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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Particle Size Distribution and Its Impacts on Ash Deposition and Radiative Transfer during Oxy-Combustion of Rice Husk–Natural Gas

Rice husk (RH) co-combustion with natural gas in highly oxygen-enriched concentrations presents a net carbon-negative energy production opportunity while minimizing flue gas recycling. However, recent experiments have shown enhanced ash deposition rates in oxygen-enriched conditions, with deposition/shedding also being dependent on the particle size distribution (PSD) of the parent RH fuel. To uncover the causative mechanisms behind these observations, add-on models for ash deposition/shedding and radiative properties were employed in computational fluid dynamics simulations. The combustion scenarios investigated encompassed two types of RH (US RH, Chinese RH) with widely varying ash contents (by % mass) and inlet fuel PSD with air and O2/CO2 (70/30 vol %, OXY70) as oxidizers. Utilizing the measured fly-ash PSDs near the deposit surface and modeling the particle viscosity accurately, particle kinetic-energy (PKE)-based capture and shedding criteria were identified as the keys to accurate deposition/shedding rate predictions. The OXY70 scenarios showed higher ash-capturing propensities due to their lower PKE. Conversely, higher erosion rates were predicted in the AIR firing scenarios. In addition, the radiative characteristics across all the scenarios were dominated by the gases and were not sensitive to the fly-ash PSD. Therefore, the higher particle concentrations in the OXY70 conditions did not negatively impact the heat extraction.

Krishnamoorthy, Gautham (ORCID:0000000255205092)↗
DOE OSTI2023

Multiscale characterization of an additively manufactured property graded Ni-base alloy for molten-salts\supercritical-CO 2 heat exchangers

The sequential optimization of strength and corrosion resistance in conventional alloy design procedures often results in a tradeoff between environmental degradation and optimal mechanical properties. A concurrent optimization of these two material properties is essential to efficiently develop high temperature alloys that can withstand harsh environments increasingly required for carbon–neutral energy technologies. In this work, we demonstrate the feasibility of directed energy deposition (DED) to manufacture a dual-corrosion resistant Ni-based alloy (Hastelloy N and Haynes 282), that meets the high temperature operation requirements of a molten-salts\supercritical-CO 2 (sCO 2 ) heat exchanger. A combination of multiscale characterization techniques and computational thermodynamics was employed to evaluate the cracking susceptibilities during fabrication and predict the microstructural stability of the material. Very good agreement was achieved between the observed and predicted phases and phase fractions of the as-printed material. A careful characterization of the transition zone between the two terminal alloy chemistries revealed potential precipitation strengthening (γ') on the Hastelloy N side while columnar-shaped M 23 C 6 and γ' precipitates that formed at grain boundaries (GBs) of the transition zone likely minimized the local interfacial energies. Both these mechanisms are believed to increase the interfacial stability but their performance at high temperatures requires further investigation.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Optimization of wall assembly for DOE Efficient New Homes Program in HUD’s Climate Zones 2 and 3

Manufactured homes provide affordable housing for over 20 million Americans. The U.S. Department of Energy’s Efficient New Homes (ENH) program has introduced new energy certification requirements that manufacturers can optionally target to ensure outstanding levels of energy savings, comfort, health, and durability. In collaboration with Clayton Homes (under User Agreement (NN-23-1469)), Oak Ridge National Laboratory evaluated multiple wall assembly options to meet ENH requirements in HUD Climate Zones 2 and 3, with a target maximum cost premium of 5%. After analyzing thermal performance, cost, and manufacturability, insulated structural sheathing was identified as the most practical and effective solution. A full-scale manufactured home prototype demonstrated that this approach meets energy performance goals with minimal disruption to existing manufacturing processes and a cost premium of less than 10% with a path to further lower costs, offering a promising path forward for more efficient manufactured housing.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

High-performance all-solid-state Li–S batteries enabled by an all-electrochem-active prelithiated Si anode

The development of all-solid-state Li–S batteries has been greatly impeded by dendrite growth and dendrite penetration, which are both related to the Li metal anode. As a compromised alternative, we report lithium silicide (Li x Si) as a dendrite-free and high-capacity anode with Li source. Here, the as-synthesized Li x Si is relatively soft, highly electronically conductive, and with a high Li diffusivity. These distinctive properties make Li x Si anode viable as an “all-electrochem-active” electrode (consisted of 100 wt.% Li x Si). Compared with the typical composite electrode, the all-electrochem-active electrode not merely maximizes the electrode-level energy density but also minimizes the electrolyte-related interfacial degradation. Li x Si symmetric cell demonstrates a reversible cycling at 4 mA cm –2 for over 320 h. Stress change and morphological evolution of the Li x Si electrode are investigated upon dealloying/alloying. When paired with a S cathode (active mass loading of 3 mg cm –2 ), Li x Si–S full cell shows a good cycling behavior over 500 cycles and rate performance (69% capacity retained at 1.2C) even at 25 °C.

