SEARCH · Search NASA
Results for “surface engineering”
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.
A Dynamic Landsat Derived Normalized Difference Vegetation Index (NDVI) Product for the Conterminous United States
Satellite derived vegetation indices (VIs) are broadly used in ecological research, ecosystem modeling, and land surface monitoring. The Normalized Difference Vegetation Index (NDVI), perhaps the most utilized VI, has countless applications across ecology, forestry, agriculture, wildlife, biodiversity, and other disciplines. Calculating satellite derived NDVI is not always straight-forward, however, as satellite remote sensing datasets are inherently noisy due to cloud and atmospheric contamination, data processing failures, and instrument malfunction. Readily available NDVI products that account for these complexities are generally at coarse resolution; high resolution NDVI datasets are not conveniently accessible and developing them often presents numerous technical and methodological challenges. Here, we address this deficiency by producing a Landsat derived, high resolution (30 m), long-term (30+ years) NDVI dataset for the conterminous United States. We use Google Earth Engine, a planetary-scale cloud-based geospatial analysis platform, for processing the Landsat data and distributing the final dataset. We use a climatology driven approach to fill missing data and validate the dataset with established remote sensing products at multiple scales. We provide access to the composites through a simple web application, allowing users to customize key parameters appropriate for their application, question, and region of interest.
Application of Aluminum Coatings in Vibrational Finishing of Steel Parts
The application of aluminum coatings to steel surfaces in vibrational finishing is experimentally studied. Such mechanical coatings are applied at elevated temperatures (120–300°С) by means of aluminum powder.
Concept of Dynamic Heat Insulation for Rotating Detonation Engines
This work introduces a new class of materials concept to dynamically reduce instantaneous heat fluxes in Rotating Detonation Engine (RDE) combustor chamber walls. The high-frequency and high-amplitude surface heat fluxes observed in RDEs arise from large instantaneous temperature differences between the detonation shockwave and chamber wall surface. These temperature gradients drive substantial energy loss and reduce the chamber gas pressure, ultimately limiting the cycle’s thermodynamic efficiency. This work introduces a concept for dynamically insulating the combustion chamber surfaces using surface layers or coatings with low thermal time scale. With such coatings, the surface temperature may follow the fluctuations of the cyclic detonation wave temperature, thus reducing the instantaneous heat flux therefore cycle-mean heat flux. To analyze these cyclic thermal phenomena, a one-dimensional analytical conduction solver was utilized with the capability to handle multilayered structures. Parametric modeling was performed using transient heat flux boundary conditions representative of a hydrogen–air RDE across a broad range of coating thermal properties and engine conditions. The coating effectiveness scaled with the product of thermal time constant and detonation wave frequency and the results were non-dimensionalized to guide future materials development. This strategy may offer benefits in increasing material survivability, reducing cooling requirements, and enhancing pressure gain.
An artificial interphase enables reversible magnesium chemistry in carbonate electrolytes
Magnesium-based batteries possess potential advantages over their lithium counterparts. However, reversible Mg chemistry requires a thermodynamically stable electrolyte at low potential, which is usually achieved with corrosive components and at the expense of stability against oxidation. In lithium-ion batteries the conflict between the cathodic and anodic stabilities of the electrolytes is resolved by forming an anode interphase that shields the electrolyte from being reduced. This strategy cannot be applied to Mg batteries because divalent Mg 2+ cannot penetrate such interphases. Here, we engineer an artificial Mg 2+ -conductive interphase on the Mg anode surface, which successfully decouples the anodic and cathodic requirements for electrolytes and demonstrate highly reversible Mg chemistry in oxidation-resistant electrolytes. The artificial interphase enables the reversible cycling of a Mg/V 2 O 5 full-cell in the water-containing, carbonate-based electrolyte. This approach provides a new avenue not only for Mg but also for other multivalent-cation batteries facing the same problems, taking a step towards their use in energy-storage applications.
