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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Structural Effects of Lanthanide Dopants on Alumina

Lanthanide (Ln 3+ ) doping in alumina has shown great promise for stabilizing and promoting desirable phase formation to achieve optimized physical and chemical properties. However, doping alumina with Ln elements is generally accompanied by formation of new phases (i.e. LnAlO3, Ln2O3), and therefore inclusion of Ln-doping mechanisms for phase stabilization of the alumina lattice is indispensable. In this study, Ln-doping (400 ppm) of the alumina lattice crucially delays the onset of phase transformation and enables phase population control, which is achieved without the formation of new phases. The delay in phase transition (θ → α), and alteration of powder morphology, particle dimensions, and composition ratios between α- and θ-alumina phases are studied using a combination of solid state nuclear magnetic resonance, electron microscopy, digital scanning calorimetry, and high resolution X-ray diffraction with refinement fitting. Loading alumina with a sparse concentration of Ln-dopants suggests that the dopants reside in the vacant octahedral locations within the alumina lattice, where complete conversion into the thermodynamically stable α-domain is shown in dysprosium (Dy)- and lutetium (Lu)-doped alumina. This study opens up the potential to control the structure and phase composition of Ln-doped alumina for emerging applications.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Nitrogen-Coordinated Single Cobalt Atom Catalysts for Oxygen Reduction in Proton Exchange Membrane Fuel Cells

Due to the Fenton reaction, the presence of Fe and peroxide in electrodes generates free radicals causing serious degradation of the organic ionomer and the membrane. Pt-free and Fe-free cathode catalysts therefore are urgently needed for durable and inexpensive proton exchange membrane fuel cells (PEMFCs). Herein, a high-performance nitrogen-coordinated single Co atom catalyst is derived from Co-doped metal-organic frameworks (MOFs) through a one-step thermal activation. Aberration-corrected electron micros-copy combined with X-ray absorption spectroscopy virtually verifies the CoN4 coordination at an atomic level in the catalysts. Through investigating effects of Co doping contents and thermal activation temperature, an atomically Co site dispersed catalyst with optimal chemical and structural properties has achieved respectable activity and stability for the oxygen reduction reaction (ORR) in challenging acidic media (e.g., half-wave potential of 0.80 V vs reversible hydrogen electrode (RHE). The performance is comparable to Fe-based catalysts and 60 mV lower than Pt/C -60 µg Pt cm-2). Fuel cell tests confirm that catalyst activity and stability can translate to high-performance cathodes in PEMFCs. The remarkably enhanced ORR performance is attrib-uted to the presence of well-dispersed CoN4 active sites embedded in 3D porous MOF-derived carbon particles, omitting any inactive Co aggregates.

Wang, Xiao X.↗
DOE OSTI2023

Chemical controls on the propagation and healing of subcritical fractures

Human activities involving subsurface reservoirs—resource extraction, carbon and nuclear waste storage—alter thermal, mechanical, and chemical steady-state conditions in these systems. Because these systems exist at lithostatic pressures, even minor chemical changes can cause chemically assisted deformation. Therefore, understanding how chemical effects control geomechanical properties is critical to optimizing engineering activities. The grand challenge in predicting the effect of chemical processes on mechanical properties lays in the fact that these phenomena take place at molecular scales, while they manifest all the way to reservoir scales. To address this fundamental challenge, we investigated chemical effects on deformation in model and real systems spanning molecular- to centimeter scales. We used theory, experiment, molecular dynamics simulation, and statistical analysis to (1) identify the effect of simple reactions, such as hydrolysis, on molecular structures in interfacial regions of stressed geomaterials; (2) quantify chemical effects on the bulk mechanical properties, fracture and displacement for granular rocks and single crystals; (3) develop initial understanding of universal scaling for individual displacement events in layered geomaterials; and (4) develop analytic approximations for the single-chain mechanical response utilizing asymptotically correct statistical thermodynamic theory. Taken together, these findings advance the challenging field of chemo-mechanics.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Wind Turbine Rotor Design Using High-Fidelity Aerostructural Optimization

Large wind turbines yield more energy but demand careful aeroelastic blade design. Coupled multiphysics design strategies can reduce wind energy costs by exploiting fluid-structure interactions. This work presents the first high-fidelity aerostructural optimization study of a large wind turbine rotor. We use blade-resolved fluid dynamics and structural solvers in a monolithic gradient-based optimization framework to explore steady-state torque and blade mass tradeoffs. The coupled-adjoint approach computes gradients efficiently, enabling the optimization of over 100 structural and geometric parameters simultaneously. Our optimization study modifies a DTU 10 MW benchmark with a simplified structure and isotropic material properties. The tightly coupled optimizations increase torque by 14% while reducing rotor mass by 9% or reduce blade mass by 27% while maintaining torque. Blade-resolved models provide greater design freedom, enabling 5% higher mass reductions than conventional parameterizations at equal torque. This framework paves the way for more detailed high-fidelity optimization studies to complement conventional design approaches.

