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Results for “Transfer function design”

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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Fierro

FIERRO is a parallel C++ code designed to simulate fluid mechanics, heat transfer, and solid mechanics in two- and three dimensional space. FIERRO is written to run on homogeneous (CPU) and heterogeneous (CPU+GPU) high performance computing machines. Fierro can aid a) modeling and design efforts that have historically relied on commercial implicit and explicit finite element codes, b) numerical methods research, c) manufacturing research, and d) computer science research. The code contains diverse numerical methods to solve the governing physics equations for both quasi-static and dynamic problems. Mathematical optimization solvers are coupled to the numerical methods to research topology and shape optimization that has application to additive manufacturing, and to create novel numerical approaches. Phase-field methods with micromechanical solvers are provided to simulate microstructure formation and evolution in manufacturing processes. The micromechanical solvers can also help research efforts create continuum-scale constitutive models for solids, as a function of the microstructure, in situ in a calculation or in a stand-alone manner. No physical data exists within the code.

Morgan, Nathaniel↗
DOE OSTI2022

Effect of single atom Platinum (Pt) doping and facet dependent on the electronic structure and light absorption of Lanthanum Titanium Oxide (La 2 Ti 2 O 7 ): A Density Functional Theory study

Charge generation and separation are two key processes for semiconductor photocatalysis. Here, we use Pt as a single atom catalyst to systematically examine the facet-dependent electronic band structure and light absorption of the layered perovskite-type wide-gap semiconductor, lanthanum titanium oxide (La 2 Ti 2 O 7 , LTO) by means of density functional theory simulations. In this study, it is found that single Pt atom doping of different LTO surfaces (here, (100), (101) and (001)) can not only create states in the bandgap that would promote the formation of recombination centers, but also shift the optical absorption edge to the visible region. Interestingly, the Pt doping forms a heterojunction, with the valence band maximum consisting of Pt states and the conduction band minimum consisting of LTO states, respectively. To study the facet dependent surface activity, acetic acid (CH 3 COOH) was used as a model molecule to investigate the adsorption and charge transfer on the (101), (100) and (001) Pt-LTO surface facets. The results show that the (101) facet could enable stronger adsorption of CH 3 COOH by promoting more electron transfer during the interfacial interaction. Our theoretical findings aim to promote the design and optimization of the single atom catalysts (SACs) for photocatalytic applications and other broad catalysis systems.

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

Fierro Version 2.x

FIERRO is a parallel C++ code designed to simulate fluid mechanics, heat transfer, and solid mechanics in two- and three-dimensional space. FIERRO is written to run on homogeneous (CPU) and heterogeneous (CPU+GPU) high performance computing machines. Fierro can aid a) modeling and design efforts that have historically relied on commercial implicit and explicit finite element codes, b) numerical methods research, c) manufacturing research, and d) computer science research. The code contains diverse numerical methods to solve the governing physics equations for both quasi-static and dynamic problems. Mathematical optimization solvers are coupled to the numerical methods to research topology and shape optimization that has application to additive manufacturing, and to create novel numerical approaches. Phase-field methods with micromechanical solvers are provided to simulate microstructure formation and evolution in manufacturing processes. The micromechanical solvers can also help research efforts create continuum-scale constitutive models for solids, as a function of the microstructure, in situ in a calculation or in a stand-alone manner. No physical data exists within the code.

Morgan, Nathaniel↗
DOE OSTI2021

Interconnected cathode-electrolyte double-layer enabling continuous Li-ion conduction throughout solid-state Li-S battery

All-solid-state lithium (Li) batteries with high energy density are a promising solution for the next-generation energy storage systems in large-scale devices. To simultaneously overcome the challenges of poor ionic conduction of solid electrolytes and shuttling of active materials, here we introduce a functional electrolyte-cathode bilayer framework with interconnected LLAZO channels from the electrolyte into the cathode for advanced solid-state Li-S batteries. Differing from the traditional solid-state batteries with separated layer compositions, the introduced bilayer framework provides ultrafast and continuous ion/electron conduction. Instead of transferring Li+ across the polymer and garnet phases which involve huge interfacial resistance, Li+ is directly conducted through the LLAZO channels created continuously from the cathode layer to the solid electrolyte layer, significantly shortening the diffusion distance and facilitating the redox reaction of sulfur and sulfides. A stable cycle life is demonstrated in the prototype Li-S solid-state batteries assembled with the introduced LLAZO-LLAZO@CNF interconnected bilayer framework. High capacity is obtained at room temperature, indicating the superior electrochemical properties of the bilayer framework that result from the unique design of the interconnected LLAZO garnet phase.

