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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales

While wetlands are the largest natural source of methane (CH 4 ) to the atmosphere, they represent a large source of uncertainty in the global CH 4 budget due to the complex biogeochemical controls on CH 4 dynamics. Here we present, to our knowledge, the first multi-site synthesis of how predictors of CH 4 fluxes (FCH4) in freshwater wetlands vary across wetland types at diel, multiday (synoptic), and seasonal time scales. In this work we used several statistical approaches (correlation analysis, generalized additive modeling, mutual information, and random forests) in a wavelet-based multi-resolution framework to assess the importance of environmental predictors, nonlinearities and lags on FCH4 across 23 eddy covariance sites. Seasonally, soil and air temperature were dominant predictors of FCH4 at sites with smaller seasonal variation in water table depth (WTD). In contrast, WTD was the dominant predictor for wetlands with smaller variations in temperature (e.g., seasonal tropical/subtropical wetlands). Changes in seasonal FCH4 lagged fluctuations in WTD by ~17 ± 11 days, and lagged air and soil temperature by median values of 8 ± 16 and 5 ± 15 days, respectively. Temperature and WTD were also dominant predictors at the multiday scale. Atmospheric pressure (PA) was another important multiday scale predictor for peat-dominated sites, with drops in PA coinciding with synchronous releases of CH 4 . At the diel scale, synchronous relationships with latent heat flux and vapor pressure deficit suggest that physical processes controlling evaporation and boundary layer mixing exert similar controls on CH 4 volatilization, and suggest the influence of pressurized ventilation in aerenchymatous vegetation. In addition, 1- to 4-h lagged relationships with ecosystem photosynthesis indicate recent carbon substrates, such as root exudates, may also control FCH4. By addressing issues of scale, asynchrony, and nonlinearity, this work improves understanding of the predictors and timing of wetland FCH4 that can inform future studies and models, and help constrain wetland CH 4 emissions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Axial H-Bonding Solvent Controls Inhomogeneous Spectral Broadening, While Peripheral H-Bonding Solvent Controls Vibronic Broadening: Cresyl Violet in Methanol

The dynamics of the nuclei of both a chromophore and its condensed-phase environment control many spectral features, including the vibronic and inhomogeneous broadening present in spectral line shapes. For the cresyl violet chromophore in methanol, we here analyze and isolate the effect of specific chromophore–solvent interactions on simulated spectral densities, reorganization energies, and linear absorption spectra. Employing both chromophore and its condensed-phase environment control many spectral features, including the vibronic and inhomogeneous broadening present in spectral line shapes. For the cresyl violet chromophore in methanol, we here analyze and isolate the effect of specific chromophore–solvent interactions on simulated spectral densities, reorganization energies, and linear absorption spectra. Employing both force field and ab initio molecular dynamics trajectories along with the inclusion of only certain solvent molecules in the excited-state calculations, we determine that the methanol molecules axial to the chromophore are responsible for the majority of inhomogeneous broadening, with a single methanol molecule that forms an axial hydrogen bond dominating the response. Furthermore, the strong peripheral hydrogen bonds do not contribute to spectral broadening, as they are very stable throughout the dynamics and do not lead to increased energy-gap fluctuations. We also find that treating the strong peripheral hydrogen bonds as molecular mechanical point charges during the molecular dynamics simulation underestimates the vibronic coupling. Including these peripheral hydrogen bonding methanol molecules in the quantum-mechanical region in a geometry optimization increases the vibronic coupling, suggesting that a more advanced treatment of these strongly interacting solvent molecules during the molecular dynamics trajectory may be necessary to capture the full vibronic spectral broadening.

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

Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

DER integration↗
DOE OSTI2020

Dynamic Model of Active Front-End Converters with 2DOF-PI Controllers for DC Bus Voltage Control

In most of the research, a conventional one degree-of-freedom type proportional-integral controller (1DOF-PI) is used for the regulation of the dc bus voltage in an Active Front-End Converter (AFEC). This paper proposes a two degree-of-freedom type proportional-integral controller (2DOF-PI) for the regulation of dc bus voltage in an AFEC. Such a controller eliminates the left-half plane (LHP) zero in the command-to-output transfer function for dc bus voltage regulation that is inherent to the conventional 1DOF-PI for this application. The dynamic model of an AFEC in the synchronous reference frame (SRF) is presented. An inner current control and outer dc voltage control loop is built for controlling the AFEC. Vector-control scheme is employed to achieve decoupled control of direct-axis and quadrature-axis currents. The outer dc voltage control loop is designed with a 2DOF-PI to complete the control system. Transfer function (TF) of the controller is derived based on the presented model and used to calculate the controller gains. Transient and steady state performance of the designed controllers are investigated in MATLAB/Simulink and results presented.

