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Results for “Selection 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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Graphene-polyelectrolyte multilayer membranes with tunable structure and internal charge

One great advantage of graphene-polyelectrolyte multilayer (GPM) membranes is their tunable structure and internal charge for improved separation performance. In this study, we synthesized GO-dominant GPM membrane with internal negatively-charged domains, polyethyleneimine (PEI)-dominant GPM membrane with internal positively-charged domains and charge-balanced dense/loose GPM membranes by simply adjusting the ionic strength and pH of the GO and PEI solutions used in layer-by-layer membrane synthesis. A combined system of quartz crystal microbalance with dissipation (QCM-D) and ellipsometry was used to analyze the mass deposition, film thickness, and layer density of the GPM membranes. The performance of the GPM membranes were compared in terms of both permeability and selectivity to determine the optimal membrane structure and synthesis strategy. One effective strategy to improve the GPM membrane permeability-selectivity tradeoff is to assemble charge-balanced dense membranes under weak electrostatic interactions. This balanced membrane exhibits the highest MgCl 2 selectivity (~86%). Another effective strategy for improved cation removal is to create PEI-dominant membranes that provide internal positively-charged barrier to enhance cation selectivity without sacrificing water permeability. Finally, these findings shine lights on the development of a systematic approach to push the boundary of permeability-selectivity tradeoff for GPM membranes.

42 ENGINEERING↗
DOE OSTI2018

Optimization, Inference and Learning for Advanced Networks

This piece of software aims at providing an implementation of methods to perform efficient optimization, probabilistic inference and model selection and learning for various type of networked systems. The software contains a suite of methods and functions that: 1) perform probabilistic inference and random sampling on networked models 2) select or learn networked models from data 3) solve optimization tasks with a networked structure Network models that are supported by these features include, but are not limited to, factor graphs and Markov random fields such as Ising models, Potts models, p-spins models, Gaussian random fields and restricted Boltzmann machine.

Hijazi, Hassan↗
NASA NTRS1991

SeGRAm - A practical and versatile tool for spacecraft trajectory optimization

An implementation of the Sequential Gradient/Restoration Algorithm, SeGRAm, is presented along with selected examples. This spacecraft trajectory optimization and simulation program uses variational calculus to solve problems of spacecraft flying under the influence of one or more gravitational bodies. It produces a series of feasible solutions to problems involving a wide range of vehicles, environments and optimization functions, until an optimal solution is found. The examples included highlight the various capabilities of the program and emphasize in particular its versatility over a wide spectrum of applications from ascent to interplanetary trajectories.

Rishikof, Brian H.↗
DOE OSTI2020

Gel-Derived Amorphous Bismuth–Nickel Alloy Promotes Electrocatalytic Nitrogen Fixation via Optimizing Nitrogen Adsorption and Activation

To achieve the electrochemical nitrogen reduction reaction (NRR) for efficient and sustainable NH 3 production, catalysts should exhibit high selectivity and activity with optimal adsorption energy. Herein we developed a three-dimensional (3D) amorphous BiNi alloy toward a significantly enhanced NRR compared with its crystalline and metal counterparts. Ni alloying enables the chemisorption of nitrogen and the lower fre-energy change for the *NNH formation, and the 3D alloy electrocatalyst exhibits high catalytic activity for NH 3 production with a yield rate of 17.5 μg h -1 mg cat -1 and Faradaic efficiency of 13.8 %. The enhanced electron transfer and increased electrochemical surface area were revealed in the interconnected porous scaffold, affording it sufficiently efficient and stable activity for potential practical applications. Furthermore, this work offers new insights into optimizing the adsorption energy of reactants and intermediates combined with tuning the crystallinity of NRR electrocatalysts.

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

Microbiome Adaptation Could Amplify Modeled Projections of Global Soil Carbon Loss With Climate Warming

Warming alters soil microbial traits through ecological and evolutionary processes, directly influencing the decomposition of organic matter, which significantly affects global soil carbon emissions. Yet, soil carbon models largely ignore these processes and their implications for global responses to warming. Here, we incorporate eco-evolutionary theory into a mechanistic model describing microbial soil carbon decomposition to address the question of whether such processes could have consequential effects on climate carbon feedbacks globally. We assume that a key trait of microbes, their resource allocation to production of exoenzymes (which facilitate decomposition of organic matter)—is optimized to environmental temperatures by natural selection. We find that eco-evolutionary optimization results in microbes allocating more resources to enzyme production under warming. When applied at the global scale, eco-evolutionary optimization enhances the biological realism of soil carbon models and significantly amplifies global soil carbon loss by 2100. Our results highlight the significant potential of microbial eco-evolutionary responses to influence carbon cycle feedbacks to climate change, and motivate an urgent need for more comprehensive data to accurately quantify the adaptive potential of microbiomes in response to climate change.

