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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Overexpression of a gibberellin 20-oxidase gene in poplar xylem led to an increase in the size of nanocellulose fibrils and improved paper properties

Cellulose, the major component of secondary cell walls, is the most abundant renewable long-chain polymer on earth. Nanocellulose has become a prominent nano-reinforcement agent for polymer matrices in various industries. We report the generation of transgenic hybrid poplar overexpressing the Arabidopsis gibberellin 20-oxidase1 gene driven by a xylem-specific promoter to increase gibberellin (GA) biosynthesis in wood. X-ray diffraction (XRD) and sum frequency generation spectroscopic (SFG) analyses showed that cellulose in transgenic trees was less crystalline, but the crystal size was larger. The nanocellulose fibrils prepared from transgenic wood had an increased size compared to those from wild type. When such fibrils were used as a reinforcing agent in sheet paper preparation, the mechanical strength of the paper was significantly enhanced. Engineering the GA pathway can therefore affect nanocellulose properties, providing a new strategy for expanding nanocellulose applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Improved thermoelectric performance of α- and β- Cu 2 Se through suppression of hole density using extrinsic copper vacancies

Modulating Cu + ion disorder in Cu 2 Se can enable control over the polymorphism and the carrier density leading to enhanced thermoelectric properties for both α- and β-Cu 2 Se. Here we report that the incorporation of Cr 3+ into the Cu 2 Se crystal lattice facilitates the stabilization of α-Cu 2 Se at 300 K leading to a large (~140%) reduction in the carrier density both below and above the phase transition. This is attributed to the reduction in the density of intrinsic copper interstitials (Cu$^{•}_{i}$) within the Cu (2-δ-λ) (Cr$^{··}_{Cu}$)λ(V$^{'}_{Cu}$)δ(Cu$^{π}_{i}$) δ-2λ (h • ) δ-2λ Se crystal lattice. Such optimization of the carrier density led to a large (63%) increase in the thermopower and a drastic (46%) reduction in the total thermal conductivity for both α- and β-Cu 2 Se matrices. Consequently, a significant enhancement of the thermoelectric performance is observed in the entire temperature range from 300 K to 773 K. This results in high average ZT values for both α-Cu 2 Se (ZT ave = 0.60) and β-Cu 2 Se (ZT ave = 0.97), which paves the way for both near room temperature and high temperatures applications. Furthermore, this work provides a new approach to optimize the thermoelectric performance of Cu 2 Se-based materials by leveraging the interaction between mobile intrinsic Cu$^{·}_{i}$ and extrinsic V$^{'}_{Cu}$ to suppress the hole density.

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

Engineered Cementitious Composites (ECC) with limestone calcined clay cement (LC 3 )

Recent research have recognized that coupled use of calcined clay, limestone and cement clinker in concrete is viable to reduce environmental footprints at manufacture and to enhance material durability. In this study, a novel application of the limestone calcined clay cement (LC 3 ) is demonstrated by substituting the Ordinary Portland Cement (OPC) in Engineered Cementitious Composites (ECC). The composite mechanical properties including σ-δ and σ-ε relationships and residual crack widths were evaluated to 28 days under uniaxial tension. Matrix chemistry was characterized using thermogravimetric analysis and X-ray diffraction, while the pore structure of matrices and composites was analyzed using mercury intrusion porosimetry. The LC 3 -based ECC showed more rapid early strength development but lower 28-day strength (~32 MPa) due to a 20% higher water-to-solid ratio for attaining adequate workability and fiber dispersion. Nevertheless, the tensile strain capacity of LC 3 -based ECC achieved over 6% with an average residual crack width less than 50 μm. Additionally, the composite pore structure exhibited a decreasing volume fraction of large pores and voids (>100 nm) after substituting LC 3 for OPC. Here, the use of LC 3 marginally decreased the embodied material energy and cost, but led to about 32% and 28% reductions in CO 2 emissions compared to traditional OPC-based ECC and concrete, respectively. As a preliminary study, LC 3 -based ECC shows promise as a greener ductile concrete compared with OPC-based ECC.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Cerium oxide impact on fresh and hardened properties of cementitious materials

Cements used in global nuclear waste treatment, storage, and disposal require thorough testing of fresh and cured properties to demonstrate regulatory compliance. Using actual radioactive wastes is often cost-prohibitive during early scoping tests necessitating the use of non-radiological surrogates. Cerium (Ce) is often used as a surrogate for actinides uranium (U) and plutonium (Pu) in spent nuclear fuel, yet few studies have explored how Ce impacts the properties used to qualify a cement for use. Here, this work compares the fresh and cured properties of three classes of cements – ordinary portland cements, ultra-high performance cement composites, and magnesium phosphate cements – each with and without ceria (CeO 2 ). Due to CeO 2 sorption of water (54 vol%) that effectively decreases the water available to hydrate and form binding cement phases, variations in key cement properties were detected that suggest surrogate CeO 2 –cementitious matrices may not be representative of matrix behaviors when using U and Pu sources.

