Integrated design and optimization of microelectronic devices
A genetic algorithm is used for design of infrared filters and in the understanding of the material structure of a resonant tunneling diode.
quantum device heterostructure transports↗
SEARCH · Engineering Papers
Results for “Optimized Structural Design”
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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Flexible Composite-Material Pressure Vessel
A proposed lightweight pressure vessel would be made of a composite of high-tenacity continuous fibers and a flexible matrix material. The flexibility of this pressure vessel would render it (1) compactly stowable for transport and (2) more able to withstand impacts, relative to lightweight pressure vessels made of rigid composite materials. The vessel would be designed as a structural shell wherein the fibers would be predominantly bias-oriented, the orientations being optimized to make the fibers bear the tensile loads in the structure. Such efficient use of tension-bearing fibers would minimize or eliminate the need for stitching and fill (weft) fibers for strength. The vessel could be fabricated by techniques adapted from filament winding of prior composite-material vessels, perhaps in conjunction with the use of dry film adhesives. In addition to the high-bias main-body substructure described above, the vessel would include a low-bias end substructure to complete coverage and react peak loads. Axial elements would be overlaid to contain damage and to control fiber orientation around side openings. Fiber ring structures would be used as interfaces for connection to ancillary hardware.
Brown, Glen↗
Continuous Trailing-Edge Flaps for Primary Flight Control of a Helicopter Main Rotor
The use of continuous trailing-edge flaps (CTEFs) for primary flight control of a helicopter main rotor is studied. A practical, optimized bimorph design with Macro-Fiber Composite actuators is developed for CTEF control, and a coupled structures and computational fluid dynamics methodology is used to study the fundamental behavior of an airfoil with CTEFs. These results are used within a comprehensive rotorcraft analysis model to study the control authority requirements of the CTEFs when utilized for primary flight control of a utility class helicopter. A study of the effect of blade root pitch index (RPI) on CTEF control authority is conducted, and the impact of structural and aerodynamic model complexity on the comprehensive analysis results is presented. The results show that primary flight control using CTEFs is promising; however, a more viable option may include the control of blade RPI, as well.
Thornburgh, Robert P.↗
Enabling Rapid and Robust Structural Analysis During Conceptual Design
This paper describes a multi-year effort to add a structural analysis subprocess to a supersonic aircraft conceptual design process. The desired capabilities include parametric geometry, automatic finite element mesh generation, static and aeroelastic analysis, and structural sizing. The paper discusses implementation details of the new subprocess, captures lessons learned, and suggests future improvements. The subprocess quickly compares concepts and robustly handles large changes in wing or fuselage geometry. The subprocess can rank concepts with regard to their structural feasibility and can identify promising regions of the design space. The automated structural analysis subprocess is deemed robust and rapid enough to be included in multidisciplinary conceptual design and optimization studies.
Eldred, Lloyd B.↗
Evaluation of Impact Energy Attenuators and Composite Material Designs of a UAM VTOL Concept Vehicle
The development of Vertical Take-off and Landing (VTOL) vehicles for the Urban Air Mobility (UAM) markets presents a need for light weight vehicle structures with effective occupant protection capabilities. The National Aeronautics and Space Administration (NASA) has been working to fill that need, recently developing a cadre of concept vehicles to help characterize UAM design feasibility. This paper describes a study, using these concept vehicles, to evaluate the use of advanced composite structure and energy attenuating designs in the UAM vehicle design space. A finite element model (FEM) of a single passenger quadrotor concept vehicle was developed in LS-Dyna® and simulated under nominal and off-nominal vertical impact conditions. A variety of energy attenuating design mechanisms were implemented within this model to quantify their effectiveness in improving occupant safety. The use of carbon composites in both the energy attenuation mechanisms and vehicle structure was evaluated. The results of this study found significant reduction in occupant injury risk with the implementation of energy absorbing composite crush tubes and landing gear within the vehicle design. Additionally the use of a carbon fiber as a structural material was found to provide significant weight reduction while maintaining similar occupant loads to that predicted with an aluminum structure. This work provides a preliminary evaluation of design mechanisms and materials that may be used to optimize occupant protection capabilities within the UAM market.