Biodiversity & Conservation↗
DOE OSTI2020

Gel‐Derived Amorphous Bismuth–Nickel Alloy Promotes Electrocatalytic Nitrogen Fixation via Optimizing Nitrogen Adsorption and Activation

Abstract To achieve the electrochemical nitrogen reduction reaction (NRR) for efficient and sustainable NH 3 production, catalysts should exhibit high selectivity and activity with optimal adsorption energy. Herein we developed a three‐dimensional (3D) amorphous BiNi alloy toward a significantly enhanced NRR compared with its crystalline and metal counterparts. Ni alloying enables the chemisorption of nitrogen and the lower free‐energy change for the *NNH formation, and the 3D alloy electrocatalyst exhibits high catalytic activity for NH 3 production with a yield rate of 17.5 μg h −1 mg cat −1 and Faradaic efficiency of 13.8 %. The enhanced electron transfer and increased electrochemical surface area were revealed in the interconnected porous scaffold, affording it sufficiently efficient and stable activity for potential practical applications. This work offers new insights into optimizing the adsorption energy of reactants and intermediates combined with tuning the crystallinity of NRR electrocatalysts.

Fang, Zhiwei↗
NASA NTRS2013

Selection of Hyperspectral Narrowbands (HNBs) and Composition of Hyperspectral Twoband Vegetation Indices (HVIs) for Biophysical Characterization and Discrimination of Crop Types Using Field Reflectance and Hyperion-EO-1 Data

The overarching goal of this study was to establish optimal hyperspectral vegetation indices (HVIs) and hyperspectral narrowbands (HNBs) that best characterize, classify, model, and map the world's main agricultural crops. The primary objectives were: (1) crop biophysical modeling through HNBs and HVIs, (2) accuracy assessment of crop type discrimination using Wilks' Lambda through a discriminant model, and (3) meta-analysis to select optimal HNBs and HVIs for applications related to agriculture. The study was conducted using two Earth Observing One (EO-1) Hyperion scenes and other surface hyperspectral data for the eight leading worldwide crops (wheat, corn, rice, barley, soybeans, pulses, cotton, and alfalfa) that occupy approx. 70% of all cropland areas globally. This study integrated data collected from multiple study areas in various agroecosystems of Africa, the Middle East, Central Asia, and India. Data were collected for the eight crop types in six distinct growth stages. These included (a) field spectroradiometer measurements (350-2500 nm) sampled at 1-nm discrete bandwidths, and (b) field biophysical variables (e.g., biomass, leaf area index) acquired to correspond with spectroradiometer measurements. The eight crops were described and classified using approx. 20 HNBs. The accuracy of classifying these 8 crops using HNBs was around 95%, which was approx. 25% better than the multi-spectral results possible from Landsat-7's Enhanced Thematic Mapper+ or EO-1's Advanced Land Imager. Further, based on this research and meta-analysis involving over 100 papers, the study established 33 optimal HNBs and an equal number of specific two-band normalized difference HVIs to best model and study specific biophysical and biochemical quantities of major agricultural crops of the world. Redundant bands identified in this study will help overcome the Hughes Phenomenon (or "the curse of high dimensionality") in hyperspectral data for a particular application (e.g., biophysical characterization of crops). The findings of this study will make a significant contribution to future hyperspectral missions such as NASA's HyspIRI. Index Terms-Hyperion, field reflectance, imaging spectroscopy, HyspIRI, biophysical parameters, hyperspectral vegetation indices, hyperspectral narrowbands, broadbands.

Vegetation↗
DOE OSTI2018

Discovery of Tarantula Venom-Derived Na V 1.7-Inhibitory JzTx-V Peptide 5-Br-Trp24 Analogue AM-6120 with Systemic Block of Histamine-Induced Pruritis

Inhibitors of the voltage-gated sodium channel Na V 1.7 are being investigated as pain therapeutics due to compelling human genetics. We previously identified Na V 1.7-inhibitory peptides GpTx-1 and JzTx-V from tarantula venom screens. Potency and selectivity were modulated through attribute-based positional scans of native residues via chemical synthesis. In this paper, we report JzTx-V lead optimization to identify a pharmacodynamically active peptide variant. Molecular docking of peptide ensembles from NMR into a homology model-derived Na V 1.7 structure supported prioritization of key residues clustered on a hydrophobic face of the disulfide-rich folded peptide for derivatization. Replacing Trp24 with 5-Br- Trp24 identified lead peptides with activity in electrophysiology assays in engineered and neuronal cells. 5-Br-Trp24 containing peptide AM-6120 was characterized in X-ray crystallography and pharmacokinetic studies and blocked histamine-induced pruritis in mice after subcutaneous administration, demonstrating systemic Na V 1.7-dependent pharmacodynamics. Our data suggests a need for high target coverage based on plasma exposure for impacting in vivo end points with selectivity-optimized peptidic Na V 1.7 inhibitors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Biomass Production and Nutrient Removal by Perennial Energy Grasses Produced on a Wet Marginal Land