36 MATERIALS SCIENCE↗
DOE OSTI2022

An antibody-free platform for multiplexed, sensitive quantification of protein biomarkers in complex biomatrices

Sensitive, multiplexed protein quantification remains challenging despite recent advancements in LC-MS assays for targeted protein biomarker quantification. High-sensitivity protein biomarker measurements usually require immuno-affinity enrichment of target protein; a process which is highly dependent on capture reagent and limited in capability to measure multiple analytes. Herein, we report a novel antibody-free platform, which measures multiple biomarkers from complex matrices employing a strategically optimized solid-phase extraction cleanup and orthogonal multidimensional LC-MS. Eight human protein biomarkers with different specifications were spiked into canine plasma as a model investigation system. The developed strategy achieved the desired sensitivity, robustness, and throughput via the following steps: (1) post digestion mixed-mode cation exchange-reverse phase SPE enrichment cleaned up the sample initially; (2) rapid, high-pH peptide fractionation further eliminated background components efficiently while selectively enriched signature peptides (SP) to provide sufficient sensitivity for multiple targets; and (3) trapping-micro-LC-MS analysis delivered high sensitivity comparable to a nano-LC-MS method but with much better robustness and throughput for the final analysis. Compared with a conventional LC-MS assay with direct protein digestion and limited clean-up, analysis with this antibody-free platform improved the LLOQ by 1–2 orders of magnitude for the eight protein biomarkers, reaching as low as 5 ng/mL in plasma, with feasible robustness and throughput. In conclusion, this platform was applied for the quantification of biomarkers of respiratory conditions in patients with various lung diseases, demonstrating real-world applicability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Silver-mediated separations: A comprehensive review on advancements of argentation chromatography, facilitated transport membranes, and solid-phase extraction techniques and their applications

The use of silver(I) ions in chemical separations, also known as argentation separations, is a powerful approach for the selective separation and analysis of many natural and synthetic organic compounds. In this review, a comprehensive discussion of the most common argentation separation techniques, including argentation-liquid chromatography (Ag-LC), argentation-gas chromatography (Ag-GC), argentation-facilitated transport membranes (Ag-FTMs), and argentation-solid phase extraction (Ag-SPE) is provided. For each of these techniques, notable advancements, optimized separations, and innovative applications are discussed. The review begins with an explanation of the fundamental chemistry underlying argentation separations, mainly the reversible π-complexation between silver(I) ions and carbon-carbon double bonds. Within Ag-LC, the use of silver(I) ions in thin-layer chromatography, high-performance liquid chromatography, as well as preparative LC are explored. This discussion focuses on how silver(I) ions are employed in the stationary and mobile phase to separate unsaturated compounds. For Ag-GC and Ag-FTMs, different silver compounds and supporting media are discussed, often with relation to olefin-paraffin separations. Ag-SPE has been widely employed for the selective extraction of unsaturated compounds from complex matrices in sample preparation. This comprehensive review of Ag-LC, Ag-GC, Ag-FTMs, and Ag-SPE techniques emphasizes the immense potential of argentation separations in separations science and serves as a valuable resource for researchers seeking to learn, optimize, and utilize argentation separations.

