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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A novel approach to build algal consortia for sustainable biomass production

In the last decade, microalgae have reemerged as a feedstock for biofuels and a diverse suite of bioproducts. Yet, considerable challenges must be overcome before algal biofuels and bioproducts become technoeconomically viable. At present, single algal strain selected for particular phenotypic traits, such as maximum specific growth rate or lipid content, are commonly scaled for cultivation in open, outdoor raceway ponds due to the low capital costs of these systems. Although this monoculture approach may maximize the production of end products, monocultures are particularly susceptible to crashes associated with environmental and biological variability. An approach that has been proposed to generate more productive and stable microalgal crops is the use of eco-engineered communities, or consortia. Yet, attempts to construct productive consortia have not been consistently successful. We argue that failures stem from the lack of an eco-engineering approach to design species combinations. Here, we used an in silico method to build consortia before testing their performance against monocultures. Focusing on consortia of Nannochloropsis and Microchloropsis, we measured growth of strains along gradients of light, temperature, and salinity and used a functional dispersion approach to generate over 8000 functionally-diverse consortia combinations. We tested the 50 most functionally diverse consortia in a laboratory experiment and found that consortia overwhelmingly outperformed monocultures. Indeed, overyielding (OY) and a positive net biodiversity effect (NBE) was found respetively in 8%-86% and 88-92% of consortia combinations over the different experimental phases. To our knowledge, this is the first application of an in silico approach to design functionally diverse consortia before laboratory and field testing. Furthermore, our results highlight the importance of employing a functional diversity approach for consortia design.

09 BIOMASS FUELS↗
DOE OSTI2020

A Machine Learning-based Approach to Predict the Aggregate Flexibility of HVAC Systems

Use of renewable energy resources can play a significant role in mitigation of climate change since they reduce the dependency on fossil fuels. However, high penetration of renewable energy resources into the power grid may cause some reliability problems. Ancillary services are key to realize the potential of renewable resources without disturbing the grid reliability. In recent years, many control approaches focused on exploiting the aggregate flexible heating, ventilation, and air-conditioning (HVAC) loads of buildings. However, there are still research gaps in assessing in advance the resulting indoor temperature responses for given total power and weather profiles before implementing the controls in the buildings. Such an assessment is critical to guarantee satisfying both the grid related objective (reliability) and the building related objective (occupants' comfort). Towards addressing this research gap, this paper presents a machine-learning approach for predicting the ratings of indoor temperature responses. In this approach, a prediction model was developed using a dataset generated by a set of model-free control simulations. The developed model achieved high prediction accuracy and showed the potential of the proposed approach.

Amasyali, Kadir↗
DOE OSTI2021

Exploring the potential benefits of high-efficiency dual-fuel combustion on a heavy-duty multi-cylinder engine for SuperTruck I

In support of the Daimler SuperTruck I team’s 55% brake thermal efficiency (BTE) pathway goal, researchers at Oak Ridge National Laboratory performed an experimental investigation of the potential efficiency and emissions benefits of dual-fuel advanced combustion approaches on a modified heavy-duty 15-L Detroit™ DD15 engine. For this work, a natural gas port fuel injection system with an independent injection control for each cylinder was added to the DD15 engine. For the dual-fuel strategies investigated, 65%–90% of the total fuel energy was supplied through the added port fuel injection natural gas (NG) fueling system. The remaining fuel energy was supplied by one or more direct injections of diesel fuel using the production high pressure diesel fueling system. The production DD15 air handling system and combustion geometry were unmodified for this study. Efficiency and emissions with dual-fuel strategies including both low temperature combustion (LTC) and non-LTC approaches such as dual fuel direct-injection were investigated along with control authority over combustion phasing. Parametric studies of dual-fuel NG/diesel advanced combustion were conducted in order to experimentally investigate the potential of high-efficiency, dual-fuel combustion strategies to improve BTE in a multi-cylinder engine, understand the potential reductions in engine-out emissions, and characterize the range of combustion phasing controllability. Characterization of mode transitions from mixing-controlled diesel pilot ignition to kinetically controlled ignition is presented. Key findings from this study included a reproducible demonstration of BTE approaching 48% at up to a 13-bar brake mean effective pressure with significant reductions in engine-out NOx and soot emissions. Additional results from investigating load transients in dual-fuel mode and initial characterization of particle size distribution during dual-fuel operation are presented.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Demonstrate the Out-of-Pile Performance of a Real-Time Measurement System to Measure Thermal Conductivity Based on Photo Thermal Radiometry

