Search NASA⌕ Search

SEARCH · Search NASA

Results for “structural dynamics testing”

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.

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

At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Role of Mg:Al ratio and Pt promotion on mitigating the deactivation of Ni-based methane steam reforming catalysts

Ni-based catalysts for CH 4 steam reforming require fine-tuning to withstand deactivation under dynamic operation conditions relevant to emerging H 2 -driven technologies. This study investigates the impact of the Mg:Al ratio and Pt presence in catalyst composition on the activity and stability of industrially relevant Ni-based mono- and bimetallic catalysts during simulated daily start-up and shut-down cycles in various gas atmospheres. The evolution of the catalyst structure during extensive testing procedures was investigated in detail by complementary electron microscopy, in situ/operando XAS and XRD. The results obtained revealed that catalyst deactivation is promoted at high Mg:Al ratios and especially affects the monometallic catalysts. By converting CH 4 at lower temperatures, Pt regulates the extent of Ni oxidation and its incorporation into MgO lattice. Further prevention of catalyst deactivation was achieved by optimizing the reactor shut-down procedure to minimize the simultaneous exposure to high temperatures and H 2 O vapors. By flushing the reactor with N 2 only around the reaction extinction temperature, a fraction of Ni species is maintained in metallic state, which is beneficial for the long-term activity and reaction operation economy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Oxide– and Silicate–Water Interfaces and Their Roles in Technology and the Environment

Interfacial reactions drive all elemental cycling on Earth and play pivotal roles in human activities such as agriculture, water purification, energy production and storage, environmental contaminant remediation, and nuclear waste repository management. The onset of the 21st century marked the beginning of a more detailed understanding of mineral aqueous interfaces enabled by advances in techniques that use tunable high-flux focused ultrafast laser and X-ray sources to provide near-atomic measurement resolution, as well as by nanofabrication approaches that enable transmission electron microscopy in a liquid cell. This leap into atomic- and nanometer-scale measurements has uncovered scale-dependent phenomena whose reaction thermodynamics, kinetics, and pathways deviate from previous observations made on larger systems. A second key advance is new experimental evidence for what scientists hypothesized but could not test previously, namely, interfacial chemical reactions are frequently driven by “anomalies” or “non-idealities” such as defects, nanoconfinement, and other nontypical chemical structures. Third, progress in computational chemistry has yielded new insights that allow a move beyond simple schematics, leading to a molecular model of these complex interfaces. In combination with surface-sensitive measurements, we have gained knowledge of the interfacial structure and dynamics, including the underlying solid surface and the immediately adjacent water and aqueous ions, enabling a better definition of what constitutes the oxide– and silicate–water interfaces. This critical review discusses how science progresses from understanding ideal solid–water interfaces to more realistic systems, focusing on accomplishments in the last 20 years and identifying challenges and future opportunities for the community to address. Further, we anticipate that the next 20 years will focus on understanding and predicting dynamic transient and reactive structures over greater spatial and temporal ranges as well as systems of greater structural and chemical complexity. Closer collaborations of theoretical and experimental experts across disciplines will continue to be critical to achieving this great aspiration.

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

Benchmarking machine learning interatomic potentials via phonon anharmonicity

Abstract Machine learning approaches have recently emerged as powerful tools to probe structure-property relationships in crystals and molecules. Specifically, machine learning interatomic potentials (MLIPs) can accurately reproduce first-principles data at a cost similar to that of conventional interatomic potential approaches. While MLIPs have been extensively tested across various classes of materials and molecules, a clear characterization of the anharmonic terms encoded in the MLIPs is lacking. Here, we benchmark popular MLIPs using the anharmonic vibrational Hamiltonian of ThO 2 in the fluorite crystal structure, which was constructed from density functional theory (DFT) using our highly accurate and efficient irreducible derivative methods. The anharmonic Hamiltonian was used to generate molecular dynamics (MD) trajectories, which were used to train three classes of MLIPs: Gaussian approximation potentials, artificial neural networks (ANN), and graph neural networks (GNN). The results were assessed by directly comparing phonons and their interactions, as well as phonon linewidths, phonon lineshifts, and thermal conductivity. The models were also trained on a DFT MD dataset, demonstrating good agreement up to fifth-order for the ANN and GNN. Our analysis demonstrates that MLIPs have great potential for accurately characterizing anharmonicity in materials systems at a fraction of the cost of conventional first principles-based approaches.

