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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

Servo-aero-gravo-elastic (SAGE) scaling and its application to a 13-MW downwind turbine

Reduced scale wind turbines can be extremely cost-effective to test new rotor concepts since prototype costs are heavily dependent on the rotor diameter. Ideally, the scaled model would have the same non-dimensional deflections, dynamics, and control behavior as the full-scale model. This would provide a high-fidelity demonstration of the full-scale performance, which is ideal if the full-scale turbine has significant aeroelastic interactions. To this end, servo-aero-gravo-elastic (SAGE) scaling is developed and applied to a 13-MW turbine that is scaled to a 20% scale model. The scaling preserves the tip-speed ratio, the rotor speed normalized by the flapping frequency, and the tip deflections normalized by the blade length. In addition, the controller employs the same control structure (gain-scheduled pitch control and variable speed torque control) and is scaled dynamically (e.g., matching non-dimensional time constant of the pitch angle, etc.). Furthermore, the thrust, gravity, and centrifugal moments are scaled such that the load angles are preserved as a function of a non-dimensional wind speed. However, the environmental scaling must consider differences in Reynolds number (since this parameter cannot be held constant) and subsequent changes in the axial induction factor. While the presented results showcase these differences during operational conditions, the non-dimensional tip deflections remain comparable through all wind speed ranges, indicating the viability of the SAGE scaling method in matching full-scale aeroelastic responses.

Kaminski, Meghan (ORCID:0000000322334784)↗
DOE OSTI2022

Invertible Neural Networks for Airfoil Design

We report the airfoil design problem, in which an engineer seeks a shape with desired performance characteristics, is fundamental to aerodynamics. Design workflows traditionally rely on iterative optimization methods using low-fidelity integral boundary-layer methods as higher-fidelity adjoint-based computational fluid dynamics methods are computationally expensive. Surrogate-based approaches can accelerate the design process but still rely on some iterative inverse design procedure. In this work, we leverage emerging invertible neural network (INN) tools to enable the rapid inverse design of airfoil shapes for wind turbines. INNs are specialized deep-learning models with well-defined inverse mappings. When trained appropriately, INN surrogate models are capable of forward prediction of aerodynamic and structural quantities for a given airfoil shape as well as inverse recovery of airfoil shapes with specified aerodynamic and structural characteristics. The INN approach offers a roughly 100 times speed-up compared to adjoint-based methods for inverse design. We demonstrate the INN tool for inverse design on three test cases of 100 airfoils each that satisfy the performance characteristics close to those of airfoils used in wind-turbine blades. All generated shapes satisfy the desired aerodynamic characteristics, demonstrating the success of the INN approach for inverse design of airfoils.

17 WIND ENERGY↗
DOE OSTI2019

Manufacturing of a Metal Matrix Composite Coating on a Polymer Matrix Composite Through Cold Gas Dynamic Spray Technique

In this work, the manufacturing through cold gas dynamic spray (cold spray or CS) of metallic composite coatings of Al-Al{sub 2}O{sub 3} on organic composite substrates with thermoplastic PLA matrix and hemp fibers was studied. Alumina powders, with a mean diameter of 50 μm, were used blended with aluminum powders in three different weight concentration percentages (0, 15, 20, and 45%) as feedstock material in order to highlight and discuss the variations of the coating surface properties depending on the alumina percentage. The coatings were produced by using a low-pressure cold spray equipment. A detailed experimental campaign, including microstructural observations and confocal microscopy, was carried out to study the structure of the coatings. Moreover, the tribological behavior of the coatings was studied through both scratch test and pin-on-disk test. The experiments showed that a small addition of alumina improves the compactness of the coating and its resistance to scratch and wear behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2023

DUET - Distribution of Understory using Elliptical Transport: A mechanistic model of leaf litter and herbaceous spatial distribution based on tree canopy structure