SUMMARY The goal of this project is to develop a fiber-based instrument to perform in-reactor thermal conductivity measurements of fuels and materials. This instrument is based on photothermal radiometry (PTR) and involves heating a sample locally and measuring the induced temperature gradient by collecting blackbody radiation [2]. Thermal conductivity of the sample is extracted by comparing experimental results with a continuum-based model [3,4]. As a laser-based technique, PTR is a non-contact measurement technique that can be performed remotely and non-destructively. In addition, it has several advantages over other photothermal techniques, making it an ideal approach for in-situ measurement of thermal conductivity of nuclear fuels. Because blackbody radiation increases with emissivity and temperature, the PTR technique works well with unprepared surfaces, and measurement accuracy increases with temperature. Moreover, this approach is capable of measuring samples with irregular or poorly defined boundary conditions, which is a common situation for friable spent fuels.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Novel Superionic Na Conductors for Solid‐State Na batteries: Materials and Interface

This project aims to advance the fundamental understanding on how the cation or anion doping influence on the crystal structure, phase stability and ion transport of novel Na superionic conductors. Specifically, we focus on the Na 3-x A x SbS 4-y X y family solid electrolytes (A: cation dopant, X: anion dopant), a class of promising materials for all-solid-state sodium batteries owing to their high ionic conductivity and compositional tunability. By integrating materials synthesis, neutron scattering, and electrochemical characterization and theoretical simulations, this project sees to elucidate 1) how does the synthesis approaches (solid-state reaction, low-temperature reaction) on the phase purity and doping process; 2) how chemical doping (A: cation dopants such as Sn; X: anion dopants such as Se, F) modulate lattice dynamics and Na+ migration as well as interface stability toward Na metal. Specifically, this project’s proposed technology involves three objectives: (1) synthesize novel Na-ion conductors (anion or cation doped Na 3 SbS 4 ) with high phase purity and examine their structural features (e.g., phase stability); (2) understand Na-ion conductive properties of new Na-ion conductors through a combination of experimental characterizations and theoretical simulations; (3) investigate the interface stability of novel Na superionic conductors with electrode materials in solid-state Na batteries. These objectives will be achieved through combined experimental synthesis/characterizations and computational approaches. The obtained knowledge will provide a comprehensive synthesis-structure-property correlation, enabling the rational design of novel solid-state conductors with excellent conductivity and stability.

25 ENERGY STORAGE↗
DOE OSTI2024

Exchangeable Liquid Crystalline Elastomers: Enabling Rapid Processing and Enhanced Actuation Stability through On-Demand Deactivation

Exchangeable liquid crystalline elastomers (xLCEs) bearing dynamic covalent bonds are promising candidates for soft actuators due to their unique capability to adjust both network structure and liquid crystalline (LC) alignment after polymerization. While current xLCEs with low exchange temperatures are convenient for processing, they suffer from issues such as creep and loss of LC alignment during repeated thermal actuation. Herein, we present an effective solution using dynamic anhydride chemistry within a thiol-ene-based xLCE. This approach enables a catalyst-free, low-temperature bond exchange of the xLCE after polymerization, allowing for the adjustment of LC orientation under mild conditions. More importantly, it enables on-demand deactivation of the bond exchange via anhydride hydrolysis, effectively eliminating creep and enhancing actuation stability for over 100 cycles. Furthermore, the hydrolysis process results in the formation of carboxylic acid groups, which can be converted into carboxylates via alkali treatment, thereby providing the xLCE with humidity responsiveness. Finally, these findings highlight the use of dynamic anhydride bonds in the fabrication and optimization of xLCEs with enhanced durability and functionality, which is expected to facilitate significant advancements in their applications in soft actuators and robotics.

36 MATERIALS SCIENCE↗
DOE OSTI2022

High Throughput Computational Framework of Materials Properties for Extreme Environments