interatomic potentials↗
DOE OSTI2024

Flow and Heat Transfer Experimental Study for 3D-Printed Solar Receiving Tubes With Helical Fins at Internal Surface

3D-printing technology was applied to fabricate novel solar thermal collection tubes that have internal heat transfer enhancement fins and external surfaces with high solar absorptivity and low emissivity due to the ability to use different materials in one tube. Helical fins were selected to introduce circumferential flow and thus minimize the circumferential temperature difference of the tube that receives sunlight on one side. The structures of the helical fins were previously optimized from computational fluid dynamics (CFD) analysis with the objective of low entropy production rate by looking for high heat transfer coefficient and relatively lower pressure loss. High-temperature alloy, Inconel-718, was used to 3D print the tubes, which can resist corrosion for the potential application of molten chloride salts as heat transfer fluid. Experimental tests were carried out using water as the heat transfer fluid with the high heat flux provided by a tubular furnace heater. The tested Reynolds number ranges from 3.9 × 10 3 to 6.1 × 10 4 . Heat transfer coefficients of up to 2.8 times that of the smooth tube could be obtained with the expense of increased pressure loss compared to that of the smooth tube. The total system entropy generation can be significantly reduced due to the benefit of heat transfer enhancement that is greater than the expenses of the increased pressure loss. The experimental results of the 3D-printed heat transfer tubes confirmed the CFD-based results of fin optimization. Furthermore, the novel heat transfer tube is recommended for application in concentrating solar power systems.

14 SOLAR ENERGY↗
DOE OSTI2019

Automatic Differentiation for Adjoint Stencil Loops

Stencil loops are a common motif in computations including convolutional neural networks, structured-mesh solvers for partial differential equations, and image processing. Stencil loops are easy to parallelise, and their fast execution is aided by compilers, libraries, and domain-specific languages. Reverse-mode automatic differentiation, also known as algorithmic differentiation, autodiff, adjoint differentiation, or back-propagation, is sometimes used to obtain gradients of programs that contain stencil loops. Unfortunately, conventional automatic differentiation results in a memory access pattern that is not stencil-like and not easily parallelisable. In this paper we present a novel combination of automatic differentiation and loop transformations that preserves the structure and memory access pattern of stencil loops, while computing fully consistent derivatives. The generated loops can be parallelised and optimised for performance in the same way and using the same tools as the original computation. We have implemented this new technique in the Python tool PerforAD, which we release with this paper along with test cases derived from seismic imaging and computational fluid dynamics applications.

Automatic Differentiation↗
DOE OSTI2023

Exploitation of Defects in High Entropy Ceramic Barrier Materials

A critical mission need exists to develop new materials that can withstand extreme environments and multiple sequential threats. High entropy materials, those containing 5 or more metals, exhibit many exciting properties which would potentially be useful in such situations. However, a particularly hard challenge in developing new high entropy materials is determining a priori which compositions will form the desired single phase material. The project outlined here combined several modeling and experimental techniques to explore several structure-property-relationships of high entropy ceramics in an effort to better understand the connection between their compositional components, their observed properties, and stability. We have developed novel machine learning algorithms which rapidly predict stable high entropy ceramic compositions, identified the stability interplay between configurational entropy and cation defects, and tested the mechanical stability of high entropy oxides using the unique capabilities at the Dynamic Compression Sector facility and the Saturn accelerator.

36 MATERIALS SCIENCE↗
DOE OSTI2017

High Strain Rate Mechanical Characterization of Carbon Fiber Reinforced Polymer Composites Using Digital Image Correlations

