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Results for “Lattice 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.

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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Non-Destructive Characterization of Subsurface Residual Stress Fields and Correlation with Microstructural Conditions in a Shot-Peened Inconel Component

Shot-peening is an important surface treatment used in a preventative way to guard against fatigue failures. The residual stress state imparted by shot-peening deters the formation and propagation of surface cracks. In this paper, we describe the measurement of residual stresses in an Inconel, IN100, sample using lattice strains measured using High Energy X-ray Diffraction (HEXD) and a Bi-Scale Optimization Method (BSOM). HEXD enabled rapid, non-destructive lattice strain measurements over a large region of the sample. Subsurface strains were obtained using a conical slit setup. The BSOM utilizes a macroscale representation of the sample and a spherical harmonic-based crystal scale representation of crystal orientation space at each experimental point (diffraction volume). A roughly biaxial stress state was predicted with a von Mises equivalent stress between 300 MPa and 400 MPa near the surface. The layer of material with high residual stress induced by shot-peening was found to be approximately 1 mm thick. Diffraction peak width, EBSD, and microhardness measurements were also made on the same sample, which rendered more qualitative measures of the plasticity-related effects of the shot-peening induced residual stress field. All of these measurements show a dimishing shot-peening plasticity with the increasing depth.

42 ENGINEERING↗
DOE OSTI2020

A regression algorithm for accelerated lattice QCD that exploits sparse inference on the D-Wave quantum annealer

We propose a regression algorithm that utilizes a learned dictionary optimized for sparse inference on a D-Wave quantum annealer. In this regression algorithm, we concatenate the independent and dependent variables as a combined vector, and encode the high-order correlations between them into a dictionary optimized for sparse reconstruction. On a test dataset, the dependent variable is initialized to its average value and then a sparse reconstruction of the combined vector is obtained in which the dependent variable is typically shifted closer to its true value, as in a standard inpainting or denoising task. Here, a quantum annealer, which can presumably exploit a fully entangled initial state to better explore the complex energy landscape, is used to solve the highly non-convex sparse coding optimization problem. The regression algorithm is demonstrated for a lattice quantum chromodynamics simulation data using a D-Wave 2000Q quantum annealer and good prediction performance is achieved. The regression test is performed using six different values for the number of fully connected logical qubits, between 20 and 64. The scaling results indicate that a larger number of qubits gives better prediction accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Component-wise reduced order model lattice-type structure design

Lattice-type structures can provide a combination of stiffness with light weight that is desirable in a variety of applications. Design optimization of these structures must rely on approximations of the governing physics to render solution of a mathematical model feasible. In this paper, we propose a topology optimization (TO) formulation that approximates the governing physics using component-wise reduced order modeling as introduced in Huynh et al. (2013); Eftang and Patera (2013), which can reduce solution time by multiple orders of magnitude over a full-order finite element model while providing a relative error in the solution of 1%. In addition, the offline training data set from such component-wise models is reusable, allowing its application to many design problems for only the cost of a single offline training phase, and the component-wise method is nearly embarrassingly parallel. We also show how the parameterization chosen in our optimization allows a simplification of the component-wise reduced order model (CWROM) not noted in previous literature, for further speedup of the optimization process. Furthermore, the sensitivity of the compliance with respect to the particular parameterization is derived solely at the component level. In numerical examples, we demonstrate a 1000x speedup over a full-order FEM model with relative error of 1% and show minimum compliance designs for two different cantilever beam examples, one smaller and one larger. Finally, error bounds for the displacement field, compliance, and compliance sensitivity of the CWROM are derived.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Clifford Circuit-Based Heuristic Optimization of Fermion-To-Qubit Mappings