Putnam, Jacob↗
Stall Margin Improvement in a Transonic Compressor with a Casing Treatment: Flow Mechanism
Effects of casing treatments for improved stall margin in transonic compressors are investigated. Two types of compressor casing treatments, circumferential grooves and axial slots casing treatments, are studied in detail. The primary objective of the current paper is to identify the underlying fluid mechanics that change the stall margin of transonic compressors with these two casing treatments. Large eddy simulation (LES) is applied to calculate the compressor flow fields with these two types of casing treatments. Calculated flow fields are first compared with measured experimental data to validate the LES simulation. Details of the calculated unsteady flow structures are examined to unearth the underlying flow mechanism for the improved stall margin. Further possible design optimization of the casing treatment for the stall margin extension without any performance penalty near the design condition is also explored.
compressor↗
Analysis of Interlaminar Damage in Refractory Composites by In-Situ Micro-X-Ray Computed Tomography
Refractory composites are relevant for hot-structure applications on re-entry vehicles. To develop optimized high-performance hot structures, accurate material property data is required. Material properties that often limit design in two-dimensional (2-D) composites are interlaminar strengths. A better understanding of the failure mechanisms for interlaminar shear (ILS), interlaminar tension (ILT), and curved-beam interlaminar tension (CB-ILT) tests can provide insight on improving the design and fabrication of refractory composites. In-situ tests were performed to capture the progressive failure in American Society for Testing and Materials (ASTM) sized ILS (ASTM D3846), ILT (ASTM D7291), and CB-ILT (ASTM D6415) specimens. The present study investigates the change in material behavior by examining the macro porosity change in in-situ ILS tests, the mesoscale and inter-ply porosity changes in flatwise in-situ ILT tests and the change in per-ply delamination in in-situ CB-ILT tests.
In-situ Damage Characterization↗
Progressive collapse analysis of stiffened panel structure with consideration of actual welding distortion and residual stresses
To date, inclusion of welding distortion and residual stresses in determining the strength of marine structures is based on empirical formulations due to welding complexity. In this study, a more feasible numerical model was developed from the perspective of inherent strain theory. The accuracy of this simplified numerical model was confirmed by transient thermo-elastic–plastic finite element analysis using a novel dynamic mesh refining and iterative substructure method which is 50 times faster than conventional finite element method. Welding distortion by the two numerical approaches was validated with experimental measurement. The collapse analyses indicated that the inherent strain model predicted a compressive strength and load-displacement curve with high accuracy, as validated by the transient thermo-mechanical model. The global stiffness and the ultimate strength of the welded structure would be overestimated by about 22% if no welding effects were considered. The proposed computation procedure for determining the ultimate strength of welded structures is accurate, simple, and practical and enables improvement of ultimate strength through optimization of welding joint design to reduce the welding residual stress and distortion.
Huang, Hui↗
GradientGraph
Under this SBIR Phase II, Reservoir Labs has developed G2 Analytics, a new technology that allows network operators to analyze bottleneck and flow performance with high precision. G2 delivers a new analytical approach and framework to resolve a variety of key problems found in modern communication networks, including: traffic engineering, routing, flow scheduling, network design, capacity planning, resiliency analysis, network slicing, or service level agreement (SLA) management, among others. G2 leverages the bottleneck structure of congestion-controlled communication networks, a recent mathematical discovery by the Reservoir team [RL19b, RL20a, RL20b, RL21a]. Bottleneck structures reveal how perturbations on flows and links propagate through the network, providing an analytical framework to measure (qualitatively and quantitatively) the ripple effects induced as they traverse the network. Leveraging the mathematics of bottleneck structures, Reservoir Labs is developing the G2 technology to provide network operators with a framework to design, optimize and troubleshoot network performance. This delivery includes the G2 software stack.