This project aims to establish a framework capable of efficiently predicting the properties of structural materials for service in harsh environments over a wide range of temperatures and over long periods of time. The approach is to develop and integrate high throughput first-principles calculations in combination with machine learning (ML) methods, perform high throughput CALPHAD (calculations of phase diagrams) modeling, and carry out finite element method (FEM) simulations. Relevant to high temperature service in fossil power system, nickel-based superalloys such as Inconel 740 and Haynes 282 as well as the associated (Ni-Cr-Co)-Al-C-Fe-Mn-Mo-Nb-Si-Ti system, were investigated. The present framework was built on the concept of phase-based property data, in which properties of individual phases are modeled as a function of internal and external independent variables. This project established an open-source infrastructure with the following capabilities: (1) High throughput implementation of first-principles calculations at finite temperatures and variable compositions using both accurate phonon calculations and the efficient Debye model for thermodynamic properties, elastic constants, diffusion coefficients, vacancy formation, stacking and twin faults, and dislocation mobility; i.e., using the developed code DFTTK; (2) Machine learning capabilities to predict the above properties so that the number of first-principles calculations can be significantly reduced; e.g., using the developed code SIPFENN; (3) High throughput CALPHAD modeling of the above properties as a function of temperature and composition using our unique capability based on ESPEI and PyCalphad; (4) New capabilities to predict the stress-strain behavior of individual phases; and (5) New models for tensile strength prediction in common FEM software with the crystal plasticity finite element simulations (CPFEM).

, Ni-based superalloys↗
DOE OSTI2021

Electrical conductivity of Sn at high pressure and temperature

To date, temperature and conductivity have many outstanding implications in extreme environments but are yet to be fully understood under high pressure and temperature dynamic conditions. Here, we introduce an approach to provide high quality electrical conductivity results under dynamic loading conditions. Emphasis is given to address the skin depth effect's influence in a dynamic loading experiment by using thin films. The thin film samples in this study were at least 100 times thinner than previous samples in dynamic electrical conductivity experiments, increasing the current density to its full potential across the sample's entire cross section. Consideration of the skin depth accounts for at minimum a 4× scaling factor to the final electrical conductivity result that has been neglected in previous dynamic electrical conductivity studies. We also obtained improved signal-to-noise ratio with custom diagnostics optimized for better electrical impedance matching. Furthermore, these considerations were applied to Sn to assess electrical conductivity at elevated pressure and temperature. The high signal-to-noise ratio with reduced skin depth influence results in Sn allowing observation of the conductivity changes related to solid-to-solid and solid-to-liquid phase transitions. Additionally, we calculate the Sn thermal conductivity using the Wiedemann-Franz law for our experiments and compare against thermal transport dependent temperature measurements from previous work.

36 MATERIALS SCIENCE↗
DOE OSTI2024

T -matrix analysis of static Wilson line correlators from lattice QCD at finite temperature

Here, we utilize a previously constructed thermodynamic T -matrix approach to the quark-gluon plasma (QGP) to derive constaints on its input by using results on Wilson line correlators (WLCs) of a static quark-antiquark pair from 2 + 1-flavor lattice-QCD (lQCD) computations with realistic pion mass. The self-consistent T -matrix results, which include previous constraints from the lQCD equation of state in the light-parton sector, can describe the lQCD data for WLCs fairly well once refinements of its driving kernel are applied. In particular, the input potential requires less screening than what has been inferred from previous T-matrix analyses. Pertinent predictions for the spectral and transport properties of the QGP are discussed, including the spatial diffusion coefficient for heavy quarks; the latter turns out to have a rather weak temperature dependence, in approximate agreement with recent unquenched lQCD results.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Using Ensemble Data Assimilation to Estimate Transient Hydrologic Exchange Flow Under Highly Dynamic Flow Conditions

Abstract Quantifying dynamic hydrologic exchange flows (HEFs) within river corridors that experience high‐frequency flow variations caused by dam regulations is important for understanding the biogeochemical processes at the river water and groundwater interfaces. Heat has been widely used as a tracer to infer steady‐state flow velocities through analytical solutions of heat transport defined by the diurnal temperature signals. Under sub‐daily dynamic flow conditions, however, such analytical solutions are not applicable due to the violation of their fundamental assumptions. In this study, we developed a data assimilation‐based approach to estimate the sub‐daily flux under highly dynamic flow conditions using multi‐depth temperature observations at a 5‐min resolution. If the hydraulic gradient is measured, Darcy's law was used to calculate the flux with permeability estimated from temperature responses below the riverbed. Otherwise, flux was estimated directly by assimilating multi‐depth temperature data at 1‐ or 2‐hr time intervals assuming one‐dimensional flow and heat transport governing equation. By comparing estimated fluxes with model‐generated synthetic truth, we demonstrated that both schemes have robust performance in estimating fluxes under highly dynamic flow conditions. This data assimilation‐based flux estimation method was able to capture the vertical sub‐daily fluxes using multi‐depth high‐resolution temperature data alone, even in the presence of multi‐dimensional flow. This approach has been successfully applied to real field temperature data collected at the Hanford site, which experiences highly dynamic HEFs. Our study shows the promise of adopting distributed 1‐D temperature monitoring to capture spatial and temporal exchange dynamics in river corridors at a watershed scale or beyond.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A Machine Learning Bias Correction on Large–Scale Environment of High–Impact Weather Systems in E3SM Atmosphere Model

