SEARCH · Engineering Papers

Results for “density functional theory development”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,153 records · Page 64

All sources NASA NTRS DOE OSTI

DOE OSTI2025

Defect modeling in semiconductors: the role of first principles simulations and machine learning

Abstract Point defects in semiconductors dictate their electronic and optical properties. Vacancies, interstitials, substitutional defects, and defect complexes can form in the semiconductor lattice and significantly impact its performance in applications such as solar absorption, light emission, electronics, and catalysis. Understanding the nature and energetics of point defects is essential for the design and optimization of next-generation semiconductor technologies. Here, we provide a comprehensive overview of the current state of research on point defects in semiconductors, focusing on the application of density functional theory (DFT) and machine learning (ML) in accelerating the prediction and understanding of defect properties. DFT has been instrumental in accurately calculating defect formation energies, charge transition levels, and other defect-related properties such as carrier recombination rates and lifetimes, and ion migration barriers. ML techniques, particularly neural networks, have emerged as powerful tools for enabling rapid prediction of defect properties at DFT-accuracy in order to overcome the expense of using large supercells and advanced functionals. We begin this article with a discussion of different types of point defects and complexes, their impact on semiconductor properties, and the experimental and DFT approaches typically used for their characterization. Through multiple case studies, we explore how DFT has been successfully applied to understand defect behavior across a variety of semiconductors, and how ML approaches integrated with DFT can efficiently predict defect properties and facilitate the discovery of new materials with tailored defect behavior. Overall, the advent of ‘DFT+ML’ promises to drive advancements in semiconductor technology, catalysis, and renewable energy applications, paving the way for the development of high-performance semiconductors which are defect-tolerant or have desirable dopability.

Rahman, Md Habibur (ORCID:000000027705984X)↗

DOE OSTI2024

Frontiers in Magnetic Materials

Magnetism is crucial to many modern technologies, a driver for condensed matter physics research and one of the most remarkable and diverse properties of matter. We propose to develop understanding of novel magnetism and magnetic related behavior in materials and use this to accelerate the discovery of forefront magnetic materials. The approach is via the connection of magnetic properties to specific structures and materials. Topics that will be addressed are (1) Metallic magnetic materials with unusually low carrier concentrations and/or moments (2) Magnetism arising from unusual chemistry including 4d and 5d magnetism and (3) Materials with strong spin-fluctuations, which can lead to quantum criticality, spin-fluctuation induced superconductivity and other novel quantum behavior. These topics overlap, for example, the 4d ruthenates include ferromagnets (perovskite SrRuO 3 ), extremely high ordering temperature antiferromagnets (honeycomb lattice SrRu 2 O 6 ) and well as quantum materials with strong spin fluctuations (layered perovskite Sr 2 RuO 4 and Sr 3 Ru 2 O 7 ). We will use of density calculations to connect magnetic properties with chemistry and structure and employ phenomenological theories to extend these results to properties that are not directly given by direct first principles methods and we will conduct tests to explore the limitations of density functional approximations and new functionals.

36 MATERIALS SCIENCE↗

DOE OSTI2021

From Femtoseconds to Gigaseconds: The SolDeg Platform for the Performance Degradation Analysis of Silicon Heterojunction Solar Cells

Heterojunction Si solar cells exhibit notable performance degradation. Here, we modeled this degradation by electronic defects getting generated by thermal activation across energy barriers over time. To analyze the physics of this degradation, we developed the SolDeg platform to simulate the dynamics of electronic defect generation. First, femtosecond molecular dynamics simulations were performed to create a-Si/c-Si stacks, using the machine learning-based Gaussian approximation potential. Second, we created shocked clusters by a cluster blaster method. Third, the shocked clusters were analyzed to identify which of them supported electronic defects. Fourth, the distribution of energy barriers that control the generation of these electronic defects was determined. Fifth, an accelerated Monte Carlo method was developed to simulate the thermally activated time-dependent defect generation across the barriers. Our main conclusions are as follows. (1) The degradation of a-Si/c-Si heterojunction solar cells via defect generation is controlled by a broad distribution of energy barriers. (2) We developed the SolDeg platform to track the microscopic dynamics of defect generation across this wide barrier distribution and determined the time-dependent defect density N(t) from femtoseconds to gigaseconds, over 24 orders of magnitude in time. (3) We have shown that a stretched exponential analytical form can successfully describe the defect generation N(t) over at least 10 orders of magnitude in time. (4) We found that in relative terms, Voc degrades at a rate of 0.2%/year over the first year, slowing with advancing time. (5) We developed the time correspondence curve to calibrate and validate the accelerated testing of solar cells. We found a compellingly simple scaling relationship between accelerated and normal times t normal ∝ t accel T(accel)/T(normal) . (6) We also carried out experimental studies of defect generation in a-Si:H/c-Si stacks. We found a relatively high degradation rate at early times that slowed considerably at longer time scales.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

