Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “TRAJECTORIES”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Room-temperature fabrication of garnet-type solid-electrolyte: Optimizing particle size for high ionic conductivity

Dense and uniform Li 6.25 Al 0.25 La 3 Zr 2 O 12 (Al-doped LLZO) solid-electrolyte film of ~24 µm thickness is successfully fabricated by room temperature aerosol deposition (AD) method. The process optimization study revealed that careful control of particle size and morphology is one of critical determinants in the development of a compact AD membrane. Notably, our method facilitated an impressive ionic conductivity of approximately 10 –5 S cm –1 , bypassing the necessity for post-annealing processes, a milestone in itself. However, it is hypothesized that the attained conductivity is somewhat restrained by factors such as smaller grain size and potential surface degradation due to moisture exposure during fabrication, indicating avenues for further research. Looking forward, detailed investigations into the film's microstructure and its impact on transport properties will be a focal point, alongside potential enhancements through post-annealing and particle coating strategies. Finally, this research hints at a promising trajectory for the development of high-efficiency solid-state battery technology.

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

The single-cell transcriptome program of nodule development cellular lineages in Medicago truncatula

Legumes establish a symbiotic relationship with nitrogen-fixing rhizobia by developing nodules. Nodules are modified lateral roots that undergo changes in their cellular development in response to bacteria, but the transcriptional reprogramming that occurs in these root cells remains largely uncharacterized. Here, we describe the cell-type-specific transcriptome response of Medicago truncatula roots to rhizobia during early nodule development in the wild-type genotype Jemalong A17, complemented with a hypernodulating mutant (sunn-4) to expand the cell population responding to infection and subsequent biological inferences. The analysis identifies epidermal root hair and stele sub-cell types associated with a symbiotic response to infection and regulation of nodule proliferation. Trajectory inference shows cortex-derived cell lineages differentiating to form the nodule primordia and, posteriorly, its meristem, while modulating the regulation of phytohormone-related genes. Gene regulatory analysis of the cell transcriptomes identifies new regulators of nodulation, including STYLISH 4, for which the function is validated.

Cell Biology↗
DOE OSTI2022

Kinetic model development and Bayesian uncertainty quantification for the complete reduction of Fe-based oxygen carriers with CH 4 , CO, and H 2 for chemical looping combustion

In this work, three kinetic models are developed and calibrated for the complete multi-step reduction of an Fe-based oxygen carrier (OC) particle with CH 4 , CO, and H 2 , using data from thermogravimetric analysis. The complete reduction rate profiles exhibit complex dynamics whose trajectory is significantly different depending on the reducing gas. A Bayesian model building and parameter estimation framework is applied for simultaneous parameter and model structure uncertainty quantification. The final models show excellent agreement between model predictions and calibration data, as well as new data not used for calibration (for the reduction of the OC with CH 4 ). Parameter uncertainty is quantified by determining joint posterior distribution, and model structure uncertainty is addressed by incorporating Gaussian process stochastic functions (represented by Bayesian smoothing splines) into the kinetic models. The final kinetic models with discrepancy functions are readily employable in equation-oriented simulation and optimization platforms.

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

Ab initio molecular dynamics simulation of Nd3+ incorporation in calcite

Trivalent rare-earth elements (REEs) readily incorporate in Ca-bearing minerals such as calcite (CaCO3), a phenomenon with important implications for the use of REEs as biogeochemical tracers as well as for environmental remediation. Despite substantial efforts, determining the incorporation modes of trivalent REEs in calcite has remained challenging due to their presence in multiple coordination environments. This is particularly problematic for the interpretation of extended X-ray absorption fine structure (EXAFS) spectroscopy. In this work, ab initio molecular dynamics (AIMD) simulations of Nd3+ incorporated in calcite were performed to resolve its incorporation modes. Nd3+ and Ca2+ have very similar ionic radii but the additional positive charge requires a charge compensation scheme (CCS). Five CCSs were considered in AIMD simulations, and the resulting atomic trajectories were then used to compute EXAFS spectra for direct fitting to a published experimental spectrum. The results of the fits indicated that Nd3+ incorporates at the Ca2+ site in calcite in a mixture of six- and seven-fold coordination environments and through three main incorporation modes: associated with a protonated nearest-neighbor Ca2+ vacancy, incorporated as NdOH2+, and charge compensated by a remote species (on the spatial scale probed by EXAFS). The incorporation of Nd3+ in calcite via multiple modes was consistent with previous work, but the AIMD-EXAFS approach allowed for resolving the nature, proportion, and structure of the different coordination environments.

