SEARCH · Search NASA
Results for “structural optimization”
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.
Unraveling and Exploiting Complex Structures in Optimization Using OptiGraphs.
Abstract not provided.
Gradient-based Optimization for Structural Isolation and Scattering Minimization.
Abstract not provided.
Improved Simulation Tools for Optimizing Structure-Based Beam Acceleration and Manipulation
Explore the source record for details and available documents.
Global Optimization of Chemical Cluster Structures: Methods, Applications, and Challenges
Chemical clusters are relevant to many applications in catalysis, separations, materials, and energy sciences. Experimentally, the structure of clusters is difficult to determine, but it is very important in understanding their chemistry and properties. Computational methods can be used to examine cluster structure, however finding the most stable structure is not simple, particularly as the cluster size increases. Global optimization techniques have long been used to tackle the problem of the most stable structure, but such approaches would have to look for a global minimum, while sampling local minima over the whole potential energy surface as well. In this review, the state-of-the-art theory of global optimization theory is summarized. First, the definition, significance, relation to experiments, and a brief history of global optimization is presented. We then discuss, in more detail, three versatile global optimization methods: the basin hopping, the artificial bee colony algorithm, and the genetic algorithm. We close with some representative application examples of global optimization of clusters since 2016 and the challenges, open questions and opportunities in this field.
A nearsighted force-training approach to systematically generate training data for the machine learning of large atomic structures
A challenge of atomistic machine-learning (ML) methods is ensuring that the training data are suitable for the system being simulated, which is particularly challenging for systems with large numbers of atoms. Most atomistic ML approaches rely on the nearsightedness principle (“all chemistry is local”), using information about the position of an atom’s neighbors to predict a per-atom energy. Here, in this work, we develop a framework that exploits the nearsighted nature of ML models to systematically produce an appropriate training set for large structures. We use a per-atom uncertainty estimate to identify the most uncertain atoms and extract chunks centered around these atoms. It is crucial that these small chunks are both large enough to satisfy the ML’s nearsighted principle (that is, filling the cutoff radius) and are large enough to be converged with respect to the electronic structure calculation. We present data indicating when the electronic structure calculations are converged with respect to the structure size, which fundamentally limits the accuracy of any nearsighted ML calculator. These new atomic chunks are calculated in electronic structures, and crucially, only a single force—that of the central atom—is added to the growing training set, preventing the noisy and irrelevant information from the piece’s boundary from interfering with ML training. The resulting ML potentials are robust, despite requiring single-point calculations on only small reference structures and never seeing large training structures. We demonstrated our approach via structure optimization of a 260-atom structure and extended the approach to clusters with up to 1415 atoms.
Optimization of metal-supported solid oxide fuel cells with a focus on mass transport
Performance of symmetric-architecture metal-supported solid oxide fuel cells was improved significantly by optimizing the catalyst infiltration process and metal support structure. Optimization of component structure and processing parameters was performed during tape-casting and fabrication of button cells. Mass transport of oxygen in the metal support was identified as a major limitation. To overcome this limitation, pore former loading and thickness of the metal support (130–250 μm) were optimized. The catalyst infiltration process was also improved by studying the impact of firing temperature (400 °C–900 °C) and infiltration cycle numbers (1–15). The maximum power density of the optimized cell was 0.9 W cm –2 at 700°C using hydrogen as a fuel, a three-fold increase over the baseline cell performance. The degradation rate of optimized cells at 550 °C, 600 °C, and 700 °C was 2%, 4.5%, and 5.5% per 100 h, respectively. Here, the phenomena of mass transport, catalyst coarsening, and chromium poisoning on the catalyst were analyzed by electrochemical impedance spectroscopy and scanning electron microscopy.
