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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 OSTI2020

Pre-Ionization Controlled Laser Plasma Formation for Ignition Applications (Final Scientific/Technical Report)

This report consists of three grand funded papers. Abstracts for each follow. 1) In this work, we present experimental and modeling studies of air pre-ionization using ultraviolet (UV) laser pulses and its effect on laser breakdown of an overlapped near-infrared (NIR) pulse. Experimental studies are conducted with a 266 nm beam (fourth harmonic of Nd:YAG) for UV pre-ionization and an overlapped 1064 nm NIR beam (fundamental of Nd:YAG), both having pulse duration of 10 ns. Results show that the UV beam produces a pre-ionized volume which assists in breakdown of the NIR beam, leading to reduction in NIR breakdown threshold by factor of >2. Numerical modeling is performed to examine the ionization and breakdown of both beams. The modeled breakdown threshold of the NIR, including assist by pre-ionization, is in reasonable agreement with the experimental results. 2) The present contribution compares the energy absorption, optical emission, temperature, and fluid dynamics of ultraviolet (UV) λ = 266 nm and near infrared (NIR) λ = 1064 nm nanosecond laser induced plasmas in ambient air. For UV pulses at the conditions studied, energy absorption by the plasmas increases relatively gradually with laser pulse energy starting at delivered energy of E~8 mJ. Corresponding measurements of plasma luminosity show that the absorption of UV radiation does not necessarily result in visible plasma emission. For the NIR induced plasmas, the energy absorption profile is far more abrupt and begins at ~55 mJ. In contrast with UV, the absorption of NIR radiation is always accompanied by intense optical emission. The temperatures of both types of plasma have been measured with Rayleigh scattering thermometry (at times after the Thomson signal sufficiently diminishes). The UV plasmas can attain a wider range of temperatures, including lower temperatures, depending on the pulse energy (e.g., T ~400–2000K for E ~7–35 mJ at Δt = 10 μs after the pulse) while the NIR plasmas show only hotter temperatures (e.g., T ~ 12 000 K for E = 75 mJ at Δt = 10 μs after the pulse) as is consistent with the literature. Differences in the fluid dynamics for UV versus NIR pulses are shown with Schlieren imaging. In this work, the contrast in the UV and NIR plasma threshold behavior is attributed to differing roles of avalanche ionization and multiphoton ionization as is also illustrated by a simple numerical model. 3) The present contribution examines the impact of plasma dynamics and plasma-driven fluid dynamics on the flame growth of laser ignited mixtures and shows that a new dual-pulse scheme can be used to control the kernel formation process in ways that extend the lean ignition limit. We perform a comparative study between (conventional) single-pulse laser ignition (λ = 1064 nm) and a novel dual-pulse method based on combining an ultraviolet (UV) pre-ionization pulse (λ = 266 nm) with an overlapped near-infrared (NIR) energy addition pulse (λ = 1064 nm). We employ OH* chemiluminescence to visualize the evolution of the early flame kernel. For single-pulse laser ignition at lean conditions, the flame kernel separates through third lobe detachment, corresponding to high strain rates that extinguish the flame. In this work, we investigate the capabilities of the dual-pulse to control the plasma-driven fluid dynamics by adjusting the axial offset of the two focal points. In particular, we find there exists a beam waist offset whereby the resulting vorticity suppresses formation of the third lobe, consequently reducing flame stretch. With this approach, we demonstrate that the dual-pulse method enables reduced flame speeds (at early times), an extended lean limit, increased combustion efficiency, and decreased laser energy requirements.

