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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Data on Cu- and Ni-Si-Mn-rich solute clustering in a neutron irradiated austenitic stainless steel

The data presented in this article is supplementary to the research article “Phase instabilities in austenitic steels during particle bombardment at high and low dose rates” (Levine et al.). Needle-shaped samples were prepared with focused ion beam milling from a 304L stainless steel that was irradiated with fast neutrons (E 0.1 MeV) in the BOR-60 reactor at 318 °C to 47.5 dpa. Atom probe tomography (APT) experiments in voltage mode were then conducted on a Cameca LEAP 5000X HR. Atom position, range, and mass spectrum files after reconstruction with Cameca’s IVAS software are included. Cu- and Ni-Si-Mn-rich solute nanoclusters were identified and analyzed using the Open Source Characterization of APT Reconstructions (OSCAR) program. Python code for OSCAR, information on the program’s underlying algorithm, and sample output files are provided. A proximity histogram of a Ni-Si-Mn-rich cluster and a 1D density/solute concentration profile of a Cu-rich cluster are given to demonstrate OSCAR’s analytical functionalities. The provided APT dataset is valuable for benchmarking phase instabilities in neutron-irradiated austenitic stainless steels that occur at high doses. The OSCAR program can be reused to process other APT data sets where solute nanoclustering is of interest.

42 ENGINEERING↗
DOE OSTI2024

Red-QAOA: Efficient Variational Optimization through Circuit Reduction

The Quantum Approximate Optimization Algorithm (QAOA) provides a quantum solution for combinatorial optimization problems. However, the optimal parameter searching process of QAOA is greatly affected by noise, leading to non-optimal solutions. This paper introduces a novel approach to optimize QAOA by exploiting the energy landscape concentration of similar instances via graph reduction, thus addressing the effect of noise. We formalize the notion of similar instances in QAOA and develop a Simulated Annealing-based graph reduction algorithm, called Red-QAOA, to identify the most similar subgraph for efficient parameter optimization. Red-QAOA outperforms state-of-the-art Graph Neural Network (GNN) based graph pooling techniques in performance and demonstrates effectiveness on a diverse set of real-world optimization problems encompassing 3200 graphs. Red-QAOA reduced the node counts and edge counts by 28% and 37%, respectively, while maintaining a low mean square error of 2%. These enable the identification of an optimal parameter set that is closer to the ideal true optimal solution in the presence of noise. By substantially streamlining the search for QAOA parameters, our approach sets the stage for the practical application of quantum algorithms in solving complex optimization problems.

Wang, Meng↗
DOE OSTI2017

Techno-economic Analysis of Advanced Zeolite Membranes for the Concentration of Tritiated Water

This report describes the techno-economic analysis of advanced molecular sieve zeolite membranes to separate and concentrate tritiated water (HTO) from dilute HTO-bearing aqueous streams. In the first phase of this effort, several monovalent and divalent cation-exchanged silicoalumino phosphate (SAPO- 34) molecular sieve zeolite membranes were synthesized on disk supports and characterized with gas and vapor permeation measurements. In the second phase, Linde Type A (LTA) zeolite membranes were synthesized on disk and tubular supports. The pervaporation process performance was evaluated for the separation and concentration of tritiated water. Techno-economic analysis (TEA) on SAPO-34 and LTA membrane based technologies was completed. However, compared to the SAPO-34 membranes, LTA zeolite membrane performance both with respect to separation factor and permeance was superior and encouraging. LTA zeolite membranes were selected as the preferred option for comparison with the Combined Electrolysis and Catalyst Exchange (CECE) process for the concentration of tritiated water from dilute HTO-bearing aqueous solutions. It is estimated that the total energy cost to process 10 L/min of tritiated water for the membrane cascade system is substantially lower compared to the CECE process. A preliminary capital cost estimate for the membrane based system and the CECE process is also reported.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Smart Manufacturing Pathways for Industrial Decarbonization and Thermal Process Intensification

