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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Full Gradient Solution to Adaptive Hybrid Control

This paper focuses on the adaptation mechanisms in adaptive hybrid controllers. Most adaptive hybrid controllers update two filters individually according to the filtered reference least mean squares (FxLMS) algorithm. Because this algorithm was derived for feedforward control, it does not take into account the presence of a feedback loop in the gradient calculation. This paper provides a derivation of the proper weight vector gradient for hybrid (or feedback) controllers that takes into account the presence of feedback. In this formulation, a single weight vector is updated rather than two individually. An internal model structure is assumed for the feedback part of the controller. The full gradient is equivalent to that used in the standard FxLMS algorithm with the addition of a recursive term that is a function of the modeling error. Some simulations are provided to highlight the advantages of using the full gradient in the weight vector update rather than the approximation.

Bean, Jacob↗
DOE OSTI2024

Designing a Dynamic Material via Interlocking Kraft Lignin with Ultrahigh Molecular Weight Poly(ethylene oxide)

The environmental issues stemming from plastic waste and the excessive use of petroleum-based chemicals are both concerning and urgent. To effectively address this problem, we need to adopt a comprehensive approach involving developing more sustainable and renewable materials. These materials should be capable of demonstrating similar or even better performance than functional polymers synthesized from petroleum-based chemicals. Our research aims to develop more sustainable materials by leveraging the intrinsic characteristics of a natural polymer, lignin, a byproduct of the biorefinery industries. We utilized the three-dimensional branching structure of lignin as a material framework. We employed an approach to co-reactive melt processing of kraft lignin with aliphatic flexible chains of soft cross-linkers through the reaction of the cross-linker epoxy chain ends with lignin functional groups. Our study demonstrates that the interlocked structure formed from the co-blending of kraft lignin with ultrahigh molecular weight poly(ethylene oxide) and the cross-linking of lignin chains through a solvent-free process can manipulate the macromolecular interactions and relaxation. This manipulation results in materials that exhibit a wide range of thermomechanical properties and self-healing and shape memory effects, with drastically improved stiffness in a single material. We have explored the fundamental understanding of the macromolecular chain relaxation dynamics originating from the interlocking structure formation. In conclusion, this investigation utilized various techniques, including thermal, mechanical, rheology, and quasi-elastic neutron scattering.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Algorithm 1028: VTMOP: Solver for Blackbox Multiobjective Optimization Problems

VTMOP is a Fortran 2008 software package containing two Fortran modules for solving computationally expensive bound-constrained blackbox multiobjective optimization problems. VTMOP implements the algorithm of [32], which handles two or more objectives, does not require any derivatives, and produces well-distributed points over the Pareto front. The first module contains a general framework for solving multiobjective optimization problems by combining response surface methodology, trust region methodology, and an adaptive weighting scheme. The second module features a driver subroutine that implements this framework when the objective functions can be wrapped as a Fortran subroutine. Lastly, support is provided for both serial and parallel execution paradigms, and VTMOP is demonstrated on several test problems as well as one real-world problem in the area of particle accelerator optimization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Coupling of Flavonoid Initiation Sites with Monolignols Studied by Density Functional Theory

Lignin recalcitrance presents a challenge for the development of a bioeconomy that employs lignocellulosic feedstocks. The efficiency of lignin deconstruction is improved by a reduction of molecular weight, and given the discovery that flavones serve as initiation sites in lignin biosynthesis, these molecular weight reductions could potentially be achieved with plant metabolic engineering to over-express flavonoids. Upon increasing the flavonoid content in lignin, the bond strengths and properties of flavonoid-monolignol linkages become increasingly important. To that end, the current work applies density functional theory calculations to elucidate the bond dissociation enthalpies (BDEs) of flavonoid-monolignol linkages, including dimers with oxidized monolignols. Specifically, the dimer bond strengths and monomer hydrogen abstraction energies for the flavonoids tricin, chrysoeriol, luteolin, apigenin, catechin, epicatechin, epigallocatechin, and epigallocatechin gallate are calculated, when coupled to seven natural and engineered monolignols. Results indicate that 4'-O-ß linkage strengths between flavonoids and monolignols are of comparable strength to inter-monolignol ß-O-4 linkages, with average flavonoid-monolignol BDEs of 70.7 kcal/mol relative to ~69.3 kcal/mol in analogous canonical monolignols. Epigallocatechin yielded the lowest 4'-O-ß bond strength of 52.3 kcal/mol when coupled to an oxidized monolignol, while the flavones overall produced lower average BDEs, relative to the flavanols. Substituents at the 3'-C and 5'-C positions on flavonoids affected the dimer linkage strengths to a greater extent than glycosylation or substituents further from the linkage. Erythro and threo stereochemistry across the flavonoid-monolignol linkage library exhibited only small energetic differences and no pronounced correlations. Taken together, the predictions from this work support the concept that higher concentrations of flavonoid initiation sites in lignin may afford linkage properties conducive to more facile lignin depolymerization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1974