On the statistical theory of self-gravitating collisionless dark matter flow

Dark matter, if it exists, accounts for five times as much as the ordinary baryonic matter. Compared to hydrodynamic turbulence, the flow of dark matter might possess the widest presence in our universe. This paper presents a statistical theory for the flow of dark matter that is compared with N-body simulations. By contrast to hydrodynamics of normal fluids, dark matter flow is self-gravitating, long-range, and collisionless with a scale-dependent flow behavior. The peculiar velocity field is of constant divergence nature on small scale and irrotational on large scale. The statistical measures, i.e., correlation, structure, dispersion, and spectrum functions, are modeled on both small and large scales, respectively. Kinematic relations between statistical measures are fully developed for incompressible, constant divergence, and irrotational flow. Incompressible and constant divergence flow share the same kinematic relations for even order correlations. The limiting correlation of velocity $\mathrm{ρ_{L}=1/2}$ on the smallest scale ( r = 0) is a unique feature of collisionless flow (⁠$\mathrm{ρ_{L}=1}$ for incompressible flow). On large scale, transverse velocity correlation has an exponential form $T_{2}∝e^{–r/r_2}$ with a constant comoving scale r 2 =21.3 Mpc/h that may be related to the horizon size at matter–radiation equality. All other correlation, structure, dispersion, and spectrum functions for velocity, density, and potential fields are derived analytically from kinematic relations for irrotational flow. On small scale, longitudinal structure function follows one-fourth law of ${S}_{2}^{1}∝r^{1/4}$. All other statistical measures can be obtained from kinematic relations for constant divergence flow. Vorticity is negatively correlated for scale r between 1 and 7 Mpc/h. Divergence is negatively correlated for r > 30 Mpc/h that leads to a negative density correlation.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Exact constraints and appropriate norms in machine-learned exchange-correlation functionals

Machine learning techniques have received growing attention as an alternative strategy for developing general-purpose density functional approximations, augmenting the historically successful approach of human-designed functionals derived to obey mathematical constraints known for the exact exchange-correlation functional. More recently, efforts have been made to reconcile the two techniques, integrating machine learning and exact-constraint satisfaction. We continue this integrated approach, designing a deep neural network that exploits the exact constraint and appropriate norm philosophy to de-orbitalize the strongly constrained and appropriately normed (SCAN) functional. The deep neural network is trained to replicate the SCAN functional from only electron density and local derivative information, avoiding the use of the orbital-dependent kinetic energy density. The performance and transferability of the machine-learned functional are demonstrated for molecular and periodic systems.

Artificial neural networks↗

DOE OSTI2026

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

Accurate and efficient interatomic potentials are essential for molecular dynamics (MD) simulations of radiation damage, gas diffusion, and phase stability in complex ceramics such as LiAlO 2 , especially under extreme conditions relevant to tritium production. Here, we evaluate the performance of six machine-learned interatomic potentials (MLIPs), moment tensor potential (MTP), Gaussian approximation potential, deep potential (DeePMD), atomic cluster expansion (ACE), message-passing ACE (multilayer atomic cluster expansion (MACE) pretrained) and MACE (trained from-scratch), all trained on the same density functional theory dataset with inclusion of tritium. The MLIPs are benchmarked against traditional Buckingham and ReaxFF potentials in terms of energy accuracy, density predictions, thermal equilibration behavior, threshold displacement energy (E d ), tritium diffusivity, and computational cost. Among the models, MTP shows the best overall balance between efficiency and accuracy, with low force and energy errors and realistic E d values for Li and Al. The ACE and MACE (pretrained and trained from scratch) models exhibit high E d (>200 eV) and unphysical pair interactions. DeePMD underestimates Ed due to overly repulsive behavior even at equilibrium distances. All models over-estimate tritium diffusion but the pretrained MACE model behaves well during tritium-diffusion simulations up to 500 K, maintaining diffusivities in the physically consistent 10 −11 m 2 /s range. Finally, we quantify the computational cost of each potential in large-scale atomic/molecular massively parallel simulator, finding that only MTP is more efficient than traditional empirical potentials, while others are significantly more expensive. These findings explain the trade-offs between accuracy and computational cost in MLIP development and provide essential guidance for use in high-throughput radiation damage and gas diffusion simulations in nuclear ceramics.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2021

