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Results for “Structural Optimization”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Discovery of Fragment-Derived Small Molecules for in Vivo Inhibition of Ketohexokinase (KHK)

Increased fructose consumption and its subsequent metabolism have been implicated in hepatic steatosis, dyslipidemia, obesity, and insulin resistance in humans. Since ketohexokinase (KHK) is the principal enzyme responsible for fructose metabolism, identification of a selective KHK inhibitor may help to further elucidate the effect of KHK inhibition on these metabolic disorders. Until now, studies on KHK inhibition with small molecules have been limited due to the lack of viable in vivo pharmacological tools. Herein we report the discovery of 12, a selective KHK inhibitor with potency and properties suitable for evaluating KHK inhibition in rat models. Key structural features interacting with KHK were discovered through fragment-based screening and subsequent optimization using structure-based drug design, and parallel medicinal chemistry led to the identification of pyridine 12.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Scidac-Data: Enabling Data Driven Modeling of Exascale Computing

Here, the SciDAC-Data project is a DOE-funded initiative to analyze and exploit two decades of information and analytics that have been collected by the Fermilab data center on the organization, movement, and consumption of high energy physics (HEP) data. The project analyzes the analysis patterns and data organization that have been used by NOvA, MicroBooNE, MINERvA, CDF, D0, and other experiments to develop realistic models of HEP analysis workflows and data processing. The SciDAC-Data project aims to provide both realistic input vectors and corresponding output data that can be used to optimize and validate simulations of HEP analysis. These simulations are designed to address questions of data handling, cache optimization, and workflow structures that are the prerequisites for modern HEP analysis chains to be mapped and optimized to run on the next generation of leadership-class exascale computing facilities. We present the use of a subset of the SciDAC-Data distributions, acquired from analysis of approximately 71,000 HEP workflows run on the Fermilab data center and corresponding to over 9 million individual analysis jobs, as the input to detailed queuing simulations that model the expected data consumption and caching behaviors of the work running in high performance computing (HPC) and high throughput computing (HTC) environments. In particular we describe how the Sequential Access via Metadata (SAM) data-handling system in combination with the dCache/Enstore-based data archive facilities has been used to develop radically different models for analyzing the HEP data. We also show how the simulations may be used to assess the impact of design choices in archive facilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Topology optimization of additively manufactured fluidic components free of internal support structures

This report integrates projection-based approaches for implementing overhang constraints with fluid topology optimization to design additively manufactured fluidic components that do not require internal support structures. Internal support structures are challenging, potentially impossible to remove in complex fluid networks, yet would degrade fluidic performance if left within the part. This presents a major challenge in coupling topology optimization with additive manufacturing in the design of fluid components. The proposed approach leverages past work in overhang constraints for solid mechanics, including projection formulations and adjoint sensitivity analysis, and considers incompressible Navier–Stokes equations for laminar fluid flow. The approach is demonstrated on 2D and 3D fluid topology optimization problems to minimize pressure drop and constrain mass flow rates in pipes and manifolds. Resulting designs are crisp, logical, satisfy performance constraints, and satisfy overhang constraints, eliminating the need for internal support structures.

42 ENGINEERING↗
DOE OSTI2019

Structure-Guided In Vitro to In Vivo Pharmacokinetic Optimization of Propargyl-Linked Antifolates

Pharmacokinetic/pharmacodynamic properties are strongly correlated with the in vivo efficacy of antibiotics. Propargyl-linked antifolates, a novel class of antibiotics, demonstrate potent antibacterial activity against both Gram-positive and Gram-negative pathogenic bacteria, including multidrug-resistant Staphylococcus aureus. Here, we report our efforts to optimize the pharmacokinetic profile of this class to best match the established pharmacodynamic properties. High-resolution crystal structures were used in combination with in vitro pharmacokinetic models to design compounds that not only are metabolically stable in vivo but also retain potent antibacterial activity. The initial lead compound was prone to both N-oxidation and demethylation, which resulted in an abbreviated in vivo half-life (~20 minutes) in mice. Stability of leads toward mouse liver microsomes was primarily used to guide medicinal chemistry efforts so robust efficacy could be demonstrated in a mouse disease model. Structure-based drug design guided mitigation of N-oxide formation through substitutions of sterically demanding groups adjacent to the pyridyl nitrogen. Additionally, deuterium and fluorine substitutions were evaluated for their effect on the rate of oxidative demethylation. The resulting compound was characterized and demonstrated to have a low projected clearance in humans with limited potential for drug-drug interactions as predicted by cytochrome P450 inhibition as well as an in vivo exposure profile that optimizes the potential for bactericidal activity, highlighting how structural data, merged with substitutions to introduce metabolic stability, are a powerful approach to drug design.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Powder X-ray diffraction of fluorometholone, C 22 H 29 FO 4