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

A coupled multipoint stress–multipoint flux mixed finite element method for the Biot system of poroelasticity

In this work, we present a mixed finite element method for a five-field formulation of the Biot system of poroelasticity that reduces to a cell-centered pressure–displacement system on simplicial and quadrilateral grids. A mixed stress–displacement–rotation formulation for elasticity with weak stress symmetry is coupled with a mixed velocity–pressure Darcy formulation. The spatial discretization is based on combining the multipoint stress mixed finite element (MSMFE) method for elasticity and the multipoint flux mixed finite element (MFMFE) method for Darcy flow. It uses the lowest order Brezzi–Douglas–Marini mixed finite element spaces for the poroelastic stress and Darcy velocity, piecewise constant displacement and pressure, and continuous piecewise linear or bilinear rotation. A vertex quadrature rule is applied to the velocity, stress, and stress–rotation bilinear forms, which block-diagonalizes the corresponding matrices and allows for local velocity, stress, and rotation elimination. This leads to a cell-centered positive-definite system for pressure and displacement at each time step. We perform error analysis for the semidiscrete and fully discrete formulations, establishing first order convergence for all variables in their natural norms. The numerical tests confirm the theoretical convergence rates and illustrate the locking-free property of the method.

42 ENGINEERING↗
DOE OSTI2020

Data-driven variational multiscale reduced order models

We propose a new data-driven reduced order model (ROM) framework that centers around the hierarchical structure of the variational multiscale (VMS) methodology and utilizes data to increase the ROM accuracy at a modest computational cost. The VMS methodology is a natural fit for the hierarchical structure of the ROM basis: In the first step, we use the ROM projection to separate the scales into three categories: (i) resolved large scales, (ii) resolved small scales, and (iii) unresolved scales. In the second step, we explicitly identify the VMS–ROM closure terms, i.e., the terms representing the interactions among the three types of scales. In the third step, we use available data to model the VMS–ROM closure terms. Thus, instead of phenomenological models used in VMS for standard numerical discretizations (e.g., eddy viscosity models), we utilize available data to construct new structural VMS–ROM closure models. Specifically, we build ROM operators (vectors, matrices, and tensors) that are closest to the true ROM closure terms evaluated with the available data. We test the new data-driven VMS–ROM in the numerical simulation of four test cases: (i) the 1D Burgers equation with viscosity coefficient v = 10 -3 ; (ii) a 2D flow past a circular cylinder at Reynolds numbers, Re = 100, Re = 500, and Re= 1000; (iii) the quasi-geostrophic equations at Reynolds number Re = 450 and Rossby number Ro = 0.0036; and (iv) a 2D flow over a backward facing step at Reynolds number Re = 1000. The numerical results show that the data-driven VMS–ROM is significantly more accurate than standard ROMs.

42 ENGINEERING↗
DOE OSTI2021

Galerkin formulations of isogeometric shell analysis: Alleviating locking with Greville quadratures and higher-order elements

Here we propose new quadrature schemes that asymptotically require only four in-plane points for Reissner–Mindlin shell elements and nine in-plane points for Kirchhoff–Love shell elements in B-spline and NURBS-based isogeometric shell analysis, independent of the polynomial degree p of the elements. The quadrature points are Greville abscissae associated with pth-order B-spline basis functions whose continuities depend on the specific Galerkin formulations, and the quadrature weights are calculated by solving a linear moment fitting problem in each parametric direction. The proposed shell element formulations are shown through numerical studies to be rank sufficient and to be free of spurious modes. The studies reveal comparable accuracy, in terms of both displacement and stress, compared with fully integrated spline-based shell elements, while at the same time reducing storage and computational cost associated with forming element stiffness and mass matrices and force vectors. The high accuracy with low computational cost makes the proposed quadratures along with higher-order spline bases, in particular polynomial orders, p = 5 and 6, good choices for alleviating membrane and shear locking in shells.

42 ENGINEERING↗
DOE OSTI2021

Gaussian process regression constrained by boundary value problems

We develop a framework for Gaussian processes regression constrained by boundary value problems. The framework may be applied to infer the solution of a well-posed boundary value problem with a known second-order differential operator and boundary conditions, but for which only scattered observations of the source term are available. Scattered observations of the solution may also be used in the regression. The framework combines co-kriging with the linear transformation of a Gaussian process together with the use of kernels given by spectral expansions in eigenfunctions of the boundary value problem. Furthermore, it benefits from a reduced-rank property of covariance matrices. We demonstrate that the resulting framework yields more accurate and stable solution inference as compared to physics-informed Gaussian process regression without boundary condition constraints.