Thapa, Anup↗
DOE OSTI2021

Model predictive control for integrated control of air-conditioning and mechanical ventilation, lighting and shading systems

Modern buildings are increasingly automated and often equipped with multiple building services (e.g., air-conditioning and mechanical ventilation (ACMV), dynamic shading, dimmable lighting). These systems are conventionally controlled individually without considering their interactions, affecting the building’s overall energy inefficiency and occupant comfort. A model predictive control (MPC) system that features a multi-objective MPC scheme to enable coordinated control of multiple building services for overall optimized energy efficiency, indoor thermal and visual comfort, as well as a hybrid model for predicting indoor visual comfort and lighting power is proposed. The MPC system was implemented in a test facility having two identical, side-by-side experimental cells to facilitate comparison with a building management system (BMS) employing conventional reactive feedback control. The MPC system coordinated the control of the ACMV, dynamic façade and automated dimmable lighting systems in one cell while the BMS controlled the building services in the other cell in a conventional manner. The MPC side achieved 15.1–20.7% electricity consumption reduction, as compared to the BMS side. Simultaneously, the MPC system improved indoor thermal comfort by maintaining the room within the comfortable range (-0.5 < predicted mean vote < 0.5) for 98.3% of the time, up from 91.8% of the time on the BMS side. Visual comfort, measured by indoor daylight glare probability (DGP) and horizontal illuminance level at work plane height, was maintained for the entire test period on the MPC side, improving from having visual comfort for 94.5% and 85.7% of the time, respectively, on the BMS side.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

All-optical control of charge transfer and interlayer excitons in transition metal dichalcogenide heterostructures

An all-optical approach to control interlayer charge transfer and interlayer exciton dynamics is demonstrated using transition metal dichalcogenide heterostructures of MoSe 2 /MoS 2 and MoSe 2 /WS 2 as examples. In the three-pulse pump-probe experiments, a control pulse injects photocarriers and produces an electric field due to the interlayer charge separation. A pump pulse excites new carriers and their dynamics under the influence of the control-injected carriers and field is time-resolved by measuring differential reflectance of a third probe pulse. In MoSe 2 /MoS 2 , the interlayer electron transfer time from MoSe 2 to MoS 2 decreases with increasing the control-injected carrier density and electric field. The dipole moment of the interlayer excitons is reduced by the control pulse, which can be utilized for ultrafast and all-optical control of interlayer excitons. The recombination of the interlayer excitons is enhanced by the control pulse, which increases the spatial overlap of the electron and hole wave functions. The effect of the control pulse on the interlayer excitons is confirmed in the MoSe 2 /WS 2 heterostructure, although its effect on the electron transfer time is not resolvable. Furthermore, these results reveal useful information to understand and control interlayer charge transfer dynamics and provide tools for ultrafast manipulation of electrons in van der Waals heterostructures.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Parallel Grid-Forming Inverter-Driven Black Start: Power-Hardware-in-the-Loop Validation: Preprint

With power systems encountering increasing deployment levels of inverter-based resources (IBRs), system restoration using grid-forming (GFM) IBRs has gained attention. Engineered to establish grid voltages in the absence of a stiff grid, black-start-capable GFM IBRs are expected to enhance power system resilience by playing a critical role in bottom-up system restoration. This paper experimentally studies the feasibility of the novel approach through power-hardware-in-the-loop (PHIL) testing and validation with a commercial GFM inverter. The PHIL test setup demonstrates an inverter-driven black start of a 5-MW unbalanced distribution feeder where two GFM inverters collectively black start the feeder: one is a commercial hardware GFM inverter interacting through the PHIL interface, and the other is a software GFM emulated by a real-time simulation with full electromagnetic transient (EMT) models. Both GFM inverters are equipped with negative-sequence voltage control to suppress the voltage imbalance resulting from the unbalanced loading, allowing us to study the dynamic interactions between the GFMs with their control parameters unknown. To evaluate the dynamic behavior of the GFM inverters under the entire black-start process, the EMT model of the distribution system details the transformer and motor dynamics to emulate their inrush and startup behaviors. It abstracts conventional grid-connected inverter dynamics, i.e., grid-following inverters. Oscilloscope measurements of the hardware GFM inverter are also presented for critical steps. Takeaways for further study and field deployment are provided.