Rapid decarbonization is fast becoming the primary environmental and sustainability initiative for many economic sectors. Industry consumes more than 30 % of all primary energy in the United States and accounts for nearly 25 % of all greenhouse gas (GHG) emissions. More than 70 % of energy consumed by the industrial sector is related to thermal processes, which are also the largest contributors of carbon emissions, overwhelmingly due to the combustion of fossil fuels. Thermal process intensification (TPI) seeks to dramatically improve the energy performance of thermal systems through technology pillars focusing on alternative energy sources and processes, supplemental technologies, and waste heat management. The impacts of TPI have significant overlap with the goals of industrial decarbonization (ID) that seeks to phase out all GHG emissions from industrial activities. Emerging supplemental technologies such as smart manufacturing (SM) and the industrial internet of things (IoT) enable significant opportunities for the optimization of manufacturing processes. Combining strategies for TPI and ID with SM and IoT can open and enhance existing opportunities for saving time and energy via approaches such as tighter control of temperature zones, better adjustment of thermal systems for variations in production levels and feedstock properties, and increased process throughput. Data collected by smart processes will also enable new advanced solutions such as digital twins and machine learning algorithms to further improve thermal system savings. Herein, this paper examines the individual pathways of TPI, ID, and SM and how the combination of all three can accelerate energy and GHG reductions.

42 ENGINEERING↗
DOE OSTI2021

Irradiation-Driven Restructuring of UO 2 Thin Films: Amorphization and Crystallization

Combustion synthesis in uranyl nitrate–acetylacetone–2-methoxyethanol solutions was used to deposit thin UO 2 films on aluminum substrates to investigate the irradiation-induced restructuring processes. Thermal analysis revealed that the combustion reactions in these solutions are initiated at ~160 °C. The heat released during the process and the subsequent brief annealing at 400 °C allow the deposition of polycrystalline films with 5–10 nm UO 2 grains. The use of multiple deposition cycles enables tuning of the film thicknesses in the 35–260 nm range. Irradiation with Ar 2+ ions (1.7 MeV energy and a fluence of up to 1 × 10 17 ions/cm 2 ) is utilized to generate a uniform distribution of atomic displacements within the films. X-ray fluorescence (XRF) and alpha-particle emission spectroscopy showed that the films were stable under irradiation and did not undergo sputtering degradation. X-ray photoelectron spectroscopy (XPS) showed that the stoichiometry and uranium ionic concentrations remain stable during irradiation. The high-resolution electron microscopy imaging and electron diffraction analysis demonstrated that at the early stages of irradiation (below 1 × 10 16 ion/cm 2 ) UO 2 films show complete amorphization and beam-induced densification (sintering), resulting in a pore-free disordered film. Prolonged irradiation (5 × 10 16 ion/cm 2 ) is shown to trigger a crystallization process at the surface of the films that moves toward the UO 2 /Al interface, converting the entire amorphous material into a highly crystalline film. This work reports on an entirely different radiation-induced restructuring of the nanoscale UO 2 compared to the coarse-grained counterpart. The preparation of thin UO 2 films deposited on Al substrates fills an area of national need within the stockpile stewardship program of the National Nuclear Security Administration and fundamental research with actinides. Here, the method reported in this work produces pure, robust, and uniform thin-film actinide targets for nuclear science measurements

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

Modeling Equilibrium Solid–Liquid Interfaces under Effective Constant Chemical Potential Using Machine Learning Interatomic Potentials

The chemical potential (μ) of species in solution is essential for understanding various chemical processes at interfaces. Molecular dynamics (MD) simulations, constrained by fixed compositions, cannot maintain constant chemical potential with reference to a targeted concentration or chemical potential under nonequilibrium or dynamic conditions, as solute species can migrate to the interface and deplete (or enrich) the bulk due to solute-interface interactions. In this study, we introduce a simple and computationally efficient approach named iterative quasi-constant chemical potential molecular dynamics (iqCμMD) simulation, which helps simulate targeted molar concentrations of species in solution. iqCμMD overcomes the limitations of conventional MD by adjusting the number of species in the solution to reach a target bulk concentration (chemical potential), which allows simulation of the interface under the bulk conditions comparable to experiment. We demonstrate our approach using machine learning interatomic potential (MLIP)-based MD simulations of the Na 2 SO 4,aq –graphene interface, and to show the transferability of our approach, we also perform classical force field-based MD simulations of NaCl aq –air and NaCl aq –graphite interfaces, which produce comparable results to previous CμMD simulations. Our results also show that the iqCμMD approach efficiently achieves the desired bulk ion concentration within two iterations, and by utilizing MLIPs, we can achieve converged results using relatively small-scale simulations compared to previous CμMD simulations. By combining iqCμMD with MLIP-driven simulations, solid–liquid interfaces can be modeled under an effective constant chemical potential with DFT-level accuracy. Here, we show that iqCμMD offers a robust and simple computational framework for constant chemical potential simulations, as its only requirement is to be able to converge interfacial simulations with a measurable bulk region.

