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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Metal nitride materials for solar-thermal ammonia production [Slides]

Solar Thermal Ammonia Production has the potential to synthesize ammonia in a green, renewable process that can greatly reduce the carbon footprint left by the conventional Haber-Bosch reaction. Co 3 Mo 3 N has been identified as a potential candidate for ammonia production. It is synthesized via oxide precursor synthesis followed by nitridation under 10% H 2 /N 2 . The synthesis method can be extended to other candidate nitrides. The Co 3 Mo 3 N → Co 6 Mo 6 N reduction is demonstrated on TGA with rapid kinetics. The formation of NH 3 is qualitatively observed, but not quantitatively determined. The material retains crystal structure, but no secondary phases are observed in XRD. Partial re-nitridation back to CMN331 of ~35% of max nitridation is observed. Reaction parameters in TGA differ from experimental conditions in the literature. Experiments at Georgia Tech better mimic re-nitridation conditions with more sensitive, quantitative analytical techniques (GC-MS). The ASU NH 3 synthesis/re-nitridation reactor is under development and will permit experiments (reduction/re-nitridation) under precisely controlled T, pH 2 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Low-cost Retrofit Kit for Integral Reciprocating Compressors (IRCs) to Reduce Emissions and Enhance Efficiency

Methane emissions from natural gas engines within the oil and gas industry pose a significant environmental challenge, contributing approximately 34.1 MMTCO2 eq to the total of 239 MMTCO2 eq of methane emissions in 2021, according to the EPA report. In response to this pressing issue, a collaborative effort involving the University of Oklahoma and key industry partners—WAGO Automation, Mid Continental Rental, Elipsa, and Perscient—has resulted in the development of a retrofit kit designed to reduce emissions from integral reciprocating compressors (IRCs), which are integrated compressors and engines. The retrofit kit developed comprises an Air Management System (AMS), Integrated Sensors, and a Cloud-Connected Control Unit with Graphical User Interface (GUI)/Human-Machine Interface (HMI). This solution enhances operational efficiency, reduces emissions, and expands the operational envelope of IRCs in the natural gas industry. The project successfully completed all tasks, including the installation of a full-size IRC at a designated site in Oklahoma, the development of an optimized AMS, integration of sensors, and implementation of a data acquisition system. Significant achievements include a notable reduction in CH 4 emissions, up to 84% at specific loads, and the successful deployment of the retrofit kit in diverse field conditions. The system's capabilities were enhanced through the creation of a feedback control algorithm for the AMS using a correlation matrix illustrating relationships between engine parameters, and the design of a predictive and preventive maintenance platform. The project concluded with the deployment of the entire retrofit kit to another location, confirming its effectiveness in reducing emissions and enhancing IRC performance. The comprehensive solution offers valuable benefits for IRCs, making them invaluable assets in the natural gas industry.

03 NATURAL GAS↗
DOE OSTI2024

Development and Validation of Home Comfort System for Total Performance Deficiency/Fault Detection and Optimal Comfort Control

In this project, we developed and tested a learning-based home thermal model that facilitates the operation of a model predictive control (MPC)-based optimization agent and an automated fault detection and diagnosis (AFDD) agent. The home thermal model was constructed using a two-node resistor-capacitor model. Moreover, two accompanying parameter identification methods were introduced, least-squares and optimization. Based on the home thermal model, the MPC-based optimization agent was developed to optimize residential HVAC operation. Using two FDD methods, the AFDD agent was constructed to detect and diagnose two prevalent residential AC faults, airflow reduction and refrigerant undercharge. The home thermal model, along with the MPC-based optimization agent and AFDD agent, were tested at the Norman Test House, Miami Test House, Pacific Northwest National Laboratory (PNNL) Test House A, and PNNL Test House B. Finally, they were also field tested in nine demonstration homes with real occupants.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