17 WIND ENERGY↗
DOE OSTI2011

Power Minimization techniques for Networked Data Centers.

Our objective is to develop a mathematical model to optimize energy consumption at multiple levels in networked data centers, and develop abstract algorithms to optimize not only individual servers, but also coordinate the energy consumption of clusters of servers within a data center and across geographically distributed data centers to minimize the overall energy cost and consumption of brown energy of an enterprise. In this project, we have formulated a variety of optimization models, some stochastic others deterministic, and have obtained a variety of qualitative results on the structural properties, robustness, and scalability of the optimal policies. We have also systematically derived from these models decentralized algorithms to optimize energy efficiency, analyzed their optimality and stability properties. Finally, we have conducted preliminary numerical simulations to illustrate the behavior of these algorithms. We draw the following conclusion. First, there is a substantial opportunity to minimize both the amount and the cost of electricity consumption in a network of datacenters, by exploiting the fact that traffic load, electricity cost, and availability of renewable generation fluctuate over time and across geographical locations. Judiciously matching these stochastic processes can optimize the tradeoff between brown energy consumption, electricity cost, and response time. Second, given the stochastic nature of these three processes, real-time dynamic feedback should form the core of any optimization strategy. The key is to develop decentralized algorithms that can be implemented at different parts of the network as simple, local algorithms that coordinate through asynchronous message passing.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Effect of Alloying Additions on Wrought FeCrAl Alloys for Accident-Tolerant Fuel Cladding

Nuclear-grade wrought Fe-Cr-Al alloys are under development as one of the candidate materials for accident-tolerant fuel (ATF) cladding for light water reactors (LWRs) because of their superior oxidation and corrosion resistance in high-temperature steam environment compared to Zr-based alloys that are currently in service. Numerous efforts have been made for the development of the FeCrAl alloys with the ATF concept. The current researches focused on optimizing the balance between the improved environmental compatibility and sufficient mechanical properties in both normal service condition and under accident scenario, as well as fabricability of seamless tube product for fuel cladding applications. Alloying addition is one of the key factors to control the various properties. The effect of Cr and Al additions on the properties in model Fe-Cr-Al-Y alloys were systematically examined by the authors, and the candidate base alloy composition with 13 wt.% Cr and 5 wt.% Al was proposed for further development. The modified FeCrAl alloys with Mo and Nb additions were also investigated in order to achieve improved strength. It was revealed that the microstructure control together with the thermal stability to retain desired microstructure were also the dominant factors for successful development of ATF FeCrAl alloys, especially when the fabricability of seamless tube production was also considered. This paper summarizes further investigation of alloying effects with a wide composition range of Cr and Al additions in the model alloys, as well as the effect of minor alloying additions of Mo, Nb, and Zr, through evaluation of the tensile properties. The combined effect of microstructure control is also discussed. Effect of alloying additions on tensile properties of wrought FeCrAl alloys was investigated. Minor alloying additions such as Al, Mo, Nb, and Zr were found to improve room-temperature tensile strength of the wrought FeCrAl alloys significantly. It was also found that stabilized deformed microstructure consisting of subgrains was also effective to improve the properties compared to the recrystallized grain structure. Optimization to balance the properties and the seamless FeCrAl tube fabricability is currently in progress. (authors)

36 MATERIALS SCIENCE↗
DOE OSTI2023

“Freedom of design” in chemical compound space: towards rational in silico design of molecules with targeted quantum-mechanical properties

The rational design of molecules with targeted quantum-mechanical (QM) properties requires an advanced understanding of the structure–property/property–property relationships (SPR/PPR) that exist across chemical compound space (CCS). In this work, we analyze these fundamental relationships in the sector of CCS spanned by small (primarily organic) molecules using the recently developed QM7-X dataset, a systematic, extensive, and tightly converged collection of 42 QM properties corresponding to ≈4.2M equilibrium and non-equilibrium molecular structures containing up to seven heavy/non-hydrogen atoms (including C, N, O, S, and Cl). By characterizing and enumerating progressively more complex manifolds of molecular property space—the corresponding high-dimensional space defined by the properties of each molecule in this sector of CCS—our analysis reveals that one has a substantial degree of flexibility or “freedom of design” when searching for a single molecule with a desired pair of properties or a set of distinct molecules sharing an array of properties. To explore how this intrinsic flexibility manifests in the molecular design process, we used multi-objective optimization to search for molecules with simultaneously large polarizabilities and HOMO–LUMO gaps; analysis of the resulting Pareto fronts identified non-trivial paths through CCS consisting of sequential structural and/or compositional changes that yield molecules with optimal combinations of these properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