We present that the introduction of carbon fiber reinforced polymer (CFRP) composites to structural components in lightweight automotive structures necessitates an assessment to evaluate that their crashworthiness dynamic response provides similar or higher levels of safety compared to conventional metallic structures. In order to develop, integrate and implement predictive computational models for CFRP composites that link the materials design, molding process and final performance requirements to enable optimal design and manufacturing vehicle systems for this study, the dynamic mechanical response of unidirectional (UD) and 2x2 twill weave CRFP composites was characterized at deformation rates applicable to crashworthiness performance. Non-standardized specimen geometries were tested on a standard uniaxial frame and an intermediate-to-high speed dynamic testing frame, equipped with high speed cameras for 3D digital image correlation (DIC). Specimen cross-sections, according to each fiber orientation tested, were consistent across strain rates to ensure results were comparable. Tensile strength and modulus were experimentally investigated over a wide range of strain rates (0.0001 to 200 s -1 ). DIC was used to estimate strain profiles on composites surfaces, and the modulus was calculated from those strain measurements. Experimental results demonstrated an increase in the tensile strength of UD CFRP composites in the longitudinal (0°) and transverse (90°) direction with increasing strain rates. In contrast, the tensile strength of 2x2 woven composites and the tensile modulus of the UD material were insensitive to increasing strain rates. Finally, comparison of failure modes provided insights on how loading rates influenced failure mechanisms.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Cloud-based implementation of white-box model predictive control for a GEOTABS office building: A field test demonstration

Model predictive control (MPC) has been proven in simulations and pilot case studies to be a superior control strategy for large buildings. MPC can utilize the weather and occupancy schedule forecasts, together with the system model, to predict the future thermal behavior of the building and minimize the overall energy use and maximize thermal comfort. However, these advantages come with the cost of increased modeling effort, computational demands, communication infrastructure, and commissioning efforts. Thus a typical approach is to, often rapidly, simplify the building modeling and MPC optimization problem while paying a price of not reaching the full performance potential. It has been shown that by employing accurate physics-based models, MPC performance can be notably increased closer to its theoretical performance bound. However, implementation of such high-fidelity MPC in real buildings remains a challenge, resulting in a lack of successful field test studies. This work presents the methodology and field test demonstration of a computationally efficient implementation of the white-box MPC in an office building in Belgium. The detailed model of the building is based on first-principle physical equations. The deployment and supervision of MPC operation in a practical setting are supported by an automated cloud-based communication infrastructure. The motivating factor behind the cloud-based architecture is its compatibility with a commercially appealing control as a service concept. The building is equipped with a ground source heat pump (GSHP) and thermally activated building structures (TABS), where the combination of both is also known as GEOTABS. From a control perspective, GEOTABS buildings are particularly challenging systems due to large scale, complex heating, ventilation and air conditioning (HVAC) system, and slow dynamics with time delays. On the other hand, there is an increased potential for energy savings due to the high thermal mass, which acts as thermal storage. The MPC operation is demonstrated during the challenging transient seasons (switching between heating and cooling), and its performance is compared to a traditional rule-based controller (RBC). We provide a proof of concept of real MPC operation for the most difficult seasons with notable GSHP energy use savings equal to 53.5% and thermal comfort improvement by 36.9%. Other MPC applications found in the literature describe tests for only cooling or only heating, and up to now only for a black-box or a grey-box approach.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

A framework for modeling and optimizing dynamic systems under uncertainty

Algebraic modeling languages (AMLs) have drastically simplified the implementation of algebraic optimization problems. However, there are still many classes of optimization problems that are not easily represented in most AMLs. These classes of problems are typically reformulated before implementation, which requires significant effort and time from the modeler and obscures the original problem structure or context. In this work we demonstrate how the Pyomo AML can be used to represent complex optimization problems using high-level modeling constructs. We focus on the operation of dynamic systems under uncertainty and demonstrate the combination of Pyomo extensions for dynamic optimization and stochastic programming. We use a dynamic semibatch reactor model and a large-scale bubbling fluidized bed adsorber model as test cases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Packaging a 650V/400A GaN Half-bridge Power Module with Ultra-low Parasitics for Electric Vehicle Drive Applications