25 ENERGY STORAGE↗
DOE OSTI2025

A Conjugated Oligoelectrolyte Exhibiting Room Temperature Spin-Correlated Radical Pair Character for Biological Sensing

We report a water-soluble conjugated oligoelectrolyte (COE) composed of carbazole-benzophenone, COE-CbzBP, that exhibits photogenerated spin-correlated radical pair (SCRP) behavior sensitive to static electric fields from DNA but not from lipid bilayers. The SCRP forms from a thermally activated, spin-polarized state enabled by partial π-conjugation disruption at the donor–acceptor (carbazole-benzophenone) nitrogen–carbon (N–C) junction, which facilitates a twisted intramolecular charge-transfer (TICT) geometry. This state minimizes the singlet–triplet energy gap (ΔE ST = 0.12 eV), radical–pair exchange coupling (J RP ∼ ΔE ST /2), and charge separation free energy (ΔG CS ) in both DNA (−0.19 eV) and lipid bilayers (−0.55 eV). Room-temperature continuous-wave electron paramagnetic resonance (CW-EPR) reveals a photogenerated spin-polarized singlet for COE-CbzBP that splits upon DNA association, consistent with modulation of J RP and hyperfine coupling (A x ), presumably via electric field-spin coupling. No spin-polarized signal was observed under dark, cryogenic conditions, or in liposomes, but was quenched by the spin trap 4-POBN. Transient absorption and spectroelectrochemistry confirmed magnetic-field sensitive long-lived excited-state absorption features attributed to charge-separated states 3 [Cbz •+ -BP •– ]*, which were lengthened by DNA, and quenched in lipid bilayers and 4-POBN. Quantum chemical simulations show that planar geometries (lipid-like) increase ΔE ST by 0.31 eV compared to TICT-optimized structures. This geometry-dependent modulation explains the absence of SCRP signatures in rigid environments, underscoring the importance of TICT states, minimized ΔE ST , and favorable ΔG CS for achieving room-temperature SCRP generation. These findings establish design principles for TICT-enabled molecules exhibiting qubit-like behavior that operate under ambient and biologically relevant conditions, with direct implications for quantum information science (QIS).

Aromatic compounds↗
DOE OSTI2023

Modeling the Environment-Dependent Kinetics of Oxygen Reduction Reaction – a Continuum Model for Electric Double Layer

Here, for proton-exchange-membrane fuel cells (PEMFCs) to achieve broad commercialization, improved energy-conversion efficiency with minimal Pt-based electrocatalyst is required. Because the sluggish rate of oxygen reduction reaction (ORR) limits the efficiency of PEMFCs, the efficiency improvement requires a better understanding of ORR kinetics and mechanism to design better catalyst. To understand the ORR mechanism, theoretical and experimental analyses have been conducted. While previous studies reasonably explained the catalyst-dependent activity on single crystal catalysts in 0.1 M perchloric acid solution, the explicit effect of electrolyte and related microenvironments is not thoroughly understood. The change in the electrolyte alters the electric-double-layer (EDL) structure and thus the local microenvironment at the electrode/electrolyte interface. Thus, the structure of the EDL should be carefully analyzed to uncover the electrolyte-dependent reaction kinetics. In this talk, we propose a multiscale continuum model to predict the EDL structure and examine the effect of perchloric acid concentration on ORR activity on Pt (111). The model includes Density Potential Functional Theory (DPFT) for electron density and Modified Poisson Boltzmann equation for species’ density and electric potential. Also, the interaction between adsorbents and electric field is taken into account by minimizing the grand potential. After model validation with experimentally measured double-layer capacity data as a function of applied potential and concentration, the effect of the perchloric acid concentration (0.02 M – 0.2 M) on ORR activity is analyzed and discussed. It is shown that the model reproduces the specific activity obtained in the experiments when assuming the oxygen adsorption is limiting the rate, which can be attributed to the large energetic barrier for solvent reorganization. Then, extension of the model to PEMFC ionomer electrolytes will be introduced. Overall, the model framework and findings provide insights into the ORR mechanism and guidance on how to tailor catalyst materials for increased PEMFC performance.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2026

