers are especially error prone and require high levels of optimization to reduce operation counts and maximize the probability the compiled program will succeed. These computers only support operations decomposed into one- and two-qubit gates and only two-qubit gates between physically connected pairs of qubits. Typical compilers first decompose operations, then route data to connected qubits. We propose a new compiler structure, Orchestrated Trios, that first decomposes to the three-qubit Toffoli, routes the inputs of the higher-level Toffoli operations to groups of nearby qubits, then finishes decomposition to hardware-supported gates. This significantly reduces communication overhead by giving the routing pass access to the higher-level structure of the circuit instead of discarding it. A second benefit is the ability to now select an architecture-tuned Toffoli decomposition such as the 8-CNOT Toffoli for the specific hardware qubits now known after the routing pass. Furthermore, we perform real experiments on IBM Johannesburg showing an average 35% decrease in two-qubit gate count and 23% increase in success rate of a single Toffoli over Qiskit. We additionally compile many near-term benchmark algorithms showing an average 344% increase in (or 4.44x) simulated success rate on the Johannesburg architecture and compare with other architecture types.

compiler↗
DOE OSTI2022

Doppler-Free Saturation Spectroscopy (DFSS) for Magnetic and Electric Field Measurements in an FRC plasma

The Cooperative Research and Development Agreement (CRADA) between Oak Ridge National Laboratory (ORNL) and TAE Technologies (TAE) focused on a feasibility study of Doppler-free saturation spectroscopy (DFSS) as a diagnostic strategy to measure the internal magnetic and electric fields of TAE’s C-2W field-reversed configuration (FRC) fusion device. Non-perturbative measurements of magnetic and electric fields in the core of FRCs can provide critical information needed to constrain equilibrium models and for the continued advancement of the fusion research program at TAE. DFSS is a well-established, laser-based absorption spectroscopy technique used to suppress inhomogeneous (Doppler) broadening linewidths and reveal Zeeman/Stark split hyperfine structures of target emission lines. The collaboration involved the use of ORNL’s suite of experimentally validated quantum mechanical modelling codes to simulate theoretical atomic spectra for a variety of machine parameters and laser beam configurations. TAE’s expertise was heavily used to provide measurements/models of the required machine parameters needed as inputs to the simulations. Results of an all-encompassing error analysis demonstrate the favourable capabilities of the diagnostic and provided invaluable insights as to how DFSS can be best used to accurately measure internal B/E-fields in C-2W.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