Growing dedicated bioenergy crops on marginal land can provide beneficial outcomes including biomass production and energy, resource management, and ecosystem services. We investigated the effects of harvest timing (peak standing crop [PEAK] or after killing frost [KF]) and nitrogen (N) fertilizer rates (0, 56, and 112 kg N ha -1 ) on yield, nutrient concentrations, and nutrient removal rates of perennial grasses on a wet marginal land. We evaluated three monocultures, including switchgrass (Panicum virgatum L., SW), Miscanthus x giganteus (MG), prairie cordgrass (Spartina pectinata Link, PCG), and a polyculture mixture of big bluestem (Andropogon gerardii Vitman), Indiangrass (Sorghastrum nutans (L.) Nash), and sideoats grama (Bouteloua curtipendula Torr., MIX). Increasing the application of N did correlate with increased biomass, concentration, and subsequent removal of nutrients across almost all treatment combinations. In all grass treatments except MG, PEAK harvesting increased yield and nutrient removal. At PEAK harvest, switchgrass is ideal for optimizing both biomass production and nutrient removal. While our results also suggest short-term plasticity for farmers when selecting harvest timing for optimal nutrient removal, KF harvest is recommended to ensure long-term stand longevity and adequate nutrient removal. If the KF harvest is adopted, MG would be the ideal option for optimizing biomass yield potential. Additionally, we found that the yield of polyculture did not vary much with harvest timing, suggesting better yield stability. Future studies should give consideration for long-term evaluation of polyculture mixtures to assess their biomass yields and nutrient removal capacities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Coordinated Optimal Control of Constrained DERs

In this paper, a coordinated optimal control is designed to simultaneously satisfy multiple power system objectives at the distribution network level. With high penetration of renewables, traditional generation and control may not be enough to compensate for intermittent changes in net load. Dynamic coordination of battery storage devices and demand response at aggregated distributed energy resource (DER) sites becomes necessary to maintain power system operation, however, each dynamic power component operates at different time scales. Frequency stabilization can be achieved by designing optimal controls for traditional generation, storage devices and demand response. The proposed optimal controls naturally render frequency separation of these load balancing mechanisms. Given a 24-hour net load forecast, optimal tracking control inputs are designed subject to constraints of selected system elements to ensure optimal performance of the power system. Simulation results are presented and discussed.

Harvey, Roland↗
DOE OSTI2022

Synthesis and Exploratory Catalysis of 3d Metals: Atom and Group-Transfer Reactions and the Activation and Functionalization of Small Molecules Including Greenhouse Gases

Determining ways to convert natural gas, with zero emissions, and to more value-added materials such as olefins is one of the main goals in my research group. Over this funding period, we explored the chemistry of early-transition metals with metal-nitrogen multiple bonds, specifically titanium and zirconium nitrides, and explored their redox properties, basicity and reactivity with small molecules including greenhouses gases such as carbon dioxide. Some of these work serve as inspiration for the chemistry of important materials such as uranium nitride along with its unprecedented basicity and ability to activate C-H bonds. Using robust chelating templates we also examined rare examples of trivalent group 4 transition metal ions, and studied these spectroscopically. Using early transition metal ions we also explored synthetic routes to new phosphide based products using the phosphaethynolate salt. In addition, we also explored the chemistry of ferrous systems, and redox active ligand that can allow us to reversibly break and make C-S as well as N-N bonds, activate dinitrogen forming unusual MNNM topologies. Our final component describes how we activate methane and dehydrocouple if with a carbene source to form ethylene. Using this information, we also discovered a simple to make iridium catalyst that activate and functionalize methane with 9:1 selectivity for mono-functionalization. We have established a pathway that leads to poisoning of the catalyst and have found optimal conditions for higher selectivity and with over 170 Turnovers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1993

Structural design utilizing updated, approximate sensitivity derivatives

A method to improve the computational efficiency of structural optimization algorithms is investigated. In this method, the calculations of 'exact' sensitivity derivatives of constraint functions are performed only at selected iterations during the optimization process. The sensitivity derivatives utilized within other iterations are approximate derivatives which are calculated using an inexpensive derivative update formula. Optimization results are presented for an analytic optimization problem (i.e., one having simple polynomial expressions for the objective and constraint functions) and for two structural optimization problems. The structural optimization results indicate that up to a factor of three improvement in computation time is possible when using the updated sensitivity derivatives.