This paper proposes a compact and efficient half-bridge power module with three 650 V / 150 A GaN dies in parallel. The power module incorporates a main power printed circuit board (PCB), an interface PCB, and a flex PCB to achieve low parasitics in both power loop and gate-side connection, resolving the issue of high parasitics typically encountered with wire bonding in high-current applications. Additionally, the interface PCB decouples the design constraints between the power loop and the gate loops. The proposed design is optimized with a vertical loop configuration to reduce power loop inductance through magnetic flux cancellation. Finite element analysis indicates that the power loop inductance is 0.58 nH at 100 MHz, while the maximum die junction temperature reaches 131 °C under an ambient temperature of 65 °C and a load current of 385 A. The proposed multi-piece PCB structure reduces the inductance of the drive circuit to minimize EMI and to mitigate false triggering. At the same time, it reduces impedance mismatches across different driver circuits, thereby achieving dynamic current sharing in multi-chip parallel configurations. Under simulation conditions of 400 V / 385 A, the current imbalance among chips was limited to 5 A. A 400 V / 385 A double-pulse test was conducted to experimentally validate the performance of the proposed power module.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2018

Characterization of Pharmaceutical Cocrystals and Salts by Dynamic Nuclear Polarization-Enhanced Solid-State NMR Spectroscopy

Multicomponent solids such as cocrystals have emerged as a way to control and engineer the stability, solubility and manufacturability of solid active pharmaceutical ingredients (APIs). Cocrystals are typically formed by solution- or solid-phase reactions of APIs with suitable cocrystal coformers, which are often weak acids. One key structural question about a given multicomponent solid is whether it should be classified as a salt, where the basic API is protonated by the acid, or as a cocrystal, where the API and coformer remain neutral and engage in hydrogen bonding interactions. It has previously been demonstrated that solid-state NMR spectroscopy is a powerful probe of structure in cocrystals and salts of APIs, however, the poor sensitivity of solid-state NMR spectroscopy usually restricts the types of experiments that can be performed. Here relayed dynamic nuclear polarization (DNP) was applied to reduce solid-state NMR experiments by one to two orders of magnitude for salts and cocrystals of a complex API. The large sensitivity gains from DNP facilitates rapid acquisition of natural isotopic abundance 13C and 15N solid-state NMR spectra. Critically, DNP enables double resonance 1H-15N solid-state NMR experiments such as 2D 1H-15N HETCOR, 1H-15N CP-build up, 15N{1H} J-resolved/attached proton tests, 1H-15N DIPSHIFT and 1H-15N PRESTO. The latter two experiments allow 1H-15N dipolar coupling constants and H-N bond lengths to be accurately measured, providing an unambiguous assignment of nitrogen protonation state and definitive classification of the multi-component solids as cocrystals or salts. In conclusion, these types of measurements should also be extremely useful in the context of polymorph discrimination, NMR crystallography structure determination and for probing hydrogen bonding in a variety of organic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Explaining and predicting human behavior and social dynamics in simulated virtual worlds: reproducibility, generalizability, and robustness of causal discovery methods

Ground Truth program was designed to evaluate social science modeling approaches using simulation test beds with ground truth intentionally and systematically embedded to understand and model complex Human Domain systems and their dynamics Lazer et al. (Science 369:1060–1062, 2020). Our multidisciplinary team of data scientists, statisticians, experts in Artificial Intelligence (AI) and visual analytics had a unique role on the program to investigate accuracy, reproducibility, generalizability, and robustness of the state-of-the-art (SOTA) causal structure learning approaches applied to fully observed and sampled simulated data across virtual worlds. In addition, we analyzed the feasibility of using machine learning models to predict future social behavior with and without causal knowledge explicitly embedded. In this paper, we first present our causal modeling approach to discover the causal structure of four virtual worlds produced by the simulation teams—Urban Life, Financial Governance, Disaster and Geopolitical Conflict. Our approach adapts the state-of-the-art causal discovery (including ensemble models), machine learning, data analytics, and visualization techniques to allow a human-machine team to reverse-engineer the true causal relations from sampled and fully observed data. We next present our reproducibility analysis of two research methods team’s performance using a range of causal discovery models applied to both sampled and fully observed data, and analyze their effectiveness and limitations. We further investigate the generalizability and robustness to sampling of the SOTA causal discovery approaches on additional simulated datasets with known ground truth. Our results reveal the limitations of existing causal modeling approaches when applied to large-scale, noisy, high-dimensional data with unobserved variables and unknown relationships between them. We show that the SOTA causal models explored in our experiments are not designed to take advantage from vasts amounts of data and have difficulty recovering ground truth when latent confounders are present; they do not generalize well across simulation scenarios and are not robust to sampling; they are vulnerable to data and modeling assumptions, and therefore, the results are hard to reproduce. Finally, when we outline lessons learned and provide recommendations to improve models for causal discovery and prediction of human social behavior from observational data, we highlight the importance of learning data to knowledge representations or transformations to improve causal discovery and describe the benefit of causal feature selection for predictive and prescriptive modeling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Effect of Different Cooling Rates on the Corrosion Behavior of High-Carbon Pearlitic Steel

