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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Thermal Shape Stability of fcc Metal Nanocrystals Synthesized with Faceted Nonequilibrium Shapes

Highly refined capabilities of the shape-controlled solution-phase synthesis of metal nanocrystals (NCs) allow the generation of NCs with faceted nonequilibrium shapes, which optimize properties for target applications such as catalysis and plasmonics. Often, for such applications and also for TEM analysis, the NCs are removed from the solution-phase environment. We explore the postsynthesis evolution of these metastable NCs in a high-vacuum TEM environment. Specifically, here we analyze their reshaping toward the equilibrium Wulff shapes mediated by surface diffusion, where such reshaping degrades the above-mentioned optimized properties. Typical sizes for these NCs range from 5 to 30 nm or 10 3 –10 6 atoms, and reshaping often occurs on the time scale of minutes for temperatures around, say, 400 °C. We discuss the development of predictive stochastic atomistic-level models for NC evolution with a realistic description of surface diffusion. These models, in contrast to Molecular Dynamics, can naturally address the relevant time and length scales for these systems. KMC simulation results for the stochastic models are described, focusing on the reshaping of slightly elongated nanorods and of mildly truncated octahedra and nanocubes. In addition, we review appropriate theoretical formulations for reshaping, which involves the nucleation and growth on 2D islands or layers on outer facets of the NC. We note the limitations of classical nucleation theory in some scenarios and demonstrate the successes of a more fundamental and general master equation-based analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1987

Efficiency characteristics of crescent-shaped wings and caudal fins

Caudal (tail) fins of fish and aquatic mammals that cruise long distances, and wings of certain birds, often have the shape of a crescent moon. This study investigates how the crescent shape contributes to the traveling performance of these animals. A steady-flow theory (Maskew, 1982) that correctly models the trailing wake was used to analyze lifting surface efficiency, which is dependent on the level of induced (or vortex) drag for a given lift and span of the lifting surface. This analysis shows that backward curvature of a wing improves induced efficiency to a value greater than that of the flat untwisted wing of elliptical shape considered optimal in classical wing theory (Prandt, 1921 and Munk, 1921). This increase of induced efficiency results from the nonplanar trailing vortex sheet produced by the crescent-shaped wing at a given angle of attack.

Van Dam, C. P.↗
NASA NTRS2016

Trajectory Design Employing Convex Optimization for Landing on Irregularly Shaped Asteroids

Mission proposals that land on asteroids are becoming popular. However, in order to have a successful mission the spacecraft must reliably and softly land at the intended landing site. The problem under investigation is how to design a fuel-optimal powered descent trajectory that can be quickly computed on- board the spacecraft, without interaction from ground control. An optimal trajectory designed immediately prior to the descent burn has many advantages. These advantages include the ability to use the actual vehicle starting state as the initial condition in the trajectory design and the ease of updating the landing target site if the original landing site is no longer viable. For long trajectories, the trajectory can be updated periodically by a redesign of the optimal trajectory based on current vehicle conditions to improve the guidance performance. One of the key drivers for being completely autonomous is the infrequent and delayed communication between ground control and the vehicle. Challenges that arise from designing an asteroid powered descent trajectory include complicated nonlinear gravity fields, small rotating bodies and low thrust vehicles. There are two previous studies that form the background to the current investigation. The first set looked in-depth at applying convex optimization to a powered descent trajectory on Mars with promising results.1, 2 This showed that the powered descent equations of motion can be relaxed and formed into a convex optimization problem and that the optimal solution of the relaxed problem is indeed a feasible solution to the original problem. This analysis used a constant gravity field. The second area applied a successive solution process to formulate a second order cone program that designs rendezvous and proximity operations trajectories.3, 4 These trajectories included a Newtonian gravity model. The equivalence of the solutions between the relaxed and the original problem is theoretically established. The proposed solution for designing the asteroid powered descent trajectory is to use convex optimization, a gravity model with higher fidelity than Newtonian, and an iterative solution process to design the fuel optimal trajectory. The solution to the convex optimization problem is the thrust profile, magnitude and direction, that will yield the minimum fuel trajectory for a soft landing at the target site, subject to various mission and operational constraints. The equations of motion are formulated in a rotating coordinate system and includes a high fidelity gravity model. The vehicle's thrust magnitude can vary between maximum and minimum bounds during the burn. Also, constraints are included to ensure that the vehicle does not run out of propellant, or go below the asteroid's surface, and any vehicle pointing requirements. The equations of motion are discretized and propagated with the trapezoidal rule in order to produce equality constraints for the optimization problem. These equality constraints allow the optimization algorithm to solve the entire problem, without including a propagator inside the optimization algorithm.