Heterogeneity in surface fuels produced by overstory trees and understory vegetation is a major driver of fire behavior and ecosystem dynamics. Previous attempts at predicting tree leaf and needle litter accumulation over time have been constrained in scope to probabilistic models that consider a limited number of key factors influencing tree litter dispersal patterns and decomposition processes. Here we present a mechanistic model for estimating variation in surface fuels called the Distribution of Understory using Elliptical Transport (DUET). DUET uses a pre-generated voxelated canopy array and distributes the leaf litter from the trees in an elliptical shape based on tree species characteristics, wind data, and location-specific features. DUET then calculates grass growth between trees and around the litter patterns and sets moisture levels based on relative humidity. In this study, we use DUET to simulate spatial distributions of leaf litter and illustrate model sensitivity to several parameters, providing inputs for testing fuel spatial heterogeneity impacts on fire behavior using process-based models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Improved Treatment of 1–4 Interactions in Force Fields for Molecular Dynamics Simulations

Traditional force fields commonly use a combination of bonded torsional terms and empirically scaled nonbonded interactions to capture 1-4 energies and forces of atoms separated by three bonds in a molecule. While this approach can yield accurate torsional energy barriers, it often leads to inaccurate forces and erroneous geometries and creates an interdependence between dihedral terms and nonbonded interactions, complicating parametrization and reducing transferability. Here, in this paper, we demonstrate that 1-4 interactions can be accurately modeled using only bonded coupling terms, eliminating the need for arbitrarily scaled nonbonded interactions altogether. Furthermore, by leveraging the automated parametrization capabilities of the Q-Force toolkit, we efficiently determine the necessary coupling terms without the need for manual adjustment. Our approach is first validated on a range of small molecule systems, encompassing both flexible and rigid structures, and shows a significant improvement in force field accuracy, obtaining subkcal/mol mean absolute error for every molecule tested. We further extend the bonded-only model for 1-4 interactions to Amber ff14sb, CHARMM36, and OPLS-AA force fields to reproduce ab initio gas and implicit solvent ϕ,ψ surfaces of alanine dipeptide.

Abdullah, Aalim S. [University of California, Berk↗
DOE OSTI2023

Optical control of ultrafast structural dynamics in a fluorescent protein

The photoisomerization reaction of a fluorescent protein chromophore occurs on the ultrafast timescale. The structural dynamics that result from femtosecond optical excitation have contributions from vibrational and electronic processes and from reaction dynamics that involve the crossing through a conical intersection. The creation and progression of the ultrafast structural dynamics strongly depends on optical and molecular parameters. When using X-ray crystallography as a probe of ultrafast dynamics, the origin of the observed nuclear motions is not known. Now, high-resolution pump–probe X-ray crystallography reveals complex sub-ångström, ultrafast motions and hydrogen-bonding rearrangements in the active site of a fluorescent protein. However, we demonstrate that the measured motions are not part of the photoisomerization reaction but instead arise from impulsively driven coherent vibrational processes in the electronic ground state. A coherent-control experiment using a two-colour and two-pulse optical excitation strongly amplifies the X-ray crystallographic difference density, while it fully depletes the photoisomerization process. A coherent control mechanism was tested and confirmed the wave packets assignment.

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

Verification methods for drift–diffusion reaction models for plasma simulations

Abstract Compared to other computational physics areas such as codes for general computational fluid dynamics, the documentation of verification methods for plasma fluid codes remains under developed. Current analytical solutions for plasma are often highly limited in terms of testing highly coupled physics, due to the harsh assumptions needed to derive even simple plasma equations. This work highlights these limitations, suggesting the method of manufactured solutions (MMSs) as a potential option for future verification efforts. To demonstrate the flexibility of MMS in verifying these highly coupled systems, the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework was utilized. Thanks to the MOOSE framework’s robustness and modularity, as well as to its physics module capabilities and ecosystem applications (i.e. Zapdos and the chemical reaction network) developed for plasma physics modeling and simulation, this report lays the groundwork for a structured method of conducting plasma fluid code verification.