Measurement of the earth resources technology satellite /ERTS-1/ multi-spectral scanner OTF from operational imagery

The optical transfer function (OTF) of some typical ERTS-1 multispectral imagery was obtained by comparison of matched sets of aircraft underflight and ERTS photographic and digital images. One-dimensional OTF analysis consisted in obtaining U-2 and ERTS microdensitometer scans followed by density to transmission conversion, microdensitometer aperture correction, exposure calibration, scan correlation scale optimization, OTF calculation, obtaining a form weighted average of the OTFs, transformation of the OTFs back to the spatial domain (giving the line spread function or LSF), and application of a window function to the LSF resulting in a smoothed OTF. Date-to-date comparison of ERTS OTFs showed a drop in quality on April 4, 1973, compared with January 4, 1973.

Schowengerdt, R. A.↗
DOE OSTI2020

Improving climate model coupling through a complete mesh representation: a case study with E3SM (v1) and MOAB (v5.x)

One of the fundamental factors contributing to the spatiotemporal inaccuracy in climate modeling is the mapping of solution field data between different discretizations and numerical grids used in the coupled component models. The typical climate computational workflow involves evaluation and serialization of the remapping weights during the preprocessing step, which is then consumed by the coupled driver infrastructure during simulation to compute field projections. Tools like Earth System Modeling Framework (ESMF) and TempestRemap offer capability to generate conservative remapping weights, while the Model Coupling Toolkit (MCT) that is utilized in many production climate models exposes functionality to make use of the operators to solve the coupled problem. However, such multistep processes present several hurdles in terms of the scientific workflow and impede research productivity. In order to overcome these limitations, we present a fully integrated infrastructure based on the Mesh Oriented datABase (MOAB) library, which allows for a complete description of the numerical grids and solution data used in each submodel. Through a scalable advancing-front intersection algorithm, the supermesh of the source and target grids are computed, which is then used to assemble the high-order, conservative, and monotonicity-preserving remapping weights between discretization specifications. The Fortran-compatible interfaces in MOAB are utilized to directly link the submodels in the Energy Exascale Earth System Model (E3SM) to enable online remapping strategies in order to simplify the coupled workflow process. We demonstrate the superior computational efficiency of the remapping algorithms in comparison with other state-of-the-science tools and present strong scaling results on large-scale machines for computing remapping weights between the spectral element atmosphere and finite volume discretizations on the polygonal ocean grids.

58 GEOSCIENCES↗
NASA NTRS2001

Superalloy Foams for Aeroshell Applications

Current thermal protection systems for reentry from space, such as that employed on the space shuttle, rely on ceramic tiles with ultra-low conductivity. These materials provide excellent thermal protection but are extremely fragile, easily degraded by environmental attack, and carry no structural loads. Future thermal protection systems being proposed in NASAs MITAS Program will attempt to combine thermal protection with improved durability and structural capability without significant increases in vehicle weight. This may be accomplished by combining several materials in a layered structure to obtain the desired function for aeroshell applications. One class of materials being considered for inclusion in this concept are high temperature metal foam. The objective of this paper was to fabricate low density, superalloy foams and conduct limited testing to evaluate their thermal and structural capabilities. Superalloys were chosen for evaluation as they possesses good strength and excellent environmental endurance over a wide range of temperatures. Utilizing superalloys as low density foams, with porosity contents greater than 90%, minimizes weight and thermal conductivity.