Commercial fluorometholone, CAS #426-13-1, crystallizes in the monoclinic space group P 2 1 (#4) with a = 6.40648(2), b = 13.43260(5), c = 11.00060(8) Å, β = 92.8203(5)°, V = 945.517(5) Å 3 , and Z = 2. A reduced cell search in the Cambridge Structural Database yielded one previous structure determination, using single-crystal data at 292 K. In this work, the sample was ordered from the United States Pharmacopeial Convention (Lot # R032K0) and analyzed as-received. The room temperature (295 K) crystal structure was refined using synchrotron ( λ = 0.412826 Å) powder diffraction data and optimized using density functional theory (DFT) techniques. Hydrogen positions were included as a part of the structure and were re-calculated during the refinement. The diffraction data were collected on beamline 11-BM at the Advanced Photon Source, Argonne National Laboratory, and the powder X-ray diffraction pattern of the compound has been submitted to ICDD® for inclusion in the Powder Diffraction File™. The agreement of the Rietveld-refined and DFT-optimized structures is excellent; the root-mean-square Cartesian displacement is 0.060 Å. In addition to the O–H⋯O hydrogen bonds observed by Park et al. (Park, Y. J., Lee, M. Y., and Cho, S. I. ( 1992 ). “Fluorometholone,” J. Korean Chem. Soc. 36 , 812–817), C–H⋯O hydrogen bonds contribute to the crystal energy.

Gonzalez, Diana↗
DOE OSTI2023

Efficient 3D Molecular Design with an E(3) Invariant Transformer VAE

This work introduces a three-dimensional (3D) invariant graph-to-string transformer variational autoencoders (VAE) (Vagrant) for generating molecules with accurate density functional theory (DFT)-level properties. Vagrant learns to model the joint probability distribution of a 3D molecular structure and its properties by encoding molecular structures into a 3D-aware latent space. Directed navigation through this latent space implicitly optimizes the 3D structure of a molecule, and the latent embedding can be used to condition a generative transformer to predict the candidate structure as a one-dimensional (1D) sequence. Additionally, we introduce two novel sampling methods that exploit the latent characteristics of a VAE to improve performance. Here, we show that our method outperforms comparable 3D autoregressive and diffusion methods for predicting quantum chemical property values of novel molecules in terms of both sample quality and computational efficiency.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Solar‐Driven CO 2 Conversion via Optimized Photothermal Catalysis in a Lotus Pod Structure

Abstract Photothermal CO 2 reduction is one of the most promising routes to efficiently utilize solar energy for fuel production at high rates. However, this reaction is currently limited by underdeveloped catalysts with low photothermal conversion efficiency, insufficient exposure of active sites, low active material loading, and high material cost. Herein, we report a potassium‐modified carbon‐supported cobalt (K + −Co−C) catalyst mimicking the structure of a lotus pod that addresses these challenges. As a result of the designed lotus‐pod structure which features an efficient photothermal C substrate with hierarchical pores, an intimate Co/C interface with covalent bonding, and exposed Co catalytic sites with optimized CO binding strength, the K + −Co−C catalyst shows a record‐high photothermal CO 2 hydrogenation rate of 758 mmol g cat −1 h −1 (2871 mmol g Co −1 h −1 ) with a 99.8 % selectivity for CO, three orders of magnitude higher than typical photochemical CO 2 reduction reactions. We further demonstrate with this catalyst effective CO 2 conversion under natural sunlight one hour before sunset during the winter season, putting forward an important step towards practical solar fuel production.

