Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “combined topology 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.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Design of a Wind Tunnel Balance using Topology Optimization Considering Multifunctional Stress Performance

This paper proposes a new topology optimization formulation for multi-functional performance of a compliant sensor structure–a wind tunnel balance. Compliant mechanism design is one of the main applications of topology optimization and plenty of successful design studies have been reported. However, design practicability is still questionable when stress concentration is critical due to complex/combined loads, especially at compliant hinge joints. In this paper, we formulate a new topology optimization formulation considering multiple loading scenarios in a compliant sensor structure design. Two separate constraints,one related to sensor performance and the other focused on structural safety in terms of maximum von Mises stress, are included in the design formulation. This formulation is to achieve excellent sensitivity of an applied axial load while maintaining structure safety with a combined general load applied which is one order higher than the axial load. This challenging problem is solved using a well-known SIMP approach with the relaxation and projection methods.

Myung Kyun Sung↗
NASA NTRS2006

A Robust Scalable Transportation System Concept

This report documents the 2005 Revolutionary System Concept for Aeronautics (RSCA) study entitled "A Robust, Scalable Transportation System Concept". The objective of the study was to generate, at a high-level of abstraction, characteristics of a new concept for the National Airspace System, or the new NAS, under which transportation goals such as increased throughput, delay reduction, and improved robustness could be realized. Since such an objective can be overwhelmingly complex if pursued at the lowest levels of detail, instead a System-of-Systems (SoS) approach was adopted to model alternative air transportation architectures at a high level. The SoS approach allows the consideration of not only the technical aspects of the NAS", but also incorporates policy, socio-economic, and alternative transportation system considerations into one architecture. While the representations of the individual systems are basic, the higher level approach allows for ways to optimize the SoS at the network level, determining the best topology (i.e. configuration of nodes and links). The final product (concept) is a set of rules of behavior and network structure that not only satisfies national transportation goals, but represents the high impact rules that accomplish those goals by getting the agents to "do the right thing" naturally. The novel combination of Agent Based Modeling and Network Theory provides the core analysis methodology in the System-of-Systems approach. Our method of approach is non-deterministic which means, fundamentally, it asks and answers different questions than deterministic models. The nondeterministic method is necessary primarily due to our marriage of human systems with technological ones in a partially unknown set of future worlds. Our goal is to understand and simulate how the SoS, human and technological components combined, evolve.

Hahn, Andrew↗
DOE OSTI2024

SMARTER Rules-Based Distributed Deconfliction of ADMS Applications

A conceptual numerical methodology derived from Grid Architecture principles is introduced for deconflicting setpoints issued by multiple advanced distribution management system applications. The methodology applies technical, economic, environmental, and social rules to eliminate non-viable combinations. The concept of temporal equipment controls budgets is introduced to preserve the health of physical assets and avoid equipment damage through repeated controls cycling. The rules are combined with a multi-criteria decision-making framework to select a near-optimal set of deconflicted setpoints using a set of qualitative and quantitative decision criteria selected by the distribution system operator. Numerical results are demonstrated on the IEEE 123-bus test feeder for three competing applications. Three alternative distributed schemes are used to decompose the problem: by topological area, by phase, and fully decentralized. The fully decentralized implementation is shown to yield near-optimal deconfliction results with significantly reduced computational time.

Anderson, Alexander A.↗
DOE OSTI2024

Buffer-layer-controlled nickeline vs zinc-blende/wurtzite-type MnTe growths on c -plane Al 2 O 3 substrates

In the recent past, MnTe has proven to be a crucial component of the intrinsic magnetic topological insulator (IMTI) family [MnTe] m [Bi2Te 3 ] n , which hosts a wide range of magneto-topological properties depending on the choice of m and n. However, bulk crystal growth allows only a few combinations of m and n for these IMTIs due to the strict limitations of the thermodynamic growth conditions. One way to overcome this challenge is to utilize atomic layer-by-layer molecular beam epitaxy (MBE) technique, which allows arbitrary sequences of [MnTe]m and [Bi 2 Te 3 ] n to be formed beyond the thermodynamic limit. For such MBE growth, finding optimal growth templates and conditions for the parent building block, MnTe, is a key requirement. Here, we report that two different hexagonal phases of MnTe - nickeline (NC) and zinc-blende/wurtzite (ZB-WZ) structures, with distinct in-plane lattice constants of 4.20 ± 0.04 Å and 4.39 ± 0.04 Å, respectively - can be selectively grown on c-plane Al 2 O 3 substrates using different buffer layers and growth temperatures. Moreover, we provide the first comparative studies of different MnTe phases using atomic-resolution scanning transmission electron microscopy and show that ZB and WZ-like stacking sequences can easily alternate between the two. Surprisingly, In 2 Se 3 buffer layer, despite its lattice constant (4.02 Å) being closer to that of the NC phase, fosters the ZB-WZ instead, whereas Bi 2 Te 3 , sharing the same lattice constant (4.39 Å) with the ZB-WZ phase, fosters the NC phase. Furthermore, these discoveries suggest that lattice matching is not always the most critical factor determining the preferred phase during epitaxial growth. Overall, this will deepen our understanding of epitaxial growth modes for chalcogenide materials and accelerate progress toward new IMTI phases as well as other magneto-topological applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Flexible silicon photonic architecture for accelerating distributed deep learning