Large–scale dynamical and thermodynamical processes are common environmental drivers of high–impact weather systems causing extreme weather events. However, such large–scale environmental conditions often display systematic biases in climate simulations, posing challenges to evaluating high–impact weather systems and extreme weather events. In this paper, a machine learning (ML) approach was employed to bias correct the large–scale wind, temperature, and humidity simulated by the atmospheric component of the Energy Exascale Earth System Model (E3SM) at ~1° resolution. The usefulness of the ML approach for extreme weather analysis was demonstrated with a focus on three high–impact weather systems, including tropical cyclones (TCs), extratropical cyclones (ETCs), and atmospheric rivers (ARs). We show that the ML model can effectively reduce climate bias in large–scale wind, temperature, and humidity while preserving their responses to imposed climate change perturbations. The bias correction is found to directly improve water vapor transport associated with ARs, and representations of thermodynamical flows associated with ETCs. When the bias–corrected large–scale winds are used to drive a synthetic TC track forecast model over the Atlantic basin, the resulting TC track density agrees better with that of the TC track model driven by observed winds. In addition, the ML model insignificantly interferes with the mean climate change signals of large–scale storm environments as well as the occurrence and intensity of three weather systems. This study suggests that the proposed ML approach can be used to improve the downscaling of extreme weather events by providing more realistic large–scale storm environments simulated by low–resolution climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Anisotropic thermal-conductivity degradation in the phase-field method accounting for crack directionality

Dynamic loads applied to metals may lead to brittle or ductile fracture depending on the imposed strain rates, material properties, and specimen geometry. With an increase in the applied velocity, brittle to ductile failure mode transition may also be observed. Furthermore, at high strain rates, ductile fracture may be preceded by shear bands, which are narrow bands of intense plastic deformation, typically accompanied by a significant rise in temperature. Reliable models are needed to predict the response of metals subject to dynamic loads. In particular, capturing the interplay between heat conduction and crack propagation using the phase-field fracture method is still an open research field. To accurately capture the heat transfer physics across crack surfaces, damage models degrading thermal-conductivity are necessary. While isotropic thermal-conductivity degradation models were proposed, they may lead to errors as they are indifferent to crack directionality. In our current work, a new anisotropic approach is proposed in which thermal-conductivity, which depends on the phase-field gradient, is degraded solely across the crack. Here, it is shown that this approach improves the near-field approximation of temperature and heat flux compared with isotropic degradation, when taking the discontinuous crack solutions as reference. Additionally, the anisotropic degradation approach is implemented in a unified dynamic fracture model and a couple of examples demonstrate the viability of this approach.

42 ENGINEERING↗
DOE OSTI2025

Predictive modeling of Néel temperature in austenitic alloys using CALPHAD and data analytics

The Néel temperature is a crucial yet often overlooked parameter in calculating the stacking fault energy (SFE) of austenitic alloys. Several empirical equations have been proposed to estimate the Néel temperature of austenitic alloys, which are then used to calculate the SFE and explain deformation mechanisms. However, these empirical equations, typically derived using linear regression algorithms, are often simplistic and may fail to capture the complex interactions among multiple alloying elements that influence the Néel temperature. Moreover, their applicability is usually limited to specific compositional ranges. In this study, we propose a CALPHAD based approach and develop a surrogate decision tree based regression model capable of capturing the interactions among multiple alloying elements to predict the Néel temperature. Predictions from both the CALPHAD approach and the regression model show close agreement with experimental measurements reported in the literature. In conclusion, the implications of accurate Néel temperature predictions on the calculated SFE and deformation mechanisms are also discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics

Process optimization of photovoltaic devices is a time-intensive, trial-and-error endeavor, which lacks full transparency of the underlying physics and relies on user-imposed constraints that may or may not lead to a global optimum. Herein, we demonstrate that embedding physics domain knowledge into a Bayesian network enables an optimization approach for gallium arsenide (GaAs) solar cells that identifies the root cause(s) of underperformance with layer-by-layer resolution and reveals alternative optimal process windows beyond traditional black-box optimization. Our Bayesian network approach links a key GaAs process variable (growth temperature) to material descriptors (bulk and interface properties, e.g., bulk lifetime, doping, and surface recombination) and device performance parameters (e.g., cell efficiency). For this purpose, we combine a Bayesian inference framework with a neural network surrogate device-physics model that is 100× faster than numerical solvers. With the trained surrogate model and only a small number of experimental samples, our approach reduces significantly the time-consuming intervention and characterization required by the experimentalist. As a demonstration of our method, in only five metal organic chemical vapor depositions, we identify a superior growth temperature profile for the window, bulk, and back surface field layer of a GaAs solar cell, without any secondary measurements, and demonstrate a 6.5% relative AM1.5G efficiency improvement above traditional grid search methods.