Enumeration as a Tool for Structure Solution: A Materials Genomic Approach to Solving the Cation-Ordered Structure of Na 3 V 2 (PO 4 ) 2 F 3
While powder diffraction methods are routinely utilized to optimize structural models for compounds whose crystal structures are known, the determination of unknown structures is far more challenging. When the unknown structure is large, structure solution can become a virtually intractable problem using standard structure solution methodologies, especially when the space group cannot be unambiguously resolved. One such system is the promising Na-ion battery cathode material Na 3 V 2 (PO 4 ) 2 F 3 whose high temperature and room temperature structures were previously solved, but whose more complex low-temperature structure could not be determined. Here, a novel materials genomic approach is demonstrated for the solution of the unknown 100 K structure of Na 3 V 2 (PO 4 ) 2 F 3 in which enumeration methods are first used to generate a large number (~3,000) of trial structures based on plausible orderings of Na ions and then automated Rietveld refinements are carried out to optimize each of these trial structures. Based on both the analysis of the ensemble of optimized trial structures and the density functional theory energy minimization of selected trial structures, the 100 K structure of Na 3 V 2 (PO 4 ) 2 F 3 is best described as belonging to the space group A2 1 am with unit cell dimensions of a = 9.01928(4), b = 27.1379(1), c = 10.73307(5). The 100 K unit cell has a large volume of 2627.07(2) Å 3 with Z = 12 and 33 independent crystallographic sites (9 Na, 3 V, 3 P, 12 O, and 6 F) that is 3x and 6x larger than the room- and high-temperature polymorphs of this phase, respectively. Finally, the novel methods described here will be generally applicable for the solution of the complex cation-ordered structures that commonly occur for battery materials.
Quantifying the Site Heterogeneities of Non‐Uniform Catalysts Using QuantEXAFS
Abstract We present the Multi‐site (MS) QuantEXAFS approach, designed to model the EXAFS data from samples containing an element in different local bonding geometries. Building upon our QuantEXAFS method, which maps experimental extended X‐ray absorption fine structures (EXAFS) data to DFT‐optimized structures, MS‐QuantEXAFS introduces the key capability to probe fractional contributions of multiple sites that may be present in an experimental sample. Specifically, we demonstrate effectiveness of this technique by investigating mixed samples containing known fractions of site‐isolated subsurface Pt/MgO with Pt nanoparticles uniformly supported on MgO. The ‘site‐fractions’ obtained through MS‐QuantEXAFS closely match (i. e., ±6%) the known fractions of the physically mixed samples. This approach has been generalized to other oxides, and thus represents an important advance in quantifying the speciation of non‐uniform catalyst samples.
Land-based wind turbines with flexible rail-transportable blades – Part 2: 3D finite element design optimization of the rotor blades
Abstract. Increasing growth in land-based wind turbine blades to enable higher machine capacities and capacity factors is creating challenges in design, manufacturing, logistics, and operation. Enabling further blade growth will require technology innovation. An emerging solution to overcome logistics constraints is to segment the blades spanwise and chordwise, which is effective, but the additional field-assembled joints result in added mass and loads, as well as increased reliability concerns in operation. An alternative to this methodology is to design slender flexible blades that can be shipped on rail lines by flexing during transport. However, the increased flexibility is challenging to accommodate with a typical glass-fiber, upwind design. In a two-part paper series, several design options are evaluated to enable slender flexible blades: downwind machines, optimized carbon fiber, and active aerodynamic controls. Part 1 presents the system-level optimization of the rotor variants as compared to conventional and segmented baselines, with a low-fidelity representation of the blades. The present work, Part 2, supplements the system-level optimization in Part 1 with high-fidelity blade structural optimization to ensure that the designs are at feasible optima with respect to material strength and fatigue limits, as well as global stability and structural dynamics constraints. To accommodate the requirements of the design process, a new version of the Numerical Manufacturing And Design (NuMAD) code has been developed and released. The code now supports laminate-level blade optimization and an interface to the International Energy Agency Wind Task 37 blade ontology. Transporting long, flexible blades via controlled flapwise bending is found to be a viable approach for blades of up to 100 m. The results confirm that blade mass can be substantially reduced by going either to a downwind design or to a highly coned and tilted upwind design. A discussion of active and inactive constraints consisting of material rupture, fatigue damage, buckling, deflection, and resonant frequencies is presented. An analysis of driving load cases revealed that the downwind designs are dominated by loads from sudden, abrupt events like gusts rather than fatigue. Finally, an analysis of carbon fiber spar caps for downwind machines finds that, compared to typical carbon fibers, the use of a new heavy-tow carbon fiber in the spar caps is found to yield between 9 % and 13 % cost savings.
Optimization of a traveling wave superconducting radiofrequency cavity for upgrading the International Linear Collider
The standing wave TESLA niobium-based superconducting radio frequency structure is limited to an accelerating gradient of about 50 MV/m by the critical rf magnetic field. To break through this barrier, we explore the option of niobium-based traveling wave (TW) structures. Optimization of TW structures was done considering experimentally known limiting electric and magnetic fields. It is shown that a TW structure can have an accelerating gradient above 70 MeV/m that is about 1.5 times higher than contemporary standing wave structures with the same critical magnetic field. The other benefit of TW structures shown is R/Q about 2 times higher than the TESLA structure that reduces the dynamic heat load by a factor of 2. A method is proposed how to make TW structures multipactor-free. Some design proposals are offered to facilitate fabrication. Further increase of the real-estate gradient (equivalent to 80 MV/m active gradient) is also possible by increasing the length of the accelerating structure because of higher group velocity and cell-to-cell coupling. Realization of this work opens paths to International Linear Collider energy upgrades beyond 1 to 3 TeV in competition with CLIC. The paper will discuss corresponding opportunities and challenges.