grid-forming inverter↗
DOE OSTI2023

ReaxFF reactive force field model enables accurate prediction of physiochemical and mechanical properties of crystalline and amorphous shape‐memory polyurethane

Abstract We investigate reactive force field (ReaxFF) prediction of physiochemical and mechanical properties, and stimuli response behavior of a shape‐memory polyurethane (SMPU) rigid segment. We used SMPUs as a platform to link the rigid/hard segment domain crystallinity with properties that can also be experimentally measured. Specifically, we developed models for crystalline and amorphous forms of a common rigid segment of SMPUs, for example, 4,4′‐diphenylmethane diisocyanate (MDI) with n‐butanediol (BDO), denoted as MDI‐BDO. ReaxFF molecular dynamics simulations with equilibrium dynamics are performed by controlling mass, temperature, and pressure or volume to predict the structural and physiochemical properties of crystalline and amorphous rigid segments of SMPU systems, followed by simulating their XRD and FTIR patterns, respectively. Non‐equilibrium ReaxFF simulations are implemented by using uniaxial box deformation technique to study response‐behavior of crystalline SMPU under tensile loading and during stress‐relaxation. The model and simulation scheme presented in this study demonstrates a robust theoretical pathway toward application‐dictated, on‐demand design of high‐performance SMPUs in applications such as soft robotics.

Afroz, Mohammad M.↗
DOE OSTI2024

Tip‐Enhanced Imaging and Control of Infrared Strong Light‐Matter Interaction

Optical antenna resonators enable control of light‐matter interactions on the nano‐scale via electron–photon hybrid states in strong coupling. Specifically, mid‐infrared (MIR) nano‐antennas coupled to saturable intersubband transitions in multi‐quantum‐well (MQW) semiconductor heterostructures allow for the coupling strength to be tuned through antenna resonance and field intensity. Here, in this study, tip‐enhanced nano‐scale variation of antenna‐MQW coupling across the antenna is demonstrated, with a spatially‐dependent coupling strength $g_{\textrm{aq}}$ varying from 73 (strong coupling) to 24 cm -1 (weak coupling). This behavior is modeled based on the spatially dependent local constructive and destructive interference between tip and antenna fields. Using a quantum‐mechanical density‐matrix model of the MQW system with its designed values of transition dipole moment, doping density, and population decay time, the picosecond IR pulse coupling to intersubband transitions and the associated tip induced strong‐field saturation effects are described. These results present a new regime of nonlinear IR light‐matter control based on the dynamic manipulation of quantum hybrid states on the nanoscale and in the infrared, with a perspective regarding extension to molecular vibrations.

Wang, Yueying↗
DOE OSTI2022

Ruddlesden–Popper Perovskites with Narrow Phase Distribution for Air-Stable Solar Cells

2D Ruddlesden-Popper perovskites have risen to prominence as stable and efficient photovoltaic materials because of their structural diversity, rich photophysics, and low moisture ingression. However, thin films processed from stoichiometric precursor solutions possess a broad phase distribution of different number of inorganic layers with random crystal orientation, crippling device performance. The effect of methylammonium chloride (MACI) and 3-amino4-phenolsulfonic acid (APSA) on the fabrication of perpendicularly oriented (PEA) 2 MA 4 Pb 5 I 16 films with narrow phase distribution using antisolvent and hot-casting processing techniques is investigated. MACI plays a critical role in suppressing parasitic n ≤ 2 and 3D-like phases. APSA performs the dual function of trap passivation and further narrowing phase polydispersity through strong coordination with Pb 2+ . Ex situ grazing-incident wide-angle X-Ray scattering (GIWAXS) and ultrafast spectroscopic characterization reveal uniformly mixed-phase distribution with disordered orientation in antisolvent treated films, while additive-assisted hot-casting treatment results in oriented, reverse-graded phase distribution, i.e., small-n on the film surface and large-n at the bottom. Arising thin films enable efficient p-i-n solar cells with an efficiency of 14.34%, and a V oc of 1.20 V, retaining 96% initial efficiency after 1440 h under ambient conditions (RH = 50-60%) without encapsulation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Exploring Saccharomycotina Yeast Ecology Through an Ecological Ontology Framework