42 ENGINEERING↗
DOE OSTI2021

Non-intrusive data-driven model reduction for differential–algebraic equations derived from lifting transformations

In this paper we present a non-intrusive data-driven approach for model reduction of nonlinear systems. The approach considers the particular case of nonlinear partial differential equations (PDEs) that form systems of partial differential–algebraic equations (PDAEs) when lifted to polynomial form. Such systems arise, for example, when the governing equations include Arrhenius reaction terms (e.g., in reacting flow models) and thermodynamic terms (e.g., the Helmholtz free energy terms in a phase-field solidification model). Using the known structured form of the lifted algebraic equations, the approach computes the reduced operators for the algebraic equations explicitly, using straightforward linear algebra operations on the basis matrices. The reduced operators for the differential equations are inferred from lifted snapshot data using operator inference, which solves a linear least squares regression problem. The approach is illustrated for the nonlinear model of solidification of a pure material. The lifting transformations reformulate the solidification PDEs as a system of PDAEs that have cubic structure. The operators of the lifted system for this solidification example have affine dependence on key process parameters, permitting us to learn a parametric reduced model with operator inference. Numerical experiments show the effectiveness of the resulting reduced models in capturing key aspects of the solidification dynamics.

42 ENGINEERING↗
DOE OSTI2022

Probing oil recovery in shale nanopores with small-angle and ultra-small-angle neutron scattering

Increasing oil production from unconventional shale reservoirs is crucial to meet growing energy demands while achieving lower carbon emission than conventional crude oil. Enhanced oil recovery (EOR) has been proposed to improve hydrocarbon recovery rates through the injection of a fluid into the reservoir to facilitate residual oil release from the shale formation. However, economical and sustainable implementation of EOR requires advanced knowledge of fluid behavior in nano-sized pore spaces in shale. In this study, we utilize small-angle neutron scattering (SANS) and ultra-small-angle neutron scattering (USANS) as experimental probes to examine decane removal from a shale nano- to micro-porous matrix, utilizing methane as the injectant. The extent of decane saturation and recovery post-methane pressurization is quantified for clay-rich and carbonate-rich shale samples. A key finding is that extraction of decane by methane on depressurization is related to the methane-decane critical point. Furthermore, we found that although clay-rich shale had a much higher porosity of 5.6%, compared with 1.2% for carbonate-rich shale, decane was more easily removed from the carbonate-rich matrix, leading to similar hydrocarbon yields. These promising results demonstrate the ability of SANS and USANS to provide key insights into oil recovery from nano- to micron-sized pores in shale matrices. Combined with effects of various fractures on fluid behavior in shale, this experimental technique can be used to assess the viability of EOR injectants.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2026

Computational methods in solution-based plastics purification

Plastic waste can be recycled into resins with near-virgin properties by solution-based purification processes that selectively dissolve polymers, remove contaminants, or detach printing residues. Here, in this review, we examine computational methods for predicting the behavior governing solution-based plastic purification, motivated by the vast polymer–solvent–contaminant compositional space. We discuss thermodynamic and machine learning methods for predicting polymer–solvent and polymer–contaminant interaction and review physics-based molecular dynamics simulations that resolve molecular-scale phenomena within polymer matrices inaccessible to screening methods. We highlight how these methods have informed experimental design for dissolution-based recycling and solvent-based contaminant removal. Finally, we discuss the prospective role of agentic AI in integrating these computational tools with real-time sorting data to adapt purification conditions to the compositional variability of real post-consumer feedstocks. This review charts a path toward computationally guided solution-based purification workflows that can respond to the complexity inherent in plastic waste streams.

Altamimi, Ali [Univ. of Wisconsin, Madison, WI (Un↗
DOE OSTI2021

Skeletal model reduction with forced optimally time dependent modes

Skeletal model reduction based on local sensitivity analysis of time dependent systems is presented in which sensitivities are modeled by forced optimally time dependent (f-OTD) modes. The f-OTD factorizes the sensitivity coefficient matrix into a compressed format as the product of two skinny matrices, i.e. f-OTD modes and f-OTD coefficients. The modes create a low-dimensional, time dependent, orthonormal basis which capture the directions of the phase space associated with most dominant sensitivities. These directions highlight the instantaneous active species, and reaction paths. Evolution equations for the f-OTD modes and coefficients are derived, and the implementation of f-OTD for skeletal reduction is described. For demonstration, skeletal reduction is conducted of the constant pressure ethylene-air burning in a zero-dimensional reactor, and new reduced models are generated. The laminar flame speed, the ignition delay, and the extinction curve as predicted by the models are compared against some existing skeletal models in literature for the same detailed model. Finally, the results demonstrate the capability of f-OTD to eliminate unimportant reactions and species in a systematic, efficient and accurate manner.