FORCE Update 2024

The Framework for Optimization of Resources and Economics (FORCE) tool suite is the U.S. Department of Energy’s Nuclear Integrated Energy Systems (IES) Program flagship tool suite for technoeconomic IES analysis of IES. This tool suite is useful for analysis designed to evaluate and improve the technoeconomics of energy production systems, particularly for systems including nuclear technology. In this report, we document the development activity for the FORCE tool suite to extend its capabilities as performed during fiscal year 2024. In addition to reliability and accessibility, capability is one of the three standards guiding the development of the FORCE tool suite and the software codes that are its constituent parts. Extending the capabilities of the FORCE tool suite allows analysis both within the IES program as well as industry, university, and laboratory partners to perform analysis with more accuracy, insight, and impactful narrative. Four areas of capability development were the focus of activity this year: economic parameter uncertainty quantification, multiresolution analysis, components-to-optimization workflow automation, and statespace construction workflows for real-time optimal control. In economic parameter uncertainty quantification, the ability of HERON to capture risk due to scenarios (weather and energy demand uncertainty) was expanded to also include uncertainties in financial parameters such as capital cost or operation and maintenance costs. By including these sources of uncertainty, which are sometimes very large compared with scenario uncertainty, HERON is better able to capture the risk posed by investment in various IES technology. Because of this, analysts can also consider the reduction in risks that can be realized by choice of some technologies. In multiresolution analysis, development activity extended on work completed previously. In fiscal year 2023, methods for decomposing time series signals, such as demand, solar and wind availability, and price profiles, were analyzed and down-selected to those most effective at splitting signals into different resolutions. These resolutions allow considering the influence of different energy demand and supply behaviors across different time scales. For example, energy demand might be divided into seasonal, weekly, and hourly profiles. In fiscal year 2024, this preliminary work was extended and implemented within the Risk Analysis Virtual Environment (RAVEN) risk and uncertainty analysis platform, which is used throughout the FORCE framework. This development of the “multi-resolution time series analysis” (MR-TSA) module in RAVEN allows training synthetic history generators on complex time series. These synthetic history generators can then be used in HERON for generating scenarios that represent possible market and weather scenarios that can be analyzed on different time scales. We envision completing this work in the future, implementing multiresolution dispatch optimization strategies that can make the most beneficial use of these stratified time histories. In components-to-optimization workflow development, workflows for translating user inputs of components into algorithms for algebraic optimization were selected and implemented. Similar algorithms within the Holistic Energy Resource Optimization Network (HERON) were separated from the main code base of HERON and gathered with the components-to-optimization workflows in the new Dispatch Optimization Variable Engine (DOVE) software library. This modularization allows FORCE users to analyze dispatch optimization and energy system duty cycles independently of HERON, which previously was a burdensome task. Additionally, these dispatch optimization algorithms, set up in an independent library, can now be used across all software applications within FORCE, especially including the real-time optimal control software Optimization of Real-time Capacity Allocation (ORCA). Allowing FORCE software to share dispatch optimization algorithms within a single library allows for improved software maintenance and reliability. In statespace characterization workflow development, alternative workflows for optimizing dispatch with additional technical accuracy was the focus, particularly to improve the real-time optimization decision making in ORCA. Using algorithms and workflows initially developed for the Feasible Actuator Range Modifier (FARM), workflows for determining the statespace representation of IES were identified and demonstrated. The resulting dispatch optimization required a more robust optimization algorithm than that originally used in HERON (and moved to DOVE), which required adding an alternate workflow to DOVE that can more accurately match the behavior of physical systems using a partial differential equation representation. In conclusion, capability developments in the FORCE tool suite in fiscal year 2024 have improved the ability of the FORCE tool suite to perform

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Investigation of the Origins of Comets as Revealed through Infrared High-resolution Spectroscopy I. Molecular Abundances

We report and analyze updated molecular abundances in 20 comets obtained by employing modern data reduction procedures and molecular models. Using box and scatter plots, we examine how the different molecular species are distributed among the comet population, while by means of pie charts, we investigate the relative proportions of these molecular species in each comet. We compare these results with the orbital parameters of the selected comets to identify trends related to their dynamical history. With these analyses, we tentatively identify at least three chemical classes based mainly on relative abundances of CO, CH{sub 3}OH, CH{sub 4}, C{sub 2}H{sub 6}, HCN, and NH{sub 3}. The combination of relative abundances and orbital parameters is then compared with recent chemical models of planetary system formation. This approach may help in investigating the origins and evolution of the material in cometary nuclei. Among other aspects, we underline the need to increase our sample size, especially for hypervolatiles (i.e., CH{sub 4} and CO) in Jupiter family comets.