On the Convergence of Overlapping Schwarz Decomposition for Nonlinear Optimal Control

Here, we study the convergence properties of an overlapping Schwarz decomposition algorithm for solving nonlinear optimal control problems (OCPs). The algorithm decomposes the time domain into a set of overlapping subdomains, and solves all subproblems defined over subdomains in parallel. The convergence is attained by updating primal-dual information at the boundaries of overlapping subdomains. We show that the algorithm exhibits local linear convergence, and that the convergence rate improves exponentially with the overlap size. We also establish global convergence results for a general quadratic programming, which enables the application of the Schwarz scheme inside second-order optimization algorithms (e.g., sequential quadratic programming). The theoretical foundation of our convergence analysis is a sensitivity result of nonlinear OCPs, which we call "exponential decay of sensitivity" (EDS). Intuitively, EDS states that the impact of perturbations at domain boundaries (i.e., initial and terminal time) on the solution decays exponentially as one moves into the domain. Here, we expand a previous analysis available in the literature by showing that EDS holds for both primal and dual solutions of nonlinear OCPs, under uniform second-order sufficient condition, controllability condition, and boundedness condition. We conduct experiments with a quadrotor motion planning problem and a partial differential equations (PDE) control problem to validate our theory, and show that the approach is significantly more efficient than alternating direction method of multipliers and as efficient as the centralized interior-point solver.

42 ENGINEERING↗
DOE OSTI2022

Inadequacy of current approaches for characterizing membrane transport properties at high salinities

Cost optimal design of osmotic membrane processes requires an accurate estimate of membrane transport parameters across their full operational range. However, standard approaches for estimating these parameters rely on empirical methods, the accuracy of which remains unquantified as a function of temperature, salinity, and measurement error. Herein, we present a systematic accuracy analysis of previously developed methods for estimation of membrane transport properties in reverse osmosis, high-pressure reverse osmosis, forward osmosis, pressure retarded reverse osmosis, and osmotically assisted reverse osmosis. We use a Monte Carlo approach to sample the full range of feasible membrane water permeabilities, salt permeabilities, structural parameters, and operating conditions for these processes. These material and process parameters are then incorporated into a physical transport model for each process. Our analysis shows that the statistical uncertainty of current empirical methods for estimating membrane parameters increases by 5 times from low-salinity to high-salinity conditions. Here, the result of this work demonstrates that empirical methods are inadequate for precisely estimating membrane transport properties at high salinity and highlight a critical need for the development of statistically validated higher accuracy methods.

42 ENGINEERING↗
DOE OSTI2022

Photovoltaic Module R&D Considerations for Soiling Mitigation

Photovoltaic (PV) modules work best in the sunniest environments. Unfortunately, often the sunniest places also have substantial amounts of airborne "dust" that deposits on the front surface of the modules and blocks the sunlight; reducing energy output. In fact, natural soiling has reduced the energy output of PV systems since the technology was first used, and viable mitigation strategies have remained elusive ever since. With the ever-increasing deployments around the world, especially in dusty environments, soiling is becoming a billion-dollar problem, worldwide. While substantial work has been done to examine and resolve some of the issues with PV soiling, often mitigation comes down to physically cleaning the modules. However, a more systematic evaluation of the different module properties correlations to soiling mitigation needs to be done. In many instances, the causal connections between module properties and soiling are simply not known. This lack of knowledge results in a substantial increase in time and effort to evaluate and qualify appropriate soiling mitigation protocols based on site specific issues and the intrinsic module properties that are typically not optimized for mitigating soiling in a given environment. Thus, module property protocols and/or standards are needed to more quickly help identify appropriate module and site-specific mitigation.

mitagation↗
DOE OSTI2016

Optimization of microtubule affinity regulating kinase (MARK) inhibitors with improved physical properties

Inhibition of microtubule affinity regulating kinase (MARK) represents a potentially attractive means of arresting neurofibrillary tangle pathology in Alzheimer’s disease. This manuscript outlines efforts to optimize a pyrazolopyrimidine series of MARK inhibitors by focusing on improvements in potency, physical properties and attributes amenable to CNS penetration. A unique cylcyclohexyldiamine scaffold was identified that led to remarkable improvements in potency, opening up opportunities to reduce MW, Pgp efflux and improve pharmacokinetic properties while also conferring improved solubility.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Co-Optimization of Internal Combustion Engines and Biofuels