The present work discusses the effect of pearlitic morphology on the corrosion behavior of high-carbon fully pearlitic steel in 3.5% NaCl solution. Four different types of pearlitic steels (furnace-cooled, as-received, air-cooled and forced-air-cooled) consisting of coarse, medium, fine and very fine microstructures, respectively, were tested. Electrochemical behavior of these steels was studied with the help of dynamic and linear polarization and AC impedance spectroscopic tests. The corrosion resistance improved with fineness of the microstructure in general. However, with further reduction in interlamellar spacing beyond a limit, the corrosion resistance reduced slightly. Formation of homogeneous distribution of microgalvanic cells between cementite and ferrite lamellae of fine pearlitic steel improved the corrosion resistance. However, entanglement of the lamellae of pearlite in very fine pearlitic structure as well as breaking of cementite lamellae due to finer pearlitic colonies was attributed to the higher corrosion of the forced-air-cooled steel as compared to the air-cooled steel.

36 MATERIALS SCIENCE↗
DOE OSTI2018

EBSD analysis of surface and bulk microstructure evolution during interrupted tensile testing of a Fe-19Cr-12Ni alloy

Highlights: • Similar deformation structures are obtained for surface and bulk grains. • A lower strain is needed for twinning in surface grains than in bulk grains. • A reduced ability to cross-slip due to low SFE leads to an increasing LAB density. • Twinning reduces the dislocation slip length. • High strain-hardening rate is maintained up to high strains. - Abstract: The microstructure evolution in both surface and bulk grains in a pure Fe-19Cr-12Ni alloy has been analyzed using electron backscatter diffraction after tensile testing interrupted at different strains. Surface grains were studied during in situ tensile testing performed in a scanning electron microscope, whereas bulk grains were studied after conventional tensile testing. The evolution of the deformation structure in surface and bulk grains displays a strong resemblance but the strain needed to obtain a similar deformation structure is lower in the case of surface grains. Both slip and twinning are observed to be important deformation mechanisms, whereas deformation-induced martensite formation is of minor importance. Since the stacking fault energy (SFE) is low, ~17 mJ/m{sup 2}, dynamic recovery by cross slip of un-dissociated dislocations is unfavorable. This reduces the annihilation of dislocations which in turn leads to a significant increase of low angle boundaries with increasing strain. The low SFE also favors formation of deformation twins which reduces the slip distance, leading to a hardening similar to the Hall-Petch relation. The combination of a low ability for cross-slip and a reduced slip distance caused by twinning is concluded to be the main reason for maintaining a high strain-hardening rate up to strains close to necking.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Nutrient Dynamics in a Coupled Terrestrial Biosphere and Land Model (ELM-FATES-CNP)

We present a representation of nitrogen and phosphorus cycling in the Functionally Assembled Terrestrial Ecosystem Simulator, a demographic vegetation model within the Energy Exascale Earth System land model. This representation is modular, and designed to allow testing of multiple hypothetical approaches for carbon-nutrient coupling in plants. Novel model hypotheses introduced in this work include, (a) the controls on plant acquisition of aqueous mineralized nutrients in the soil and (b) fairly straight forward methods of allocating nutrients to specific plant organs and their losses through live plant turnover as well as litter fluxes generated through plant mortality. This combines the new with pre-existing hypotheses (such as nitrogen fixation and soil decomposition) into a system that can accommodate plant-soil dynamics for a large number of size- and functional-type-resolved plant cohorts within a time-since-disturbance-resolved ecosystem. Root uptake of nutrients is governed by fine root biomass, and plants vary in their fine root biomass allocation in order to balance carbon and nutrient limitations to growth. We test the sensitivity of the model to a wide range of parameter variations and structural representations, and in the context of observations at Barro Colorado Island, Panama. A key model prediction is that plants in the high-light-availability canopy positions allocate more carbon to fine roots than plants in low-light understory environments, given the widely different carbon versus nutrient constraints of these two niches within a given ecosystem. This model provides a basis for exploring carbon-nutrient coupling with vegetation demography within Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Modes of strain accommodation in Cu-Nb multilayered thin film on indentation and cyclic shear