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

Binning is Sinning (Supernova Version): The Impact of Self-calibration in Cosmological Analyses with Type Ia Supernovae

Recent cosmological analyses (e.g., JLA, Pantheon) of Type Ia supernovae (SNe Ia) have propagated systematic uncertainties into a covariance matrix and either binned or smoothed the systematic uncertainty vectors in redshift space. We demonstrate that systematic error budgets of these analyses can be improved by a factor of ~ 1.5 × with the use of unbinned and unsmoothed covariance matrices. To understand this, we employ a separate approach that simultaneously fits for cosmological parameters and additional self-calibrating scale parameters that constrain the size of each systematic. We show that the covariance-matrix approach and scale-parameter approach indeed yield equivalent results, implying that in both cases the data can self-calibrate certain systematic uncertainties, but that this ability is hindered when information is binned or smoothed in redshift space. We review the top systematic uncertainties in current analyses and find that the reduction of systematic uncertainties in the unbinned case depends on whether a systematic is solely degenerate with the cosmological model in redshift space or whether it can be described by additional correlations between supernova properties and luminosity. Furthermore, we show that the power of self-calibration increases with the size of the data set, which presents a tremendous opportunity for upcoming analyses of photometrically classified samples, like those of Legacy Survey of Space and Time (LSST) and the Nancy Grace Roman Telescope (NGRST). However, to take advantage of self-calibration in large, photometrically classified samples, we must first address the issue that binning is required in currently used photometric analysis methods.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Parametric model-order-reduction development for unsteady convection

A time-averaged error indicator with POD- h Greedy is developed to drive parametric model order reduction (pMOR) for 2D unsteady natural convection in a high-aspect ratio slot parameterized with the Prandtl number, Rayleigh number, and slot angle with respect to the gravity. The error indicator is extended to accommodate the energy equation and Leray regularization. Despite being two-dimensional and laminar, the target flow regime presents several challenges: 1) there is a bifurcation in the angle parameter space; 2) the solution can be multivalued, even at steady state; and 3) the solution exhibits spatio-temporal chaos at several points in the parameter space. The authors explore several reduced-order models (ROMs) and demonstrate that Leray-regularized Galerkin ROMs provide a robust solution approach for this class of flows. They further demonstrate that error-indicated pMOR can efficiently predict several QOIs, such as mean flow, mean Nusselt number and mean turbulent kinetic energy, even in the presence of a bifurcation. Finally, they show that spatio-temporal chaos can lead to lack of reproducibility in both the full-order model and the reduced-order model and that the variance in the full-order model provides a lower bound on the pMOR error in these cases.

leray regularization↗
DOE OSTI2023

Probabilistic partition of unity networks for high–dimensional regression problems

We explore the probabilistic partition of unity network (PPOU-Net) model in the context of high-dimensional regression problems and propose a general framework focusing on adaptive dimensionality reduction. With the proposed framework, the target function is approximated by a mixture of experts model on a low-dimensional manifold, where each cluster is associated with a fixed-degree polynomial. We present a training strategy that leverages the expectation maximization (EM) algorithm. During the training, we alternate between (i) applying gradient descent to update the DNN coefficients; and (ii) using closed-form formulae derived from the EM algorithm to update the mixture of experts model parameters. Under the probabilistic formulation, step (ii) admits the form of embarrassingly paralleliazable weighted least-squares solves. The PPOU-Nets consistently outperform the baseline fully-connected neural networks of comparable sizes in numerical experiments of various data dimensions. Here, we also explore the proposed model in applications of quantum computing, where the PPOU-Nets act as surrogate models for cost landscapes associated with variational quantum circuits.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Mineral scaling on brass and aluminum surfaces with a range of wettability