Kerisit, Sebastien N.↗
DOE OSTI2020

A Tchebycheff-based multi-objective combined with a PSO–SQP dynamic real-time optimization framework for cycling energy systems

The aim in this work is to develop an optimization framework to address cycling of baseload energy systems due to the penetration of renewables into the grid. The developed strategy corresponds to a multi-objective and dynamic real-time optimization (MOO-DRTO) framework applied to a postcombustion MEA-based CO 2 capture process from a baseload coal-fired power plant under cycling conditions. A Tchebycheff-based method is used for the multi-objective optimization (MOO) component. Also, a hybrid approach consisting of Particle Swarm Optimization (PSO) and Sequential Quadratic Programming (SQP) is implemented for the first time in process systems engineering to solve the dynamic real-time optimization (DRTO) component. The objectives considered in the MOO-DRTO framework are economic and environmental. The proposed MOO-DRTO strategy is successfully implemented and 24-h optimal output trajectories for the carbon capture system under cycling are generated. Also, the optimal compromise is chosen from the Pareto front according to a set of selected weights for the objectives with minimal interaction between the framework and decision maker. The results indicated that the developed framework has potential to be extended to plant-wide optimization applications under cycling.

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

COVID-19 dynamics across the US: A deep learning study of human mobility and social behavior

This paper presents a deep learning framework for epidemiology system identification from noisy and sparse observations with quantified uncertainty. The proposed approach employs an ensemble of deep neural networks to infer the time-dependent reproduction number of an infectious disease by formulating a tensor-based multi-step loss function that allows us to efficiently calibrate the model on multiple observed trajectories. The method is applied to a mobility and social behavior-based SEIR model of COVID-19 spread. The model is trained on Google and Unacast mobility data spanning a period of 66 days, and is able to yield accurate future forecasts of COVID-19 spread in 203 US counties within a time-window of 15 days. Interestingly, a sensitivity analysis that assesses the importance of different mobility and social behavior parameters reveals that attendance of close places, including workplaces, residential, and retail and recreational locations, has the largest impact on the effective reproduction number. Furthermore, the model enables us to rapidly probe and quantify the effects of government interventions, such as lock-down and re-opening strategies. Taken together, the proposed framework provides a robust workflow for data-driven epidemiology model discovery under uncertainty and produces probabilistic forecasts for the evolution of a pandemic that can judiciously provide information for policy and decision making. All codes and data accompanying this manuscript are available at https://github.com/PredictiveIntelligenceLab/DeepCOVID19.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Bayesian inference of heterogeneous epidemic models: Application to COVID-19 spread accounting for long-term care facilities

Here we propose a high dimensional Bayesian inference framework for learning heterogeneous dynamics of a COVID-19 model, with a specific application to the dynamics and severity of COVID-19 inside and outside long-term care (LTC) facilities. We develop a heterogeneous compartmental model that accounts for the heterogeneity of the time-varying spread and severity of COVID-19 inside and outside LTC facilities, which is characterized by time-dependent stochastic processes and time-independent parameters in ~ 1500 dimensions after discretization. To infer these parameters, we use reported data on the number of confirmed, hospitalized, and deceased cases with suitable post-processing in both a deterministic inversion approach with appropriate regularization as a first step, followed by Bayesian inversion with proper prior distributions. To address the curse of dimensionality and the ill-posedness of the high-dimensional inference problem, we propose use of a dimension-independent projected Stein variational gradient descent method, and demonstrate the intrinsic low-dimensionality of the inverse problem. We present inference results with quantified uncertainties for both New Jersey and Texas, which experienced different epidemic phases and patterns. Moreover, we also present forecasting and validation results based on the empirical posterior samples of our inference for the future trajectory of COVID-19.