Simulation of interacting Fermionic Hamiltonians is one of the most promising applications of quantum computers. However, the feasibility of analyzing Fermionic systems with a quantum computer hinges on the efficiency of Fermion-to-qubit mappings that encode nonlocal Fermionic degrees of freedom in local qubit degrees of freedom. While recent studies have highlighted the importance of designing Fermion-to-qubit mappings that are tailored to specific problem Hamiltonians, the methods proposed so far either are restricted to a narrow class of mappings or they use computationally expensive and unscalable brute-force search algorithms. Here, in this work, we address this challenge by designing a heuristic numerical optimization framework for Fermion-to-qubit mappings. To this end, we first translate the Fermion-to-qubit mapping problem to a Clifford circuit optimization problem and then use simulated annealing to optimize the average Pauli weight of the problem Hamiltonian. For all Fermionic Hamiltonians we have considered, the numerically optimized mappings outperform their conventional counterparts, including ternary-tree-based mappings that are known to be optimal for single creation and annihilation operators. We find that our optimized mappings yield between 15% and 40% improvements on the average Pauli weight when the simulation Hamiltonian has an intermediate level of complexity. Most remarkably, the optimized mappings improve the average Pauli weight for 6 × 6 nearest-neighbor hopping and Hubbard models by more than 40% and 20%, respectively. Surprisingly, we also find specific interaction Hamiltonians for which the optimized mapping outperforms any ternary-tree-based mapping. Our results establish heuristic numerical optimization as an effective method for obtaining mappings tailored for specific Fermionic Hamiltonian.

Hamiltonians↗
NASA NTRS1985

Noise constraints effecting optimal propeller designs

A preliminary design tool for advanced propellers was developed combining a fast vortex lattice aerodynamic analysis, a fast subsonic point source noise analysis, and an optimization scheme using a conjugate directions method. Twist, chord and sweep distributions are optimized to simultaneously improve both the aerodynamic performance and the noise observed at a fixed relative position. The optimal noise/performance tradeoffs for straight and advanced concept blades are presented. The techniques used include increasing the blade number, blade sweep, reducing the rotational speed, shifting the spanwise loading and diameter changes.

Miller, C. J.↗
NASA NTRS1985

Noise constraints effecting optimal propeller designs

A preliminary design tool for advanced propellers was developed combining a fast vortex lattice aerodynamic analysis, a fast subsonic point source noise analysis, and an optimization scheme using a conjugate directions method. Twist, chord and sweep distributions are optimized to simultaneously improve both the aerodynamic performance and the noise observed at a fixed relative position. The optimal noise/performance tradeoffs for straight and advanced concept blades are presented. The techniques used include increasing the blade number, blade sweep, reducing the rotational speed, shifting the spanwise loading and diameter changes.

Miller, C. J.↗
DOE OSTI2022

Parallel simulated annealing with embedded machine learning and multifidelity models for reactor core design

This paper presents extensions to a penalty-free, parallel simulated annealing (SA) algorithm for multi-constrained combinatorial optimization with the aim of embedding multi-fidelity physics models into the annealing procedure. The method uses a low-fidelity, quickly executing model for rapid design space exploration and a high-fidelity model for detailed constraint resolution and on-the-fly bias correction. Machine learning models updated within the annealing procedure were used to bridge the gap between the multi-fidelity models, which led to accurate rapid exploration and efficient detailed constraint resolution. A software implementation of the new multi-fidelity optimization methods, called ML-PSA, was demonstrated on a continuous multi-fidelity optimization problem and a constrained combinatorial PWR lattice design problem. These problems demonstrate some of the features, parallel performance characteristics, and extensible nature of the multi-fidelity SA methods. This paper shows that the developed software and procedure are a general optimization tool that can be applied to a wide variety of scientific and engineering design optimization applications. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Organizing Cells Within Non-Periodic Microarchitectured Materials That Achieve Graded Thermal Expansions