Wang, Hongmin↗
DOE OSTI2023

Solar–Driven CO 2 Conversion via Optimized Photothermal Catalysis in a Lotus Pod Structure

Photothermal CO 2 reduction is one of the most promising routes to efficiently utilize solar energy for fuel production at high rates. However, this reaction is currently limited by underdeveloped catalysts with low photothermal conversion efficiency, insufficient exposure of active sites, low active material loading, and high material cost. Herein, we report a potassium-modified carbon-supported cobalt (K + –Co–C) catalyst mimicking the structure of a lotus pod that addresses these challenges. As a result of the designed lotus-pod structure which features an efficient photothermal C substrate with hierarchical pores, an intimate Co/C interface with covalent bonding, and exposed Co catalytic sites with optimized CO binding strength, the K + –Co–C catalyst shows a record-high photothermal CO 2 hydrogenation rate of 758 mmol gcat –1 h –1 (2871 mmol gCo –1 h –1 ) with a 99.8 % selectivity for CO, three orders of magnitude higher than typical photochemical CO 2 reduction reactions. Here, we further demonstrate with this catalyst effective CO 2 conversion under natural sunlight one hour before sunset during the winter season, putting forward an important step towards practical solar fuel production.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Optimization of sensors based on surface waves in flat-layered structures

A method for increasing the sensitivity of surface-wave sensors in multifilm structures is proposed based on the results of a theoretical study of the reflection of a plane electromagnetic wave from a flat-layered structure containing homogeneous films. A method for optimizing the parameters of the films forming the surface structure is developed and tested. The devices proposed are characterized by high field amplification in a surface layer of multifilm structure and a higher sensitivity to variations in the optical properties of the thin near-surface layer. Application of the proposed multifilm structures is expected to increase significantly the sensitivity of the existing sensors based on surface-plasmon polaritons, which are applied in modern optical multichannel biological, chemical, and physical sensor systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Autonomous phase mapping of gold nanoparticles synthesis with differentiable models of spectral shape

Autonomous experimentation–or self-driving labs–offers a systematic approach to accelerate materials discovery by integrating automated synthesis, characterization, and data-driven decision-making. We present a closed-loop workflow for the on-demand synthesis and structural characterization of colloidal gold nanoparticles, enabling direct mapping from composition to nanoscale structure. Our framework leverages differentiable models of spectral shape to address two central tasks in self-driving labs: (a) phase mapping, or identifying compositional regions with distinct structural behavior; and (b) material retrosynthesis, or optimizing compositions for target structure. Using functional data analysis, we develop a data-driven model with generative pre-training, active learning, and high-throughput experiments to predict spectral responses across composition space. We demonstrate the approach on seed-mediated growth of gold nanoparticles, showcasing its ability to extract design rules, reveal secondary interactions, and efficiently navigate morphology space. Gradient-based optimization of the models enables inverse design, making this a unified platform.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Quantitative and mechanistic insights into proton dynamics for fast energy storage

Proton conduction in hydrogen-bond-rich protic electrolytes enables fast mass and charge transport, crucial for electrochemical energy storage and power conversion. Such transport can give proton-based batteries exceptional rate capability and low-temperature operation beyond other working ions. Here we show that in phosphoric acid (H 3 PO 4 ) electrolytes, vehicular and structural proton transport coexist, and their contributions to conductivity can be quantitatively distinguished. We link structural diffusion directly to hydrogen-bond strength, enabling the precise tuning of proton migration. Guided by this, we reveal a double conductivity peak from regulated structural diffusion. The optimal electrolyte (5.8-M H 3 PO 4 ) achieves high overall (232.9 mS cm −1 ) and structural (164.9 mS cm−1) conductivity. A MoO 3 ‖CuFe-TBA battery with this electrolyte outperforms a deep-eutectic benchmark (8.3-M H 3 PO 4 ), delivering >17,474 W kg −1 at room temperature and retaining 15.1 Wh kg −1 at −75 °C. These findings provide a framework for designing advanced protic electrolytes across electrochemical systems.