Yellamraju, Sruthi↗
A Single-Lap Joint Adhesive Bonding Optimization Method Using Gradient and Genetic Algorithms
A natural process for any engineer, scientist, educator, etc. is to seek the most efficient method for accomplishing a given task. In the case of structural design, an area that has a significant impact on the structural efficiency is joint design. Unless the structure is machined from a solid block of material, the individual components which compose the overall structure must be joined together. The method for joining a structure varies depending on the applied loads, material, assembly and disassembly requirements, service life, environment, etc. Using both metallic and fiber reinforced plastic materials limits the user to two methods or a combination of these methods for joining the components into one structure. The first is mechanical fastening and the second is adhesive bonding. Mechanical fastening is by far the most popular joining technique; however, in terms of structural efficiency, adhesive bonding provides a superior joint since the load is distributed uniformly across the joint. The purpose of this paper is to develop a method for optimizing single-lap joint adhesive bonded structures using both gradient and genetic algorithms and comparing the solution process for each method. The goal of the single-lap joint optimization is to find the most efficient structure that meets the imposed requirements while still remaining as lightweight, economical, and reliable as possible. For the single-lap joint, an optimum joint is determined by minimizing the weight of the overall joint based on constraints from adhesive strengths as well as empirically derived rules. The analytical solution of the sin-le-lap joint is determined using the classical Goland-Reissner technique for case 2 type adhesive joints. Joint weight minimization is achieved using a commercially available routine, Design Optimization Tool (DOT), for the gradient solution while an author developed method is used for the genetic algorithm solution. Results illustrate the critical design variables as a function of adhesive properties and convergences of different joints based on the two optimization methods.
Smeltzer, Stanley S., III↗
Lightweight Metal Stamping Optimization Enabled by Artificial Intelligence
Successfully manufacturing an automotive body structure made via the sheet metal stamping process depends upon simultaneous consideration of component design, tooling design, stamping process control, and material properties. In many cases, introducing lightweight sheet materials (e.g., aluminum alloys, magnesium alloys, advanced high strength steels) holds the potential to significantly reduce vehicle weight, but challenges the stamping process by introducing materials with inherently less ductility. Successful and repeatable applications require co-developing the stamping process controls with the varying material properties, including formability. During the stamping process, as soon as the forming limit of the sheet is exceeded, the material shows localized necking which quickly leads to splits. Controlling process variability to avoid these material splits will enable deployment of less formable, lighter, and stronger materials for stamped automotive components. A typical optimization procedure for manufacturing requires an iterative process involving parameter setting, execution of computational simulations, and modifying the parameters. The entire process demands substantial computational time, making it impractical for real-time feedback towards rapid corrective actions required for in-line control for running production processes. To overcome this challenge, artificial intelligence (AI) can be leveraged to determine optimal manufacturing parameters within a single manufacturing cycle time. This research proposes an in-line optimization framework incorporating a trained AI model to predict kidney-shaped die forming. Preliminary results indicate that the AI framework can accurately predict draw-in values based on a given parameter set, a process referred to as forward prediction. Furthermore, the AI framework can also predict the optimal parameter set that leads to the desired draw-in values, referred to as inverse optimization (or backward prediction). This research has been performed in collaborations with USCAR (US Council for Automotive Research) and AutoForm. The members of USCAR are Ford, GM, and Stellantis.