Chemical structure↗
DOE OSTI2021

Manipulation and statistical analysis of the fluid flow of polymer semiconductor solutions during meniscus-guided coating

Recent work in structure–processing relationships of polymer semiconductors have demonstrated the versatility and control of thin-film microstructure offered by meniscusguided coating (MGC) techniques. Here, we analyze the qualitative and quantitative aspects of solution shearing, a model MGC method, using coating blades augmented with arrays of pillars. The pillars induce local regions of high strain rates—both shear and extensional—not otherwise possible with unmodified blades, and we use fluid mechanical simulations to model and study a variety of pillar spacings and densities. We then perform a statistical analysis of 130 simulation variables to find correlations with three dependent variables of interest: thin-film degree of crystallinity and transistor field-effect mobilities for charge-transport parallel (Μ para ) and perpendicular (Μ perp ) to the coating direction. Our study suggests that simple fluid mechanical models can reproduce substantive correlations between the induced fluid flow and important performance metrics, providing a methodology for optimizing blade design.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Ammonium Looping with Membrane Absorber and Distributed Stripper for Enhanced Algae Growth

The objective of this project is to conduct a comprehensive investigation to develop and demonstrate a practical, reliable, and cost-effective integrated carbon dioxide (CO 2 ) capture and biofixation process for algae production. This process utilizes a chemical hindered ammonium solution (NH 4 OH) as both a capture reagent and an algae nutrient. The membrane absorber employed in the system ensured minimal ammonia (NH 3 ) emissions in the treated flue gas. Additionally, distributed solar-energy powered strippers located near the bioreactor modules facilitate solvent regeneration and enable just-in-time delivery of CO 2 and NH 3 to the algae, thereby minimizing the pH swing for enhanced productivity. The proposed membrane CO 2 absorber and solar-powered stripper is designed and seamlessly integrated with the existing 0.1 MWth bench-scale CO 2 capture system (CCS) and open raceway ponds (ORPs) at the University of Kentucky Center for Applied Energy (UK CAER) campus. The entire integrated process has undergone construction, operation, testing, and analysis. Furthermore, technology has been evaluated with a techno-economic analysis (TEA), technology gap analysis (TGA), life-cycle analysis (LCA), and a technology maturation plan (TMP). This project was performed within two budget periods, 54 months in duration. There are fourteen Project Tasks, twelve Milestones and four Success Criteria.

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

Development of Perovskite (PVSK) Technology Related to Solar Cell Manufacturing Equipment (CRADA CRD-18-733 Final Report)

This CRADA between Energy Everywhere and NREL is to develop a process to fabricate flexible photovoltaic modules using a solution coated PVSK absorber with interlayers comprising materials deposited by economically feasible methods. Energy Everywhere™ will replicate and scale this process in Berkeley, California where it will ultimately commercialize the process, fabrication equipment, and PV modules.

14 SOLAR ENERGY↗
DOE OSTI2018

Impact of nitrate on the redox chemistry and solubility of Tc(IV) in alkaline, dilute to concentrated aqueous NaCl solutions

Here, the impact of nitrate on the redox behaviour and solubility of 99 Tc(IV) was investigated in 0.1–5.0 M NaCl–NaNO 3 –NaOH solutions with 0.1 M ≤ [NO 3 – ] ≤ 1.0 M and 8.5 ≤ pH m ≤ 14.5 (with pH m = –log [H + ]) in the presence or absence of reducing agents (as Fe powder, Na 2 S 2 O 4 , Sn(II)). The evolution of the redox conditions as a function of time (for t ≤ 450 days) was investigated: (i) in the absence of technetium (inactive samples), (ii) in the presence of 99 Tc(IV) (added as 1–2 mg of TcO 2 ∙0.6H 2 O(am)), and (iii) in the presence of 137 Cs(I) (and absence of 99 Tc). All experiments were conducted in an Ar-glovebox (O 2 < 1 ppm) at T = (22 ± 2) °C. An improved understanding of Tc redox processes in nitrate-rich alkaline solutions is relevant to predict Tc behaviour in specific nuclear waste disposal scenarios and a topic of fundamental scientific interest.