Crystallization fouling, or mineral scaling, is fundamental to various surfaces in energy and environmental applications. This paper considers, for the first time, a systematic investigation of scaling of nonwetting aluminum and brass surfaces relative to their bare counterparts. Metallic surfaces with a range of nonwetting characteristics from hydrophilic to superhydrophobic and lubricant infused surfaces were fabricated using a facile etching method. Systematic dynamic flow scaling experiments are conducted on the six different surface types at three flow levels and two temperature levels. The interactive effects of the different parameters are analyzed relative to their correspondence to the nucleation theory. The study reveals that nonwetting metallic superhydrophobic or hydrophobic surfaces reduce fouling by over 50% compared to bare counterparts. Further, lubricant infused metallic surfaces demonstrate superior anti-scaling performance by over 60% and up to 90% fouling reduction. The study is the first to present fundamental insights into the engineering and anti-scaling performance of nonwetting brass and aluminum surfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Status of the WPEC subgroup 46 - Efficient and effective use of integral experiments for nuclear data validation

The present paper summarizes the current status of the activities of the on-going WPEC subgroup 46 which was the last significant initiative of Massimo before he passed away. The goal of WPEC/SG46 is to define, test and document a methodology to provide unambiguous feedback to the nuclear data evaluator community, based on the joint use of integral experiments and data assimilation techniques. Part of this effort resulted in a renewed analysis of the original Target Accuracy Requirement (TAR) exercise of WPEC/SG26, by adding more diverse nuclear systems and parameters, including reaction channels correlations and a coarser energy group structure; and making use of the progress made in the most recent evaluations in terms of both nuclear data and covariance matrices. The preliminary outcomes of the updated TAR exercise, documented by various groups worldwide are clear already: uncertainty reductions are required for many nuclide-reaction pairs in a variety of energy range if the target uncertainty requirements set by the industry are to be met, especially for k{sub eff}. The inclusion of correlations between the various reaction channels had a major impact on the magnitude of the required uncertainty reduction. Those uncertainty reductions are unlikely to be met by differential measurements alone. However, the selection of relevant integral experiment and a subsequent adjustment procedure may help meet these requirements. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Imprints of High-Density Nuclear Symmetry Energy on Crustal Fraction of Neutron Star Moment of Inertia

The density dependence of nuclear symmetry energy E sym (ρ) remains the most uncertain aspect of the equation of state (EOS) of supradense neutron-rich nucleonic matter. Utilizing an isospin-dependent parameterization of the nuclear EOS, we investigate the implications of the observational crustal fraction of the neutron star (NS) moment of inertia ΔI/I for the E sym (ρ). We find that symmetry energy parameters significantly influence the ΔI/I, while the EOS of symmetric nuclear matter has a negligible effect. In particular, an increase in the slope L and skewness J sym of symmetry energy results in a larger ΔI/I, whereas an increase in the curvature K sym leads to a reduction in ΔI/I. Moreover, the ΔI/I is shown to have the potential for setting a lower limit of symmetry energy at densities exceeding 3 ρ 0 , particularly when L is constrained to values less than 60 MeV, thereby enhancing our understanding of supradense NS matter.

equation of state↗
DOE OSTI2022

Improving fatigue performance of additively manufactured Ti-6Al-4V using sulfur-based self-terminating etching processes

The adoption of Additively Manufactured (AM) Ti-6Al-4V (Ti64) is continually increasing in the industry due to its benefits over the conventional manufacturing techniques. However, AM Ti64 parts are prone to poor surface finish, which significantly affects their fatigue performance. Therefore, AM Ti64 parts need post-processing to enhance surface finish and fatigue performance. Prior work with a sulfur-based self-terminating etching process for AM Ti64 demonstrated support removal and surface roughness reduction. To further this work, this manuscript details the impact of this sulfur-based post-processing technique on the mechanical properties and the fatigue life of AM Ti64 by characterizing the surface roughness, microstructural evolution, and fracture surfaces. Finally, this post-processing technique decreased the surface roughness parameter - S v by 55 % from 84 ± 11 µm to 38 ± 19 µm and increased the average fatigue life by 340 % from about 7000 cycles to 30,000 cycles.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A quantitative risk assessment framework for fault reactivation in underground hydrogen storage: Coupled simulation and deep learning approach