First indirectly driven liquid-DT filled double shell implosions at the National Ignition Facility
Double shell implosions aim to explore material mixing under fusion conditions in a volume burn geometry using high-Z metal pushers. High-Z pushers are more compressible than low-Z pushers enabling high stagnation pressure which reduces the required implosion speed while maintaining a low pusher adiabat despite strong shock heating. Additionally, the use of a small fuel mass reduces the fuel internal energy required for ignition, thus achieving stable platforms with reasonable fusion output to conduct controlled experiments. These factors make volume burn in a double shell implosion highly promising. Recently, a series of liquid-DT filled, indirectly driven, double shell implosions were conducted at the National Ignition Facility with laser drives reaching up to 1.5 MJ. These experiments achieved a maximum DT neutron yield of 1.67 × 10 14 (yield—479 J), DT ion temperature of 2.6 keV, fuel areal density (ρR) of 0.15 g/cm 2 , and stagnation pressure of 79 Gbar. Over the course of these shots, the DT neutron yield has increased by an order of magnitude largely from improved mitigation of outer shell assembly joint driven instability growth using thicker gold plating at the assembly joint. Further performance improvements are expected by enhancing outer-to-inner shell kinetic energy transfer and refined mitigation of degradations from engineering features such as the outer shell joint, fill tube, and surface roughness.
AI-Enabled Discovery and Physics-Based Optimization of Energy Efficient Processing Strategies for Advanced Turbine Alloys (Final Technical Report)
In this project, the multi-organizational team of academic and industrial researchers from the University of Kentucky an aerospace and energy generation OEM partner has leveraged novel Digital Process Twin (DPT) models of process/structure interactions (i.e., process-induced surface integrity) to advance a paradigm of fully integrated computational materials engineering (ICME). Using efficient process models as the core of a digital process simulator for a reinforcement learning algorithm, the team has integrated industrial data and metrics of structure/performance/energy relationships and manufacturing-related energy metrics to optimize dynamic processing parameters for significantly improved life-cycle energy efficiency of advanced γ-TiAl low-pressure turbine (LPT) alloys, as indicated by a set of design relevant parameters (e.g., residual stresses and scrap rate). The key objective and anticipated outcome of the project was at least a 10% reduction in life-cycle embodied energy for a recently developed, γ-TiAl low-pressure turbine (LPT) alloy and nickel-based superalloy Inconel 718, through the adoption of the proposed AI-enabled process optimization approach. The final project outcomes significantly exceeded this original target, realizing manufacturing-related energy efficiency improvements of more than 130% for TiAl and up to 80% for Inconel 718. Rather than following the prevailing and highly inefficient empirical paradigm, the proposed study demonstrated the feasibility of adopting a digital, physics-based process design and optimization paradigm. The recurring need for manual intervention, rework, reinspection causes significant production bottlenecks and unnecessary expense associated with delivering the requisite component quality. The OEM partner, and turbine industry in general, expect to reap significant cost and resource savings if an AI-optimized set of parameters can be applied to specific machining operations. The technical scope of the proposed project involved the paving of a realistic path towards model-based and AI-enabled Integrated Computational Materials Engineering (ICME), and away from inefficient empirical process optimization and legacy manufacturing practices, which are no longer able to efficiently process novel high-performance turbine alloy materials. The project team will address the fundamental knowledge gap that currently exists within the ICME paradigm with respect to the process/structure/performance/energy impacts of finishing processes. While significant resources have been devoted to the ‘early stages’ of manufacturing, such as alloy design, primary and secondary processing, finishing processes have not been adequately integrated within ICME. To provide an actionable path towards model-based finishing process design (e.g., machining, burnishing, grinding, polishing), we will employ a novel AI-enabled process optimization paradigm, based on a computationally efficient, physics-based process simulator. Through limited experimental work to calibrate and validate our process simulator model via an advanced in-situ characterization technique and process optimization via reinforcement learning, the project will seek to demonstrate a viable alternative to the inefficient ‘legacy’ processing strategies, empirical testing and broad scope machining learning approaches, all of which fail to adequately consider complex process physics. The project team has identified an intermetallic γ-TiAl LPT alloy, which is currently being used as part of the OEM partner’s advanced gas turbine designs. This particular alloy poses significant manufacturing challenges during finishing operations, which limit the degree to which the current turbine design can be manufactured in an energy- and cost-efficient manner. Empirical testing and numerical modeling efforts to optimize processing parameters for γ-TiAl have not been able to resolve these manufacturing challenges, so the proposed physics-based AI-enabled optimization technology would offer a truly novel and transformative capability. The multi-organizational team of academic and industry experts from the UKY and the OEM partner will work together closely to demonstrate the analytical and experimental critical function and characteristic proof of concept of this novel approach.