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

Enriched immersed boundary method (EIBM) for interface-coupled multi-physics and applications to convective conjugate heat transfer

It is challenging to develop numerical methods that simultaneously maintain accuracy of resolving boundary conditions and mesh flexibility to handle the interface in interface-coupled multi-physics problems involving large property discontinuity. On the one hand, boundary-fitted methods possess high accuracy in capturing the interfacial phenomenon but involve complicated volumetric mesh generation, mesh-motion, and even re-meshing procedures. On the other hand, immersed boundary methods (IBM) provide mesh flexibility but sometimes suffer from inferior interface representations, leading to poor enforcement of boundary conditions. This paper presents an enriched immersed boundary method (EIBM) to overcome this challenge and demonstrates its efficacy in conjugate heat transfer, a representative example in interface-coupled multi-physics systems that have implications for many industrial processes. Further, the core technique of the method is to enhance IBM’s accuracy of the fluid–solid interface by enriching the degrees of freedom of the cut elements to enforce temperature and flux compatibilities and resolve all the physical unknowns on the background mesh to simplify the volumetric mesh generation. We implement the EIBM under the framework of a variational multiscale formulation for coupled Navier–Stokes and thermodynamics equations. The enriched DoFs enable better enforcement of temperature and flux compatibilities with large conductivity ratios across the fluid–solid interface. At the same time, the immersed nature of the proposed method still attains mesh flexibility. The EIBM’s accuracy is thoroughly evaluated through a set of examples, ranging from benchmark problems with analytical solutions to real-world cooling processes of a moving metallic structure with complex geometry.

42 ENGINEERING↗
DOE OSTI2017

Advanced Techniques for Sample Processing of the Reusable Metal-Containing Raw Material (Review)

A review of the sample processing of different types of reusable metal-containing material (catalytic agents, e-wastes, cuttings, etc.) is given in the article; the purpose of the sample processing is the further analysis in the form of solutions: dilution in the mineral acid mix, alloying with different fluxes, leaching, etc. Increase in the temperature and pressure, ultrasonic impact, and microwave radiation are applied for the purpose of sample processing intensification. Methods of sample processing with application of sorbate concentration for the further definition of not only the noble, nonferrous, and rare metals but also the toxic elements are described. The review shows that the stage of sample processing is very important for achieving the potential of the applied analytical methods.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Physics-informed KNN milling stability model with process damping effects

This paper describes a k-nearest neighbors, or KNN, model for milling stability including process damping effects. A physics-based, frequency domain milling stability solution is used to generate the training data, but does not incorporate process damping effects. The data set is then updated using limited tests to capture the process damping behavior. A “stair step” approach is used to select the test points, where a first spindle speed-axial depth combination is selected based on the physics-based stability map, subsequent tests are defined using the previous test result, and data points are updated by knowledge of process damping behavior and the test results. Furthermore, the KNN modeling approach demonstrates the ability to predict both stable and unstable results, including process damping behavior.

42 ENGINEERING↗
DOE OSTI2022

Process Interactions Can Change Process Ranking in a Coupled Complex System Under Process Model and Parametric Uncertainty

For a complex hydrologic system with multiple processes and process interactions, global sensitivity analysis is often used to identify important or influential parameters for model development and improvement. The identification is complicated by process model uncertainty, when a system process can be represented by multiple process models. This study develops a new total-effect process sensitivity index to identify influential processes under model uncertainty. This is done by extending Sobol's total-effect parameter sensitivity index for one system model to total-effect process sensitivity index for multiple system models to account for uncertainty in process models and model parameters. The total-effect process sensitivity index includes not only the first-order process sensitivity index for measuring the importance of individual processes but also higher-order indices that account for process interactions. The total-effect process sensitivity index can identify an influential process that itself and its interactions with other processes influence a model output. Here, the total-effect process sensitivity index is applied to two numerical examples: (a) Sobol's G*-functions with analytical solutions of first-order and total-effect process sensitivity indices, and (b) groundwater flow models with interactions between recharge, geology, and snowmelt processes. The second evaluation shows that, due to second-order and higher-order process interactions, the first-order and total-effect process sensitivity indices give different process ranking. It is thus necessary to estimate both first-order and total-effect process sensitivity indices to appreciate the difference between the first-order impact of a process alone and the overall total-effect impact of the process itself and its interactions with other processes on a model output.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Building Efficiency Technologies by Tomorrow’s Engineers and Researchers (BETTER) Capstone. Final Technical Report

BETTER Capstone supported 29 student project teams consisting of 155 students over two years in developing transformative building energy efficiency technologies through a capstone design experience. Capstone is the culmination of an undergraduate student’s engineering education. Interdisciplinary teams of students spent a semester designing and prototyping a technological solution for a variety building energy efficiency problems. During this experience students utilized the full design process, including the manufacturing and testing of a prototype solution, as well as publically demonstrating the solution at the Capstone Design Expo. As part of this project, students explored modern manufacturing techniques and gained hands-on experience with these techniques to produce their prototype technologies. This research added to the understanding of the challenges within building technology education and engagement with industry. One goal of the project was to help break the chicken-and-egg problem with getting students to engage more deeply with the building technology industry. It was learned however that this industry is less interested in trying innovative new concept but rather interested in hiring graduates for existing conventional building efforts. While none of the projects yielded commercial success, much individual student growth and learning was accomplished, which is a long-term benefit to the public at large.