Mechanical engineering↗
DOE OSTI2026

A multiscale packed-bed reactor model for sustainable ethylene production via chemical looping oxidative coupling of methane

The rising global warming concerns and shale gas discovery have prompted research in the direction of greenhouse gas (GHG), such as methane, reduction and conversion. Oxidative coupling of methane (OCM) offers a pathway to low carbon-intense valorization of methane while producing ethylene, a chemical regarded as central to the petrochemical industry. Even after decades of OCM discovery, researchers keep understanding the process and underlying chemical reactions in a pursuit to achieve industrial viability for OCM. Here, in general, OCM suffers from low C 2 selectivity, yield and reactor temperature runaways due to highly exothermic nature of its reactions. Computational Fluid Dynamics (CFD) tools help analyze spatial gradients within the reactor to deeply understand the diffusion of species, mass and heat transfer phenomena. Furthermore, challenges associated with scaling up such as hot spot formation and parametric sensitivity can be addressed without having to expend on costly experiments. The current paper presents a multiscale packed-bed reactor CFD model coupled with a chemical kinetic model for the chemical looping OCM. The CFD model includes two scales i.e., macroscale for catalyst bed and microscale for individual pellets. Moreover, a chemical kinetic model based on 10 gas-phase reactions is integrated with the CFD model. An additional surface reaction for the formation of gas-phase oxygen from catalyst surface is added to account for the absence of feed oxygen. The model is calibrated against experimental results. The calibrated model captures trends in CH 4 conversion, C 2 selectivity and C 2 yield within a ± 4.35 % range across a temperature range of 700-900 °C. Moreover, model fidelity is evaluated by varying key computational parameters such as mesh resolution and time step size. The model is also verified by varying the inlet methane concentration and the gas hourly space velocity (GHSV) and comparing the results with literature. A sensitivity analysis and scale-up of the current model is undergoing.

Chemical looping↗
DOE OSTI2020

Influence of physical and chemical hydrology on bioremediation of a U-contaminated aquifer informed by reactive transport modeling incorporating 238 U/ 235 U ratios

Microbially-catalyzed reductive immobilization of aqueous uranium (U) as a solid phase has been proposed as a U remediation technique. Both laboratory and field experiments have demonstrated that this reduction reaction alters the 238 U/ 235 U ratio, producing a 238 U-enriched U(IV) solid. In contrast, other major U reactive transport processes fractionate these isotopes much less. This suggests the potential to quantify the extent of bioreduction occurring in groundwater containing U using the 238 U/ 235 U ratio, which would substantially improve upon current practices largely relying on U concentration measurements alone. Current reactive transport models for uranium dynamics include only concentration measurements, which are strongly influenced by highly coupled reactive transformation and aqueous transport processes. The complex physical and chemical behavior of U potentially compromises such quantitative analysis of U storage and release. Here we report the first numerical reactive transport model which explicitly incorporates variations in the 238 U/ 235 U ratio of U and demonstrates improved interpretation of the principal chemical reactions and groundwater transport processes affecting the subsurface mobility and distribution of this widespread contaminant. Recent U bioreduction studies performed in a contaminated aquifer in Rifle, Colorado, USA applied Rayleigh distillation models to interpret U stable isotope fractionation observed as a result of acetate amendment. These simplified models were unable to resolve the spatiotemporal pattern of U isotope fractionation recorded in the aqueous solutes. Here, we employ the multi-component, isotope-enabled CrunchTope reactive transport software to interpret these measured U isotope ratios, and demonstrate accurate reproduction of observed trends in both geochemistry and 238 U/ 235 U ratios for two consecutive years of field experiments. Overall, our results indicate that accurately modeling both the U concentration and isotope ratio distributions greatly constrains the parameter space of the model. We find that the transport properties of U in the Rifle aquifer are governed by the presence of low-permeability regions, which the isotopes are uniquely sensitive to. When U reduction is spatially constrained by these low-permeability regions, the shift in the 238 U/ 235 U ratio becomes more muted. Accurate modeling of observed U isotope ratios thus provides a powerful means to better understand bioremediation, and the current study serves to advance the application of this novel method.