The increasing size and complexity of deep learning (DL) models have led to the wide adoption of distributed training methods in datacenters (DCs) and high-performance computing (HPC) systems. However, communication among distributed computing units (CUs) has emerged as a major bottleneck in the training process. In this study, we propose Flex-SiPAC, a flexible silicon photonic accelerated compute cluster designed to accelerate multi-tenant distributed DL training workloads. Flex-SiPAC takes a co-design approach that combines a silicon photonic hardware platform with a tailored collective algorithm, optimized to leverage the unique physical properties of the architecture. The hardware platform integrates a novel wavelength-reconfigurable transceiver design and a micro-resonator-based wavelength-reconfigurable switch, enabling the system to achieve flexible bandwidth steering in the wavelength domain. The collective algorithm is designed to support reconfigurable topologies, enabling efficient all-reduce communications that are commonly used in DL training. The feasibility of the Flex-SiPAC architecture is demonstrated through two testbed experiments. First, an optical testbed experiment demonstrates the flexible routing of wavelengths by shuffling an array of input wavelengths using a custom-designed spatial-wavelength selective switch. Second, a four-GPU testbed running two DL workloads shows a 23% improvement in job completion time compared to a similarly sized leaf-spine topology. We further evaluate Flex-SiPAC using large-scale simulations, which show that Flex-SiPAC is able to reduce the communication time by 26% to 29% compared to state-of-the-art compute clusters under representative collective operations.

Wu, Zhenguo (ORCID:0000000322847985)↗
DOE OSTI2020

Wave Prediction using X-Band Radar (Final Summary Report)

Model Predictive Control (MPC) is the best controls framework available to wave energy today. It allows for the maximization of energy yield from a WEC system while respecting various constraints and losses in the system. This type of constrained optimization is a critical capability for the techno-economic optimization of WEC devices and is difficult to do effectively with causal controls frameworks. MPC requires a future prediction of wave excitation forces, requiring wave prediction on a future time horizon of up to 30 seconds. The future horizon required is highly dependent on the WEC and PTO type available. To our knowledge no-one has been able to implement MPC on a WEC device at sea, due to the fact that phase-resolved wave-prediction is not a capability that has been sufficiently developed to date. The key objectives of this project are focused on developing “industry-ready” wave prediction technology building blocks that can be applied to a wide range of different wave energy conversion topologies. Our collaborative work with device developers has demonstrated that the level of improvement attainable for any particular device is heavily dependent on the device and PTO topology chosen. The range of annual average power capture improvements is on the order of 10% to over 100%. However, the important aspect is that we can get within about 10% of theoretical upper limits even when considering errors in the wave prediction and more importantly, we can optimize performance while respecting various constraints such as motion amplitude or peak structural loads. The combination of improved performance while limiting structural loads has a net effect of reducing the levelized cost of energy from these emerging technologies and allows us to design control laws that meet an economic optimum. Efforts under a previous project on controls has focused on developing a set of wave-prediction algorithms that work by leveraging a network of wave sensing measurement buoys that are equipped with real-time telemetry. The present work is focusing primarily on using X-band radar to measure the wave-field around the WEC to predict the future excitation forces on the structure and combining the measurements of these different sources to create improvements in the wave-prediction accuracy.