42 ENGINEERING↗
DOE OSTI2017

Asymptotic behaviour of time averages for non-ergodic Gaussian processes

In this work, we study the behaviour of time-averages for stationary (non-ageing), but ergodicity-breaking Gaussian processes using their representation in Fourier space. We provide explicit formulae for various time-averaged quantities, such as mean square displacement, density, and analyse the behaviour of time-averaged characteristic function, which gives insight into rich memory structure of the studied processes. Moreover, we show applications of the ergodic criteria in Fourier space, determining the ergodicity of the generalised Langevin equation’s solutions. - Highlights: Ergodic criteria for Gaussian models which use Fourier transform are provided. Smooth, field Hamiltonian models are ergodic, discrete models are non-ergodic. Fractal Fourier structure can induce recurring correlations and non-mixing. Non-ergodic models exhibit non-linear dynamics and complex constants of motion. This non-linearity can be studied using time-averaged characteristic function.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Positive Role of Fluorine Impurity in Recovered LiNi 0.6 Co 0.2 Mn 0.2 O 2 Cathode Materials

Lithium-ion battery (LIB) recycling is considered as an important component to enable industry sustainability. A massive number of LIBs in portable electronics, electric vehicles, and grid storage will eventually end up as wastes, leading to serious economic and environmental problems. Hence, tremendous efforts have been made to improve the hydrometallurgical recycling process because it is the most promising option for handling end-of-life LIBs owing to its wide applicability, low cost, and high productivity. Despite these advantages, some extra elements (Al, Fe, C, F, and so forth) remain as impurities in the removal process and are retained in the solution, which is a great challenge to obtain high-quality cathode materials. In this work, the impacts caused by fluorine impurity on the LiNi 0.6 Co 0.2 Mn 0.2 O 2 (NCM622) cathode are intensively investigated via hydrometallurgical coprecipitation for the first time. Our results show that up to 1 at. % fluorine impurity brings a positive influence on the recovered material due to a higher Ni 2+ ratio on the surface of cathode particles. In addition, the presence of fluoride ions during coprecipitation could lead to the formation of holes in cathode particles, which improves the rate capability and cyclability dramatically. Compared to the virgin material, the capacity of the NCM622 material with 0.2 at. % fluorine impurity is boosted by ~8% (167.7 mA h/g) with a remarkable capacity retention of 98.0% after 100 cycles at 0.33 C. Besides, the cathode with 0.2 at. % fluorine impurity shows a far better rate performance, especially at high rates (~7% increased at 5 C) than that of virgin. Furthermore, these results convince that a low concentration of fluorine impurity is desirable in the hydrometallurgical recycling process. More importantly, this study offers implications in the design of high-performance NCM622 cathode materials via coprecipitation production with ion doping in the near future.

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

Online Monitoring of Catalytic Processes by Fiber-Enhanced Raman Spectroscopy

An innovative solution for real-time monitoring of reactions within confined spaces, optimized for Raman spectroscopy applications, is presented. This approach involves the utilization of a hollow-core waveguide configured as a compact flow cell, serving both as a conduit for Raman excitation and scattering and seamlessly integrating into the effluent stream of a cracking catalytic reactor. The analytical technique, encompassing device and optical design, ensures robustness, compactness, and cost-effectiveness for implementation into process facilities. Notably, the modularity of the approach empowers customization for diverse gas monitoring needs, as it readily adapts to the specific requirements of various sensing scenarios. As a proof of concept, the efficacy of a spectroscopic approach is shown by monitoring two catalytic processes: CO 2 methanation (CO 2 + 4H 2 → CH 4 + 2H 2 O) and ammonia cracking (2NH 3 → N 2 + 3H 2 ). Leveraging chemometric data processing techniques, spectral signatures of the individual components involved in these reactions are effectively disentangled and the results are compared to mass spectrometry data. This robust methodology underscores the versatility and reliability of this monitoring system in complex chemical environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