Understanding Cu incorporation in the Cu 2x Hg 2-x GeTe 4 structure using resonant x-ray diffraction

The ability to control carrier concentration based on the extent of Cu solubility in the Cu 2x Hg 2-x GeTe 4 alloy compound (where 0 ≤ x ≤ 1) makes Cu 2x Hg 2-x GeTe 4 an interesting case study in the field of thermoelectrics. While Cu clearly plays a role in this process, it is unknown exactly how Cu incorporates into the Cu 2x Hg 2-x GeTe 4 crystal structure and how this affects the carrier concentration. In this work, we use a combination of resonant energy x-ray diffraction (REXD) experiments and density functional theory (DFT) calculations to elucidate the nature of Cu incorporation into the Cu 2x Hg 2-x GeTe 4 structure. REXD across the Cu k edge facilitates the characterization of Cu incorporation in the Cu 2x Hg 2-x GeTe 4 alloy and enables direct quantification of antisite defects. Here, we find that Cu substitutes for Hg at a 2:1 ratio, wherein Cu annihilates a vacancy and swaps with a Hg atom. DFT calculations confirm this result and further indicate that the incorporation of Cu occurs preferentially on one of the z = 1/4 or z = 3/4 planes before filling the other plane. Furthermore, the amount of Cu Hg antisite defects quantified by REXD was found to be directly proportional to the experimentally measured hole concentration, indicating that the Cu Hg defects are the driving force for tuning carrier concentration in the Cu 2x Hg 2-x GeTe 4 alloy. The link uncovered here between crystal structure, or more specifically antisite defects, and carrier concentration can be extended to similar cation-disordered material systems and will aid the development of improved thermoelectric and other functional materials through defect engineering.

36 MATERIALS SCIENCE↗

DOE OSTI2021

iSPECTRON: a simulation interface for linear and nonlinear spectra with ab-initio quantum chemistry software

We introduce iSPECTRON, an open source (under the Educational Community License version 2.0) program that parses data from common quantum chemistry software (NWChem, OpenMolcas, Gaussian, Cobramm, etc.), produces the input files for the simulation of linear and nonlinear spectroscopy of molecules with the Spectron code, and analyzes the spectra with a broad range of tools. Vibronic spectra are expressed in term of the electronic eigenstates, obtained through quantum chemistry computations, and vibrational/bath effects are incorporated in the framework of the displaced harmonic oscillator model, where all required quantities are computed at the Franck-Condon point. The code capabilities are illustrated by simulating linear absorption, transient absorption and two dimensional electronic spectra of the pyrene molecule. Two levels of electronic structure theory, TDDFT (with NWChem) and RASSCF/RASPT2 (with OpenMolcas), are compared where possible. Acknowledgements: F.S., A.N., D.R.N., N.G., S.M, M.G. acknowledge support from the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Chemical Sciences, Geosciences, and Biosciences Division under Award Nos. DE-SC0019484, KC-030103172684. The Spectron code was developed with support from the National Science Foundation (Grant CHE- 1953045). This research benefited from computational resources provided by EMSL, a DOE Office of Science User Facility sponsored by the Office of Biological and Environmental Research and located at PNNL. PNNL is operated by Battelle Memorial Institute for the United States Department of Energy under DOE Contract No. DE-AC05-76RL1830.