Scotti, Stephen J.↗
DOE OSTI2023

Development of Building Design Optimization Methodology: Residential Building Applications

Building design optimization is a highly complex problem, requiring long computational running processes because of the many options that exist when a building is being designed. This paper introduces an integrated approach through which to perform this optimization within an acceptable time frame. The approach includes the methods of variable selection, model simplification, and a sequential optimization process. Using singular value decomposition, a large number of design variables is reduced to a smaller subset that can be solved more quickly through the optimization algorithm. To expedite the variable selection process, a modeling approach that quickly simulates annual energy consumption was developed to replace full annual energy simulations. The developed methodology was applied to two residential buildings in the US, and the results are discussed herein. To assess the accuracy of the integrated optimization methodology, the optimized life cycle costs are compaa variables demonstrating the strongest contributions in the optimization study were identified. The proposed methodology significantly shortened the time requirements for the optimization processes of the two case studies by 74% and 84%; the optimized life cycle costs were within 0.05% and 0.06%, respectively, of the optimum point.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1990

Rapid near-optimal trajectory generation and guidance law development for single-stage-to-orbit airbreathing vehicles

General problems associated with on-board trajectory optimization, propulsion system cycle selection, and with the synthesis of guidance laws were addressed for an ascent to low-earth-orbit of an air-breathing single-stage-to-orbit vehicle. The NASA Generic Hypersonic Aerodynamic Model Example and the Langley Accelerator aerodynamic sets were acquired and implemented. Work related to the development of purely analytic aerodynamic models was also performed at a low level. A generic model of a multi-mode propulsion system was developed that includes turbojet, ramjet, scramjet, and rocket engine cycles. Provisions were made in the dynamic model for a component of thrust normal to the flight path. Computational results, which characterize the nonlinear sensitivity of scramjet performance to changes in vehicle angle of attack, were obtained and incorporated into the engine model. Additional trajectory constraints were introduced: maximum dynamic pressure; maximum aerodynamic heating rate per unit area; angle of attack and lift limits; and limits on acceleration both along and normal to the flight path. The remainder of the effort focused on required modifications to a previously derived algorithm when the model complexity cited above was added. In particular, analytic switching conditions were derived which, under appropriate assumptions, govern optimal transition from one propulsion mode to another for two cases: the case in which engine cycle operations can overlap, and the case in which engine cycle operations are mutually exclusive. The resulting guidance algorithm was implemented in software and exercised extensively. It was found that the approximations associated with the assumed time scale separation employed in this work are reasonable except over the Mach range from roughly 5 to 8. This phenomenon is due to the very large thrust capability of scramjets in this Mach regime when sized to meet the requirement for ascent to orbit. By accounting for flight path angle and flight path angle rate in construction of the flight path over this Mach range, the resulting algorithm provides the means for rapid near-optimal trajectory generation and propulsion cycle selection over the entire Mach range from take-off to orbit.

Calise, A. J.↗
NASA NTRS1980

Focal-plane optimization for detector noise limited performance in cryogenic Fourier transform spectrometer /FTS/ sensors

A study was performed to determine the optimum focal plane configuration including optics, filters and detector-preamplifier selection. The configuration was optimized particularly with respect to minimizing the noise level, but fabrication considerations for a cryogenic environment were also taken into account. The noise terms from source, background, detector electronics and charged particle radiation were quantitatively evaluated. It appears that noise equivalent spectral radiance less than 10 to the -11th W/sq cm per sr per kayser can be achieved between 2.5 and 20 microns.

Mcguirk, M.↗
DOE OSTI2022

Optimal high-throughput virtual screening pipeline for efficient selection of redox-active organic materials

As global interest in renewable energy continues to increase, there has been a pressing need for developing novel energy storage devices based on organic electrode materials that can overcome the shortcomings of the current lithium-ion batteries. One critical challenge for this quest is to find materials whose redox potential (RP) meets specific design targets. In this study, we propose a computational framework for addressing this challenge through the effective design and optimal operation of a high-throughput virtual screening (HTVS) pipeline that enables rapid screening of organic materials that satisfy the desired criteria. Starting from a high-fidelity model for estimating the RP of a given material, we show how a set of surrogate models with different accuracy and complexity may be designed to construct a highly accurate and efficient HTVS pipeline. We demonstrate that the proposed HTVS pipeline construction and operation strategies substantially enhance the overall screening throughput.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Bayesian Model Selection as a Continuous-Variable Helmholtz Machine

We show that Bayesian model selection is equivalent to optimization of a suitably defined free energy. The entropy term in the free energy is defined over the space of possible model selections and model parameters for a given model. Bayesian model selection thus follows a minimum description length principle.

97 MATHEMATICS AND COMPUTING↗