Gayda, John↗
DOE OSTI2020

Predicting a Sharp Decline in Selectivity for Catalytic Esterification of Alcohols from van der Waals Interactions

Controlling the selectivity of catalytic reactions is a critical aspect of improving energy efficiency in the chemical industry; thus, predictive models are of key importance. Herein the performance of a heterogeneous, nanoporous Au catalyst is predicted for the complex catalytic self-coupling of the series of C 2 -C 4 alkyl alcohols, based solely on the known kinetics of the elementary steps of the catalytic cycle for methanol coupling, using scaling methods augmented by density functional theory. Notably, a sharp decrease in selectivity for ester formation with increasing molecular weight to favor the aldehyde due to van der Waals interactions of reaction intermediates with the surface was predicted and subsequently verified quantitatively by experiment. Furthermore, the agreement between theory and experiment clearly demonstrates the efficacy of this approach for building a predictive model of catalytic behavior for a homologous set of reactants using a small set of experimental information.

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

Predicting a Sharp Decline in Selectivity for Catalytic Esterification of Alcohols from van der Waals Interactions

Controlling the selectivity of catalytic reactions is a critical aspect of improving energy efficiency in the chemical industry; thus, predictive models are of key importance. Herein the performance of a heterogeneous, nanoporous Au catalyst is predicted for the complex catalytic self-coupling of the series of C 2 –C 4 alkyl alcohols, based solely on the known kinetics of the elementary steps of the catalytic cycle for methanol coupling, using scaling methods augmented by density functional theory. Notably, a sharp decrease in selectivity for ester formation with increasing molecular weight to favor the aldehyde due to van der Waals interactions of reaction intermediates with the surface was predicted and subsequently verified quantitatively by experiment. Further, the agreement between theory and experiment clearly demonstrates the efficacy of this approach for building a predictive model of catalytic behavior for a homologous set of reactants using a small set of experimental information.

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

Improved Architectures and Training Algorithms for Deep Operator Networks

Operator learning techniques have recently emerged as a powerful tool for learning maps between infinite-dimensional Banach spaces. Trained under appropriate constraints, they can also be effective in learning the solution operator of partial differential equations (PDEs) in an entirely self-supervised manner. In this work we analyze the training dynamics of deep operator networks (DeepONets) through the lens of Neural Tangent Kernel theory, and reveal a bias that favors the approximation of functions with larger magnitudes. To correct this bias we propose to adaptively re-weight the importance of each training example, and demonstrate how this procedure can effectively balance the magnitude of back-propagated gradients during training via gradient descent. We also propose a novel network architecture that is more resilient to vanishing gradient pathologies. Taken together, our developments provide new insights into the training of DeepONets and consistently improve their predictive accuracy by a factor of 10-50x, demonstrated in the challenging setting of learning PDE solution operators in the absence of paired input-output observations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Gradient-informed Hamiltonian Monte Carlo for multicomponent CALPHAD model optimization and uncertainty quantification

CALPHAD model parameter optimization is inherently challenging due to non-smooth objective functions, high-dimensional parameter spaces, and the need for uncertainty quantification (UQ). Traditional weighted nonlinear least squares approaches are computationally efficient but local, whereas black-box global optimizers and ensemble Markov Chain Monte Carlo (MCMC) methods provide broader exploration at substantial computational cost. The objective of this work is to combine the global exploration capability of gradient-informed Hamiltonian Monte Carlo – specifically the No-U-Turn Sampler (NUTS) – with local deterministic refinement using BFGS to efficiently optimize multicomponent CALPHAD models with minimal manual intervention. Analytic gradients are computed via the Jansson derivative framework. The methodology is demonstrated on the Cr—Fe binary system and extended to the Cr—Fe—Ni ternary system with 32 degrees of freedom. For Cr—Fe, NUTS achieves comparable or superior optimality relative to ensemble MCMC while requiring over an order-of-magnitude fewer likelihood evaluations. Parameter uncertainties are quantified through NUTS sampling and propagated to thermodynamic observables using local expansion, demonstrating a novel modular approach that combines binary and ternary parameter subsets without requiring global relaxation. These results establish gradient-informed exploration as a scalable strategy for multicomponent CALPHAD optimization and provide a practical route towards efficient higher-order database development with quantified uncertainty.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A Physically-Based Model for Thermo-Oxidative and Hydrolytic Aging of Elastomers