16 TIDAL AND WAVE POWER↗
DOE OSTI2020

Training Spiking Neural Networks Using Combined Learning Approaches

Spiking neural networks (SNNs), the class of neural networks used in neuromorphic computing, are difficult to train using traditional back-propagation techniques. Spike timingdependent plasticity (STDP) is a biologically inspired learning mechanism that can be used to train SNNs. Evolutionary algorithms have also been demonstrated as a method for training SNNs. In this work, we explore the relationship between these two training methodologies. We evaluate STDP and evolutionary optimization as standalone methods for training networks, and also evaluate a combined approach where STDP weight updates are applied within an evolutionary algorithm. We also apply Bayesian hyperparameter optimization as a meta learner for each of the algorithms. We find that STDP by itself is not an ideal learning rule for randomly connected networks, while the inclusion of STDP within an evolutionary algorithm leads to similar performance, with a few interesting differences. This study suggests future work in understanding the relationship between network topology and learning rules.

Elbrecht, Daniel↗
DOE OSTI2021

Discovery of a weak topological insulating state and van Hove singularity in triclinic RhBi2

Abstract Time reversal symmetric (TRS) invariant topological insulators (TIs) fullfil a paradigmatic role in the field of topological materials, standing at the origin of its development. Apart from TRS protected strong TIs, it was realized early on that more confounding weak topological insulators (WTI) exist. WTIs depend on translational symmetry and exhibit topological surface states only in certain directions making it significantly more difficult to match the experimental success of strong TIs. We here report on the discovery of a WTI state in RhBi 2 that belongs to the optimal space group P $$\bar{1}$$ 1 ¯ , which is the only space group where symmetry indicated eigenvalues enumerate all possible invariants due to absence of additional constraining crystalline symmetries. Our ARPES, DFT calculations, and effective model reveal topological surface states with saddle points that are located in the vicinity of a Dirac point resulting in a van Hove singularity (VHS) along the (100) direction close to the Fermi energy ( E F ). Due to the combination of exotic features, this material offers great potential as a material platform for novel quantum effects.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Enhancement of the Magnetoresistance in the Mobility‐Engineered Compensated Metal Pt 5 P 2

Abstract The magnetoresistance (MR) in nonmagnetic materials continues to be a fertile research area in materials science. The search for giant, positive MR has been limited to a rather small window of materials such as high‐mobility semimetals in single‐crystalline form. Here, the observation of a very large positive MR in metallic Pt 5 P 2 in polycrystalline form is reported. The observations reveal that improvement of the crystallinity results in a significant enhancement of the positive MR, exceeding 10 000% at 9 T, comparable to high‐mobility semimetals. Based on first‐principles calculations combined with magnetotransport and thermoelectric measurements, the Fermi surface of Pt 5 P 2 is found to consist of a collection of multiple electron and hole pockets compensating one another, along with a characteristic pocket continuously connected to the adjacent Brillouin zone, together with possible topologically protected band crossings. This work extends the landscape of high MR candidate materials to polycrystalline metals, which demonstrates the importance of crystallinity and purity of the samples for the optimization of the MR.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Experimental investigation and thermodynamic modeling of the binary Pt–Sn system

Here, the thermodynamics of the Pt–Sn system is revisited with attention to phase equilibria involving PtSn 4 , a topological quantum material with interesting physical properties. The phase relationship in the Sn-rich region of the Pt–Sn binary system was investigated by a combination of differential scanning calorimetry and equilibration experiments. Based on the present experiments and a critical evaluation of the available thermodynamic and phase diagram data reported in the literature, thermodynamic assessments of the entire Pt–Sn system were performed using the CALPHAD approach. Two sets of thermodynamic parameters were optimized, where the liquid phase was described using the Bragg-Williams random mixing model and the associate solution model, respectively. The modeling results are compared with the reliable experimental data and are shown to provide good representations of the phase equilibria and thermodynamic properties in this system.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Multi-Fidelity Stochastic Economic Dispatch for Operating Low Carbon Power Grids

Grid operators can address the inherently stochastic nature of renewables by solving a two-stage stochastic programming model that minimizes the cost of dispatch decisions while accounting for the complex grid dynamics. It is common to use a sample average approximation to estimate the expectation of the second stage costs in this model. However, the large sample count needed for numerical accuracy makes effective modeling large-scale electric grids computationally intractable. We introduce a control variate multi-fidelity estimator for the second-stage recourse that enables high quality dispatch decisions in real-time with a reduced computational burden. We obtain a hierarchy of model fidelities by linearizing the AC power flow system representation to DC power flow, and by relaxing transmission and voltage network constraints. We evaluate the performance of our proposed method on a synthetic grid with 73 buses against a deterministic baseline with persistence forecast and a high-fidelity reference. Our analysis shows a computational speed-up of 7.62x with a minimal loss in accuracy. The multi-fidelity method is well suited to fidelity combinations that use a simplified network topology in their lower fidelity model and is an attractive option for applications where accurate grid modeling needed on a limited computational budget.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Towards automating structural discovery in scanning transmission electron microscopy *