42 ENGINEERING↗
DOE OSTI2022

Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inference

Operator inference learns low-dimensional dynamical-system models with polynomial nonlinear terms from trajectories of high-dimensional physical systems (non-intrusive model reduction). Here, this work focuses on the large class of physical systems that can be well described by models with quadratic and cubic nonlinear terms and proposes a regularizer for operator inference that induces a stability bias onto learned models. The proposed regularizer is physics informed in the sense that it penalizes higher-order terms with large norms and so explicitly leverages the polynomial model form that is given by the underlying physics. This means that the proposed approach judiciously learns from data and physical insights combined, rather than from either data or physics alone. Additionally, a formulation of operator inference is proposed that enforces model constraints for preserving structure such as symmetry and definiteness in linear terms. Numerical results demonstrate that models learned with operator inference and the proposed regularizer and structure preservation are accurate and stable even in cases where using no regularization and Tikhonov regularization leads to models that are unstable.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Computational assessment of smooth and rough parameter dependence of statistics in chaotic dynamical systems

An assumption of smooth response to small parameter changes, of statistics or long-time averages of a chaotic system, is generally made in the field of sensitivity analysis, and the parametric derivatives of statistical quantities are critically used in science and engineering. In this paper, we propose a numerical procedure to assess the differentiability of statistics with respect to parameters in chaotic systems. Here, we numerically show that the existence of the derivative depends on the Lebesgue integrability of a certain density gradient function, which we define as the derivative of logarithmic SRB density along the unstable manifold. We develop a recursive formula for the density gradient that can be efficiently computed along trajectories, and demonstrate its use in determining the differentiability of statistics. Our numerical procedure is illustrated on low-dimensional chaotic systems whose statistics exhibit both smooth and rough regions in parameter space.

42 ENGINEERING↗
DOE OSTI2020

A statistical model to predict ignition probability

Ignition capability is a critical design constraint for aeronautical gas turbines. However the current trend toward overall lean burn is detrimental to the engine ignition and relight and the ignition system must be adapted to ensure a fast and reliable light-round in all circumstances. As ignition is a stochastic phenomenon, the optimization of an ignition system requires to build ignition probability maps, which is difficult and costly with either experiment or numerical simulation as both require many tests. Here, this work proposes a model to predict the ignition probability map, knowing only flow statistics in non-reacting conditions, i.e., with only one test. The originality of the model is to construct statistics of the flame kernel trajectory, which are then combined with local flow indicators to evaluate the ignition probability at the considered sparking location. Application to a swirled burner operated in premixed, non-premixed and spray combustion modes illustrates the model concepts and demonstrates its ability to recover the experimental ignition map with good accuracy.

42 ENGINEERING↗
DOE OSTI2022

Potassium carbonate decomposition modeling within rotating detonation engines for direct power extraction applications