A Hybrid Biophysical‐Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy ( LE ) and sensible heat ( H ) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R 2 = 0.81–0.94) and H (R 2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

evapotranspiration↗
DOE OSTI2026

Data for: A hybrid biophysical-machine learning framework for diurnal surface energy flux estimation using proximal sensing

Thermal-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal datasets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for specific surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of an ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81-0.94) and H (R2 = 0.46-0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical – machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

Agricultural Sciences↗
DOE OSTI2021

Exploring the Grid Value of Offshore Wind Energy in Oregon

The significant offshore wind energy potential of Oregon faces several challenges, including a power grid which was not developed for the purpose of transmitting energy from the ocean. The grid impacts of the energy resource are considered through the lenses of (i) resource complementarity with Variable Renewable Energy resources; (ii) correlations with load profiles from the four balancing authorities with territory in Oregon; and (iii) spatial value to regional and coastal grids as represented through a production cost model of the Western Interconnection. The capacity implications of the interactions between offshore wind and the historical east-to-west power flows of the region are discussed. The existing system is shown to accommodate more than two gigawatts of offshore wind interconnections with minimal curtailment. Through three gigawatts of interconnection, transmission flows indicate a reduction of coastal and statewide energy imports as well as minimal statewide energy exports.

17 WIND ENERGY↗
DOE OSTI2023

Reliability-informed end-of-use decision making for product sustainability using two-stage stochastic optimization

The concept of circular economy has been diffused in recent decades to promote economic growth that does not add to the burden on natural resource extraction. Re-X options (e.g., reuse, repair, refurbish, remanufacture, recycle) have been gradually adopted in the product development process and optimized to reduce or eliminate waste and pollution. Although manufacturing incorporating Re-X options can be more environmentally friendly, it involves more sources of uncertainty than traditional manufacturing since the end-of-use products can be collected from multiple origins with various quantities and qualities, and the market demand for both new and remanufactured products cannot be forecasted perfectly. Thus, there is a need to optimize the Re-X policy to alleviate the negative impacts of the higher uncertainty. One option is using the reliability information of new products to estimate the end-of-use conditions and applying multi-stage stochastic optimization to capture multiple demand scenarios. This paper develops a two-stage stochastic optimization model to optimize the quality thresholds for reuse, recycling, and remanufacturing options. Our objective is to minimize the total cost, energy consumption, and environmental impact of producing and providing warranty service for a product family. The model employs reliability information of product components to estimate the warranty service cost and the end-of-use conditions of the returned resources. A case study on a general product family is implemented to illustrate the efficacy of the optimization model. Finally, results show that the two-stage optimization can achieve cost and environmental impact reduction for a hybrid manufacturing and remanufacturing process.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A FETI approach to domain decomposition for meshfree discretizations of nonlocal problems

We propose a domain decomposition method for the efficient simulation of nonlocal problems. Our approach is based on a multi-domain formulation of a nonlocal diffusion problem where the subdomains share “nonlocal” interfaces of the size of the nonlocal horizon. This system of nonlocal equations is first rewritten in terms of minimization of a nonlocal energy, then discretized with a meshfree approximation and finally solved via a Lagrange multiplier approach in a way that resembles the finite element tearing and interconnect method. Specifically, we propose a distributed projected gradient algorithm for the solution of the Lagrange multiplier system, whose unknowns determine the nonlocal interface conditions between subdomains. Several two-dimensional numerical tests on problems as large as 191 million unknowns illustrate the strong and the weak scalability of our algorithm, which outperforms the standard approach to the distributed numerical solution of the problem. Finally, this work is the first rigorous numerical study in a two-dimensional multi-domain setting for nonlocal operators with finite horizon and, as such, it is a fundamental step towards increasing the use of nonlocal models in large scale simulations.