DeChant, Corey (ORCID:0000000293779732)↗
DOE OSTI2025

Vertical canopy gradients of respiration drive plant carbon budgets and leaf area index

Despite its importance for determining global carbon fluxes, leaf respiration remains poorly constrained in land surface models (LSMs). We tested the sensitivity of the Energy Exascale Earth System Model Land Model – Functionally Assembled Terrestrial Ecosystem Simulator (ELM-FATES) to variation in the canopy gradients of leaf maintenance respiration (R dark ). We ran global and point simulations varying the canopy gradient of R dark to explore the impacts on forest structure, composition, and carbon cycling. In global simulations, steeper canopy gradients of R dark lead to increased understory survival and leaf biomass. Leaf area index (LAI) increased up to 77% in tropical regions compared with the default parameterization, improving alignment with remotely sensed benchmarks. Global vegetation carbon varied from 308 Pg C to 449 Pg C across the ensemble. In tropical forest simulations, steeper gradients of R dark had a large impact on successional dynamics. Results show the importance of canopy gradients in leaf traits and fluxes for determining plant carbon budgets and emergent ecosystem properties such as competitive dynamics, LAI, and vegetation carbon. The high-model sensitivity to canopy gradients in R dark highlights the need for more observations of how leaf traits and fluxes vary along light micro-environments to inform critical dynamics in LSMs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Final Technical Report for DE-SC0008135, Modulating Thermal Transport Phenomena in Nanostructures via Elastic Strain at Extreme Limits of Strength

Nanoscale materials fabricated with nearly pristine crystal structure are often endowed with ultra-strength behavior, where material failure occurs at a significant fraction of its ideal limit. The thermal conductivity exhibited by such materials is also uniquely affected by the high surface-to-volume ratio at the nanoscale. The juxtaposition of the vastly increased dynamic range of elastic strain available in ultra-strength nanomaterials and altered thermal transport shows promise for tunable thermal properties. This project aims to exploit these properties of high-strength nanostructures to elucidate the coupling between large mechanical strains and thermal conductivity (both electron and phonon) leading to better understanding and control of the thermal performance in these materials. Unique fabrication methods to produce nanosized quasi-defect free single crystals and modern nanomechanical testing will be used to identify the size-dependent dynamic range of elastic strain and understand deformation mechanisms near the ideal limit. Identifying and quantifying thermal transport phenomena as a function of mechanical strain in these nanostructures will open the door to using elastic strain engineering in high-strength nanomaterials to tune thermal transport. The results of these investigations will be used to improve the performance, efficiency, and versatility of advanced thermal management and energy conversion devices with tunable response.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Quantifying Subsurface Biogeochemical Variability in a High Altitude Watershed During Winter Isolation

Shallow subsurface microbial and geochemical processes in watersheds are dynamic and responsive to seasonal and long-term environmental change. At high elevation or latitude, such changes may occur during winter months when normal sampling is impossible, impractical, unsafe, or may actually alter the processes being studied. Yet accurate modeling of biogeochemical parameters in these environments requires sampling that can capture events and persistent cold climate trends. Gaps in current models exist where data have not been collected during extended snow- or ice-covered periods. The goals of our exploratory study were to examine the biogeochemical processes occurring in a well-studied high altitude watershed during winter when the system is largely inaccessible. We tested the hypothesis that during snow cover in the East River (ER) watershed, episodic excursions of microbial community structure and biogeochemical processes and concentrations fluctuated from values extrapolated from pre- and post-snow time periods. Through our research we identified previously unresolved subsurface-surface transport phenomena and a heretofore under-appreciated methane cycle occurring during wintertime months, thus providing valuable insight into watershed processes. Our research showed that autonomous sampling techniques, first used in marine systems, is extremely versatile and can be adapted for use in harsh continental settings to examine temporal changes in freshwater chemistry and microbial community assemblage. Further, we found that by sampling during periods when systems are hard to access, will lend insight and better accountability for global budgets for methane, an important greenhouse gas. By strengthening our knowledge of wintertime biogeochemical relationships in high altitude watersheds our research leads to more complete descriptions of cryptic processes and ultimately will inform and improve the reactive transport models that simulate processes in these systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Data‐driven variational method for discrepancy modeling: Dynamics with small‐strain nonlinear elasticity and viscoelasticity