We report that recently, there has been a significant interest in detonation-based combustion systems, such as rotating detonation engines (RDE), due to potential performance advantages in propulsion and energy applications. This includes improvements in thermodynamic efficiency, inherently high gas velocities, and the possibility of achieving an increase in total pressure (i.e. pressure gain) through the combustion process. Due to approximating a constant volume combustion process, these devices also tend to exhibit extremely high local gas temperatures relative to a comparable constant pressure combustor. These advantages overlap well with desirable performance characteristics of direct power extraction (DPE) technologies, such as a magnetohydrodynamic (MHD) generator. Typically, in DPE systems hot combustion products are seeded with an easily ionizable material such as potassium carbonate (K 2 CO 3 ) in order to boost the electrical conductivity. However, due to the short gas residence times within an RDE, it was unclear whether forming an electrically conductive combustion plasma would be feasible for integration with a downstream MHD generator. A model is presented which describes the heating, decomposition, and ionization of solid K 2 CO 3 particles and aqueous solutions of K 2 CO 3 in water, for a given initial particle diameter. This model was combined with available computational fluid dynamics (CFD) data for an oxygen-methane RDE in a one-way coupled Eulerian-Lagrangian framework to predict particle trajectories and the corresponding heating, decomposition, and ionization histories. Electrical conductivities were computed using a previously developed model, and a method was proposed to determine an equivalent average electrical conductivity. Results show that particle sizes below ~30 µm are able to fully decompose before reaching the exit of the RDE. While the one-way coupled nature of the simulations precluded rigorous evaluation of the effects of seed material on detonability, a substantial temperature reduction is expected at the detonation wave front due to heating and decomposition. A preliminary comparison is presented between the RDE and an equivalent constant pressure adiabatic combustor, showing a potential performance advantage for the RDE.

42 ENGINEERING↗
DOE OSTI2023

Automated identification and calculation of prompt effects in kinetic mechanisms using statistical models

The kinetics of prompt dissociation involves rovibrationally excited species (generally formed by exothermic reactions) which may dissociate or isomerize prior to thermalization via collisions with the bath gas. Treating such rovibrationally excited species (so-called "hot" species) with standard kinetic phenomenology may result in incorrect macroscopic representation of their reactivity. Here this work presents the first fully automated methodology for the calculation of prompt effects of a chosen species in a kinetic mechanism, including (i) reaction selection; (ii) theoretical calculation of rate constants and prompt branching fractions; and (iii) final rate constant fitting. The energy partition between hot fragments is estimated using a variety of statistical models, including a new physically sound microcanonical statistical model based on the rovibrational density of states of the fragments. The methodology is validated against literature data for the prompt dissociations of HCO and C 3 H 7 radicals. The microcanonical statistical model is in better agreement with trajectory simulations for larger species and is thus applicable for practical systems that typically involve large molecules, for which direct dynamics calculations are impractical. The automated workflow is applied to the evaluation of the effects of prompt dissociation for two isomeric radicals C 4 H 7 1-3 (1-methylallyl) and C 4 H 7 1-4 (3-buten-1-yl). Twelve H-atom abstraction reactions are selected and the corresponding rate constants are computed with first principles theory. The microcanonical statistical model predicts that prompt dissociations of C 4 H 7 1-3 and C 4 H 7 1-4 are already significant at 1000K, resulting in differences of up to an order of magnitude at 2000K with respect to the phenomenological thermal rate constants. To illustrate the effects of prompt dissociation on simulations of experimental data, the calculated prompt rate constants are implemented in both CRECK and C3MechV3.3 kinetic mechanisms. Simulations of experimental flame data illustrate the noticeable impact of prompt dissociation kinetics on the high-temperature combustion reactivity of C 4 H 8 -1 and C 4 H 8 -2.

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

An experimental, theoretical, and kinetic modeling study of post-flame oxidation of ammonia

