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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
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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 OSTI2022

Load Mitigating Feedback Control of WECs

This report outlines the development of load-mitigating feedback control for wave energy converters. A simple, self-tuning multi-objective controller is demonstrated in simulation for a 3-DOF (surge, heave, pitch) point absorber. In previous work, the proposed control architecture has been shown to be effective in experiment for a variety of device archetypes for the single objective of the maximization of electrical power capture: here this architecture is extended to reduce device loading as well. In particular, PTO actuation forces and the minimization of fatigue damage (determined from the sum of wave-exerted and PTO forces) are considered as additional objectives for the self-tuning controller. This controller is demonstrated for two similar, but distinct systems: one described by the identified linear models from physical testing of the WaveBot device, and another based upon a WEC-Sim simulation that expands upon boundary element method data from the WaveBot device. In both cases, because the power surface is consistently fairly flat in the vicinity of control parameters that maximize power capture in contrasting sea-states, it is found to be generally possible to mitigate either fatigue damage or PTO load. However, PTO load is found to conflict with fatigue damage in some sea-states, limiting the efficacy of control objectives that attempt to mitigate both simultaneously. Additionally, coupling between the surge and pitch DOFs also limits the extent to which fatigue damage can be mitigated for both DOFs in some sea-states. Because control objectives can be considered a function of the sea-state (e.g., load mitigation may not be a concern until the sea is sufficiently large) a simple transition strategy is proposed and demonstrated. This transition strategy is found to be effective with some caveats: firstly, it cannot circumvent the aforementioned objective contradictions. Secondly, this objective transition is too slow to act as a system constraint, and objective thresholds must thus be considered quite conservatively. Improvement of the adjustment strategy is demonstrated through the addition of an integral term. Selection of well-performing transition parameters can be a function of sea-state. While a simple selection procedure is proposed, it is non-optimal, and a more robust selection procedure is suggested for future work.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Identifying Green Solvent Mixtures for Bioproduct Separation Using Bayesian Experimental Design

Liquid–liquid extraction (LLE) is a widely used technique for the separation and purification of liquid-phase products with applications in various industries, including pharmaceuticals, petrochemicals, and renewable chemistry. A critical step in the design of an LLE process is the selection of appropriate solvents. This study presents a new methodology for identifying solvent mixtures for bioproduct separation using Bayesian experimental design (BED). Motivated by the need for environmentally friendly and effective separation methods, we address the challenge of selecting solvent systems that balance separation efficiency, selectivity, and environmental impact while also tackling the difficulty of separating multiple bioproducts using complex solvent systems. Our approach specifically seeks to predict product partition coefficients (log10 Kp values) as thermodynamic parameters underlying solvent selection. The iterative approach integrates Bayesian optimization with experimental measurements to guide solvent selection and leverages COSMO-RS simulations to enhance high-throughput experimentation. Using the design of solvent systems for the separation of lignin-derived aromatic products via centrifugal partition chromatography (CPC) as a case study, we show that within seven iterations/cycles of the methodology, we can identify new mixtures of green solvents that align with CPC design principles. Furthermore, these results demonstrate the efficacy of the BED framework in optimizing green solvent systems for complex separations, highlighting the potential of this method to advance the field of green chemistry and contribute to the development of sustainable industrial processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Measurement of $\Delta m^2_{32}$ and $\sin^2\!\theta_{23}$ using Muon Neutrino and Antineutrino Beams in the NOvA Experiment

NOvA is a long-baseline neutrino oscillation experiment located in the mid-west United States. It consists of two functionally identical tracking calorimeters, known as the near and far detector, that measure neutrino interactions induced by the NuMI beam at baselines of 1~km and 810~km. NuMI can be configured to provide a muon neutrino or antineutrino beam. Analysis of \numu~+~\numubar disappearance allows constraint of the $|\Delta m^2_{32}|$ and \sint oscillation parameters. This thesis presents the first NOvA disappearance results using both neutrino and antineutrino data - previous NOvA analyses have only used neutrino beam data. Two analysis improvements are delineated in dedicated chapters - the design of selection criteria to identify events that are fully contained in the far detector and the optimization of particle identification selection criteria in a multi-dimensional parameter space. The full-detector equivalent beam exposures used for this thesis are $8.85\ti mes 10^{20}$ and $6.91\times 10^{20}$~protons~on~target for neutrino and antineutrino data respectively. Under the assumption of a normal (inverted) neutrino mass hierarchy, analysis of the data gives $\Delta m^2_{32}=+2.49^{+0.09}_{-0.07}\times10^{-3}~\text{eV}^2$ ($\Delta m^2_{32}=-2.54\pm0.08\times10^{-3}~\text{eV}^2$) and $\sin^2\theta_{23} = 0.59\pm0.03$ ($\sin^2\theta_{23} = 0.44\pm0.03$). Maximal mixing ($\sin^2\theta_{23} = 0.5$) is disfavoured at the 1.7~\sig level.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

RouteE: A Vehicle Energy Consumption Prediction Engine

The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts. The pre-trained RouteE models are established using NREL’s Future Automotive Systems Technology Simulator paired with approximately 1 million miles of drive cycle data from the Transportation Secure Data Center, resulting in energy consumption behavior estimates over a representative sample of driving conditions for the United States. Validations have been performed using on-road fuel consumption data for conventional and electrified vehicle powertrains. Transferring the results of the on-road validation to a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology in a green-routing application would accurately select the route that consumes the least fuel 90% of the time. The novel machine learning techniques used in RouteE make it a flexible and robust tool for a variety of transportation applications.

33 ADVANCED PROPULSION SYSTEMS↗
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↗
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↗
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↗
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↗