The aim of this paper is to introduce an approach for optimally organizing a variety of different unit cell designs within a large lattice such that the bulk behavior of the lattice exhibits a desired Young’s modulus with a graded change in thermal expansion over its geometry. This lattice, called a graded microarchitectured material, can be sandwiched between two other materials with different thermal expansion coefficients to accommodate their different expansions or contractions caused by changing temperature while achieving a desired uniform stiffness. First, this paper provides the theory necessary to calculate the thermal expansion and Young’s modulus of large multi-material lattices that consist of periodic (i.e., repeating) unit cells of the same design. Then it introduces the theory for calculating the graded thermal expansions of a large multimaterial lattice that consists of non-periodic unit cells of different designs. An approach is then provided for optimally designing and organizing different unit cells within a lattice such that both of its ends achieve the same thermal expansion as the two materials between which the lattice is sandwiched. A MATLAB tool is used to generate images of the undeformed and deformed lattices to verify their behavior and various examples are provided as case studies. The theory provided is also verified and validated using finite element analysis and experimentation.

Young’s modulus↗
NASA NTRS1993

A New Approach to Structural Failure Detection Based on Adaptive Lattice Filtering

A new approach to structural failure detection and localization is introduced based on acoustic reflections. It is shown that the cross-sectional areas of structural elements can be computed directly in terms of the reflection coefficients of an optimal finite impulse response Wiener filter realized in lattice form.

structural↗
DOE OSTI2025

LatticeAnalytics: Strut-Level Visualization and Inspection of Additively Manufactured Lattice Structures

Additive manufacturing (AM) is revolutionizing the production of custom components with complex internal geometries, essential for high-performance applications in diverse fields such as medicine and defense. These AM parts optimize strength while minimizing weight by utilizing internal lattice structures consisting of large quantities of small interconnected struts. However, the complexity of these structures, combined with the challenges of using X-ray Computed Tomography (XCT) data, makes validation of part reliability difficult. This ultimately inhibits the development of novel parts for our collaborating material scientists. Here, we introduce LatticeAnalytics, a novel framework specifically designed for visual inspection of defects in these lattice structures. Our framework offers an end-to-end solution that includes the data management of XCT scans, enables remote access for geographically dispersed teams through a web-based dashboard, and incorporates novel visualizations. Our analysis is facilitated by a coarse alignment between the lattice’s nominal model, a spatial graph, and the XCT data. We employ a simple VR-based approach for fast and rough alignment, followed by an offline registration and identification of the struts. With the nodes and struts aligned and identified in the volume, our framework allows querying of subvolumes containing a single strut at multiple resolutions. This avoids computation over the entire lattice and also allow for easy parallelization of down-stream computations, such as strut-specific metrics. To depict a fast overview of the strut quality, we introduce two innovative visual encodings, crucial for our collaborators’ research in creating novel AM parts: the Contour View and the Roughness Map, which depict critical geometrical and surface features of individual struts in standardized two 2D views. We evaluated the integrated system through expert interviews. The feedback confirms the framework’s practicality and its effectiveness in enhancing current inspection workflows. It solves major bottlenecks for our collaborators, ultimately helping them create novel parts with advanced properties.

Miao, Haichao [Lawrence Livermore National Laborat↗
DOE OSTI2022

Platinum-like HER onset in a GNR/MoS 2 quantum dot heterostructure through curvature-dependent electron density reconfiguration

Tailoring the curvature-directed lattice strain in GNRs along with optimal surface anchoring of molybdenum disulfide (MoS 2 ) quantum dots (QDs) can lead to a unique heterostructure with Pt-like HER activity (onset potential –60 mV). The curvature-induced electronic charge redistribution at the curved region in the graphene nanoribbons allows a facile GNR-MoS 2 interfacial charge transfer in the heterostructure, making the interfacial sulfur (S) more active towards the HER. Finally, the density functional theory (DFT) calculations confirmed electronically activated interfacial S-based catalytic centers in the curved GNR-based heterostructure leading to Pt-like HER activity.