Pinson, Robin M.↗
DOE OSTI2021

Enhanced Data Efficiency Using Deep Neural Networks and Gaussian Processes for Aerodynamic Design Optimization

Adjoint-based optimization methods are attractive for aerodynamic shape design primarily due to their computational costs being independent of the dimensionality of the input space and their ability to generate high-fidelity gradients that can then be used in a gradient-based optimizer. This makes them very well suited for high-fidelity simulation based aerodynamic shape optimization of highly parametrized geometries such as aircraft wings. However, the development of adjoint-based solvers involve careful mathematical treatment and their implementation require detailed software development. Furthermore, they can become prohibitively expensive when multiple optimization problems are being solved, each requiring multiple restarts to circumvent local optima. In this work, we propose a machine learning enabled, surrogate-based framework that replaces the expensive adjoint solver, without compromising on predicting predictive accuracy. Specifically, we first train a deep neural network (DNN) from training data generated from evaluating the high-fidelity simulation model on a model-agnostic design of experiments on the geometry shape parameters. The optimum shape may then be computed by using a gradient-based optimizer coupled with the trained DNN. Subsequently, we also perform a gradient-free Bayesian optimization, where the trained DNN is used as the prior mean. We observe that the latter framework (DNN-BO) improves upon the DNN-only based optimization strategy for the same computational cost. Overall, this framework predicts the true optimum with very high accuracy, while requiring far fewer high-fidelity function calls compared to the adjoint-based method. Furthermore, we show that multiple optimization problems can be solved with the same machine learning model with high accuracy, to amortize the offline costs associated with constructing our models. Our methodology finds applications in the early stages of aerospace design. (C) 2021 Published by Elsevier Masson SAS.

Renganathan, S. Ashwin↗
NASA NTRS1999

Engineering Overview of a Multidisciplinary HSCT Design Framework Using Medium-Fidelity Analysis Codes

An objective of the HPCC Program at NASA Langley has been to promote the use of advanced computing techniques to more rapidly solve the problem of multidisciplinary optimization of a supersonic transport configuration. As a result, a software system has been designed and is being implemented to integrate a set of existing discipline analysis codes, some of them CPU-intensive, into a distributed computational framework for the design of a High Speed Civil Transport (HSCT) configuration. The proposed paper will describe the engineering aspects of integrating these analysis codes and additional interface codes into an automated design system. The objective of the design problem is to optimize the aircraft weight for given mission conditions, range, and payload requirements, subject to aerodynamic, structural, and performance constraints. The design variables include both thicknesses of structural elements and geometric parameters that define the external aircraft shape. An optimization model has been adopted that uses the multidisciplinary analysis results and the derivatives of the solution with respect to the design variables to formulate a linearized model that provides input to the CONMIN optimization code, which outputs new values for the design variables. The analysis process begins by deriving the updated geometries and grids from the baseline geometries and grids using the new values for the design variables. This free-form deformation approach provides internal FEM (finite element method) grids that are consistent with aerodynamic surface grids. The next step involves using the derived FEM and section properties in a weights process to calculate detailed weights and the center of gravity location for specified flight conditions. The weights process computes the as-built weight, weight distribution, and weight sensitivities for given aircraft configurations at various mass cases. Currently, two mass cases are considered: cruise and gross take-off weight (GTOW). Weights information is obtained from correlations of data from three sources: 1) as-built initial structural and non-structural weights from an existing database, 2) theoretical FEM structural weights and sensitivities from Genesis, and 3) empirical as-built weight increments, non-structural weights, and weight sensitivities from FLOPS. For the aeroelastic analysis, a variable-fidelity aerodynamic analysis has been adopted. This approach uses infrequent CPU-intensive non-linear CFD to calculate a non-linear correction relative to a linear aero calculation for the same aerodynamic surface at an angle of attack that results in the same configuration lift. For efficiency, this nonlinear correction is applied after each subsequent linear aero solution during the iterations between the aerodynamic and structural analyses. Convergence is achieved when the vehicle shape being used for the aerodynamic calculations is consistent with the structural deformations caused by the aerodynamic loads. To make the structural analyses more efficient, a linearized structural deformation model has been adopted, in which a single stiffness matrix can be used to solve for the deformations under all the load conditions. Using the converged aerodynamic loads, a final set of structural analyses are performed to determine the stress distributions and the buckling conditions for constraint calculation. Performance constraints are obtained by running FLOPS using drag polars that are computed using results from non-linear corrections to the linear aero code plus several codes to provide drag increments due to skin friction, wave drag, and other miscellaneous drag contributions. The status of the integration effort will be presented in the proposed paper, and results will be provided that illustrate the degree of accuracy in the linearizations that have been employed.