238U/235U↗
DOE OSTI2020

Solvation at metal/water interfaces: An ab initio molecular dynamics benchmark of common computational approaches

Determining the influence of the solvent on electrochemical reaction energetics is a central challenge in our understanding of electrochemical interfaces. To date, it is unclear how well existing methods predict solvation energies at solid/liquid interfaces, since they cannot be assessed experimentally. Ab initio molecular dynamics (AIMD) simulations present a physically highly accurate, but also a very costly approach. In this work, we employ extensive AIMD simulations to benchmark solvation at charge-neutral metal/water interfaces against commonly applied continuum solvent models. We consider a variety of adsorbates including *CO, *CHO, *COH, *OCCHO, *OH, and *OOH on Cu, Au, and Pt facets solvated by water. The surfaces and adsorbates considered are relevant, among other reactions, to electrochemical CO 2 reduction and the oxygen redox reactions. We determine directional hydrogen bonds and steric water competition to be critical for a correct description of solvation at the metal/water interfaces. As a consequence, we find that the most frequently applied continuum solvation methods, which do not yet capture these properties, do not presently provide more accurate energetics over simulations in vacuum. We find most of the computed benchmark solvation energies to linearly scale with hydrogen bonding or competitive water adsorption, which strongly differ across surfaces. Furthermore, we determine solvation energies of adsorbates to be non-transferable between metal surfaces, in contrast to standard practice.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Comparing storm resolving models and climates via unsupervised machine learning

Global storm-resolving models (GSRMs) have gained widespread interest because of the unprecedented detail with which they resolve the global climate. However, it remains difficult to quantify objective differences in how GSRMs resolve complex atmospheric formations. This lack of comprehensive tools for comparing model similarities is a problem in many disparate fields that involve simulation tools for complex data. To address this challenge we develop methods to estimate distributional distances based on both nonlinear dimensionality reduction and vector quantization. Our approach automatically learns physically meaningful notions of similarity from low-dimensional latent data representations that the different models produce. This enables an intercomparison of nine GSRMs based on their high-dimensional simulation data (2D vertical velocity snapshots) and reveals that only six are similar in their representation of atmospheric dynamics. Furthermore, we uncover signatures of the convective response to global warming in a fully unsupervised way. Our study provides a path toward evaluating future high-resolution simulation data more objectively.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Pressure feedback control of electro-hydraulic actuators using fixed displacement hydraulic machines

This paper presents the development of a control strategy for Electro-Hydraulic Actuator (EHA) systems based on fixed displacement hydraulic machines. Although some previous studies on EHAs show benefits in terms of energy efficiency and regeneration capability compared to conventional hydraulic systems, few studies focus on the dynamic performance of EHAs. Unlike traditional valve-controlled systems, an EHA based on a fixed displacement pump typically relies on variation of the electric motor shaft speed to control the actuator velocity. This study shows that pressure feedback can be used to control the speed of the electric machine, thus achieving the required actuator/machine dynamic response. A simulation model is developed by including the EM, the hydraulic system, and the actuation system, which can give a realistic prediction of the EHA performance for a typical duty cycle. An open-circuit EHA previously proposed by the authors is used to validate the control strategy, and the simulation results show superior dynamic performance, exhibiting a reduction in response time and oscillatory behavior, compared to a feedforward EHA controller. Following the method presented in this study, it is possible to design a controller with a pressure feedback strategy for EHA systems with different size and actuation requirements

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Measuring Photoexcited Electron and Hole Dynamics in ZnTe and Modeling Excited State Core-Valence Effects in Transient Extreme Ultraviolet Reflection Spectroscopy

Transient extreme ultraviolet (XUV) spectroscopy is becoming a valuable tool for characterizing solar energy materials because it can separate photoexcited electron and hole dynamics with element specificity. We use surface-sensitive femtosecond XUV reflection spectroscopy to separately measure photoexcited electron, hole, and band gap dynamics of ZnTe, a promising photocathode for CO 2 reduction. We develop an ab initio theoretical framework based on density functional theory and the Bethe-Salpeter equation to robustly assign the complex transient XUV spectra to the material's electronic states. Applying this framework, we identify the relaxation pathways and quantify their time scales in photoexcited ZnTe, including subpicosecond hot electron and hole thermalization, surface carrier diffusion, ultrafast band gap renormalization, and evidence of acoustic phonon oscillations.