A computationally efficient model is proposed to capture the loss of mechanical performance due to chemical aging that are formed as the competition of chain scission and cross-link formation/dissolution, such as thermo-oxidative aging or hydrolytic aging. The model should be considered an extension of our recent models [1, 2, 3] which further simplifies the matrix behavior based on the assumption of independence of environmental and mechanical damage. The model uses this assumption to reduce the necessary material parameters needed to model constitutive and inelastic behavior of elastomers during aging. To this end, the model can provide accurate predictions of the material performance with the significantly fewer number of fitting parameters. The model is relevant for all decay mechanisms formed by the occurrence of two simultaneous micro-mechanisms; (i) formation/reduction of the cross-links, and (ii) chain scission, both of which are present in thermo-oxidation and hydrolytic aging. Assuming the alteration of the chain density along the aging trajectory is identical to the peroxide cross-link density for thermo-oxidation, and the change of the average molecular weight for hydrolysis, the strain energy of polymer matrix can be rewritten as a function of deformation, deformation history, storage time and aging temperature. Next, the modified network alteration model is formulated for implementation into Finite Element (FE) simulations. The model is built on the presumption of homogeneous and consistent oxygen/water absorption and thus is mainly relevant for relatively thin samples exposed to environmental loads for a long time. The proposed model includes only six physically inspired material parameters. Therefore, while it is computationally efficient, it shows good agreement with own experimental data, which performed on various range of accelerated aging temperatures and times. With respect to its computational efficiency, simplicity, accuracy, and interpret-ability, the model is the right choice for advanced implementations in FE programs.

42 ENGINEERING↗
DOE OSTI2020

Wet Brush Homopolymers as “Smart Solvents” for Rapid, Large Period Block Copolymer Thin Film Self-Assembly

The spontaneous formation of periodic nanopatterns through the self-assembly of block copolymer (BCP) thin films offers an enticing path to engineer functional material properties over large areas, but the prohibitively slow ordering of ultrahigh molecular weight BCPs that generate large pattern periods (>100 nm) severely restricts the design space in which BCPs may be used. Here, we demonstrate that blending lamellae-forming polystyrene-block-poly(methyl methacrylate) diblock copolymers with very low molecular weight polystyrene and poly(methyl methacrylate) homopolymers can reduce by an order-of-magnitude the times required to form ordered large period patterns through solvent vapor annealing. Using in situ grazing-incidence small-angle X-ray scattering and ex situ electron microscopy, we show that the added homopolymer plasticizers act as a “smart solvent”: They increase the mobility of the long BCP chains by amplifying the dilution effect during solvent vapor annealing but segregate to like domains during subsequent thermal annealing to produce high-quality patterns with periods that may exceed 200 nm, which are readily transferred to other materials. The dramatic impact of homopolymer plasticizers may help extend the use of BCPs into new areas, particularly for visible light photonic applications that demand larger period nanopatterns.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Self-Assembly Driven Microlithography via Near-Infrared Light Activation

Current vat photopolymerization (VP) relies on UV or visible light to start the rapid crosslinking of liquid photocurable resins into 3D-printed structures. Here, we develop a self-assembly-driven photopatterning approach to photothermally generate polymeric solids by combining thermoplasmonic nanoparticles and thermoresponsive polymers, in which near-infrared (NIR) light activates thermoplasmonic heating of nanoparticles, triggering the irreversible self-assembly of thermoresponsive polymers into insoluble mesoglobules. A small amount of thermal initiator and crosslinker leads to irreversible self-assembly of polymer nanocomposites. NIR light offers deeper penetration and reduced scattering compared to UV, enabling more uniform curing of thicker or filled materials and expanded process control for composites or opaque systems. Thermoplasmonic heat generation is achieved using surface-modified gold nanorods (AuNRs) with a longitudinal localized surface plasmon resonance peak in the NIR region. Key variables such as polymer composition, molecular weight, physical interactions at the nanoparticle–polymer interface, which can be tuned by surface functionalization, AuNR concentration, and pH can be used to tailor the assembly behavior of these systems, including photothermal effect, flocculation, and cloud point temperature, and the mechanical properties of the final structures. Collectively, these results highlight a platform for photothermally-driven microlithography of polymer solids with diverse, tunable macroscopic properties, enabled by low-energy NIR light-activated self-assembly.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Catalytic Chemical Recycling of Post-Consumer Polyethylene