Yeasts in the subphylum Saccharomycotina are found across the globe in disparate ecosystems. A major aim of yeast research is to understand the diversity and evolution of ecological traits, such as carbon metabolic breadth, insect association, and cactophily. This includes studying aspects of ecological traits like genetic architecture or association with other phenotypic traits. Genomic resources in the Saccharomycotina have grown rapidly. Ecological data, however, are still limited for many species, especially those only known from species descriptions where usually only a limited number of strains are studied. Moreover, ecological information is recorded in natural language format limiting high throughput computational analysis. To address these limitations, we developed an ontological framework for the analysis of yeast ecology. A total of 1,088 yeast strains were added to the Ontology of Yeast Environments (OYE) and analyzed in a machine-learning framework to connect genotype to ecology. This framework is flexible and can be extended to additional isolates, species, or environmental sequencing data. Widespread adoption of OYE would greatly aid the study of macroecology in the Saccharomycotina subphylum.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Harnessing on-machine metrology data for prints with a surrogate model for laser powder directed energy deposition

In this study, we leverage the massive amount of multi-modal on-machine metrology data generated from Laser Powder Directed Energy Deposition (LP-DED) to construct a comprehensive surrogate model of the 3D printing process. By employing Dynamic Mode Decomposition with Control (DMDc), a data-driven technique, we capture the complex physics inherent in this extensive dataset. This physics-based surrogate model emphasizes thermodynamically significant quantities, enabling us to accurately predict key process outcomes. The model ingests 21 process parameters, including laser power, scan rate, and position, while providing outputs such as melt pool temperature, melt pool size, and other essential observables. Furthermore, it incorporates uncertainty quantification to provide bounds on these predictions, enhancing reliability and confidence in the results. We then deploy the surrogate model on a new, unseen part and monitor the printing process as validation of the method. Our experimental results demonstrate that the predictions align with actual measurements with high accuracy, confirming the effectiveness of our approach. Furthermore, this methodology not only facilitates real-time predictions but also operates at process-relevant speeds, establishing a basis for implementing feedback control in LP-DED.

Digital twins↗
DOE OSTI2022

Gross primary production (GPP) and red solar induced fluorescence (SIF) respond differently to light and seasonal environmental conditions in a subalpine conifer forest

The phenology of montane conifer forests is likely to shift in response to climate change and altered seasonal dynamics of light, temperature, and moisture. Solar-induced fluorescence (SIF) is expected to provide substantial improvement for mapping temporal changes in evergreen gross primary production (GPP) over greenness-based remote sensing indices. The utility of SIF to monitor seasonal changes in the phenology of conifer photosynthesis depends on the degree to which GPP and SIF respond in synchrony to key environmental drivers. However, to what extent SIF and GPP become decoupled by responding differently to the combined effects of light and other environmental conditions remains unknown. The goal of this study was to characterize the responses of GPP and SIFred to a suite of environmental drivers at the half-hour time scale and determine how these relationships change across seasons. We analyzed one year of tower-based SIFred and eddy covariance-derived GPP data from a conifer forest at Niwot Ridge, Colorado. We compared the light responses of GPP and SIFred across the year, finding that SIFred increased in response to light earlier in the year than did GPP. The light response of GPP had a positive temperature dependence in spring, and this dependency reversed in summer due to increased evaporative demand, while the light response of SIFred was less temperature dependent. Using artificial neural network ensemble analysis, we found that from spring to summer, SIFred did not exhibit a parallel response to the seasonally dynamic temperature and moisture controls on GPP. In summer SIFred was not correlated with canopy conductance, suggesting that SIF is less sensitive to stomatal control than GPP. Furthermore, our results suggest that, in conifers, photosystems begin to activate in spring prior to when water becomes available for photosynthesis, presenting a challenge for the use of SIF as a phenological indicator in conifer forests.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