14 SOLAR ENERGY↗
DOE OSTI2021

Dynamically Unveiling Metal–Nitrogen Coordination during Thermal Activation to Design High‐Efficient Atomically Dispersed CoN 4 Active Sites

Abstract We elucidate the structural evolution of CoN 4 sites during thermal activation by developing a zeolitic imidazolate framework (ZIF)‐8‐derived carbon host as an ideal model for Co 2+ ion adsorption. Subsequent in situ X‐ray absorption spectroscopy analysis can dynamically track the conversion from inactive Co−OH and Co−O species into active CoN 4 sites. The critical transition occurs at 700 °C and becomes optimal at 900 °C, generating the highest intrinsic activity and four‐electron selectivity for the oxygen reduction reaction (ORR). DFT calculations elucidate that the ORR is kinetically favored by the thermal‐induced compressive strain of Co−N bonds in CoN 4 active sites formed at 900 °C. Further, we developed a two‐step (i.e., Co ion doping and adsorption) Co‐N‐C catalyst with increased CoN 4 site density and optimized porosity for mass transport, and demonstrated its outstanding fuel cell performance and durability.

He, Yanghua↗
DOE OSTI2023

Impact of automated battery sorting for mineral recovery from lithium-ion battery recycling in the United States

The United States has identified several lithium-ion battery materials as critical for reaching the national emission reduction targets that have been set in accordance with the 2015 Paris Agreement. However, with few natural resources available domestically, there is a rapidly growing focus on the development of a domestic recycling industry to recover these materials from end-of-life batteries. Here, we use the Lithium-Ion Battery Resources Assessment (LIBRA) system dynamics model to evaluate the impact of automated battery sorting technology in terms of the shares of cobalt and nickel that are recovered through recycling. Findings show that automated sorting has clear benefits over manual sorting methods by helping recyclers selectively process high-cobalt batteries. By maximizing cobalt recovery, recycling becomes more profitable and drives greater investment in recycling capacity, resulting in a higher share of nickel and cobalt recovered from EOL batteries over time.

25 ENERGY STORAGE↗
DOE OSTI2025

Robustness of inflation to kinetic inhomogeneities

We investigate the effects of large inhomogeneities in both the inflaton field and its momentum. We find that in general, large kinetic perturbations reduce the number of e-folds of inflation. In particular, we observe that inflationary models with sub-Planckian characteristic scales are not robust even to kinetic energy densities that are sub-dominant to the potential energy density, unless the initial field configuration is sufficiently far from the minimum. This strengthens the results of our previous work. In inflationary models with super-Planckian characteristic scales, despite a reduction in the number of e-folds, inflation is robust even when the potential energy density is initially sub-dominant. For the cases we study, the robustness of inflation strongly depends on whether the inflaton field is driven into the reheating phase by the inhomogeneous scalar dynamics.

gravity↗
DOE OSTI2021

Nonlinear Characterization of a Joint Exhibiting a Reduction in Damping at High Energy

Even with the advent of additive manufacturing, the vast majority of complex structures are comprised of individual components held together with bolted joints. However, bolted joints present a challenge for mechanical design as they are a source of nonlinearity and increase the uncertainty in the overall behavior of the system in a dynamic environment. While many advances have been made in the ability to accurately model and test bolted joints, it is still an open area of research. Modes of vibration that exercise bolted joints typically exhibit nonlinear behavior where, with increased excitation level, the natural frequency decreases (i.e. softens) and the damping increases. However, the system under study for this work has an axial mode which does not follow this trend; it does soften as expected, but, after an initial increase, the apparent damping decreases with excitation amplitude. At the highest excitation level, the frequency of the mode decreases to that of a nearby bending mode and the response is amplified nearly 500% above that at lower levels. It is unclear whether the decrease in damping is due to the coupling of the two modes or if it is a characteristic of the axial mode. Therefore, the objective of this project is to investigate the coupling between the axial and bending modes and the dynamics leading to the decrease in damping.