Abstract Scanning transmission electron microscopy is now the primary tool for exploring functional materials on the atomic level. Often, features of interest are highly localized in specific regions in the material, such as ferroelectric domain walls, extended defects, or second phase inclusions. Selecting regions to image for structural and chemical discovery via atomically resolved imaging has traditionally proceeded via human operators making semi-informed judgements on sampling locations and parameters. Recent efforts at automation for structural and physical discovery have pointed towards the use of ‘active learning’ methods that utilize Bayesian optimization with surrogate models to quickly find relevant regions of interest. Yet despite the potential importance of this direction, there is a general lack of certainty in selecting relevant control algorithms and how to balance a priori knowledge of the material system with knowledge derived during experimentation. Here we address this gap by developing the automated experiment workflows with several combinations to both illustrate the effects of these choices and demonstrate the tradeoffs associated with each in terms of accuracy, robustness, and susceptibility to hyperparameters for structural discovery. We discuss possible methods to build descriptors using the raw image data and deep learning based semantic segmentation, as well as the implementation of variational autoencoder based representation. Furthermore, each workflow is applied to a range of feature sizes including NiO pillars within a La:SrMnO 3 matrix, ferroelectric domains in BiFeO 3 , and topological defects in graphene. The code developed in this manuscript is open sourced and will be released at github.com/nccreang/AE_Workflows .

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Design optimization of lightweight automotive seatback through additive manufacturing compression overmolding of metal polymer composites

With the growing demand for enhanced automotive fuel efficiency and environmental sustainability, there is a need for lightweighting automotive components through innovative design and manufacturing processes. Here, this study leverages a combination of numerical iterative design optimization and hybrid additive manufacturing–compression molding (AM-CM) technique for metal polymer composites to lightweight an automotive seatback. The AM-CM process enables robust mechanical interlocking between metals and composites, boasting high stiffness and strength with low overall density. Replacing metallic components with such metal polymer composites allows for comparable mechanical performance while significantly reducing the overall weight. First, the automotive seatback design space is reduced to critical load carrying regions using topology optimization and high stress concentration areas are identified using finite element analysis. Next, a lightweight metal polymer subcomponent is designed for a high stress concentration region. The full seatback frame with spatially heterogeneous material-specific design is then iteratively optimized to enable enhanced stiffness with minimal weight. Overall, the automotive seatback frame designed with location-specific metal, polymer, and metal polymer composite materials weighs 20% less than the metal-only design while exhibiting similar stiffness.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Modeling and Analysis of DC Microgrids as Stochastic Hybrid Systems

This study proposes a method of predicting the influence of random load behavior on the dynamics of dc microgrids and distribution systems. This is accomplished by combining stochastic load models and deterministic microgrid models. Together, these elements constitute a stochastic hybrid system. The resulting model enables straightforward calculation of dynamic state moments, which are used to assess the probability of desirable operating conditions. Specific consideration is given to systems based on the dual active bridge (DAB) topology. Bounds are derived for the probability of zero voltage switching (ZVS) in DAB converters. A simple example is presented to demonstrate how these bounds may be used to improve ZVS performance as an optimization problem. In conclusion, predictions of state moment dynamics and ZVS probability assessments are verified through comparisons to Monte Carlo simulations.

42 ENGINEERING↗
DOE OSTI2024

Advanced Permanent Magnet Generator Topologies Using Multimaterial Shape Optimization and 3D Printing: Preprint

A vast majority of utility-scale wind turbine generators in the United States are dependent on foreign- sourced rare-earth permanent magnets that are vulnerable to supply chain uncertainties. Many small wind original equipment manufacturers are motivated to pursue continuous improvements to the generator design to lower the material and production costs and improve performance by lowering cogging torque and increasing the efficiency. Traditional design and manufacturing offer limited opportunities. In this work, we demonstrate advanced design approaches for a 15-kW baseline wind turbine generator by making use of recent progress in three-dimensional (3D) printing of polymer- bonded magnets and, electrical and structural steel. We explore three methods of magnet parametrization using Bezier curves resulting in symmetric, asymmetric and multimaterial magnet designs. We employ a multiphysics approach combining parametric computer-aided design modeling, finite- element analysis and targeted sampling to identify novel designs with more opportunities for reducing rare-earth material, improving efficiency and minimizing cogging torque. The results show that asymmetric-pole design and multimaterial-pole designs offer a greater opportunity to minimize rare-earth magnet materials by up to 35% with similar performance as the baseline generator, suggesting newer opportunities with design freedom beyond traditional limits of symmetry and as allowed by 3D printing.