The role of SAXS and molecular simulations in 3D structure elucidation of a DNA aptamer against lung cancer
Aptamers are short, single-stranded DNA or RNA oligonucleotide molecules that function as synthetic analogs of antibodies and bind to a target molecule with high specificity. Aptamer affinity entirely depends on its tertiary structure and charge distribution. Therefore, length and structure optimization are essential for increasing aptamer specificity and affinity. Here, we present a general optimization procedure for finding the most populated atomistic structures of DNA aptamers. Based on the existed aptamer LC-18 for lung adenocarcinoma, a new truncated LC-18 (LC-18t) aptamer LC-18t was developed. A three-dimensional (3D) shape of LC-18t was reported based on small-angle X-ray scattering (SAXS) experiments and molecular modeling by fragment molecular orbital or molecular dynamic methods. Molecular simulations revealed an ensemble of possible aptamer conformations in solution that were in close agreement with measured SAXS data. The aptamer LC-18t had stronger binding to cancerous cells in lung tumor tissues and shared the binding site with the original larger aptamer. The suggested approach reveals 3D shapes of aptamers and helps in designing better affinity probes.
Crystal structure of perfluorononanoic acid, C 9 HF 17 O 2
The crystal structure of perfluorononanoic acid (PFNA) was solved via parallel tempering using synchrotron powder diffraction data obtained from the Brockhouse X-ray Diffraction and Scattering (BXDS) Wiggler Lower Energy (WLE) beamline at the Canadian Light Source. PFNA crystallizes in monoclinic space group P2 1 /c (#14) with lattice parameters a = 26.172(1) Å, b = 5.6345(2) Å, c = 10.9501(4) Å, and β = 98.752(2)°. The crystal structure is composed of dimers, with pairs of PFNA molecules connected by hydrogen bonds via the carboxylic acid functional groups. The Rietveld-refined structure was compared to a density functional theory-optimized structure, and the root-mean-square Cartesian difference was larger than normally observed for correct powder structures. The powder data likely exhibited evidence of disorder which was not successfully modeled.
Architecting the Third Dimension of Electrochemical Energy Storage
Three-dimensional (3D) architectural design has emerged as a powerful strategy to push electrochemical energy storage (EES) devices beyond the intrinsic limitations of conventional two-dimensional (2D) electrodes. While planar architectures enable high packing density and mature manufacturing, they suffer from limited ion transport and low active-material loading. In contrast, 3D architectures introduce low-tortuosity networks and high surface area that enhance charge and mass transport while supporting thick, high mass-loading electrodes. However, their practicality remains hindered by challenges in volumetric density, mechanical stability, and large-scale manufacturability. Here, this Perspective examines the key evaluation and design principles that govern 3D device performance. We discuss the fundamental trade-offs between porosity, volumetric density, and mechanical stability that shape 3D design and highlight emerging strategies for integrating materials engineering, structural optimization, device integration, computational modeling, and scalable manufacturing. By aligning structural functionality with manufacturability, 3D architectures can evolve from laboratory prototypes to commercially viable energy storage systems.
Process-structure-property relationships for droplet-on-demand liquid-metal-jetted parts
Liquid metal jetting droplet-on-demand additive manufacturing is an attractive alternative to industry-standard, laser-based metal additive manufacturing techniques that use powder feedstocks and produce large heat affected zones. Liquid metal jetting provides a low feedstock footprint, minimal materials waste, less contamination and inclusions from feedstock, and is compatible with a large range of materials. However, little has been reported on the liquid metal jetting process-structure-property relationships that are crucial to understanding how to consistently print high quality metal parts. We investigate the effects of build plate temperature, infill pattern, and track raster rate on the macro- and microstructure, density, hardness, and tensile strength of metal parts created with our custom droplet-on-demand setup using pure tin feedstock as a surrogate for more relevant structural materials. We found that although the build plate temperature had the largest influence on the structure and properties, the infill pattern is an important factor for optimal structural integrity. It was observed that the track raster rate influenced track-to-track porosity and should be considered in toolpath development. Despite printing in ambient air, we could not identify oxide on the surface or in the bulk of the parts and saw no adverse effects on microstructure or tensile performance. The results and methods of this study will help guide future work in optimizing the liquid metal jetting process to reliably print parts using any liquid metal jetting system for a wide variety of metal feedstocks.