Optimization of La 2–x Sr x CuO 4 Single Crystal Film Growth via Molecular Beam Epitaxy
Atomic layer-by-layer molecular beam epitaxy (ALL-MBE) combined with ozone is one of the best methods to fabricate single-crystal thin films of complex oxides. Cuprate such as La 2-x Sr x CuO 4 (LSCO) is a representative complex-oxide high-temperature superconductor (HTS) material. Our group utilizes this method to produce high-quality single-crystal HTS films with atomically smooth surfaces and interfaces. In addition, ALL-MBE enables us to engineer multilayer heterostructures with atomic precision. This allows the fabrication of tunnel junctions, various nanostructures, and other HTS devices of interest for superconducting electronics. We have synthesized over three thousand LSCO thin films in the past two decades. These films’ structural and electronic properties have been studied and characterized by various methods. Here, we distill the extensive experience we accumulated into a step-by-step protocol to fabricate atomically perfect LSCO films. The recipe includes substrate preparation, ozone generation and distillation, source calibration, the in situ monitoring of the film synthesis, post-growth annealing, and ex situ characterization. It discloses a reproducible way to fabricate single-crystal LSCO films for basic research and HTS electronic applications.
Nano-micro-structured Graphene Film Electrodes for Enhanced Performance of Supercapacitors
The graphene structures at either nanoscale and microscale are important to the film electrode performance. At nanoscale, a molecular engineering approach (i.e., molecular ligand grafting) was used to control the interspacing between restacked graphene sheets. At microscale, this work applied an external field to align the graphene sheets along the field direction during the film deposition from liquid slurry of molecularly engineered graphene. Five different ligand molecules were effectively grafted on the surfaces of well-dispersed graphene oxides (GOs) in liquid suspension and chemically reduced to GOs (rGOs), which then were modified by grafted molecules to form (m-rGOs). XRD data shows well controlled nanometer sized interlayer spacings between the re-stacked sheets of m-rGOs. Then, we investigated the effects of ligand grafting on the deposit film structure. Their baseline electrochemical cell performance was evaluated by different aqueous electrolytes (e.g., H2SO4 vs. KOH) for the following external field alignment. Initially, the m-rGO film electrodes exhibited higher capacitance performance with H2SO4 electrolyte, compared with KOH. Also, the external field has rendered the graphene sheets oriented velrtically to the film surface, resulting in enhanced capacitance due to enhanced diffusion and accessibility of electrolyte ions into the graphene film structure. Aligning graphene nanosheets in film deposits via external field could be a novel versatile fabrication technique to tailor microscopic architecture of graphene coatings for high performance supercapacitor.
Approaching PetaVolts per Meter Plasmonics Using Structured Semiconductors
A newly uncovered class of plasmons in the strongly excited limit opens access to unprecedented Petavolts per meter electromagnetic fields with wide-ranging, transformative impact. Unlike conventional plasmons, such plasmons are constituted by non-perturbative, large-amplitude oscillations of the ultradense, delocalized free electron Fermi gas inherent in conductive media. Here structured semiconductors doped to have an appropriate conduction electron density are introduced to tune the properties of the Fermi gas for matched excitation of large-amplitude plasmons using readily available electron beams which enables immediate experimental validation. Specifically, an electrostatic, surface “crunch-in” plasmon is collisionlessly excited by the beam launched inside a tube. Strong excitation due to matching results in relativistic oscillations of the electron gas and unravels unique phenomena. Relativistically induced ballistic electron transport comes about due to relativistic multifold increase in the mean free path and also leads to unconventional heat deposition beyond Ohm’s law. This explains the absence of observed damage or solid-plasma formation in past experiments on conductive samples interacting with electron bunches shorter than 10-13 seconds. Furthermore, relativistic momentum leads to copious tunneling of electron gas across the surface, which then crunches inside the tube. Relativistic effects along with large, localized electron density variations underlying these modes necessitate kinetic approach to theoretical and computational modeling. Kinetic model presented here demonstrates experimental viability of observing tens of gigavolts per meter plasmonic fields excited by matching readily available electron beams to plasmons in semiconductors with 1018cm-3 free electron density, and paves the way for Petavolts per meter plasmonics.
The Vacancy-Induced Electronic Structure of the SrTiO 3-δ Surface
The emergence of a 2D electron gas (2DEG) on the (001) surface of oxygen-deficient strontium titanate (SrTiO 3-δ ) is investigated. Using in situ soft X-ray spectroscopy and effective mass modeling, a series of quantitative band diagrams are developed to describe the evolution of near-surface and bulk carrier concentrations, downward band bending, and Fermi level along a lateral gradient of oxygen vacancies formed on SrTiO 3-δ by direct-current resistive heating under ultrahigh vacuum conditions. Electrons are accumulated over a 3 nm region near the surface, confined within a potential well with saturated 300 meV downward band bending. The relation between Fermi levels and carrier concentrations near the surface suggests the density of states near the surface is much lower than the bulk density of states, which is consistent with the quantum-confined subbands of a 2DEG. The quantitative relationship between the surface and bulk electronic structures developed in this work provides a guide for precise engineering of the oxygen-vacancy-induced 2DEG in SrTiO 3 .