Abstract The effective inclusion of a priori knowledge when embedding known data in physics‐based models of dynamical systems can ensure that the reconstructed model respects physical principles, while simultaneously improving the accuracy of the solution in the previously unseen regions of state space. This paper presents a physics‐constrained data‐driven discrepancy modeling method that variationally embeds known data in the modeling framework. The hierarchical structure of the method yields fine scale variational equations that facilitate the derivation of residuals which are comprised of the first‐principles theory and sensor‐based data from the dynamical system. The embedding of the sensor data via residual terms leads to discrepancy‐informed closure models that yield a method which is driven not only by boundary and initial conditions, but also by measurements that are taken at only a few observation points in the target system. Specifically, the data‐embedding term serves as residual‐based least‐squares loss function, thus retaining variational consistency. Another important relation arises from the interpretation of the stabilization tensor as a kernel function, thereby incorporating a priori knowledge of the problem and adding computational intelligence to the modeling framework. Numerical test cases show that when known data is taken into account, the data driven variational (DDV) method can correctly predict the system response in the presence of several types of discrepancies. Specifically, the damped solution and correct energy time histories are recovered by including known data in the undamped situation. Morlet wavelet analyses reveal that the surrogate problem with embedded data recovers the fundamental frequency band of the target system. The enhanced stability and accuracy of the DDV method is manifested via reconstructed displacement and velocity fields that yield time histories of strain and kinetic energies which match the target systems. The proposed DDV method also serves as a procedure for restoring eigenvalues and eigenvectors of a deficient dynamical system when known data is taken into account, as shown in the numerical test cases presented here.

Masud, Arif↗
DOE OSTI2025

Deep reinforcement learning control for co-optimizing energy consumption, thermal comfort, and indoor air quality in an office building

With the recent demand for decarbonization and energy efficiency, advanced HVAC control using Deep Reinforcement Learning (DRL) becomes a promising solution. Due to its flexible structures, DRL has been successful in energy reduction for many HVAC systems. However, only a few researches applied DRL agents to manage the entire central HVAC system and control multiple components in both the water loop and the air loop, owing to its complex system structures. Moreover, those researches have not extended their applications by incorporating the indoor air quality, especially both CO2 and PM2.5concentrations, on top of energy saving and thermal comfort, as achieving those objectives simultaneously can cause multiple control conflicts. What's more, DRL agents are usually trained on the simulation environment before deployment, so another challenge is to develop an accurate but relatively simple simulator. Therefore, we propose a DRL algorithm for a central HVAC system to co-optimize energy consumption, thermal comfort, indoor CO2 level, and indoor PM2.5 level in an office building. To train the controller, we also developed a hybrid simulator that decoupled the complex system into multiple simulation models, which are calibrated separately using laboratory test data. The hybrid simulator combined the dynamics of the HVAC system, the building envelope, as well as moisture, CO2, and particulate matter transfer. Three control algorithms (rule-based, MPC, and DRL) are developed, and their performances are evaluated on the hybrid simulator environment with a realistic scenario (i.e., with stochastic noises). The test results showed that, the DRL controller can save 21.4 % of energy compared to a rule-based controller, and has improved thermal comfort, reduced indoor CO2 concentration. The MPC controller showed an 18.6 % energy saving compared to the DRL controller, mainly due to savings from comfort and indoor air quality boundary violations caused by unmeasured disturbances, and it also highlights computational challenges in real-time control due to non-linear optimization. Finally, we provide the practical considerations for designing and implementing the DRL and MPC controllers based on their respective pros and cons.