Segatta, Francesco↗

DOE OSTI2020

The synergistic role of Mn and Zr/Ti in producing θ'/L12 co-precipitates in Al-Cu alloys

Microstructural stability is a critical factor to consider when designing new alloys for high-temperature applications. An Al-Cu alloy with Mn and Zr additions has recently been developed to withstand extended exposures of up to 350 °C. The addition of Mn in combination with Zr and their segregation to precipitate interfaces play a significant role in stabilizing the metastable θ ' precipitates responsible for the alloy's hardness; however, adding Zr and Mn separately only improves the stability to 200 °C and 300 °C, respectively. To this end, the effect of the synergistic additions on interfacial structure and chemistry was studied in detail using atom probe tomography (APT) and scanning transmission electron microscopy (STEM) for Al-Cu-Mn-Zr/Ti-containing alloys subjected to long-term annealing (up to 2,100 h) in the critical temperature range, 300 °C and 350 °C, to investigate the role of Zr/Ti in increasing the θ '-precipitate stability. The APT and STEM results reveal that Mn additions stabilize θ ' long enough for the slower diffusing Zr atoms to segregate to coherent θ ' interfaces that eventually create a θ '/ L1 2 -Al 3 (Zr x ,Ti 1-x ) co-precipitate structure. The co-precipitate is highly stable, as shown by density functional theory calculations, and is a key factor that governs microstructural stability beyond 300 °C. This study reveals how solute additions with different stabilization mechanisms can work in concert to stabilize a desired microstructure, and the results provide insights that can be applied to other high-temperature alloy systems.

36 MATERIALS SCIENCE↗

DOE OSTI2023

VTAnDeM: A python toolkit for simultaneously visualizing phase stability, defect energetics, and carrier concentrations of materials

Phase stability, defect formation energies, and carrier concentrations are closely interrelated features of semiconductors. Due to their joint dependence on the multidimensional chemical potential space, it is challenging to quantitatively establish patterns between these quantities in a given semiconductor, especially when the semiconductor is comprised of multiple elements. To enable synchronous visualization and analysis of these complementary material properties and their interdependence, we developed the Visualization Toolkit for Analyzing Defects in Materials (VTAnDeM). This python-based toolkit allows users to interactively explore how defect formation energies and carrier concentrations vary across the composition and chemical potential spaces of multicomponent semiconductors. Here, we illustrate the computational workflow that employs VTAnDeM as a post-processing tool for first-principles calculations and describe the data organization and theory underlying the visualization scheme. Furthermore, we believe that this software will serve as a useful tool for simultaneously visualizing the often complex and non-intuitive chemical potential – defect – carrier concentration phase space of semiconductors.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Photoinduced anomalous Hall effect in two-dimensional transition metal dichalcogenides

A circularly polarized ac pump field illuminated near resonance on two-dimensional transition metal dichalcogenides (TMDs) produces an anomalous Hall effect in response to a dc bias field. In this work, we develop a theory for this photoinduced anomalous Hall effect in undoped TMDs irradiated by a strong coherent laser field. The strong field renormalizes the equilibrium bands and opens up a dynamical energy gap where single-photon resonance occurs. The resulting photon dressed states, or Floquet states, are treated within the rotating-wave approximation. A quantum kinetic equation approach is developed to study the nonequilibrium density matrix and time-averaged transport currents under the simultaneous influence of the strong ac pump field and the weak dc probe field. Dissipative effects are taken into account in the kinetic equation that captures relaxation and dephasing. The photoinduced longitudinal and Hall conductivities display notable resonant signatures when the pump field frequency reaches the spin-split interband transition energies. Rather than valley polarization, we find that the anomalous Hall current is mainly driven by the intraband response of photon-dressed electron populations near the dynamical gap at both valleys, accompanied by a smaller contribution due to the interband response. In conclusion, these findings highlight the importance of photon-dressed bands and nonequilibrium distribution functions in achieving a proper understanding of the photoinduced anomalous Hall effect in a strong pump field.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Efficient First-Principles Methodology for the Calculation of the All-Phonon Inelastic Scattering in Solids