The post -flame oxidation rate of ammonia was investigated in a novel atmospheric pressure flow reactor at temperatures of 1280 ± 16 K and as a function of residence time and mixture composition (1-10% O 2 , dry and moist). The experimental results, as well as selected data from literature, were analyzed using an updated detailed chemical kinetic model. The medium temperature, very lean conditions enhance the importance of reactions of the nitroxyl (HNO) intermediate. High-level theory was used to calculate the rate constant for HNO + NH 2 , indicating that this step is significantly faster than values used in literature. Furthermore, a trajectory based approach was used to determine collision efficiencies for selected bath gases for HNO + M. The experimental results show that the NH 3 oxidation rate increases with temperature and O 2 concentration, while the presence of water vapor slightly inhibits reaction. Formation of NO and N 2 O was strongly promoted at higher levels of O 2 . Modeling results agreed well with the measurements, except at the lowest level of O 2 . The predicted oxidation rate of NH 3 was shown to result from a delicate balance between chain branching and terminating steps involving NH 2 , H 2 NO, and HNO. Recent theoretical work on reactions of these species by Klippenstein and coworkers and Stagni et al. was instrumental in improving modeling predictions. After initiation, NO reached a pseudo -steady-state level, where the pathways to NO were largely balanced by the NH 2 + NO reaction. Nitric oxide was partly oxidized to NO 2 , with the NH 2 + NO 2 reaction responsible for most of the N 2 O formation. Novelty and significance statement: This study provides the first detailed kinetic analysis of the lean postflame oxidation of ammonia, based on time -resolved flow reactor data in a novel reactor. In addition to the post -flame oxidation rate of ammonia, data for formation of NO and N 2 O were compared with modeling predictions. The medium temperature, very lean conditions enhance the importance of reactions of the HNO and H 2 NO intermediates. Inclusion in the model of results from recent high-level theoretical work, including present calculations for HNO + NH 2 and HNO + M, was crucial for capturing the observed behavior. It is argued that the post -initiation steady-state NH 3 oxidation rates constitute important data for model validation, along with ignition delays and laminar flame speeds.

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

Migration energy barriers and diffusion anisotropy of point defects on tungsten surfaces

We report tungsten is one of the most promising candidates for plasma-facing materials in future fusion devices, owing to its high performance under extreme irradiation conditions. However, irradiation-induced surface morphology varies significantly, depending on the irradiation type, fluence, and flux. Therefore, it is critical to examine the dynamics of point defects on tungsten (W) surfaces to understand how irradiation affects surface morphology. In this study, we employ the Self-Evolving Atomistic Kinetic Monte Carlo (SEAKMC) method to search for potential migration paths of point defects on W (1 0 0), (1 1 0) and (1 1 1) surfaces. The first-principles calculations are then used to accurately determine the migration energy barriers. The obtained paths and barriers are incorporated into a kinetic Monte Carlo (KMC) model to determine trajectories and diffusivities, which are described by diffusion tensors to demonstrate their anisotropic features. Multiple diffusion mechanisms have been identified on different surfaces, with various anisotropy factors. Particularly, point defects on the W (1 1 0) surface have the highest diffusivities, with anisotropy factors independent of temperatures. In comparison, the anisotropy of the W (1 0 0) surface decreases as temperature increases, while the W (1 1 1) surface is isotropic for point defects. This study provides insights into defect transport properties on different surfaces, which are essential for understanding the early stages of irradiation-induced microstructural evolutions and surface morphology of tungsten.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Accelerated computation of lattice thermal conductivity using neural network interatomic potentials

We report with the development of the density functional theory (DFT) and ever-increasing computational capacity, an accurate prediction of lattice thermal conductivity based on the Boltzmann transport theory becomes computationally feasible, contributing to a fundamental understanding of thermal conductivity as well as a choice of the optimal materials for specific applications. However, steep costs in evaluating third-order force constants limit the theoretical investigation to crystals with high symmetry and few atoms in the unit cell. Currently, machine learning potentials are garnering attention as a computationally efficient high-fidelity model of DFT, and several studies have demonstrated that the lattice thermal conductivity could be computed accurately via the machine learning potentials. However, test materials were mostly crystals with high symmetries, and the applicability of machine learning potentials to a wide range of materials has yet to be demonstrated. Furthermore, establishing a standard training set that provides consistent accuracy and computational efficiencies across a wide range of materials would be useful. To address these issues, herein we compute lattice thermal conductivities at 300 K using neural network interatomic potentials. As test materials, we select 25 materials with diverse symmetries and a wide range of lattice thermal conductivities between 10 -1 and 10 3 Wm -1 K -1 . Among various choices of training sets, we find that molecular dynamics trajectories at 50–700 K consistently provide results at par with DFT for the test materials. In contrast to pure DFT approaches, the computational cost in the present approach is uniform over the test materials, yielding a speed gain of 2–10 folds. When a smaller reduced training set is used, the relative efficiency increases by up to ~50 folds without sacrificing accuracy significantly. The current work will broaden the application scope of machine learning potentials by establishing a robust framework for accurately computing lattice thermal conductivity with machine learning potentials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Understanding collective human movement dynamics during large-scale events using big geosocial data analytics