Li, Ziyue [Fudan University, Shanghai (China, Peop↗
DOE OSTI2018

3-D Semantic Information Inference from Airborne Video. Final Report

We developed a full 3D modeling pipeline focusing on an important application scenario: city-scale 3D reconstruction from aerial imagery. We propose an approach to solve camera pose estimation and dense reconstruction from Wide Area Aerial Surveillance (WAAS) videos captured by an airborne platform. Our approach solves them in an online fashion: it incrementally updates a sparse 3D map and estimates the camera pose as each new frame arrives; depth maps of selected key frames are computed using a variational method and integrated to produce a full 3D model via volumetric reconstruction. In practice, aerial imagery is usually captured using a multicamera system. We propose an approach for camera pose estimation of multi-camera aerial imagery which is parallelized on multiple GPUs for efficiency. The approach is also extended for progressive 3D model acquisition with a hand-held camera. We also developed an offline approach since online approach is not a necessity and accuracy has higher priority over efficiency in many scenarios. We presented MeshRecon, a mesh-based offline system composed of three modules: a dense point cloud is generated using multi-resolution plane sweep method; an initial mesh model is extracted from the point cloud via global optimization considering visibility information of all images; the mesh model is then iteratively refined to capture structural details by optimizing the photometric consistency and spatial regularization. The major processes are parallelized on GPU for efficiency. For the aerial imagery case, we evaluate our system on several real-world multicamera aerial imagery datasets, each covering an urban scenario of several square kilometers. Quantitative result shows that the reconstructed geometric 3D model is highly accurate with error smaller than 1 meter over the entire city. Besides aerial imagery, we also evaluate its performance on general geometric 3D model acquisition of real-world objects. Result shows that the system is robust and flexible for various types of objects at different scales in both indoor and outdoor environments. Based on city 3D models reconstructed at different times, we present a system for city-scale geometry change detection by performing comparisons at the 3D geometry level. Our system is able to detect geometry changes at different scales, ranging from a building cluster to small-scale vegetation changes, with high accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Modeling the Impact of Grain Size on Corrosion Behavior of Ni-Based Alloys in Molten Chloride Salt via Cellular Automata

Molten chloride salts hold significant promise as both thermal transfer and storage media for next-generation concentrated solar power (CSP) systems. However, molten chlorides pose a considerable corrosion risk to structural materials, particularly Ni-based alloys. One approach to enhancing corrosion resistance is through the optimization of grain structure; however, it remains uncertain whether increasing or decreasing grain size enhances corrosion resistance. A cellular automata (CA) program was developed to evaluate the interplay between grain size and corrosion in Ni-based alloy. Our CA program tracks alloy composition, surface roughness, and thickness loss via a graphical user interface, displaying corrosion and diffusion status, and multiple user input cards for tuning the simulation. CA simulations of Inconel 625 indicate enhanced corrosion resistance with increased grain size, with passivating oxides offering limited protection. Additionally, the temporal evolution of alloy surface roughness demonstrates notable fluctuations, with abrupt increases attributed to corrosion along vertical grain boundaries and sudden decreases to grain detachment from the protective film.

Materials Science↗
DOE OSTI2018

Pareto Optimal Spectrally Selective Emitters for Thermophotovoltaics via Weak Absorber Critical Coupling

Tailoring the emission spectra of a thermophotovoltaic (TPV) emitter away from that of a blackbody has the potential to minimize transmission and thermalization loss in a photovoltaic receiver. Selective TPV emitters can lead to solar energy conversion with efficiency greater than the Shockley-Queisser limit and can facilitate the generation of useful energy from waste heat. A new design is introduced to radically tune thermal emission that leverages the interplay between two resonant phenomena in simple planar nanostructures-absorption in weakly absorbing nanofilms and reflection in multilayer dielectric stacks. A virtual screening approach is employed to identify promising structures for a selective thermal emitter from a search space of millions, several of which approach the ideal values of a step-function selective thermal emitter. One of these structures is experimentally fabricated and evaluated, which includes a weakly absorbing alloy with tailored optical properties fabricated by atomic layer deposition (ALD). The versatility of the design and fabrication approach result in an emitter with excellent spectral density (0.8 W cm(-2) sr(-1)) and spectral efficiency (46.8%) at 1373 K. Future experimental challenges to a more accurate realization of the optimal structures calculated are also considered.

atomic layer deposition↗
DOE OSTI2019

A Real-Time Big Data Control-Theoretical Framework for Cyber-Physical-Human Systems [Book Chapter]