Inelastic scattering experiments are key methods for mapping the full dispersion of fundamental excitations of solids in the ground as well as nonequilibrium states. A quantitative analysis of inelastic scattering in terms of phonon excitations requires identifying the role of multiphonon processes. Here, we develop an efficient first-principles methodology for calculating the all-phonon quantum mechanical structure factor of solids. We demonstrate our method by obtaining excellent agreement between measurements and calculations of the diffuse scattering patterns of black phosphorus, showing that multiphonon processes play a substantial role. The present approach constitutes a step towards the interpretation of static and time-resolved electron, x-ray, and neutron inelastic scattering data.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Biomimetic Fe–Cu Porphyrrole Aerogel Electrocatalyst for Oxygen Reduction Reaction

The development of bioinspired catalysts for oxygen reduction reaction is one of the most prominent pathways in the search for active materials to replace Pt-based catalysts in fuel cells. Herein, we report innovative bioinspired catalysts using a directed synthetic pathway to create adjacent Cu and Fe sites. This catalyst is composed of a covalent 3D framework in an aerogel form. Aerogels are high surface area and porous hierarchical structures that can allow the formation of ultrahigh active site density and optimized mass transport of reactants and products to and from the catalytic sites. The aerogel-based catalyst exhibits high performance in a half-cell in 0.1 M KOH, with an onset potential of 0.94 V vs RHE and half-wave potential of E 1/2 = 0.80 V vs RHE, high selectivity toward the four-electron reduction of oxygen to hydroxide anions, and high durability. These results are well-translated to the anion exchange membrane fuel cell (AEMFC), reaching an open circuit potential of 0.97 V and iR-corrected peak power density of 0.51 W cm –2 . Based on density functional theory calculations, the improved activity relative to the Fe-porphyrin and Cu-corrole is ascribed to the effect of the extended carbon network and the proximity of the metal sites.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

In-Silico Analysis of High Refractive Index Materials Through Principles of Materials Design

The intent of the paper is to use specific principles of Materials Design that were developed and applied in the electronics industry for enabling understanding and design of improved high refractive index materials. Further, by combining first-principle based ab-initio, semiempirical interatomic potential methods, and machine learning approaches in conjunction with experimental data, we identified specific determinants of high refractive index materials, which can be critically applied for informing materials design and accelerating discovery. Specifically, it was demonstrated that chalcogenides and perovskites as bulk materials can exhibit higher refractive indices with appropriate engineering of specific aspects of the materials.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Biochemical and structural characterization of a sphingomonad diarylpropane lyase for cofactorless deformylation

Lignin valorization is being intensely pursued via tandem catalytic depolymerization and biological funneling to produce single products. In many lignin depolymerization processes, aromatic dimers and oligomers linked by carbon–carbon bonds remain intact, necessitating the development of enzymes capable of cleaving these compounds to monomers. Recently, the catabolism of erythro -1,2-diguaiacylpropane-1,3-diol ( erythro -DGPD), a ring-opened lignin-derived β-1 dimer, was reported in Novosphingobium aromaticivorans . The first enzyme in this pathway, LdpA (formerly LsdE), is a member of the nuclear transport factor 2 (NTF-2)-like structural superfamily that converts erythro -DGPD to lignostilbene through a heretofore unknown mechanism. In this study, we performed biochemical, structural, and mechanistic characterization of the N. aromaticivorans LdpA and another homolog identified in Sphingobium sp. SYK-6, for which activity was confirmed in vivo. For both enzymes, we first demonstrated that formaldehyde is the C 1 reaction product, and we further demonstrated that both enantiomers of erythro -DGPD were transformed simultaneously, suggesting that LdpA, while diastereomerically specific, lacks enantioselectivity. We also show that LdpA is subject to a severe competitive product inhibition by lignostilbene. Three-dimensional structures of LdpA were determined using X-ray crystallography, including substrate-bound complexes, revealing several residues that were shown to be catalytically essential. We used density functional theory to validate a proposed mechanism that proceeds via dehydroxylation and formation of a quinone methide intermediate that serves as an electron sink for the ensuing deformylation. Overall, this study expands the range of chemistry catalyzed by the NTF-2-like protein family to a prevalent lignin dimer through a cofactorless deformylation reaction.