42 ENGINEERING↗
DOE OSTI2022

Q-POP-Thermo: A general-purpose thermodynamics solver for ferroelectric materials

We report that Q-POP-Thermo is a program designed to compute thermodynamic monodomain equilibrium states and their properties for ferroelectric single crystals and thin films based on the Landau-Ginzburg-Devonshire (LGD) Theory. Utilizing symbolic manipulation with the SymPy Library, the governing equations along with appropriate boundary conditions are solved for speedy minimization of the free energy of a crystal. Utilizing the popular Differential Evolution algorithm, with appropriate hybridization, multiple phase diagrams, such as the pressure-temperature phase diagram for bulk single crystals and the common strain-temperature phase diagram for monodomain thin-film systems can be readily generated. Furthermore, a variety of material properties of stable ferroelectric phases, including dielectric, piezoelectric, and electrocaloric properties, can simultaneously be calculated. Validation studies are presented for both thin-film and single crystal systems to test the effectiveness and capability of the open-source program.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

On the convexity of phase-field fracture formulations: Analytical study and comparison of various degradation functions

Efficient and accurate fracture modeling is of great importance in applications where catastrophic outcomes under extreme scenarios are possible. The phase-field (PF) approach to fracture received significant attention over the past decade, due to its capability to capture complicated fracture patterns (e.g., crack merging and branching). Specifically, crack initiation and propagation are modeled via minimization of the total energy functional, which is regularized with the aid of a phase field. Despite the promising results and modeling capabilities of the PF method in many applications, the solution of fracture problems remains computationally challenging mainly due to the non-convexity of the total energy functional with respect to the combined unknown (phase field and displacement) fields. Understanding the effects of their coupling on convexity is crucial in order to address frequently encountered hurdles in fracture modeling (e.g., inefficient solvers and non-physical crack nucleation). In this paper, we develop convexity criteria for a wide class of PF fracture formulations. For this class of formulations, the second variation of the total energy functional is expressed in terms of Hessian matrices (evaluated at individual material points). Depending on the choice of geometric crack functions and degradation functions, we classify the formulations into three categories and analytically study each one separately. To study the sign of the second variation, we derive inequalities which are satisfied at material points when the Hessian matrix is locally positive semi-definite. These inequalities provide objective criteria for comparing degradation functions. Finally, the applicability of the proposed convexity criteria is demonstrated in the context of a one-dimensional problem, solved using a conventional monolithic solver.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Effect of static local distortions vs. dynamic motions on the stability and band gaps of cubic oxide and halide perovskites

Ternary ABX 3 perovskites made of corner-sharing BX 6 octahedra have long featured prominently in solid-state chemistry and condensed matter physics. Still, the joint understanding of their two main subgroups—halides and oxides—has not been fully developed. Indeed, unlike the case in simpler compounds having a single, robust repeated motif (“monomorphous”), certain cubic perovskites can manifest a non-thermal (= intrinsic) distribution of local motifs (“polymorphous networks”). Such static deformations can include positional degrees of freedom (e.g., atomic displacements and octahedral tilting) or magnetic moment degrees of freedom in paramagnets. Unlike thermal motion, such static distortions do not time-average to zero, being an expression of the intrinsic symmetry breaking preference of the chemical bonding. Here, the present study compares electronic structure features of oxide and halide perovskites starting from the static polymorphous distribution of motifs described by Density Functional Theory (DFT) minimization of the internal energy, continuing to finite temperature thermal disorder modeled via finite temperature DFT molecular dynamics. We find that (i) different oxide vs. halide ABX 3 compounds adopt different energy-lowering symmetry-breaking modes. The calculated pair distribution function (PDF) of SrTiO 3 from the first-principles agrees with recently measured PDF. (ii) In both oxides and halides, such static distortions lead to band gap blueshifts with respect to undistorted cubic Pm-3m structure. (iii) For oxide perovskites, high-temperature molecular dynamics simulations initiated from the statically distorted polymorphous structures reveal that the thermally-induced distortions can lead to a band gap redshift. (iv) In contrast, for cubic halide perovskite CsPbI 3 , both the intrinsic distortions and the thermal distortions contribute in tandem to band gap blueshift, the former, intrinsic effect being dominant. (v) In the oxide SrTiO 3 and CaTiO 3 (but not in halide) perovskites, octahedral tilting leads to the emergence of a distinct Γ–Γ direct band gap component as a secondary valley minimum to the well-known indirect R–Γ gap. Understanding such intrinsic vs. thermal effects on oxide vs. halide perovskites holds the potential for designing target electronic properties.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Shape Control of Colloidal Semiconductor Nanocrystals through Thermodynamically Driven Aggregative Growth