36 MATERIALS SCIENCE↗
Particle Swarm Optimisation for group structure optimization for radiotherapy shielding
Neutron transport simulations are ubiquitous in nuclear engineering because they allow one to model experimental systems and render a model platform for easy perturbation of experimental designs. In addition, simulations allow one to gain experimental insight without actually having to go through the trouble of building a physical experiment. Neutron transport simulations can be stochastic or deterministic based. Stochastic neutron transport simulations are typically simulated using the Monte Carlo method and yield very accurate solutions but are computationally expensive, while deterministic methods are typically faster but can be less accurate. Here we focus on optimizing the accuracy of deterministic neutron transport simulations for radiotherapy simulations. Deterministic neutron transport requires discretization of angle, energy, and space to appropriately analyze the system one is trying to model. Discretization of energy is challenging because of the highly variable neutron flux at certain neutron energies. Improper discretization of energy in the transport model can lead to erroneous results and therefore inaccurate interpretations of the solution. In this study, we evaluate Particle Swarm Optimization (PSO) as a mechanism for selecting optimal group structures for radiotherapy shielding. We tested the particle swarm optimization algorithm on radiotherapy shielding problems using Los Alamos National Laboratory's (LANL) main deterministic transport code PARTISN. Results show that the optimized energy group structures generated from the optimization algorithm outperformed LANL's standard energy group structures, and therefore demonstrate utility in using PSO to expedite computation times due to the increased accuracy obtained with a smaller but optimized group structure. (authors)
43 PARTICLE ACCELERATORS↗
Coupled Multi-Disciplinary Optimization for Structural Reliability and Affordability
A computational simulation method is presented for Non-Deterministic Multidisciplinary Optimization of engine composite materials and structures. A hypothetical engine duct made with ceramic matrix composites (CMC) is evaluated probabilistically in the presence of combined thermo-mechanical loading. The structure is tailored by quantifying the uncertainties in all relevant design variables such as fabrication, material, and loading parameters. The probabilistic sensitivities are used to select critical design variables for optimization. In this paper, two approaches for non-deterministic optimization are presented. The non-deterministic minimization of combined failure stress criterion is carried out by: (1) performing probabilistic evaluation first and then optimization and (2) performing optimization first and then probabilistic evaluation. The first approach shows that the optimization feasible region can be bounded by a set of prescribed probability limits and that the optimization follows the cumulative distribution function between those limits. The second approach shows that the optimization feasible region is bounded by 0.50 and 0.999 probabilities.
Abumeri, Galib H.↗
Genetic Optimization of a Tensegrity Structure
Marshall Space Flight Center (MSFC) is charged with developing advanced technologies for space telescopes. The next generation of space optics will be very large and lightweight. Tensegrity structures are built of compressive members (bars), and tensile members (strings). For most materials, the tensile strength of a longitudinal member is larger than its buckling strength; therefore a large stiffness to mass ratio can be achieved by increasing the use of tensile members. Tensegrities are the epitome of lightweight structures, since they take advantage of the larger tensile strength of materials. The compressive members of tensegrity structures are disjoint allowing compact storage of the structure. The structure has the potential to eliminate the requirement for assembly by man in space; it can be deployed by adjustments in its cable tension. A tensegrity structure can be more reliably modeled since none of the individual members experience bending moments. (Members that experience deformation in more than one dimension are much harder to model.) Structures that can be more precisely modeled can be more precisely controlled. Furthermore, an astoundingly wide variety of natural systems, including carbon atoms, water molecules, proteins, viruses, cells, tissues and even human and other living creatures are tensegrity structures. Through the process of evolution, nature continually improves the design of living creatures for the environment they live in. Since tensegrities are nature's structure of choice, it is conceivable that they have other benefits we are unaware of. A. Keane and S. Brown designed a satellite boom truss system with an enhanced vibration performance. They started with a standard truss system, then used a genetic algorithm to alter the design, while optimizing the vibration performance. An improvement of over 20,000% in frequency-averaged energy levels was obtained using this approach. In this report an introduction to tensegrity structures is given, along with a description of how to generate the nodal coordinates and connectivity of a multiple stage cylindrical tensegrity structure. A description of how finite elements can be used to develop a stiffness and mass matrix so that the modes of vibration can be determined from the eigenvalue problem is shown. A brief description of a micro genetic algorithm is then presented.