25 ENERGY STORAGE↗
DOE OSTI2021

Smart Meters Enabling Voltage Monitoring and Control Functionalities: The Last-Mile Voltage Stability Issue

It is a demanding yet challenging task to design the next generation of smart meters. This work investigates the new voltage monitoring and control function for the next-generation smart meters, and identifies its added values for power system voltage stability issues. In terms of voltage monitoring, the risk benefit analysis for adding voltage magnitudes to smart meter measurements is presented, and the risk mitigation strategies along with the co-simulation validation via GridLAB-D and NS-3 are proposed, all providing insight into the new function evaluation. In terms of voltage control, a new voltage stability control scheme is developed, which uses voltage measurements from smart meters and utilizes the observability and controllability of distributed energy resources and controllable loads. Different from traditional voltage control schemes focusing on voltage regulation and power quality monitoring from the utility perspective, the proposed control scheme can achieve the maximum real power transfer margin at the end user side, enabling the voltage stability issues being solved at the grid edge, i.e. the last-mile segment. It is the first work that considers voltage monitoring and control collectively for smart meter investment. It can help power engineering community supplement smart meter applications and shape the next-generation smart meters.

42 ENGINEERING↗
DOE OSTI2020

A Sub-Threshold Low-Power Integrated Bandpass Filter for Highly-Integrated Spectrum Analyzers

Low-power analog filter banks provide frequency analysis with minimal power and space requirements, making them viable solutions for integrated remote audio- and vibration-sensing applications. Compared with their digital counterparts, analog filter banks are better suited to achieve the lower power consumption necessary for IoT applications, while maintaining high dynamic range and a wide operational frequency range. In this work, the design and implementation of a sub-threshold complementary metal-oxide semiconductor (CMOS) integrated low-power tunable analog filter channel for signal spectrum analysis and signal discrimination is presented. Project specifications required a programmable, high-order, monolithic bandpass filter channel with small chip area, low power consumption, high dynamic range, and wide operational frequency range. The 8th-order filter channel presented in this work provides an effective Q-factor of 4.5 and a minimum dynamic range (DR) of approximately 85 dB, while allowing for tuning across a range of center frequencies from 2 kHz to 100 kHz, with power consumption of a single 8th-order filter channel measured at 256 µW nominally at the highest center frequency. An integrated analog Gm-C filter topology was selected for this application. Functionally, the high-Q bandpass filter transfer function is implemented via four cascaded 2nd-order filter cells, resulting in a single 8th-order filter channel, fabricated in 130-nm 1.2-V CMOS technology, suitable for use in monolithic integrated spectral analysis (MISA1) applications.

Long, Gavin B.↗
DOE OSTI2022

Thermal conductivity of salvaged fused cast alumina used in the glass industry

Fused cast alumina (FCA) has been and continues to be used as a refractory material in energy intensive industries such as glass melting and chemical processing. In-service degradation due to high temperature exposure in harsh environment affects the designed furnace thermal profiles and energy consumption. Phase transformation may occur at the refractory hot face during glass melting altering the properties. Three FCA blocks recovered from industrial furnaces were investigated in this study. The as-received FCA consists primarily of a mixture of alpha (α) and beta (β) alumina that has a thermal conductivity value of 5–6 W/mK. The Hot Disk method was used to obtain thermal conductivity directly on the refractory blocks. At the hot face, a transformation from β to α alumina occurred and was confirmed by an X-ray diffraction study. Thermal conductivity measurements as a function of position also showed a clear transition from β to α alumina at both ends of a complete block with no voids. Thermal conductivity of the α alumina tripled compared to β alumina. This study provides important information of heat transfer and thermal conductivity evolution to refractory manufacturers and users.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Compact Peptoid Molecular Brushes for Nanoparticle Stabilization