Bezier curves↗
DOE OSTI2025

Dark Energy Survey Year 3 results: $w$CDM cosmology from simulation-based inference with persistent homology on the sphere

We present cosmological constraints from Dark Energy Survey Year 3 (DES Y3) weak lensing data using persistent homology, a topological data analysis technique that tracks how features like clusters and voids evolve across density thresholds. For the first time, we apply spherical persistent homology to galaxy survey data through the algorithm TopoS2, which is optimized for curved-sky analyses and HEALPix compatibility. Employing a simulation-based inference framework with the Gower Street simulation suite, specifically designed to mimic DES Y3 data properties, we extract topological summary statistics from convergence maps across multiple smoothing scales and redshift bins. After neural network compression of these statistics, we estimate the likelihood function and validate our analysis against baryonic feedback effects, finding minimal biases (under $0.3σ$) in the $Ω_\mathrm{m}-S_8$ plane. Assuming the $w$CDM model, our combined Betti numbers and second moments analysis yields $S_8 = 0.821 \pm 0.018$ and $Ω_\mathrm{m} = 0.304\pm0.037$-constraints 70% tighter than those from cosmic shear two-point statistics in the same parameter plane. Our results demonstrate that topological methods provide a powerful and robust framework for extracting cosmological information, with our spherical methodology readily applicable to upcoming Stage IV wide-field galaxy surveys.

Prat, J. [Nordita; Royal Inst. Tech., Sodertalje; ↗
NASA NTRS2014

Aeroelastic Tailoring of the NASA Common Research Model via Novel Material and Structural Configurations

This work explores the use of tow steered composite laminates, functionally graded metals (FGM), thickness distributions, and curvilinear rib/spar/stringer topologies for aeroelastic tailoring. Parameterized models of the Common Research Model (CRM) wing box have been developed for passive aeroelastic tailoring trade studies. Metrics of interest include the wing weight, the onset of dynamic flutter, and the static aeroelastic stresses. Compared to a baseline structure, the lowest aggregate static wing stresses could be obtained with tow steered skins (47% improvement), and many of these designs could reduce weight as well (up to 14%). For these structures, the trade-off between flutter speed and weight is generally strong, although one case showed both a 100% flutter improvement and a 3.5% weight reduction. Material grading showed no benefit in the skins, but moderate flutter speed improvements (with no weight or stress increase) could be obtained by grading the spars (4.8%) or ribs (3.2%), where the best flutter results were obtained by grading both thickness and material. For the topology work, large weight reductions were obtained by removing an inner spar, and performance was maintained by shifting stringers forward and/or using curvilinear ribs: 5.6% weight reduction, a 13.9% improvement in flutter speed, but a 3.0% increase in stress levels. Flutter resistance was also maintained using straightrotated ribs although the design had a 4.2% lower flutter speed than the curved ribs of similar weight and stress levels were higher. These results will guide the development of a future design optimization scheme established to exploit and combine the individual attributes of these technologies.

Jutte, Christine V.↗
DOE OSTI2023

PCAfold 2.0—Novel tools and algorithms for low-dimensional manifold assessment and optimization

We describe an update to our open-source Python package, PCAfold, designed to help researchers generate, analyze and improve low-dimensional data manifolds. In the current version, PCAfold 2.0, we introduce novel tools and algorithms for assessing and optimizing low-dimensional manifolds. This includes a method that generates a “map” of local feature sizes that can help pinpoint researchers to problematic regions on a manifold. We introduce a novel cost function that characterizes the quality of a manifold topology with a single number. We develop two algorithms for feature selection based on principal component analysis (PCA) that use the cost function as an objective function to minimize. We introduce a quantity of interest (QoI)-aware dimensionality reduction strategy where data projections are computed using an artificial neural network and are directly optimized towards representing various projection-independent and projection-dependent QoIs. We also introduce an implementation of partition of unity networks (POUnets) for efficient reconstruction of QoIs from low-dimensional manifolds based on combining neural network classification with localized polynomial regression. Our software can be broadly applicable in all domains of science and engineering that aim to reduce data dimensionality, as well as in the fundamental research on representation learning.

97 MATHEMATICS AND COMPUTING↗