Jaime R. Taylor↗
Efficient identification of critical stresses in structures subject to dynamic loads
Optimum structural design problems generally employ constraints which are parametric in terms of space and time variables. A parametric constraint may be replaced by equivalent critical point constraints at its local minima for optimization applications. In complex structures, accurate identification of such critical points is computationally expensive due to the cost of finite element analyses. Three techniques are described for efficiently and accurately identifying critical points for space- and time-dependent parametric constraints. An adaptive search technique and a spline interpolation technique are developed for exactly known response. A least squares spline approximation is suggested for noisy behavior. A helicopter tail-boom structure subjected to transient loading is used as an example to demonstrate the techniques described. All three techniques are shown to be computationally efficient for critical point identification and the least squares approximation also removes noise from the data. The case of multiple constraints per element is shown to be particularly suited to the use of spline techniques.
Haftka, R. T.↗
Domain knowledge-informed, process-mapping AI graph for designing Fe-based alloys
<span style="font-family: Calibri, sans-serif; font-size: 12pt;">Continuous improvement in efficiency of a power plant relies on designing materials for use at increasingly higher temperature and/or pressure, for 100,000s hours of operation. Due to complexity, non-linearity and high-dimensionality of the problem, traditional Machine Learning (ML) approaches require unreasonably large datasets for the data-driven model development. Science-based material and process engineering complements hard data with, sometimes soft and intuitive, empirical domain knowledge. Artificial Intelligence (AI) was used in this study to incorporate such knowledge into computational graph architecture (process-mimicking artificial neuron design, causal layer and graph structures, ensemble modeling of latent states) and learning procedures (variable transformation, fuzzy physics pre-training and freezing of deep layers, virtual microstructure representation, and adversarial multi-objective optimization). The first alloys design pathways suggested by the AI tool (pyroMind) passed a preliminary engineering review on soundness and transparency.</span>
Romanov, Vyacheslav↗
Design Optimization of Medium-Frequency Transformer for DAB Converters With DC Bias Capacity
Medium-frequency transformer (MFT) dc bias is critical for the safe operation of dual-active-bridge (DAB) converters. To analyze the dc bias, expressions of dc bias are derived for SiC MOSFET DAB converters. It shows that the dc bias is a current source in steady state; thus, a dc bias current capacity is defined and used as a design constraint in the optimization of MFT. The dc bias current capacity is related to core materials, geometric parameters, and flux density, which is analyzed with analytical expression. Optimization equations with geometric variables are derived, which can be solved theoretically or numerically for both interleaved winding (IW) and separated winding (SW) MFT without and/or with leakage inductance design. Based on the optimization equations, different MFT designs for a 150-kW SiC MOSFET DAB converter are compared on the aspects of core materials, winding structures, and without/with leakage inductance design. The combination of IW structure MFT with auxiliary phase shift inductors is chosen, and the assembled prototype is tested with the 150-kW DAB converter. Here, the designed dc bias capacity, temperature rise, and efficiency are verified.
42 ENGINEERING↗
Constructing an Adaptive Heterojunction as a Highly Active Catalyst for the Oxygen Evolution Reaction
Electrochemical water splitting is of prime importance to green energy technology. Particularly, the reaction at the anode side, namely the oxygen evolution reaction (OER), requires a high overpotential associated with O—O bond formation, which dominates the energy-efficiency of the whole process. Activating the anionic redox chemistry of oxygen in metal oxides, which involves the formation of superoxo/peroxo-like (O 2 ) n – , commonly occurs in most highly active catalysts during the OER process. Here, a highly active catalyst is designed: electrochemically delithiated LiNiO 2 , which facilitates the formation of superoxo/peroxo-like (O 2 ) n – species, i.e., NiOO*, for enhancing OER activity. The OER-induced surface reconstruction builds an adaptive heterojunction, where NiOOH grows on delithiated LiNiO 2 (delithiated-LiNiO 2 /NiOOH). At this junction, the lithium vacancies within the delithiated LiNiO 2 optimize the electronic structure of the surface NiOOH to form stable NiOO* species, which enables better OER activity. This finding provides new insight for designing highly active catalysts with stable superoxo-like/peroxo-like (O 2 ) n – for water oxidation.
36 MATERIALS SCIENCE↗