42 ENGINEERING↗
DOE OSTI2023

Novel Calibration Approach for Monitoring Aerosol Hydrogen Isotopes Using Laser-Induced Breakdown Spectroscopy for Molten Salt Reactor Off-Gas Streams

Online monitoring is a key challenge for the continued development of molten salt reactor (MSR) technology. Laser-induced breakdown spectroscopy (LIBS) has previously been demonstrated to be a viable tool for monitoring aerosolized species and noble gases in real time, but the ability to discern varying isotopes in these streams has not yet been investigated for MSR applications. Tritium will form in MSRs from ternary fission and from (n,α)-reactions occurring in lithium-containing salts. This study compares three spectrometers of varying resolutions and types for measuring hydrogen isotope shifts in LIBS spectra of wetted filter paper. For each spectrometer, multivariate models were built (i.e., principal component regression, partial least squares regression, and multivariate curve resolution) to quantify the isotope ratio. The top models were then modified and corrected to apply the models to aerosol samples with varying isotope ratios. This novel calibration strategy offers an 82% reduction in volume of the calibration samples needed and is a more viable pathway for calibrating deployable LIBS systems. Lastly, this calibration model was compared with an all-aerosol trained model for monitoring hydrogen isotopes during a real-time test where the protium/deuterium ratio, along with representative salt species (i.e., lithium, sodium, and potassium) were adjusted dynamically. Results of this test validated the predictive capabilities of the transferred model and highlighted the capabilities of LIBS for real-time monitoring of MSR effluent streams.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Impact of a dynamic grid mix and climate on operational carbon emissions modeling for different building typologies and climate zones

Calculating operational carbon emissions through a building’s lifecycle is complex due to the dynamic nature of influencing factors such as climate and energy grid mix. This paper introduces a novel methodology for modeling 30-year operational carbon impacts of buildings and applies this method to mid-rise office and residential typologies across various US climate zones. The method accounts for these temporal variabilities using new and scarcely cited data sources. Key findings indicate that future changes in the climate, while impactful, play a relatively modest role in operational carbon emissions compared to significant reductions with modeling scenarios using the projected decarbonization of the electricity grid. Here, the study also finds that using annual, month-hourly, or hourly grid emission factors have a minimal impact on carbon accounting, except in certain climates and program types where emission patterns do not align with a building’s energy consumption. Warmer climates like Miami, Florida and Tucson, Arizona, which rely heavily on cooling, demonstrate larger variations in carbon emissions when using higher temporal resolution emission factors. Ultimately, this study underscores the critical role of grid decarbonization in reducing long-term emissions and the importance of incorporating this variable in life cycle assessment (LCA) modeling.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Decoding diffraction and spectroscopy data with machine learning: A tutorial

This Tutorial provides a step-by-step guide on how to apply supervised machine-learning techniques to analyze diffraction and spectroscopy data. This Tutorial details four models—a reconstruction-focused model, a regression-focused model, a hybrid reconstruction/regression model, and a multimodal model—that use x-ray diffraction profiles and vibrational density of states spectra to predict various microstructural descriptors. In this Tutorial, we cover data pre-processing steps, constructions of the models via dimensionality reduction and regression, training, and analysis of these models. Comparisons of the model’s performance are provided, highlighting the strength and weakness of the various approaches utilized.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Scout Benchmark Scenarios for U.S. Building Energy and CO2 Emissions to 2050

Overview and Intended Use Cases: These scenarios establish a range of futures for U.S. buildings sector energy use and CO2 emissions to 2050 using Scout (scout.energy.gov), a reproducible and granular model of U.S. building energy use, emissions, and consumer costs developed by the U.S. national labs for the U.S. Department of Energy's Building Technologies Office (BTO). Scout benchmark scenario data are suitable for the following example use cases: setting high-level policy goals for the U.S. buildings sector to 2050 (e.g., X% building CO2 emissions reductions vs. 2005 levels by 2030, Y% reductions vs. 2005 levels by 2050); exploring the effects of key dynamics driving U.S. buildings sector energy and CO2 emissions to 2050 that could be affected by policy levers (e.g., raising minimum technology performance levels; accelerating electrification and/or retrofit rates; introducing breakthrough technologies to the market); determining priority segments (regions, building types, and end use/technology types) and sequencing of U.S. buildings sector energy and CO2 emissions reductions to 2050 under a given set of assumptions; and/or identifying the energy and emissions impacts or cost effectiveness of specific technologies or operational approaches of interest—in isolation or after considering competition with other measures in a scenario portfolio. Scenario Summary: A total of 8 scenarios explore the effects of changes across both the demand- and supply-side of building energy use on annual U.S. building energy use and CO2 emissions from 2022–2050. Scenarios are organized into three groups representing low, moderate, and best-case potentials for building decarbonization, respectively.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Task-oriented machine learning surrogates for tipping points of agent-based models