The development of advanced engines has significant potential advantages in reduced aftertreatment costs for air pollutant emission control, and just as importantly for efficiency improvements and associated greenhouse gas emission reductions. There are significant opportunities to leverage fuel properties to create more optimal engine designs for both advanced spark-ignition and compression-ignition combustion strategies. The fact that biofuel blendstocks offer a potentially low-carbon approach to fuel production, leads to the idea of optimizing the entire fuel production-utilization value chain as a system from the standpoint of life cycle greenhouse gas emissions. This is a difficult challenge that has yet to be realized. This presentation will discuss the relationship between chemical structure and critical fuel properties for more efficient combustion, survey the properties of a range of biofuels that may be produced in the future, and describe the ongoing challenges of fuel-engine co-optimization.

09 BIOMASS FUELS↗
DOE OSTI2022

Structure/property relationship of semicrystalline polymers during tensile deformation: a molecular dynamics approach

Molecular dynamics (MD) simulation with an ab initio numerical model is conducted to study the structure–property relationship of semicrystalline polymers during uniaxial deformation. The effects of chain length, temperature, and strain rate on mechanical properties are discussed. The influences of microstructural evolution such as bond length, entanglement density, and chain orientation are also studied quantitatively. The temperature will greatly affect the chain conformation in amorphous domains, and the results revealed that the interaction of amorphous and crystalline domains played a crucial role during stretching. Accordingly, the yielding of semicrystalline polymers follows different mechanisms at temperatures above and below T g . A melt-recrystallization scheme is observed during yielding at higher temperatures, while destruction of crystal structures is observed at lower temperatures at the yield point. The correlated effects of different temperatures and strain rates on mechanical properties are examined. This research is part of our efforts to develop a digital twin of a real experiment for efficient optimization of polymer material properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Stochastic minibatch approach to the ptychographic iterative engine

The ptychographic iterative engine (PIE) is a widely used algorithm that enables phase retrieval at nanometer-scale resolution over a wide range of imaging experiment configurations. By analyzing diffraction intensities from multiple scanning locations where a probing wavefield interacts with a sample, the algorithm solves a difficult optimization problem with constraints derived from the experimental geometry as well as sample properties. The effectiveness at which this optimization problem is solved is highly dependent on the ordering in which we use the measured diffraction intensities in the algorithm, and random ordering is widely used due to the limited ability to escape from stagnation in poor-quality local solutions. In this study, we introduce an extension to the PIE algorithm that uses ideas popularized in recent machine learning training methods, in this case minibatch stochastic gradient descent. Our results demonstrate that these new techniques significantly improve the convergence properties of the PIE numerical optimization problem.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

P‐Doping Modulated RuIr Nanoparticles Anchored on Co/N/C Catalysts with Improved Alkaline Hydrogen Evolution Activity and Stability

Achieving efficient and stable hydrogen evolution reactions in alkaline conditions is crucial for hydrogen production. In this study, a RuIr/Co (SA) NC-P catalyst featuring RuIr alloys alongside P-doping and CoNx sites is developed. RuIr alloying optimizes the electronic structure between Ru and Ir, promoting electron transfer from Ru to Ir. P-doping further modulates the electronic properties of RuIr alloys, optimizing hydrogen binding energy and weakening Ru─OH binding energy, facilitating rapid H 2 generation and OH ad transfer. Meanwhile, CoNx promotes water dissociation, providing a fast proton delivery path for RuIr alloys. The catalyst exhibits enhanced HER activity with a low overpotential of 20 mV at 10 mA cm −2 , a Tafel slope of 20.4 mV dec −1 , and a turnover frequency of 19.5 H 2 s −1 at 150 mV overpotential. Moreover, catalyst stability is improved 8 times by mitigating RuIr alloy dissolution/agglomeration via P-doping. In conclusion, this work introduces a promising approach for developing efficient and stable HER electrocatalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

On-the-fly closed-loop materials discovery via Bayesian active learning

Active learning—the field of machine learning (ML) dedicated to optimal experiment design—has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate an autonomous materials discovery methodology for functional inorganic compounds which allow scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. This robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. The real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) is implemented at the synchrotron beamline to accelerate the interconnected tasks of phase mapping and property optimization, with each cycle taking seconds to minutes. We also demonstrate an embodiment of human-machine interaction, where human-in-the-loop is called to play a contributing role within each cycle. This work has resulted in the discovery of a novel epitaxial nanocomposite phase-change memory material.

36 MATERIALS SCIENCE↗