Controlling the interfaces and interactions of colloidal nanoparticles (NPs) via tethered molecular moieties is crucial for NP applications in engineered nanomaterials, optics, catalysis, and nanomedicine. Despite a broad range of molecular types explored, there is a need for a flexible approach to rationally vary the chemistry and structure of these interfacial molecules for controlling NP stability in diverse environments, while maintaining a small size of the NP molecular shell. Here, we demonstrate that low-molecular-weight, bifunctional comb-shaped, and sequence-defined peptoids can effectively stabilize gold NPs (AuNPs). The generality of this robust functionalization strategy was also demonstrated by coating of silver, platinum, and iron oxide NPs with designed peptoids. Each peptoid (PE) is designed with varied arrangements of a multivalent AuNP-binding domain and a solvation domain consisting of oligo-ethylene glycol (EG) branches. Among designs, a peptoid (PE5) with a diblock structure is demonstrated to provide a superior nanocolloidal stability in diverse aqueous solutions while forming a compact shell (similar to 1.5 nm) on the AuNP surface. We demonstrate by experiments and molecular dynamics simulations that PE5-coated AuNPs (PE5/AuNPs) are stable in select organic solvents owing to the strong PE5 (amine)-Au binding and solubility of the oligo-EG motifs. At the vapor-aqueous interface, we show that PE5/AuNPs remain stable and can self-assemble into ordered 2D lattices. The NP films exhibit strong near-field plasmonic coupling when transferred to solid substrates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Numerical Investigation of Susceptor-Catalyst Design for Ethylene Generation in Radio Frequency Based Reactors

Ethylene is the most widely produced petrochemical component in the world. Whether reactors are heated directly or indirectly via steam, manufacturers use economies of scale to overcome inherent thermodynamic inefficiencies when burning fossil fuels. While allowing large-scale operations to use alternative sources of energy and raw materials, new methods of supplying energy to chemical reactor systems can reduce the energy waste produced by conventional processes. One viable method for effectively supplying energy to reactor systems is electromagnetic (EM) induction heating. Using the properties of radio frequency (RF) waves, heterogeneous catalysts can be precisely targeted for heating inside reactors. Site-selective heating can greatly lower the energy requirements of the process by supplying heat to reaction sites while reducing needless heat transfer elsewhere. In this study, a microscale model was used to help create guidelines for susceptors and catalysts to improve ethylene production. The oxidative dehydrogenation of ethane is investigated by utilizing several catalysts and potential catalyst/susceptor combinations, with heat provided by an EM susceptor. Having the susceptors and catalyst function separately results in higher gradients in both heat and mass transfer, which drives transport through the catalyst region, while still providing adequate heating for endothermic reactions. However, using a susceptive core with a catalyst covering produces the maximum ethylene concentration.

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

Accurate Machine Learning for Predicting the Viscosities of Deep Eutectic Solvents

Deep eutectic solvents (DESs) are emerging as environmentally friendly designer solvents for mass transport and heat transfer processes in industrial applications; however, the lack of accurate tools to predict and thus control their viscosities under both a range of environmental factors and formulations hinders their general application. While DESs may serve as designer solvents, with nearly unlimited combinations, this unfortunately makes it experimentally infeasible to comprehensively measure the viscosities of all DESs of potential industrial interest. To assist in the design of DESs, we have developed several new machine learning (ML) models that accurately and rapidly predict the viscosities of a diverse group of DESs at different temperatures and molar ratios using, to date, one of the most comprehensive data sets containing the properties of over 670 DESs over a wide range of temperatures (278.15–385.25 K). Three ML models, including support vector regression (SVR), feed forward neural networks (FFNNs), and categorical boosting (CatBoost), were developed to predict DES viscosity as a function of temperature and molar ratio and contrasted with multilinear and two-factor polynomial regression baselines. Further, quantum chemistry-based, COSMO-RS-derived sigma profile (σ-profile) features were used as inputs for the ML models. The CatBoost model is excellent at externally predicting DES viscosity, as indicated by high R 2 (0.99) and low root-mean-square-error (RMSE) and average absolute relative deviations (AARD) (5.22%) values for the testing data sets, and 98% of the data points lie within the 15% of AARD deviations. Furthermore, SHapley additive explanation (SHAP) analysis was employed to interpret the ML results and rationalize the viscosity predictions. The result is an ML approach that accurately predicts viscosity and will aid in accelerating the design of appropriate DESs for industrial applications.

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

Physics‐based iterative reconstruction for dual‐source and flying focal spot computed tomography