Underground hydrogen storage (UHS) is emerging as a critical solution for large-scale energy storage. However, like all subsurface fluid injection activities, UHS poses the risk of injection-induced fault reactivation. Accurate risk assessment is essential to ensuring the safety and efficiency of UHS operations. This study presents the development of deep-learning surrogate models for fault reactivation prediction in UHS, trained on a comprehensive database of fully coupled fluid flow-geomechanics simulations. Our findings reveal that analytical models often yield unreliable estimates, with errors up to 54% in the allowable injection pressure, potentially leading to a 40% reduction in UHS operational capacity. The developed surrogate models were incorporated into a quantitative risk assessment (QRA) framework, enabling probabilistic evaluation of fault reactivation risk while accounting for uncertainties in the input variables. Site-specific features, such as horizontal stress gradients, fault’s dip and strike angles, and operational parameters like bottom-hole injection pressure and well-fault distance, were identified as the primary drivers of fault reactivation across various stress regimes. Whereas other hydraulic, geological, and poroelastic reservoir properties were found to have a secondary impact. Notably, we observed that the risk of fault reactivation for a critically oriented fault with a static friction coefficient greater than 0.55 remains below 10% in a normal faulting stress regime. However, the risk significantly increases as the stress regime transitions from normal to strike-slip and ultimately to reverse faulting conditions. These findings underscore the importance of rigorous site characterization and comprehensive QRA evaluations to optimize UHS performance and minimize geomechanical risks.

25 ENERGY STORAGE↗
DOE OSTI2021

Computationally Accelerated Discovery and Experimental Demonstration of Gd0.5La0.5Co0.5Fe0.5O3 for Solar Thermochemical Hydrogen Production

Solar thermochemical hydrogen (STCH) production is a promising method to generate carbon neutral fuels by splitting water utilizing metal oxide materials and concentrated solar energy. The discovery of materials with enhanced water-splitting performance is critical for STCH to play a major role in the emerging renewable energy portfolio. While perovskite materials have been the focus of many recent efforts, materials screening can be time consuming due to the myriad chemical compositions possible. This can be greatly accelerated through computationally screening materials parameters including oxygen vacancy formation energy, phase stability, and electron effective mass. In this work, the perovskite Gd 0.5 La 0.5 Co 0.5 Fe 0.5 O 3 (GLCF), was computationally determined to be a potential water splitter, and its activity was experimentally demonstrated. During water splitting tests with a thermal reduction temperature of 1,350°C, hydrogen yields of 101 μmol/g and 141 μmol/g were obtained at re-oxidation temperatures of 850 and 1,000°C, respectively, with increasing production observed during subsequent cycles. This is a significant improvement from similar compounds studied before (La 0.6 Sr 0.4 Co 0.2 Fe 0.8 O 3 and LaFe 0.75 Co 0.25 O 3 ) that suffer from performance degradation with subsequent cycles. Confirmed with high temperature x-ray diffraction (HT-XRD) patterns under inert and oxidizing atmosphere, the GLCF mainly maintained its phase while some decomposition to Gd 2-x La x O 3 was observed.