Weston, R. P.↗
DOE OSTI2022

Design-Space Exploration for Inverse-Design of Wind Turbine Blades Using Data-Driven Methods

The state-of-the-practice aerodynamic design methods for wind turbine blades is typically based on Blade-Element Momentum (BEM) theory using a pre-designed frozen family of airfoils. The airfoils are themselves typically designed using panel methods. The design of the next-generation of large flexible rotors will need to capture the non-linear aerodynamics and three-dimensional flow to reduce the levelized cost of wind energy. Data-driven methods for aerodynamic design using data generated by computational fluid dynamics offer an attractive alternative to BEM-based methods that captures the non-linear aerodynamics of the component airfoils as well as the root and tip sections. In this work, we develop and demonstrate a framework to "smartly" explore the relevant design space in combination with an appropriate automated CFD pipeline to evaluate the aerodynamics of each design. The design-space exploration framework uses appropriate perturbations to the airfoil shape and induction profile from a baseline shape in combination with the inverse-design using BEM. The resulting designs are evaluated using an automated CFD pipeline using the in-house CFD solver framework "Mercury". We perform verification and validation to establish the capability of the Mercury framework to predict the aerodynamic performance of wind turbines. The CFD simulation of the perturbed blade shapes are optimized to restart from the converged baseline simulation to reduce the computational time. Finally, we demonstrate the design-space exploration technique for the design of the outboard section and the full rotor using perturbations to the shape and operating conditions of the NREL 5-MW turbine.

aerodynamic design methods↗
DOE OSTI2019

Optimization of the size and shape of the scanning aperture in autocollimator-based deflectometric profilometers

Deflectometric profilometers based on industrial electronic autocollimators (ACs), as the ELCOMAT-3000, have become indispensable tools for precision form measurements of optical surfaces. A growing number of labs at synchrotron and free electron laser x-ray facilities are going for BESSY-II NOM-like versions of the AC-based profilometers. These tools have proven capable of characterizing state-of-the-art aspherical x-ray optics with an accuracy on the level of 100 nrad (root-mean-square) over the spatial frequency range limited by the size of the aperture used in the profilometer. Normally, a round aperture with a diameter of about 2.5 mm is used. Previous investigations have shown that with the optimally aligned 2.5-mm aperture, the spatial resolution of a NOM-like profilometer corresponding to the first zero-crossing of the optical transform function (OTF) is ~1.2 mm. In this paper, we investigate the performance of an AC ELCOMAT-3000 for a slope profilometer with different aperture sizes and shapes. The results of angular calibration of the AC equipped with circular and rectangular apertures placed at different distances from the AC are discussed. The calibration was performed at the Physikalisch-Technische Bundesanstalt using the original experimental arrangements, also discussed in the paper. The OTF measurements with the specially developed test sample with chirped surface slope profiles were performed at the Advanced Light Source X-Ray Optics Laboratory (XROL) in application to a new optical surface measuring system under development at the XROL. In the OTF measurements, we have shown that application of a rectangular aperture with dimensions of 1.5 mm × 3 mm improves the spatial resolution in the tangential direction by a factor of ~1.4 compared to that of the standard circular aperture of 2.5-mm diameter. We postulate that the conclusions of our investigations are crucial for reaching fundamental metrological limits in deflectometric profilometry utilizing state-of-the-art electronic autocollimators.