Guo, Fangzhou↗
DOE OSTI2015

Rational design of a carboxylic esterase Rh Est1 based on computational analysis of substrate binding

A new carboxylic esterase RhEst1 which catalyzes the hydrolysis of (S)-(+)-2,2-dimethylcyclopropanecarboxylate (S-DmCpCe), the key chiral building block of cilastatin, was identified and subsequently crystallized in our previous work. Mutant Rh Est 1A147I/V148F/G254A was found to show a 5-fold increase in the catalytic activity. In this work, molecular dynamic simulations were performed to elucidate the molecular determinant of the enzyme activity. Our simulations show that the substrate binds much more strongly in the A147I/V148F/G254A mutant than in wild type, with more hydrogen bonds formed between the substrate and the catalytic triad and the oxyanion hole. The OH group of the catalytic residue Ser101 in the mutant is better positioned to initiate the nucleophilic attack on S-DmCpCe. Interestingly, the "170-179" loop which is involved in shaping the catalytic sites and facilitating the product release shows remarkable dynamic differences in the two systems. Based on the simulation results, six residues were identified as potential "hot-spots" for further experimental testing. Consequently, the G126S and R133L mutants show higher catalytic efficiency as compared with the wild type. In conclusion, this work provides molecular-level insights into the substrate binding mechanism of carboxylic esterase Rh Est1, facilitating future experimental efforts toward developing more efficient Rh Est1 variants for industrial applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

An Open-Source Tool for Automated Power Grid Stress Level Prediction at Balancing Authorities

The behavior of modern power systems becomes more stochastic and dynamic, due to the increased penetration of variable generation, demand response, new power market structure, extreme weather conditions, contingencies, and unexpected events. It is critically important to predict potential system operational issues so that grid planners and operators can take preventive actions to mitigate the impact, e.g., lack of operational reserves. In this paper, an innovative software tool is presented to assist power grid operators in a balancing authority (BA) to predict the grid stress level over the next operating day. It periodically collects necessary information from public domain such as weather forecasts, electricity demand, and automatically estimates the stress levels on a daily basis. Advanced Neural Network and regression tree algorithms are developed as the prediction engines to achieve this goal. The tool has been tested on a few key balancing authorities that successfully predicates the growing system peak load and increased stress levels under extreme heat waves in the United States of America.

Berscheid, Alan P.↗
DOE OSTI2022

OC6 Phase Ia Definition Document: Validation of Nonlinear Hydrodynamic Loading on the DeepCwind Semisubmersible

The objective of OC6 Phase I is to investigate the persistent under-prediction (about 20% on average) of the structural loads in the OC5-DeepCwind floating semisubmersible wind system, as observed within the previous OC5 Phase II project. The OC5 results indicate that much of this under-prediction originates from the low-frequency response of the system at the surge/pitch natural frequencies, resulting from nonlinear hydrodynamic loading and/or overprediction of hydrodynamic damping. This work package therefore focuses on better understanding this low-frequency response behavior, the applicability of computational-fluid-dynamics (CFD) models and engineering-level hydrodynamic models for predicting the nonlinear hydrodynamic loading, and the level of uncertainty in the measured response characteristics. To address these objectives, two experimental campaigns were developed and performed in 2017 and 2018 by a sub-group of the OC5 project: Floating test campaign: A simplified OC5-DeepCwind semisubmersible model (no turbine, rigid tower) was moored to three taut-spring lines and subjected to motion decay and wave tests (regular and irregular waves), during which motion amplitudes of the model were measured (see Figure 1a). The purpose of this test campaign was to examine the repeatability of the low-frequency response of the system to wave excitation and determine the level of uncertainty in the response behavior.Constrained test campaign: The same model, but without the tower present, was fixed to a carriage and subjected to towing, forced oscillation and wave tests (regular and irregular waves), during which forces exerted upon the model were measured (see Figure 1b). The purpose of this test campaign was to examine the hydrodynamic loading contributions by separating the wave excitation and radiation forces. Both model test campaigns were performed in the concept basin of the Maritime Research Institute Netherlands (MARIN) in Wageningen, the Netherlands and the first test was within the framework of the MARINET2 project. Both experimental campaigns were examined within Phase Ia of the OC6 project. The purpose of this report is to supply the needed information to participants of the OC6 project to build and simulate the experimental campaigns.