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

Modeling the performance and faradaic efficiency of solid oxide electrolysis cells using doped barium zirconate perovskite electrolytes

Y-doped BaZrO 3 (BaZr 1 x Y x O 3 δ , or “BZY”), a proton-conducting ceramic featuring high bulk conductivity and good chemical stability, is a promising electrolyte material for solid oxide electrolysis cells. Further doping with Ce and/or Yb (creating materials “BCZY” and “BCZYYb”) can improve conductivity and sintering properties, but at significant penalty to cells’ faradaic efficiency (FE). Studies have proposed that reduction of lattice Ce can occur in the hydrogen electrode, which consumes some hydrogen produced by the hydrogen evolution reaction, leading to decreased FE. Despite studies suggesting this phenomenon, the mechanism is largely unknown. In this work, we developed a multiphysics model to study the transport of multiple defect species and the performance of BZY, BCZY, and BCZYYb, capturing the tradeoff between enhanced performance at the cost of FE for BCZY and BCZYYb electrolytes compared to BZY. We also found that increasing the water content of the anode gas supply lowers the current output of the cell but results in better FE. The model, which uses several parameters previously unavailable in the literature, was validated to experiments varying temperature, steam water content, and electrolyte material, as well as two performance metrics (performance curves and FE). Results verify and explain observed trends, informing future work on Ce-doped BZY electrolytes.

08 HYDROGEN↗
DOE OSTI2023

High Affinity Electrostatic Interactions Support the Formation of CdS Quantum Dot:Nitrogenase MoFe Protein Complexes

Nitrogenase MoFe protein can be coupled with CdS nanocrystals (NCs) to enable photocatalytic N 2 reduction. The nature of interactions that support complex formation is of paramount importance in intermolecular electron transfer that supports catalysis. In this work we have employed microscale thermophoresis to examine binding interactions between 3-mercaptopropionate capped CdS quantum dots (QDs) and MoFe protein over a range of QD diameters (3.4-4.3 nm). The results indicate that the interactions are largely electrostatic, with the strength of interactions similar to that observed for the physiological electron donor. In addition, the strength of interactions is sensitive to the QD diameter, and the binding interactions are significantly stronger for QDs with smaller diameters. Furthermore, the ability to quantitatively assess NC protein interactions in biohybrid systems supports strategies for understanding properties and reaction parameters that are important for obtaining optimal rates of catalysis in biohybrid systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Recent advances in hybrid metal oxide–zeolite catalysts for low-temperature selective catalytic reduction of NOx by ammonia

A hybrid catalytic system comprised of metal oxide and Cu or Fe exchanged zeolite components represents a potential advancement of state-of-the-art for selective catalytic reduction (SCR) of NOx by ammonia, including an elegant solution to the current challenge of improving the low-temperature efficiency of SCR catalysts. The idea is to enable in-situ NO oxidation over metal oxide component and concomitant fast SCR over zeolite component in the so-called “bifunctional mechanism”. This review is presented in the wake of growing interest in this innovative catalytic system. We begin this review by presenting key parameters that contribute to the synergy between metal oxide and zeolite components. Then, we discuss the materials selection for both components and their possible interactions in the system, followed by a summary of recent NH3-SCR investigations over multiple metal oxide–zeolite pairs and additional discussion on pairing techniques potentially explorable in upcoming studies. Finally, we end the review by providing the perspectives on future challenges in the development of this catalytic system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

FL‐ADS: Federated learning anomaly detection system for distributed energy resource networks

Abstract With the ongoing development of Distributed Energy Resources (DER) communication networks, the imperative for strong cybersecurity and data privacy safeguards is increasingly evident. DER networks, which rely on protocols such as Distributed Network Protocol 3 and Modbus, are susceptible to cyberattacks such as data integrity breaches and denial of service due to their inherent security vulnerabilities. This paper introduces an innovative Federated Learning (FL)‐based anomaly detection system designed to enhance the security of DER networks while preserving data privacy. Our models leverage Vertical and Horizontal Federated Learning to enable collaborative learning while preserving data privacy, exchanging only non‐sensitive information, such as model parameters, and maintaining the privacy of DER clients' raw data. The effectiveness of the models is demonstrated through its evaluation on datasets representative of real‐world DER scenarios, showcasing significant improvements in accuracy and F1‐score across all clients compared to the traditional baseline model. Additionally, this work demonstrates a consistent reduction in loss function over multiple FL rounds, further validating its efficacy and offering a robust solution that balances effective anomaly detection with stringent data privacy needs.

Purohit, Shaurya [Iowa State University Ames Iowa ↗