42 ENGINEERING↗
DOE OSTI2023

A projection-based analytical Jacobian framework for chemical kinetics applications

A major challenge in simulating complex combustion systems with detailed chemical kinetic models is the cost of integrating the chemical source terms, often done using stiff ODE solvers that require frequent Jacobian evaluations. Using analytically derived Jacobian matrices instead of divided-difference-based numerical Jacobian approximations can significantly reduce the associated computational cost. However, ambiguities arise in the formulation of analytical Jacobians because the chemical state of the system, or state vector, can be expressed in multiple ways, involving variables that are typically not independent from one another. Here, in this work, the consequences of those ambiguities on practical calculations are characterized in detail, and a generalized, projection-based framework is proposed as a mitigation strategy. Performances are assessed in a series of test cases involving a variety of configurations and numerical solution approaches. Results show that with proper treatment, commonly used analytical Jacobian formulations can be considered as equivalent for practical purposes, thereby alleviating concerns that the state vector chosen to express the governing equations and corresponding analytical Jacobian may significantly impact the accuracy of the simulations.

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

Comparison of three state-of-the-art crystal plasticity based deformation twinning models for magnesium alloys

In magnesium alloys, deformation twinning and its interactions with dislocation slip are responsible for a sigmoidal shape stress–strain behavior and an asymmetrical tension–compression yield strength in magnesium alloys. The sensitivity of twinning to the underlying microstructure renders the crystal plasticity method the most commonly adopted modeling approach for magnesium-twinning. This paper compares three state-of-the-art crystal plasticity-based twinning models from the literature, namely the elastic-viscoplastic self-consistent twinning-detwinning (EVPSC-TDT) model, crystal plasticity finite element model based on enhanced predominate twin reorientation approach (CPFE-ePTR), and the crystal plasticity finite element model based on “discrete twinning” approach (CPFE-DT). A polycrystalline microstructure is simulated with all three methods to compare the resulting stress–strain curves and lattice strains to those from the experimentally measured data. All three methods showed the capability of capturing the experimental results with different levels of accuracy. Additionally, the EVPSC-TDT method avoids solving the finite element matrices and showed the highest computational efficiency. The CPFE-ePTR model shows a higher accuracy in capturing the lattice strain. The CPFE-DT relies on high-resolution finite element mesh and is much slower than the other two methods, but it captured the local deformation concentration and stress reversal phenomena near the twin band, which was not possible with the other two methods. Based on the comparison, guidance for the selection of the appropriate model based on the specific modeling target is provided in this paper.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A direct-adjoint approach for material point model calibration with application to plasticity

Here, this paper proposes a new approach for the calibration of material parameters in local elastoplastic constitutive models. The calibration is posed as a constrained optimization problem, where the constitutive model evolution equations for a single material point serve as constraints. The objective function quantifies the mismatch between the stress predicted by the model and corresponding experimental measurements. To improve calibration efficiency, a novel direct-adjoint approach is presented to compute the Hessian of the objective function, which enables the use of second-order optimization algorithms. Automatic differentiation is used for gradient and Hessian computations. Two numerical examples are employed to validate the Hessian matrices and to demonstrate that the Newton–Raphson algorithm consistently outperforms gradient-based algorithms such as L-BFGS-B.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Random field optimization

Herein we present a new modeling paradigm for optimization that we call random field optimization. Random fields are a powerful modeling abstraction that aims to capture the behavior of random variables that live on infinite-dimensional spaces (e.g., space and time) such as stochastic processes (e.g., time series, Gaussian processes, and Markov processes), random matrices, and random spatial fields. This paradigm involves sophisticated mathematical objects (e.g., stochastic differential equations and space-time kernel functions) and has been widely used in neuroscience, geoscience, physics, civil engineering, and computer graphics. Despite of this, however, random fields have seen limited use in optimization; specifically, existing optimization paradigms that involve uncertainty (e.g., stochastic programming and robust optimization) mostly focus on the use of finite random variables. This trend is rapidly changing with the advent of statistical optimization (e.g., Bayesian optimization) and multi-scale optimization (e.g., integration of molecular sciences and process engineering). Our work extends a recently-proposed abstraction for infinite-dimensional optimization problems by capturing more general uncertainty representations. Moreover, we discuss solution paradigms for this new class of problems based on finite transformations and sampling, and identify open questions and challenges.

97 MATHEMATICS AND COMPUTING↗