Purpose For single‐source helical Computed Tomography (CT), both Filtered‐Back Projection (FBP) and statistical iterative reconstruction have been investigated. However, for dual‐source CT with flying focal spot (DS‐FFS CT), a statistical iterative reconstruction that accurately models the scanner geometry and acquisition physics remains unknown to researchers. Therefore, our purpose is to present a novel physics‐based iterative reconstruction method for DS‐FFS CT and assess its image quality. Methods Our algorithm uses precise physics models to reconstruct from the native cone‐beam geometry and interleaved dual‐source helical trajectory of a DS‐FFS CT. To do so, we construct a noise physics model to represent data acquisition noise and a prior image model to represent image noise and texture. In addition, we design forward system models to compute the locations of deflected focal spots, the dimension, and sensitivity of voxels and detector units, as well as the length of intersection between x‐rays and voxels. The forward system models further represent the coordinated movement between the dual sources by computing their x‐ray coverage gaps and overlaps at an arbitrary helical pitch. With the above models, we reconstruct images by an advanced Consensus Equilibrium (CE) numerical method to compute the maximum a posteriori estimate to a joint optimization problem that simultaneously fits all models. Results We compared our reconstruction with Siemens ADMIRE, which is the clinical standard hybrid iterative reconstruction (IR) method for DS‐FFS CT, in terms of spatial resolution, noise profile, and image artifacts through both phantoms and clinical scan datasets. Experiments show that our reconstruction has a higher spatial resolution, with a Task‐Based Modulation Transfer Function (MTF task ) consistently higher than the clinical standard hybrid IR. In addition, our reconstruction shows a reduced magnitude of image undersampling artifacts than the clinical standard. Conclusions By modeling a precise geometry and avoiding data rebinning or interpolation, our physics‐based reconstruction achieves a higher spatial resolution and fewer image artifacts with smaller magnitude than the clinical standard hybrid IR.

Wang, Xiao↗
DOE OSTI2026

Structural coherence model for predicting molten salt thermal conductivity informed by the pair distribution function

To enable thermal behavior prediction and design optimization of molten salt reactors, thermal conductivity of molten salts must be characterized in terms of salt composition and temperature. Current theoretical models fail to provide consistent approximations for all halide mixtures, particularly actinide-bearing melts. This study aims to link the short-range order structure of molten salts to the mean free path of energy carriers through a simple structural coherence model informed by the partial pair distribution function. The proposed method is used to predict the thermal conductivity of 33 alkali and alkaline earth halide salts. Predictions approximate experimental measurements with a mean absolute error of 15.7% for dissociating, complexing, and actinide salts, including unary LiCl, NaCl, and MgCl 2 as well as mixtures LiF–NaF–KF (FLiNaK), LiF–BeF 2 (FLiBe), and NaCl–UCl 3 . The work provides evidence for the validity of energy carrier descriptions of molecular-level heat transfer in molten salts, with implications for improved theories of liquid energy transport in general.

Actinide mixtures↗
DOE OSTI2021

Determining the Structure-Property Relationships of Quasi-Two-Dimensional Semiconductor Nanoplatelets.

We report a theoretical study of CdSe nanoplatelets aimed at identifying the main factors determining their photophysical properties. Using atomic configurations optimized with density functional theory calculations, we computed quasiparticle and exciton binding energies of nanoplatelets with two to seven monolayers. We employed many body perturbation theory at the GW level and solved the Bethe-Salpeter equation to obtain absorption spectra and excitonic properties. Our results, which agree well with recent experiments, were then used to design a model that allows us to disentangle the effects of quantum confinement, strain induced by passivating ligands, and dielectric environment on the electronic properties of nanoplatelets. We found that, for the model to accurately reproduce our first principle results, it is critical to account for surface stress and consider a finite potential barrier and energy-dependent effective masses when describing quantum confinement. Our findings call into question previous assumptions on the validity of an infinite barrier to describe carrier confinement in nanoplatelets, suggesting that it may be possible to optimize interfacial charge transfer and extraction by appropriately choosing passivating ligands. The model developed here is generalizable to core-shell platelets and enables the description of system sizes not yet directly treatable by first-principles calculations.

Greenwood, Arin R.↗
DOE OSTI2026

Tunable Electronic Energy Level Alignment and Exciton Diversity in Organic–Inorganic van der Waals Heterostructures

van der Waals stacking of two-dimensional (2D) materials offers a powerful platform for engineering material interfaces with tailored electronic and optical properties. While most van der Waals multilayers have featured inorganic monolayers, incorporating molecular monolayers introduces additional degrees of tunability and functionality. Here, in this study, we investigate hybrid bilayers composed of atomically thin perylene-based molecular crystals interfaced with monolayer transition metal dichalcogenides (TMDs), specifically MoS 2 and WS 2 . Using the ab initio many-body perturbation theory within the GW approximation and the Bethe-Salpeter equation approach, we predict emergent properties beyond those of the isolated constituent systems. Notably, we find substantial renormalization of monolayer molecular crystal band gap due to TMD-induced polarization. Furthermore, by varying the TMD monolayer, we demonstrate tuning of the energy level alignment of the bilayer and subsequent control over a diversity of lowest-energy excitons, which include strongly bound hybrid excitons and long-lived charge-transfer excitons. These findings establish organic-inorganic van der Waals heterostructures as a promising class of materials for tunable optoelectronic devices and quantum excitonic phenomena, expanding the design space for low-dimensional systems.