It is known that cyber-physical-human systems naturally arise from interdependent infrastructure systems and smart connected communities. Such applications require ubiquitous information sensing and processing, intelligent machine-to-machine communication for a seamless coordination, as well as intelligent interactions between humans and machines. This chapter presents a control-theoretical framework to model heterogeneous physical dynamic systems, information and communication, as well as cooperative controls and/or distributed optimization of such interconnected systems. It is shown that efficient analytical and computational algorithms can be modularly designed and hierarchically implemented to operate and optimize cyber-physical-human systems, first to quantify individually the input–output relationship of nonlinear dynamic behaviors of every physical subsystem, then to coordinate locally both cyber-physical interactions of neighboring agents as well as physical-human interactions, and finally to dynamically model and optimize the overall networked system. The hierarchical structure makes the overall optimization and control problem scalable and solvable. Moreover, the three levels integrate individual designs and optimization, distributed cooperative optimization, and decision-making through real-time, data-driven, model-based learning and control. Specifically, one of the contributions of the chapter is to demonstrate how the combination of dissipativity theory and cooperative control serves as a natural framework and promising tools to analyze, optimize, and control such large-scale system. Application to digital grid is investigated as an illustrative example.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Metal Substitution as the Method of Modifying Electronic Structure of Metal–Organic Frameworks

Targeted modification of electronic structure is an important step in the optimization of metal-organic frameworks (MOFs) for photovoltaic, sensing, and photocatalytic applications. The key parameters to be controlled include the band gap, the absolute energy position of band edges, the excited state charge separation, and degree of hybridization between metal and ligand sites. Partial metal replacement, or metal doping, within secondary building units is a promising, yet relatively unexplored route to modulate these properties in MOFs. Therefore, in the present study, a general method for selecting metal dopant is worked out in theory and validated by experiment, retaining MIL-125 and UiO-66 as the model systems. Metal mixing enables targeted optimization of key electronic structure parameters. This method is applicable to any MOF architecture and can serve as a roadmap for future synthesis of MOFs with predefined properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Progress toward favorable landscapes in quantum combinatorial optimization

The performance of variational quantum algorithms relies on the success of using quantum and classical computing resources in tandem. Here, we study how these quantum and classical components interrelate. In particular, we focus on algorithms for solving the combinatorial optimization problem MaxCut, and study how the structure of the classical optimization landscape relates to the quantum circuit used to evaluate the MaxCut objective function. In order to analytically characterize the impact of quantum features on the critical points of the landscape, we consider a family of quantum circuit ansätze composed of mutually commuting elements. We identify multiqubit operations as a key resource and show that overparameterization allows for obtaining favorable landscapes. Namely, we prove that an ansatz from this family containing exponentially many variational parameters yields a landscape free of local optima for generic graphs. However, we further prove that these ansätze do not offer superpolynomial advantages over purely classical MaxCut algorithms. Here, we then present a series of numerical experiments illustrating that noncommutativity and entanglement are important features for improving algorithm performance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Minimum feature size control in level set topology optimization via density fields

A level set topology optimization approach that uses an auxiliary density field to nucleate holes during the optimization process and achieves minimum feature size control in optimized designs is explored. The level set field determines the solid-void interface and the density field describes the distribution of a fictitious porous material using the solid isotropic material with penalization. These fields are governed by two sets of independent optimization variables which are initially coupled using a penalty for hole nucleation. The strength of the density field penalization and projection is gradually increased during the optimization process to promote a 0-1 density distribution. In addition, a second penalty regulates the evolution of the density field in the void phase. The treatment of the density field combined with the second penalty mitigate the appearance of small design features. The minimum feature size of optimized designs is controlled by the radius of the linear filter applied to the density optimization variables. The structural response is predicted by the extended finite element method, the sensitivities by the adjoint method, and the optimization variables are updated by a gradient-based optimization algorithm. Numerical examples investigate the robustness of this approach with respect to algorithmic parameters and mesh refinement. The results show the applicability of the combined density level set topology optimization approach for both optimal hole nucleation and for minimum feature size control in 2D and 3D. This comes, however, at the cost of a more complex problem formulation and additional computational cost due to an increased number of optimization variables.

42 ENGINEERING↗