HHL algorithm with mapping function and enhanced sampling for model predictive control in microgrids

Here, this paper presents a refined quantum Harrow Hassidim Lloyd (HHL) algorithm for microgrid control. The first novelty of the developed method is that a mapping shift function enables the original HHL algorithm to handle general linear equations with non-singular and indefinite matrix. Second, a method of Matrix Extension for Amplifying Sampling Probabilities of Intended Solution (ME-ASPI) is proposed to design the reformulated linear algebraic equations, allowing for improved sampling efficiency of the quantum tomography in the refined HHL algorithm. Then, we applied the method to solve the model predictive control (MPC) problem in nonlinear dynamical microgrids. Specifically, with the ME-ASPI method, the refined HHL algorithm can effectively obtain the intended partial optimal control inputs for MPC. The optimization of quadratic programming problem in each time step of MPC is transformed into a linear system problem, which is addressed by the proposed quantum solver through using only partial information, with the time complexity improved from $\mathscr{O}(\mathscr{N}^{2.37286})$ classically to $\mathscr{O}(\mathscr{N}^{2} log \mathscr{N}$ x $p$ log $p)$ in quantum. Numerical examples have validated the effectiveness of the refined HHL algorithm with the proposed mapping function and the ME-ASPI method. By leveraging quantum properties, the proposed method provides a hybrid quantum–classical framework for microgrid control. This generic method can also potentially tackle many other challenges in analyzing and controlling general complex engineered systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Multiscale Concurrent Atomistic-Continuum (CAC) modeling of multicomponent alloys

We report strengthening in complex multicomponent systems such as solid solution alloys is controlled primarily by the dynamic interactions between dislocation lines and heterogeneously distributed solute species. Modeling of extended defect length scales in such multicomponent systems becomes prohibitively expensive, motivating the development of reduced order approaches. This work explores the application of the Concurrent Atomistic-Continuum (CAC) method to model dislocation mobility in random alloys at extended length scales. By employing recently developed average-atom interatomic potentials, the average “bulk” material response in coarse-grained regions interacts with true random solute species in the atomistic-scale domain. We demonstrate that spurious stresses in domain resolution transition regions are eliminated entirely due to the CAC formulation. Simultaneously, the key details of local stress fluctuation due to randomness in the dislocation core region are captured, and fluctuating stress smoothly decays to the long-range dislocation stress field response. Dislocation mobility calculations, for line lengths over 400 nm, are computed as a function of alloy composition in the model FeNiCr system and compared to full molecular dynamics (MD). The results capture the composition-dependent trends, while reducing degrees of freedom by nearly 40%. This approach can be readily extended to any system described by an EAM potential and facilitates the study of large-scale defect dynamics in complex solute environments to support computational alloy design.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Implementation of extrinsic cohesive zone model (ECZM) in 2D finite-discrete element method (FDEM) using node binding scheme

The combined finite-discrete element method (FDEM) has been widely used for rock fracturing simulations. Conventionally, FDEM is realized using the intrinsic cohesive zone model (ICZM); however, it has the drawback of artificial compliance and high computational expense. As a complement, the extrinsic cohesive zone model (ECZM) is seen to be realized in FDEM recently, whereas the node splitting scheme utilized is cumbersome. Here, within the framework of ICZM-based FDEM, we propose a node binding scheme to efficiently bind the pre-discretized finite elements and thus guarantee the continuum behavior of materials in the elastic stage. The yield surfaces, controlled by ECZM, are dynamically embedded by invoking the pre-inserted cohesive elements. The effectiveness and efficiency of the proposed approach are validated and tested by performing a suite of numerical experiments. Compared with ICZM-based FDEM, the proposed approach can correctly capture material deformation and reduce the computation cost. In contrast to the existing ECZM-based FDEM, the proposed approach can overcome the frequent and complex element topology updating. Finally, this work provides a novel perspective that fully inherits the advantages of both ICZM and ECZM, but circumvents their shortcomings, which guarantees a more efficient and effective simulation of brittle material evolution from continuum to discontinuum.

58 GEOSCIENCES↗