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

Nonlinear discrete wavefront shaping for spatiotemporal pulse compression with multicore fibers

Here in this work, we apply an effective discrete phase front focusing method to control the coherent combining and temporal compression of laser pulses by means of nonlinear multicore fibers. We numerically demonstrate the possibility of combining almost entirely the input optical pulses injected in all cores, into the central core of a hexagonal lattice multicore fiber. We investigate the optimal operational conditions for the most effective pulse combining and analyze the influence of a positive input pulse temporal chirp and spatial phase modulation. We demonstrate that a pulse, which has been focused into an arbitrary core of the fiber, also undergoes temporal compression.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Proposal for a proton-bunch compression experiment at IOTA in the strong space-charge regime

The longitudinal compression of intense proton bunches with strong space-charge force is an essential component of a proton-based muon source for a muon collider. This paper discusses a proton-bunch compression experiment at the Integrable Optics Test Accelerator (IOTA) storage ring at Fermilab to explore optimal radio frequency (RF) cavity and lattice configurations. IOTA is a compact fixed-energy storage ring that can circulate a 2.5-MeV proton beam with varying beam parameters and lattice configurations. The study will aim to demonstrate a bunch-compression factor of at least 2 in the IOTA ring while examining the impact of intense space-charge effects on the compression process.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Accelerator physics at NSLS-II: research accomplishments in 2020

NSLS-II accelerator physicists provide scientific support for user operations, as well as for improvements and developments of the accelerator systems. This work includes optimization of the NSLS-II performance with high beam current and low vertical emittance, improvement of the beam stability, development of new software tools for operations and beam studies. The accelerator physicists productively worked on a number of research activities such as the conceptual design of NSLS-II accelerator upgrade; design and optimization of advanced low-emittance lattices; development of novel methods to study nonlinear beam dynamics; ultrafast electron diffraction and microscopy. The physicists also contributed to the BNL electron-ion collider project, to the APS and ALS upgrade projects. Research accomplishments achieved in 2020, are summarized in this report.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Proposal for a Proton-Bunch Compression Experiment at IOTA in the Strong Space-Charge Regime

The longitudinal compression of intense proton bunches with strong space-charge force is an essential component of a proton-based muon source for a muon collider. This paper discusses a proton-bunch compression experiment at the Integrable Optics Test Accelerator (IOTA) storage ring at Fermilab to explore optimal radio frequency (RF) cavity and lattice configurations. IOTA is a compact fixed-energy storage ring that can circulate a 2.5-MeV proton beam with varying beam parameters and lattice configurations. The study will aim to demonstrate a bunch-compression factor of at least 2 in the IOTA ring while examining the impact of intense space-charge effects on the compression process.

43 PARTICLE ACCELERATORS↗
NASA NTRS1985

Development and testing of tip devices for horizontal axis wind turbines

A theoretical and field experimental program has been carried out to investigate the use of tip devices on horizontal axis wind turbine rotors. The objective was to improve performance by the reduction of tip losses. While power output can always be increased by a simple radial tip extension, such a modification also results in an increased gale load both because of the extra projected area and longer moment arm. Tip devices have the potential to increase power output without such a structural penalty. A vortex lattice computer model was used to optimize three basic tip configuration types for a 25 kW stall limited commercial wind turbine. The types were a change in tip planform, and a single-element and double-element nonplanar tip extension (winglets). A complete data acquisition system was developed which recorded three wind speed components, ambient pressure, temperature, and turbine output. The system operated unattended and could perform real-time processing of the data, displaying the measured power curve as data accumulated in either a bin sort mode or polynomial curve fit. Approximately 270 hr of perormance data were collected over a three-month period. The sampling interval was 2.4 sec; thrus over 400,000 raw data points were logged. Results for each of the three new tip devices, compared with the original tip, showed a small decrease (of the order of 1 kW) in power output over the measured range of wind speeds from cut-in at about 4 m/s to over 20 m/s, well into the stall limiting region. Changes in orientation and angle-of-attack of the winglets were not made. For aircraft wing tip devices, favorable tip shapes have been reported and it is likely that the tip devices tested in this program did not improve rotor performance because they were not optimally adjusted.

Gyatt, G. W.↗