We present a machine learning framework bridging manifold learning, neural networks, Gaussian processes, and Equation-Free multiscale approach, for the construction of different types of effective reduced order models from detailed agent-based simulators and the systematic multiscale numerical analysis of their emergent dynamics. The specific tasks of interest here include the detection of tipping points, and the uncertainty quantification of rare events near them. Our illustrative examples are an event-driven, stochastic financial market model describing the mimetic behavior of traders, and a compartmental stochastic epidemic model on an Erdös-Rényi network. We contrast the pros and cons of the different types of surrogate models and the effort involved in learning them. Importantly, the proposed framework reveals that, around the tipping points, the emergent dynamics of both benchmark examples can be effectively described by a one-dimensional stochastic differential equation, thus revealing the intrinsic dimensionality of the normal form of the specific type of the tipping point. This allows a significant reduction in the computational cost of the tasks of interest.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

CFD modeling of near-wall combustion and unburned methane prediction in natural gas spark ignition engines

Natural gas-powered engines play a critical role in gas drilling, compression, and transmission sectors, but methane (CH 4 ) from engine combustion slip can be significant over their lifespan, contributing to atmospheric pollution and signaling reduced engine efficiency. Here, to address this challenge, computational fluid dynamics (CFD) simulations offer valuable insights into the in-cylinder combustion process, enabling the optimization of combustion strategies and engine designs to minimize unburned CH 4 slip. This study aims to evaluate and improve combustion models for simulating the combustion process and predicting unburned CH 4 concentrations in natural gas spark-ignition (SI) engines, including engines that are part of combined reformer-engine systems. Specifically, the performance of two flamelet-based combustion models—the Extended Coherent Flame Model (ECFM) and the G-equation model—was assessed using experimental engine data collected under varying excess-air ratio (λ) conditions and fuel compositions, including natural gas and syngas blends. In addition, to enhance the predictive capabilities of the G-equation model, a flame-wall interaction (FWI) sub-model was integrated into its framework. The effects of its model parameters, such as quenching and influence distance, on combustion behavior and unburned methane predictions were analyzed in detail. The ECFM tended to predict delayed combustion phasing under diluted mixture conditions, resulting in overprediction of unburned CH 4 concentrations. In contrast, the G-equation model provided reasonable predictions of combustion pressure, while representing higher the CH 4 reduction rate across the operating condition compared to experimental data. Incorporating the FWI sub-model—with the quenching distance calculated based on a pressure-dependent relation (P -0.48 ) and a fixed influence distance of 1.5 mm—further improved the G-equation model’s accuracy in predicting CH 4 reduction rates without compromising its ability to simulate the combustion process.

Combustion model↗
DOE OSTI2026

Path integral molecular dynamics: A high-fidelity approach to quantum dynamics of electrons

We investigate electron transport in the uniform electron gas using ring-polymer molecular dynamics (RPMD). Working in the weakly coupled, non-degenerate regime, we use RPMD to probe how the onset of quantum diffraction effects at high temperature reshapes electron–electron collisions and leads to a classical-to-quantum crossover in macroscopic transport properties. Static thermodynamics obtained with RPMD are consistent with the weak-coupling equation of state, confirming correct quantum Boltzmann sampling. Real-time transport extracted from mean square displacements exhibits the expected ballistic-to-diffusive transition and a systematic reduction of the electronic self-diffusivity as quantum effects strengthen, due to quantum diffraction modifying electron–electron collisions. Direct ring-polymer scattering simulations reveal diffractive “softening” of binary deflections, providing a micro-to-macro link between collision physics and diffusion. The present study establishes RPMD as a quantitative, trajectory-based tool for electron transport across the classical–quantum crossover and furnishes benchmarks for improving Coulomb-log interpolation models. We outline extensions to multi-component plasmas and a path to incorporate Fermi–Dirac statistics within path-integral dynamics.

Electronic transport↗