Conventional approaches for modeling human mobility pattern often focus on human activity and movement dynamics in their regular daily lives and cannot capture changes in human movement dynamics in response to large-scale events. With the rapid advancement of information and communication technologies, many researchers have adopted alternative data sources (e.g., cell phone records, GPS trajectory data) from private data vendors to study human movement dynamics in response to large-scale natural or societal events. Big geosocial data such as georeferenced tweets are publicly available and dynamically evolving as real-world events are happening, making it more likely to capture the real-time sentiments and responses of populations. However, precisely-geolocated geosocial data is scarce and biased toward urban population centers. In this research, we developed a big geosocial data analytical framework for extracting human movement dynamics in response to large-scale events from publicly available georeferenced tweets. The framework includes a two-stage data collection module that collects data in a more targeted fashion in order to mitigate the data scarcity issue of georeferenced tweets; in addition, a variable bandwidth kernel density estimation(VB-KDE) approach was adopted to fuse georeference information at different spatial scales, further augmenting the signals of human movement dynamics contained in georeferenced tweets. To correct for the sampling bias of georeferenced tweets, we adjusted the number of tweets for different spatial units (e.g., county, state) by population. To demonstrate the performance of the proposed analytic framework, we chose an astronomical event that occurred nationwide across the United States, i.e., the 2017 Great American Eclipse, as an example event and studied the human movement dynamics in response to this event. Finally, this analytic framework can easily be applied to other types of large-scale events such as hurricanes or earthquakes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Analysis of tow architecture variability in biaxially braided composite tubes

Spatial deviations in composite braid unit cell dimensions limit mechanical performance and are challenging to model. Full-field measurements of the irregular tow placement in biaxially braided composite tubes were obtained by digital image correlation. Analysis of the reconstructed tow paths showed long-range defects along the hoop direction with short-range defects dominating the biased tow direction. A route toward numerical simulation of the composite response is established by developing a 3D model of the composite using the measured tow trajectories. Finally, a comparison to previously-published triaxial braid results indicates axial tows work to constrain unit cells dimensions along the hoop direction.

3-Dimensional reinforcement↗
DOE OSTI2021

Formation and reconfiguration of tight multi-lane platoons

Advances in vehicular communication technologies are expected to facilitate cooperative driving in the future. Connected and Automated Vehicles (CAVs) are able to collaboratively plan and execute driving maneuvers by sharing their perceptual knowledge and future plans. In this paper, an architecture for autonomous navigation of tight multi-lane platoons traveling on public roads is presented. Using the proposed approach, CAVs are able to form single or multi-lane platoons of various geometrical configurations. They are able to reshape and adjust their configurations according to changes in the environment. The proposed architecture consists of two main components: an offline motion planner system and an online hierarchical control system. The motion planner uses an optimization-based approach for cooperative formation and reconfiguration in tight spaces. A constrained optimization scheme is used to plan smooth, dynamically feasible and collision-free trajectories for all the vehicles within the platoon. The paper addresses online computation limitations by employing a family of maneuvers precomputed offline and stored on a look-up table on the vehicles. The online hierarchical control system is composed of three levels: a traffic operation system (TOS), a decision-maker, and a path-follower. The TOS determines the desired platoon reconfiguration. The decision-maker checks the feasibility of the reconfiguration plan based on real-time information about the surrounding traffic. The reconfiguration maneuver is executed by a low-level path-following feedback controller in real-time. The effectiveness of the approach is demonstrated through simulations of three case studies: (1) formation reconfiguration (2) obstacle avoidance, and (3) benchmarking against behavior-based planning in which the desired formation is achieved using a sequence of motion primitives.

42 ENGINEERING↗