47 OTHER INSTRUMENTATION↗
NASA NTRS2019

High-Fidelity Multidisciplinary Sensitivity Analysis Framework for Multipoint Rotorcraft Optimization

A multidisciplinary, gradient-based sensitivity-analysis methodology is evaluated for optimization of rotorcraft configurations. The tightly coupled discipline models include physics-based fluid dynamics and rotorcraft comprehensive analysis. A discretely-consistent adjoint method accounts for sensitivities of the unsteady flow and unstructured, dynamic, over set grids, while sensitivities of structural responses to aerodynamic loads are computed using a complex-variable method. The methodology is applied to optimize the shape of UH-60A Blackhawk helicopter blades for hover and forward flight conditions. The objective of the multipoint design is to simultaneously increase the rotorcraft figure of merit in a hover flight and reduce the rotor power in a forward flight. Trimmed loose-coupling solutions for the baseline configuration are used to initiate the tight-coupling multidisciplinary analysis. Target thrust and rolling and pitching moments are enforced as optimization constraints. The optimized configuration improves the optimization metrics at both design points. The improved performance and all constraints are maintained over many revolutions beyond the optimization interval, satisfying the required flight conditions. Computational cost of the optimization cycle is assessed in a high-performance computing environment and found affordable for design of rotorcraft in general level-flight conditions.

Wang, Li↗
DOE OSTI2021

Hundreds of weak lensing shear-selected clusters from the Hyper Suprime-Cam Subaru Strategic Program S19A data

Abstract We use the Hyper Suprime-Cam Subaru Strategic Program S19A shape catalog to construct weak lensing shear-selected cluster samples. From aperture mass maps covering ∼510 deg2 created using a truncated Gaussian filter, we construct a catalog of 187 shear-selected clusters that correspond to mass map peaks with signal-to-noise ratio larger than 4.7. Most of the shear-selected clusters have counterparts in optically selected clusters, from which we estimate the purity of the catalog to be higher than 95%. The sample can be expanded to 418 shear-selected clusters with the same signal-to-noise ratio cut by optimizing the shape of the filter function and by combining weak lensing mass maps created with several different background galaxy selections. We argue that dilution and obscuration effects of cluster member galaxies can be mitigated by using background source galaxy samples and adopting a filter function with its inner boundary larger than about 2′. The large samples of shear-selected clusters that are selected without relying on any baryonic tracer are useful for detailed studies of cluster astrophysics and cosmology.

Oguri, Masamune↗
DOE OSTI2022

Single-stage gradient-based stellarator coil design: Optimization for near-axis quasi-symmetry

Here we present a new coil design paradigm for magnetic confinement in stellarators. Our approach directly optimizes coil shapes and coil currents to produce a vacuum quasi-symmetric magnetic field with a target rotational transform on the magnetic axis. This approach differs from the traditional two-stage approach in which first a magnetic configuration with desirable physics properties is found, and then coils to approximately realize this magnetic configuration are designed. The proposed single-stage approach allows us to find a compromise between confinement and engineering requirements, i.e., find easy-to-build coils with good confinement properties. Using forward and adjoint sensitivities, we derive derivatives of the physical quantities in the objective, which is constrained by a nonlinear periodic differential equation. In two numerical examples, we compare different gradient-based descent algorithms and find that incorporating approximate second-order derivative information through a quasi-Newton method is crucial for convergence. We also explore the optimization landscape in the neighborhood of a minimizer and find many directions in which the objective is mostly flat, indicating ample freedom to find simple and thus easy-to-build coils.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

An optimization-based approach to tailor the mechanical response of soft metamaterials undergoing rate-dependent instabilities