17 WIND ENERGY↗
DOE OSTI2016

Numerical investigation of the flow inside the helical coil steam generator test section - 18589

A Helical Coil Steam Generator (HCSG) is one type of heat exchangers used for the purpose of increasing the efficiency of heat transfer with low pressure drop in recent design of high temperature reactors (HTRs). To investigate the fluid phenomena around the coil bundle of HCSG, the experimental facility had been designed and computational fluid dynamics (CFD) simulations had been conducted to compare the results of experimental ones. In this study, various types of turbulent models in the commercial CFD code (STAR-CCM+) were tested with various grid sizes on the target Reynolds number, Re{sub D} = 163,000, to enhance the validity of the simulation result. The computational domain used to run the CFD simulation had the same geometry of the experimental test section which had the half-coil shaped ribs on each curved channel wall. The working fluid properties employed in the simulation was scaled to the target Reynolds number at room temperature. The averaged static pressure drop induced by coil bundle inside the test section was obtained between the inlet and outlet of the test section. In addition, flow structures inside the flow channel were examined going through the velocity field distribution around the coil and the effect of vortical structures in the flow channel. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2019

Social-ecological network analysis for sustainability sciences: a systematic review and innovative research agenda for the future

Social-ecological network (SEN) concepts and tools are increasingly used in human-environment and sustainability sciences. We take stock of this budding research area to further show the strength of SEN analysis for complex human-environment settings, identify future synergies between SEN and wider human-environment research, and provide guidance about when to use different kinds of SEN approaches and models. We characterize SEN research along a spectrum specifying the degree of explicit network representation of system components and dynamics. We then systematically review one end of this spectrum, what we term ‘fully articulated SEN’ studies, which specifically model unique social and ecological units and relationships. Results show a larger number of papers focus on methodological advancement and applied ends. While there has been some development and testing of theories, this remains an area for future work and would help develop SENs as a unique field of research, not just a method. Authors have studied diverse systems, while mainly focusing on the problem of social-ecological fit alongside a scattering of other topics. There is strong potential, however, to engage other issues central to human-environment studies. Analyzing the simultaneous effects of multiple social, environmental, and coupled processes, change over time, and linking network structures to outcomes are also areas for future advancement. This review provides a comprehensive assessment of (fully articulated) SEN research, a necessary step that can help scholars develop comparable cases and fill research gaps.

environmental governance↗
DOE OSTI2022

Data-driven linear time advance operators for the acceleration of plasma physics simulation

In this study, we demonstrate the application of data-driven linear operator construction for time advance with a goal of accelerating plasma physics simulation. We apply dynamic mode decomposition (DMD) to data produced by the nonlinear SOLPS-ITER (Scrape-off Layer Plasma Simulator - International Thermonuclear Experimental Reactor) plasma boundary code suite in order to estimate a series of linear operators and monitor their predictive accuracy via online error analysis. We find that this approach defines when these dynamics can be represented by a sequence of approximate linear operators and is essential for providing consistent projections when compared to an unconstrained application. For linear diffusion and advection–diffusion fluid test problems, we construct and apply operators within explicit and implicit time advance schemes, demonstrating that stability can be robustly guaranteed in each case. We further investigate the use of the linear time advance operators within several integration methods including forward Euler, backward Euler, and the matrix exponential. The application of this method to simulation data from SOLPS-ITER, with varying levels of Markov chain Monte Carlo numerical noise, shows that constrained DMD operators yield a capability to identify, extract, and integrate a (slow) subset of the present timescales. Example applications show that for projected speedup factors of [Formula: see text], and [Formula: see text], a mean relative error of 3%, 5%, and 8% and maximum relative error less than 20% are achievable, which appears acceptable for typical SOLPS-ITER steady-state simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