The optoelectronic properties of colloidal semiconductor nanocrystals (NCs) can be manipulated by changing their geometric shapes. The precise synthetic control over particle morphologies, however, has remained elusive. Conventional growth techniques rely on the kinetic assembly of atomic units, where supersaturation and precipitation processes can lead to a broad distribution of particle shapes. In this paper, we demonstrate that replacing atomic precursors with small-size nanocrystals as building blocks for larger colloids offers an easier, more predictive control over nanoparticle shape evolution. The reported growth strategy is illustrated via shape-selective syntheses of CdSe and CdS NC cubes, spheres, rods, as well as unprecedented “donut” and ring-like structures. Growth of desired particle morphologies was achieved by choosing an appropriate reaction solvent that minimizes the surface free energy through a unique shape formation. This so-called "aggregative growth mechanism" is explained using a thermodynamic model for interacting viscous colloids. We expect that this novel growth strategy will offer a viable platform for shape–selective synthesis of many inorganic colloids, providing pathways to optimizable optoelectronic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Improving the Accuracy of Variational Quantum Eigensolvers with Fewer Qubits Using Orbital Optimization

Near-term quantum computers will be limited in the number of qubits on which they can process information as well as the depth of the circuits that they can coherently carry out. To date, experimental demonstrations of algorithms such as the Variational Quantum Eigensolver (VQE) have been limited to small molecules using minimal basis sets for this reason. In this work we propose incorporating an orbital optimization scheme into quantum eigensolvers wherein a parametrized partial unitary transformation is applied to the basis functions set in order to reduce the number of qubits required for a given problem. The optimal transformation is found by minimizing the ground state energy with respect to this partial unitary matrix. Through numerical simulations of small molecules up to 16 spin orbitals, we demonstrate that this method has the ability to greatly extend the capabilities of near-term quantum computers with regard to the electronic structure problem. Finally, we find that VQE paired with orbital optimization consistently achieves lower ground state energies than traditional VQE when using the same number of qubits and even frequently achieves lower ground state energies than VQE methods using more qubits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Thermal Weight Determination and Interstate Coupling in State-Averaged ADAPT-VQE

Characterizing electronic thermal states at low temperatures is an important but challenging task in quantum chemistry and condensed matter physics, making it a prime candidate for a useful application in quantum computing. One of the most successful methods for state preparation on quantum computers is the Adaptive, Problem-Tailored (ADAPT) Variational Quantum Eigensolver (VQE), which has recently been generalized to treat excited states within a state-averaged framework as well as Gibbs states. In this work, we introduce Helmholtz-Optimized Thermal (HOT) ADAPT-VQE, an ancilla-free strategy for preparing Gibbs states that directly minimizes the Helmholtz free energy by targeting the dominant eigenstates of the thermal ensemble. We demonstrate the usefulness of HOT-ADAPT-VQE by predicting the free energy of two model systems with strongly correlated ground states: (1) the Fe 2+ cation in a magnetic field and (2) a [Cu 2 O 7 ] 10– fragment of the Mott insulator La 2 CuO 4 . Our results demonstrate that HOT-ADAPT-VQE significantly improves upon Gibbs-state estimates from multistate variants of ADAPT-VQE, often with substantially shallower quantum circuits, making it a promising candidate for thermal-state calculations.

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

How Well Does Kohn–Sham Regularizer Work for Weakly Correlated Systems?

Kohn–Sham regularizer (KSR) is a differentiable machine learning approach to finding the exchange-correlation functional in Kohn–Sham density functional theory that works for strongly correlated systems. Here we test KSR for a weak correlation. We propose spin-adapted KSR (sKSR) with trainable local, semilocal, and nonlocal approximations found by minimizing density and total energy loss. We assess the atoms-to-molecules generalizability by training on one-dimensional (1D) H, He, Li, Be, and Be 2+ and testing on 1D hydrogen chains, LiH, BeH 2 , and helium hydride complexes. The generalization error from our semilocal approximation is comparable to other differentiable approaches, but our nonlocal functional outperforms any existing machine learning functionals, predicting ground-state energies of test systems with a mean absolute error of 2.7 mH.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