An optimization-based design framework is proposed to tune the response of soft metamaterials involving both geometric instabilities and nonlinear viscoelastic material behavior. Designing the response of soft metamaterials to harness instabilities and undergo large, tailored configuration changes will enable advancements in soft robotics, shock and vibration mitigation, and flexible electronics. In line with the metamaterial concept, the response of these materials is governed to a large extent by the geometric and topological makeup of their small-scale features. However, the link between structure and response is less intuitive for soft metamaterials due to their reliance upon highly nonlinear responses triggered by geometric instabilities. This is further complicated by the effects of viscoelastic relaxation, which recent studies have shown to alter the emergence of instabilities in non-intuitive ways. Here, these effects are accounted for in our framework to achieve various design objectives, including tailored force–displacement response and maximized energy absorption from both geometric and material effects. To fully automate this process, it is essential to have a completely robust equation solver for forward problems involving instabilities and viscoelastic relaxation. We achieve this by casting the search for stable mechanical equilibrium — i.e. the forward problem — as a minimization problem and utilize a trust region algorithm to robustly handle instabilities and follow energetically-favorable equilibrium paths through critical points.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Multi‐Stage Optimization of Pore Size and Shape in Pore‐Space‐Partitioned Metal–Organic Frameworks for Highly Selective and Sensitive Benzene Capture

Abstract Compared to exploratory development of new structure types, pushing the limits of isoreticular synthesis on a high‐performance MOF platform may have higher probability of achieving targeted properties. Multi‐modular MOF platforms could offer even more opportunities by expanding the scope of isoreticular chemistry. However, navigating isoreticular chemistry towards best properties on a multi‐modular platform is challenging due to multiple interconnected pathways. Here on the multi‐modular pacs (partitioned acs) platform, we demonstrate accessibility to a new regime of pore geometry using two independently adjustable modules (framework‐forming module 1 and pore‐partitioning module 2). A series of new pacs materials have been made. Benzene/cyclohexane selectivity is tuned, progressively, from 4.5 to 15.6 to 195.4 and to 482.5 by pushing the boundary of the pacs platform towards the smallest modules known so far. The exceptional stability of these materials in retaining both porosity and single crystallinity enables single‐crystal diffraction studies of different crystal forms (as‐synthesized, activated, guest‐loaded) that help reveal the mechanistic aspects of adsorption in pacs materials.

Chen, Yichong↗
DOE OSTI2024

Multi–Stage Optimization of Pore Size and Shape in Pore–Space–Partitioned Metal–Organic Frameworks for Highly Selective and Sensitive Benzene Capture

Compared to exploratory development of new structure types, pushing the limits of isoreticular synthesis on a high-performance MOF platform may have higher probability of achieving targeted properties. Multi-modular MOF platforms could offer even more opportunities by expanding the scope of isoreticular chemistry. However, navigating isoreticular chemistry towards best properties on a multi-modular platform is challenging due to multiple interconnected pathways. Here on the multi-modular pacs (partitioned acs) platform, we demonstrate accessibility to a new regime of pore geometry using two independently adjustable modules (framework-forming module 1 and pore-partitioning module 2). A series of new pacs materials have been made. Benzene/cyclohexane selectivity is tuned, progressively, from 4.5 to 15.6 to 195.4 and to 482.5 by pushing the boundary of the pacs platform towards the smallest modules known so far. The exceptional stability of these materials in retaining both porosity and single crystallinity enables single-crystal diffraction studies of different crystal forms (as-synthesized, activated, guest-loaded) that help reveal the mechanistic aspects of adsorption in pacs materials.