Among commercial plastics, polyolefins are the most widely produced worldwide but have limited recyclability. Here, we report a chemical recycling route for the conversion of post-consumer high-density polyethylene (HDPE) into telechelic macromonomers suitable for circular reprocessing. Unsaturation was introduced into HDPE by catalytic dehydrogenation using an Ir-POCOP catalyst without an alkene acceptor. Cross-metathesis with 2-hydroxyethyl acrylate followed by hydrogenation transformed the partially unsaturated HDPE into telechelic macromonomers. The direct repolymerization of the macromonomers gave a brittle material due to the low overall weight-average molecular weight. Aminolysis of telechelic macromonomers with a small amount of diethanolamine increased the overall functionality. The resulting macromonomers were repolymerized through transesterification to generate a polymer with comparable mechanical properties to the starting post-consumer HDPE waste. Finally, depolymerization of the repolymerized material catalyzed by an organic base regenerated the telechelic macromonomers, thereby allowing waste polyethylene materials to enter a chemical recycling pathway.

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

Application of real‐time nonlinear model predictive control for wave energy conversion

Abstract This article presents an approach to implement a Nonlinear Model Predictive Controller (NMPC) in real‐time with a non‐standard cost index. The proposed technique's applications are presented to maximize the energy produced by a Wave Energy Converter (WEC) when the cost index is a non‐quadratic piecewise discontinuous functional of some design variables. The presented framework is based on pseudo‐quadratisation and weight scheduling, which is implemented using the ACADO toolkit for MATLAB/Simulink. The proposed strategy features code generation and deployment on the real‐time target machines for industrial applications. The simulations and experiments confirm the success of the proposed approach in achieving the feasible operation of the NMPC and an optimal power capture by the wave energy converters.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

Neuromorphic Computing is Turing-Complete

Neuromorphic computing is a non-von Neumann computing paradigm that performs computation by emulating the human brain. Neuromorphic systems are extremely energy-efficient and known to consume thousands of times less power than CPUs and GPUs. They have the potential to drive critical use cases such as autonomous vehicles, edge computing and internet of things in the future. For this reason, they are sought to be an indispensable part of the future computing landscape. Neuromorphic systems are mainly used for spike-based machine learning applications, although there are some non-machine learning applications in graph theory, differential equations, and spike-based simulations. These applications suggest that neuromorphic computing might be capable of general-purpose computing. However, general-purpose computability of neuromorphic computing has not been established yet. In this work, we prove that neuromorphic computing is Turing-complete and therefore capable of general-purpose computing. Specifically, we present a model of neuromorphic computing, with just two neuron parameters (threshold and leak), and two synaptic parameters (weight and delay). We devise neuromorphic circuits for computing all the μ-recursive functions (i.e., constant, successor and projection functions) and all the μ-recursive operators (i.e., composition, primitive recursion and minimization operators). Given that the μ-recursive functions and operators are precisely the ones that can be computed using a Turing machine, this work establishes the Turing-completeness of neuromorphic computing.

Date, Prasanna↗
DOE OSTI2025

Automated shaker placement and regularized input estimation for MIMO testing.

Multi-input, multi-output (MIMO) testing is used in component qualification to reproduce operational responses in the laboratory. It is often preferred to single-input and base-shake testing because of the potential for equivalent or better tests using smaller actuators and shorter test suites. Given a target response, two key steps in MIMO test design are selecting actuator locations and solving for input loads. Actuator locations are often manually selected using expert judgment. If an automatic method is used, locations are usually determined by simulating the vibration control problem and minimizing a combination of the input energy and control residuals. To select a configuration, the relative importance of input energy and residuals must be specified. Specifying relative weights is, in general, a manual and subjective process. This paper develops an objective function that compares actuator configurations based on control accuracy and required input energy without any manual parameter tuning. The objective function uses an optimally selected tradeoff parameter for each candidate configuration. To choose actuator locations using the new objective function, a pivoting algorithm for integer programming problems is developed. Starting with an initial configuration (such as the one generated by a greedy algorithm), the pivoting algorithm guarantees an objective function decrease in each iteration until convergence is reached. In a simulation featuring a structure excited by a diffuse acoustic field, electrodynamic shaker locations and regularized inputs are solved for without any analyst-specified parameters. Simulations are performed in MIMO configurations where the number of target responses is less than, equal to, and greater than the number of actuators.

Multi-input multi-output↗