14 SOLAR ENERGY↗
DOE OSTI2021

Toward efficient elastocaloric systems: Predicting material thermal properties with high fidelity

A critical need to accurately model thermal behaviors of materials that exhibit strong elastocaloric effects, including predicting the effects themselves at varying stresses and temperatures, has been addressed using a simple and versatile physics-based approach. The key factor leading to the high precision is approximating the underlying elastic phase transition as a smooth modification of lattice entropy between coexisting phases. Once the phase transformation entropy is modeled to match experimentally measured strain as a function of temperature and applied stress, estimating the heat capacity, entropy, and isothermal entropy and adiabatic temperature changes in temperature-stress coordinates becomes straightforward. This approach provides insight into how thermal properties of elastocaloric materials vary through the transition based on strain measurements that are simple to perform and interpret. In addition to aiding in the rapid evaluation of new and existing elastocaloric materials, this advance is expected to prove invaluable for accurate heat transfer modeling aimed at designing efficient regenerative elastocaloric cooling devices.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Measuring the thermal and ionization state of the low- z IGM using likelihood free inference

ABSTRACT We present a new approach to measure the power-law temperature density relationship $T=T_0 (\rho/ \bar{\rho })^{\gamma -1}$ and the UV background photoionization rate $\Gamma _{{{{\rm H\, {\small I}}}}{}}$ of the intergalactic medium (IGM) based on the Voigt profile decomposition of the Ly α forest into a set of discrete absorption lines with Doppler parameter b and the neutral hydrogen column density $N_{\rm H\, {\small I}}$. Previous work demonstrated that the shape of the $b-N_{{{{\rm H\, {\small I}}}}{}}$ distribution is sensitive to the IGM thermal parameters T0 and γ, whereas our new inference algorithm also takes into account the normalization of the distribution, i.e. the line-density dN/dz, and we demonstrate that precise constraints can also be obtained on $\Gamma _{{{{\rm H\, {\small I}}}}{}}$. We use density-estimation likelihood-free inference (DELFI) to emulate the dependence of the $b-N_{{{{\rm H\, {\small I}}}}{}}$ distribution on IGM parameters trained on an ensemble of 624 nyx hydrodynamical simulations at z = 0.1, which we combine with a Gaussian process emulator of the normalization. To demonstrate the efficacy of this approach, we generate hundreds of realizations of realistic mock HST/COS data sets, each comprising 34 quasar sightlines, and forward model the noise and resolution to match the real data. We use this large ensemble of mocks to extensively test our inference and empirically demonstrate that our posterior distributions are robust. Our analysis shows that by applying our new approach to existing Ly α forest spectra at z ≃ 0.1, one can measure the thermal and ionization state of the IGM with very high precision ($\sigma _{\log T_0} \sim 0.08$ dex, σγ ∼ 0.06, and $\sigma _{\log \Gamma _{{{{\rm H\, {\small I}}}}{}}} \sim 0.07$ dex).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

A machine learning degradation model for electrochemical capacitors operated at high temperature

Electrochemical capacitors (ECs) have only recently been considered as an alternative power source for telemetry sensors of drilling equipment for geothermal or oil and gas exploration. The lifecycle analysis and modelling of ECs is underrepresented in literature in comparison to other storage devices e.g. Li-ion batteries. This paper investigates the degradation of ECs when cycled outside the manufacturer-specified operating temperature envelope and proposes a machine learning-based approach for modelling the degradation. Experimental results show that end of life, defined as a 30% decrease in capacitance, occurs at 1,000 cycles when the environmental temperature exceeds the maximum operating temperature by 30%. The life-cycle test data is then used as an input to a Gaussian process regression (GPR) algorithm to predict the capacitance fade trend. The GPR is validated on a total of nine commercial cells from two different manufacturers, achieving an average root mean squared percent error of less than 2% and a mean calibration score of 93% when referenced to a 95% confidence interval. The model can be utilized to determine the EC degradation rate at a range of operating temperature values.

42 ENGINEERING↗