Orientated Growth of Ultrathin Tellurium by van der Waals Epitaxy
Abstract Tellurium, as an elemental van der Waals semiconductor, has intriguing anisotropic physical properties owing to its inherent 1D crystal structure. To exploit the anisotropic and thickness‐dependent behavior, it is important to realize orientated growth of ultrathin tellurium. Here, van der Waals epitaxial growth of Te on the surface of 2D transition metal dichalcogenides is systematically investigated. Orientated growth of Te with a thickness down to 5 nm is realized on three‐fold symmetric substrates (WSe 2 , WS 2 , MoSe 2 , and MoS 2 ), where the atomic chains of Te are aligned with the armchair directions of substrates. 1D/2D moiré superlattices are observed for the Te/WSe 2 heterostructure. This method is extended to the growth of SeTe alloys, providing flexibility for band engineering. Finally, growth of textured Te film is demonstrated on the lower‐symmetry surface of WTe 2 .
Algorithm 1028: VTMOP: Solver for Blackbox Multiobjective Optimization Problems
VTMOP is a Fortran 2008 software package containing two Fortran modules for solving computationally expensive bound-constrained blackbox multiobjective optimization problems. VTMOP implements the algorithm of [32], which handles two or more objectives, does not require any derivatives, and produces well-distributed points over the Pareto front. The first module contains a general framework for solving multiobjective optimization problems by combining response surface methodology, trust region methodology, and an adaptive weighting scheme. The second module features a driver subroutine that implements this framework when the objective functions can be wrapped as a Fortran subroutine. Lastly, support is provided for both serial and parallel execution paradigms, and VTMOP is demonstrated on several test problems as well as one real-world problem in the area of particle accelerator optimization.
Ultrafast Sharpening of a Hard-Alloy Tool
The sharpening of hard-alloy cutting tools affects their life and reliability and the quality of the machined surface. In the case of imported tools for high-precision machining, the rounding radius of the cutting edge after sharpening is 10–15 µm. That permits machining with chip thickness no less than 20–30 µm. This limit is often critical, since the machining precision of some important components is 3–5 µm. In addition, such sharpening shortens the potential tool life. If sharpening is improved so that the rounding radius of the cutting edge is 3–5 µm, the machining precision is enhanced and tool life is extended. That improves machining [2].
Surface Quality of AMg2 Aluminum Alloy with Ultrafine Grain Structure after Machining. 1. Turning
The influence of cutting on the surface roughness and undulation of AMg2 aluminum alloy with regular and ultrafine grain structure is considered. The quantitative influence of profile filters on its surface roughness and undulation is established, on the basis of 2D parameters. In the machining of materials with ultrafine grain structure, the surface quality is better than for materials with regular grain structure.
A coupled rate theory-Monte Carlo model of helium bubble evolution in plasma-facing micro-engineered tungsten
A multiscale model of helium bubble evolution in plasma-facing materials is developed. The model links different stages of helium bubble evolution: deposition, nucleation, growth, motion, and coalescence. Helium deposition is simulated with the SRIM Monte Carlo program to give spatial information on helium and displacement damage distributions near the surface. This deposition profile is then introduced into a space-dependent rate theory of bubble nucleation and growth to describe the early stages of the distribution and size of helium bubbles. The coarsening stage of bubble evolution as a result of whole bubble motion, interaction, and coalescence is modeled by a new Object Kinetic Monte Carlo (OKMC) model, for which initial conditions are taken from the mean-field rate theory calculations. The model is compared to experimental data on low-energy helium plasma interaction with micro-engineered tungsten (W), and on high-energy helium ion deposition in flat W samples. Novel features of the multiscale model include: (1) space-dependent rate theory; (2) OKMC model of bubble motion in stress and temperature fields; and (3) application of the model to micro-engineered materials, and comparison with experiments on the same time-scale. At low helium ion energy, it is found that the mechanism of trap mutation is essential in achieving good agreement with experimental measurements. On the other hand, good agreement with experiments at high incident ion energy and temperature showed the importance of bubble coalescence and coarsening as main mechanisms. The results of the model are compared with experiments on flat W surfaces irradiated at high ion energy (30 keV), and with micro-engineered W, where the surface is coated with high-density micro-pillars at low ion energy (around 100 eV). The predicted average bubble radius and density are in qualitative agreement with experimental results.