Two-phase layered thin films with a high density of semi-coherent interfaces exhibit excellent mechanical properties and thermal stability. Here, in this study, a magnetron-sputtered Cu-Nb dual-layered thin film (~500 nm for Cu and ~150 nm for Nb) having an amorphous interface between Cu and Nb with a high density of aligned growth twins in Cu is subjected to severe surface deformation. The material is loaded using indentation and cyclic shear under tribological testing. The strain accommodation in the subsurface microstructure after deformation varies based on the local structure and deformation mode. Grain refinement and crack formations in the stressed region of the Nb layer and localized crystallization of the amorphous interface are observed after indentation and scratch testing. Pronounced detwinning of growth twins in the Cu layer under the cyclic shear strain leaves large dislocations sites and loops which are observed both by high-resolution transmission electron microscopy and experiment-guided molecular dynamic (MD) simulations. Our simulations provided insights into understanding the pathway for the detwinning process under cyclic shear loading.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Achievements and Prospects of Global Broadband Seismographic Networks After 30 Years of Continuous Geophysical Observations

Global seismographic networks (GSNs) emerged during the late nineteenth and early twentieth centuries, facilitated by seminal international developments in theory, technology, instrumentation, and data exchange. The mid- to late-twentieth century saw the creation of the World-Wide Standardized Seismographic Network (1961) and International Deployment of Accelerometers (1976), which advanced global geographic coverage as seismometer bandwidth increased greatly allowing for the recording of the Earth's principal seismic spectrum. The modern era of global observations and rapid data access began during the 1980s, and notably included the inception of the GEOSCOPE initiative (1982) and GSN (1988). Through continual improvements, GEOSCOPE and the GSN have realized near-real time recording of ground motion with state-of-art data quality, dynamic range, and timing precision to encompass 180 seismic stations, many in very remote locations. Data from GSNs are increasingly integrated with other geophysical data (e.g., space geodesy, infrasound and Interferometric Synthetic Aperture Radar). Globally distributed seismic data are critical to resolving crust, mantle, and core structure; illuminating features of the plate tectonic and mantle convection system; rapid characterization of earthquakes; identification of potential tsunamis; global nuclear test verification; and provide sensitive proxies for environmental changes. As the global geosciences community continues to advance our understanding of Earth structure and processes controlling elastic wave propagation, GSN infrastructure offers a springboard to realize increasingly multi-instrument geophysical observatories. Here, we review the historical, scientific, and monitoring heritage of GSNs, summarize key discoveries, and discuss future associated opportunities for Earth Science.

58 GEOSCIENCES↗
DOE OSTI2023

Dynamics of plasma atomic layer etching: Molecular dynamics simulations and optical emission spectroscopy

Atomic layer etching is intrinsically dynamic as it involves sequential and repeated exposures of a surface to be etched with different species at different energies. The composition and structure of the near surface region change in both time and depth. Full understanding of this process requires resolving both temporal and spatial variations. In this work, we consider silicon (Si) atomic layer etching (ALE) by alternating exposure to chlorine gas (Cl 2 ) and argon ions (Ar + ). Molecular dynamics (MD) simulations are compared to experimental measurements with the aim of better understanding the dynamics of ALE and to test the simulation procedure. The simulations help to more fully interpret the experimental measurements. Further, optical emission measured just above the surface being etched can be related to etch products and can, therefore, be directly compared to simulation predictions. The simulations capture the measured initial product distribution leaving the surface and match the measured etch per cycle reasonably well. While simulations demonstrate the importance of ion-induced surface damage and mixing into a layer below the surface, the depth of which depends mainly on ion energy, the experiments suggest there is more Cl mixed into the layer than the MD procedure predicts.

74 ATOMIC AND MOLECULAR PHYSICS↗