Insulating moiré homobilayers lack a threefold symmetric second-harmonic generation
Atoms within moiré bilayers relax in plane to minimize elastic energy; such relaxation brings their space group symmetries down to P1. Here, the ab initio second harmonic generation (SHG) of twisted and atomistically optimized hBN bilayers was determined at four twist angles (θ = 38.21°, 60.00°, 73.17°, and 98.21°) and for three displacements τ measured away from the ground state AA' configuration. All moiré bilayers have a P1 space symmetry after structural optimization. This situation is quite different to monolayers with hexagonal lattices, which retain a threefold symmetry. We point out that the actual symmetries of the SHG reported for hBN bilayers on two experimental works do not coincide with the sixfold symmetric theoretical profiles they provide [either sin 2 (3Φ) or cos 2 (3Φ)], and show that the intrinsic low structural symmetry of (atomically optimized) hBN bilayer moirés can in fact be read out from experimental SHG intensity profiles—which are tunable by θ and by the frequency of light ω: The SHG is most definitely not sixfold symmetric because moirés do not retain a threefold symmetry. Furthermore, an extrinsic twofold symmetry of the SHG emission is realized by tilting the pump by an angle α away from the 2D material's normal, regardless of θ and ω. Furthermore, the design of in-plane and ultrathin sources of SHG with low symmetry could be useful for the eventual creation of entanglement sources from 2D materials.
Constraining the Varied Response of Northern Hemisphere Winter Circulation Waviness to Climate Change
Abstract While a large latitudinal displacement of the westerly jet brings about disproportionate socioeconomic impacts over Northern Hemisphere midlatitude continents, it is not well understood as to whether the winter circulation will become wavier or less in response to climate change. Here, using observations and large ensembles of climate models, we show that changes in atmospheric waviness can be estimated from the optimal structures of the westerly jet for wavier circulation, which are obtained from an advection‐diffusion model. Thus, the changes in westerly jet structure in climate models under climate change provide a physical constraint on changes in atmospheric waviness, indicating that the North Atlantic wave activity will experience a robust decline in a warmer climate, while future North Pacific wave activity is obscured by model uncertainty rather than internal variability. These findings highlight the changes to jet stream structure as a constraint for regional circulation waviness in a changing climate.
Accelerating defect predictions in semiconductors using graph neural networks
First-principles computations reliably predict the energetics of point defects in semiconductors but are constrained by the expense of using large supercells and advanced levels of theory. Machine learning models trained on computational data, especially ones that sufficiently encode defect coordination environments, can be used to accelerate defect predictions. Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of group IV, III–V, and II–VI zinc blende semiconductors, powered by crystal Graph-based Neural Networks (GNNs) trained on high-throughput density functional theory (DFT) data. Using an innovative approach of sampling partially optimized defect configurations from DFT calculations, we generate one of the largest computational defect datasets to date, containing many types of vacancies, self-interstitials, anti-site substitutions, impurity interstitials and substitutions, as well as some defect complexes. We applied three types of established GNN techniques, namely crystal graph convolutional neural network, materials graph network, and Atomistic Line Graph Neural Network (ALIGNN), to rigorously train models for predicting defect formation energy (DFE) in multiple charge states and chemical potential conditions. We find that ALIGNN yields the best DFE predictions with root mean square errors around 0.3 eV, which represents a prediction accuracy of 98% given the range of values within the dataset, improving significantly on the state-of-the-art. We further show that GNN-based defective structure optimization can take us close to DFT-optimized geometries at a fraction of the cost of full DFT. The current models are based on the semi-local generalized gradient approximation-Perdew–Burke–Ernzerhof (PBE) functional but are highly promising because of the correlation of computed energetics and defect levels with higher levels of theory and experimental data, the accuracy and necessity of discovering novel metastable and low energy defect structures at the PBE level of theory before advanced methods could be applied, and the ability to train multi-fidelity models in the future with new data from non-local functionals. The DFT-GNN models enable prediction and screening across thousands of hypothetical defects based on both unoptimized and partially optimized defective structures, helping identify electronically active defects in technologically important semiconductors.