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

Techno-economic design of a linear Fresnel reflector for industrial process heat

A techno-economic model of a Concentrating Solar Thermal (CST) system using a Linear Fresnel Reflector (LFR) has been developed. LFRs can deliver process heat suitable for a range of industries, including food and beverage production. This model uses an adaptive algorithm to calculate the optimal secondary reflector shape given the geometry and optical properties of the rest of the system. A ray-tracing program is used to calculate optical efficiency over a wide range of longitudinal and transversal incidence angles and subsequently evaluate the annual efficiency at a given geographical location. A specific LFR design developed by Hyperlight Energy was modelled, and this industrial partner provided a detailed cost breakdown which was used as the basis of an economic model. Combining the technical and economic data facilitates the calculation of the Levelized Cost of Heat (LCOH). The influence of a number of parameters on the annual efficiency and LCOH is explored; notable parameters include the absorber height, the number, width, and spacing of the primary mirrors, the aim point, and the secondary reflector shape and width. By identifying an optimal combination of these parameters, we reduce the LCOH of the industry partner’s system design by 9.2%, from 14.4 $\$/MWh_{th}$ to 13.0 $\$/MWh_{th}$. In comparison, the LCOH of a natural gas boiler delivering the same annual quantity of heat is 29 $\$/MWh_{th}$, which indicates that LFRs can be a competitive heat source for industrial processes.

14 SOLAR ENERGY↗
DOE OSTI2019

X-ray Scattering and Coarse-Grained Simulations for Clustering and Interactions of Monoclonal Antibodies at High Concentrations

Attractive protein-protein interactions (PPI) in concentrated monoclonal antibody (mAb) solutions may lead to reversible oligomers (clusters) that impact colloidal stability and viscosity. Herein, the PPI are tuned for two mAbs via the addition of arginine (Arg), NaCl, or ZnSO 4 as characterized by the structure factor ( S eff (q)) with small angle x-ray scattering (SAXS). The SAXS data are fit with molecular dynamics simulations by placing a physically relevant short-ranged attractive interaction on selected beads in coarse-grained 12 bead models of the mAb shape. The optimized 12-bead models are then used to differentiate key microstructural properties, including center of mass radial distribution functions ( g COM (r)), coordination numbers and cluster size distributions (CSD). The addition of co-solutes results in more attractive Seff(q) relative to the no co-solute control for all systems tested, with the most attractive systems showing an upturn at low q . Only the All1 model with an attractive site in each Fab and Fc region (possessing Fab-Fab, Fab-Fc and Fc-Fc interactions) can reproduce this upturn, and the corresponding CSDs show the presence of larger clusters compared to the control. In general, for models with similar net attractions, i.e., second osmotic virial coefficients, the size of the clusters increases as the attraction is concentrated on a smaller number of evenly distributed beads. Here, the cluster size distributions from simulations are used to improve the understanding and prediction of experimental viscosities. The ability to discriminate between models with bead interactions at particular Fab and Fc bead sites from SAXS simulations, and to provide real space properties (CSD and g COM (r)), will be of interest in engineering protein sequence and formulating protein solutions for weak PPI to minimize aggregation and viscosities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

A New Global Storage-Area-Depth Data Set for Modeling Reservoirs in Land Surface and Earth System Models

Reservoir storage-area-depth relationships are the most important factors controlling thermal stratification in reservoirs and, more broadly, the water, energy and biogeochemical dynamics in the reservoirs and subsequently their impacts on downstream rivers. However, most land surface or earth system models do not account for the gradual changes of reservoir surface area and storage with the changing depth, inhibiting a consistent and accurate representation of mass, energy and biogeochemical balances in reservoirs. Here we present a physically coherent parameterization of reservoir storage-area-depth dataset at the global scale. For each reservoir, the storage-area-depth relationships were derived from an optimal geometric shape selected iteratively from five possible regular geometric shapes that minimizes the error of total storage and surface area estimation. We applied this algorithm to 6,800 reservoirs included in the Global Reservoir and Dam database (GRanD). The relative error between the estimated and observed total storage is no more than 5% and 50% for 66% and 99% of all GRanD reservoirs, respectively. More importantly, the storage-depth profiles derived from the approximated reservoir geometry compared well with remote sensing based estimation at 40 major reservoirs from previous studies, and ground-truth measurements for 34 reservoirs in the United States and China. Furthermore, the new global reservoir storage-area-depth dataset is critical for advancing future modeling and understanding of reservoir processes and subsequent effects on the terrestrial hydrological, ecological and biogeochemical cycles at the regional and global scales.

13 HYDRO ENERGY↗