09 BIOMASS FUELS↗

DOE OSTI2026

ZENN: A thermodynamics-inspired computational framework for heterogeneous data–driven modeling

Traditional entropy-based methods—such as cross-entropy loss in classification problems—have long been essential tools for representing the information uncertainty and physical disorder in data and for developing artificial intelligence algorithms. However, the rapid growth of data across various domains has introduced new challenges, particularly the integration of heterogeneous datasets with intrinsic disparities. To address this, we introduce a zentropy-enhanced neural network (ZENN), extending zentropy theory into the data science domain via intrinsic entropy, enabling more effective learning from heterogeneous data sources. ZENN simultaneously learns both energy and intrinsic entropy components, capturing the underlying structure of multisource data. To support this, we redesign the neural network architecture to better reflect the intrinsic properties and variability inherent in diverse datasets. We demonstrate the effectiveness of ZENN on classification tasks and energy landscape reconstructions, showing its superior generalization capabilities and robustness-particularly in predicting high-order derivatives. In image and text classification tasks, ZENN demonstrates superior generalization by introducing a learnable temperature variable that models latent multisource heterogeneity, allowing it to surpass state-of-the-art models on CIFAR-10/100, BBC News, and AG News. As a practical application in materials science, we employ ZENN to reconstruct the Helmholtz energy landscape of Fe3Pt using data generated from density functional theory and capture key material behaviors, including negative thermal expansion and the critical point in the temperature–pressure space. Overall, this work presents a zentropy-grounded framework for data-driven machine learning, positioning ZENN as a versatile and robust approach for scientific problems involving complex, heterogeneous datasets.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Methane Partial Oxidation over Multifunctional 2-D Materials

The objective of this research is to design, synthesize, and evaluate highly selective, active, and stable multifunctional catalysts for the low temperature (< 500 Kelvin (K)) partial oxidation of methane to methanol (MTM) with molecular oxygen: CH 4 (g) + $\frac{1}{2}$O 2 (g) → CH 3 OH(g). Methane, the primary component of natural gas, is a source of energy and economic growth as well as an environmental concern. Recent developments in horizontal drilling and enhanced extraction methods have resulted in production of an estimated 62.4 trillion m 3 of ‘stranded’, or uneconomic, natural gas. Uneconomical natural gas is often flared or vented at remote oil production sites. Leaked, flared, and/or vented gas represents a "lost opportunity”, and this research project aims to maximize the value of the resource. Conventional catalysts for MTM suffer from low methanol selectivity since they exhibit ~0.55 eV higher barrier for C-H bond activation of methane compared to methanol. Without breaking these scaling relations, methanol oxidation is orders of magnitude faster than methane oxidation and it is very challenging to envision a process with economically viable single-pass yield. Here, we chose to investigate single-atom catalysts embedded and stabilized in two-dimensional materials such as graphene (GR) and "supported" on Group VIII and IB transition metals such as nickel. The electronic atomic monolayer-metal support interaction (EAMSI) present in these systems could promote methanol selectivity by breaking the scaling relations of the C-H bond activation of methane and methanol. A density functional theory (DFT) based computational study focused on predicting families of GR-based catalysts that could be active and selective for MTM. The catalyst systems predicted by the computational study were synthesized and evaluated for the gas phase MTM under relevant conditions. Unfortunately, the experimental activity and selectivity was lower than computationally predicted. The origin for the discrepancy is likely related to difficulties in synthesizing single atom catalysts in a threecomponent catalyst system at high density and with high selectivity. Future work in our groups is thus focused on reducing the system complexity to a two-component catalyst system. Finally, a techno-economic analysis (TEA) was also conducted to identify critical bottlenecks that inhibit future commercialization.

03 NATURAL GAS↗

DOE OSTI2021

Modeling the spectral modification of lower hybrid wave in the presence of drift-wave type density fluctuation in the scrape-off-layer of the EAST tokamak

In this work, the spectrum change of the lower hybrid (LH) waves caused by a low-frequency density fluctuation in the scrape-off-layer is studied by applying the wave scattering model developed by Bonoli and Ott [Phys. Fluids 25, 359 (1982)] via a Monte Carlo method. A ray-tracing model specific to this purpose is developed to evaluate the probability distribution of both the poloidal refractive index (N θ ) and the parallel refractive index (N ∥ ) of the LH wave at the last closed flux surface (LCFS).The probability distributions of N ∥ and N θ at the LCFS are studied using the EAST parameters as a function of wave frequency, the initial N ∥ , and the polar injection position, which may influence the lower hybrid current drive efficiency.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