GW-BSE calculations↗
DOE OSTI2023

Economic competitiveness of pultruded fiber composites for wind turbine applications

Pultrusion manufacturing of fiber reinforced polymers has been shown to yield some of the highest mechanical properties for unidirectional composites, having a high degree of fiber alignment with consistent performance. Pultrusions offer a low-cost manufacturing approach for producing unidirectional composites with a constant cross-section and are used in many applications, including spar caps of wind turbine blades. However, as an intermediate processing step for wind blades, the additional cost of manufacturing pultrusions must be accompanied by sufficient increases in mechanical performance and system benefits. Wind turbine blades are manufactured using vacuum-assisted resin transfer molding with infused unidirectional fiberglass or carbon pultrusions for the spar cap. Infused fiberglass composites are among the most cost-effective structural materials available and replacing this material in the cost-driven wind industry has proven challenging, where infused fiberglass spar caps are still the predominant material system in use. To evaluate alternative material systems in a pultruded composite form, it is necessary to understand the costs for this additional manufacturing step which are shown to add 33%–55% on top of the material costs. A pultrusion cost model has been developed and used to quantify cost sensitivities to various processing parameters. The mechanical performance for pultruded composites is improved versus resin-infusion manufacturing with a 17% increase in design strength at a constant fiber volume fraction, but also enables higher achievable fiber volume fractions. The cost-specific mechanical performance is compared as a function of processing parameters for pultruded composites to identify the opportunities for alternative material and manufacturing approaches for wind turbine spar caps. Finally, four materials are compared in a representative wind turbine blade model to assess the performance of pultruded carbon fiber systems and pultruded fiberglass relative to infused fiberglass, where the pultruded systems produce lower weight blades with various cost distinctions.

42 ENGINEERING↗
DOE OSTI2020

Fouling modeling and prediction approach for heat exchangers using deep learning

In this article, we develop a generalized and scalable statistical model for accurate prediction of fouling resistance using commonly measured parameters of industrial heat exchangers. This prediction model is based on deep learning where a scalable algorithmic architecture learns non-linear functional relationships between a set of target and predictor variables from large number of training samples. Here, the efficacy of this modeling approach is demonstrated for predicting fouling in an analytically modeled cross-flow heat exchanger, designed for waste heat recovery from flue-gas using room temperature water. The performance results of the trained models demonstrate that the mean absolute prediction errors are under 10 –4 KW –1 for flue-gas side, water side and overall fouling resistances. The coefficients of determination (R 2 ), which characterize the goodness of fit between the predictions and observed data, are over 99%. Even under varying levels of measurement noise in the inputs, we demonstrate that predictions over an ensemble of multiple neural networks achieves better accuracy and robustness to noise. We find that the proposed deep-learning fouling prediction framework learns to follow heat exchanger flow and heat transfer physics, which we confirm using locally interpretable model agnostic explanations around randomly selected operating points. Overall, we provide a robust algorithmic framework for fouling prediction that can be generalized and scaled to various types of industrial heat exchangers.

42 ENGINEERING↗
DOE OSTI2022

Single-Atom Pt Boosting Electrochemical Nonenzymatic Glucose Sensing on Ni(OH) 2 /N-Doped Graphene

Conventional nanomaterials in electrochemical non-enzymatic sensing face huge challenge due to their complex size-, surface- and composition- dependent catalytic properties and low active site density. In this work, we designed a single-atom Pt supported on Ni(OH) 2 nanoplates/nitrogen-doped graphene (Pt 1 /Ni(OH) 2 /NG) as the first example for constructing a single-atom catalyst based electrochemical non-enzymatic glucose sensor. The resulting Pt 1 /Ni(OH) 2 /NG exhibited a low anode peak potential of 0.48 V and high sensitivity of 220.75 μA mM -1 cm -2 towards glucose, which are 45 mV lower and 12 times higher than those of Ni(OH) 2 , respectively. The catalyst also showed excellent selectivity for several important interferences, short response time of 4.6 s, and high stability over 4 weeks. Experimental and density functional theory (DFT) calculated results reveal that the improved performance of Pt 1 /Ni(OH) 2 /NG could be attributed to stronger binding strength of glucose on single-atom Pt active centers and their surrounding Ni atoms, combined with fast electron transfer ability by the adding of the highly conductive NG. Finally, this research sheds light on the applications of SACs in the field of electrochemical non-enzymatic sensing.

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

Highly transferable atomistic machine-learning potentials from curated and compact datasets across the periodic table

Machine learning atomistic potentials trained using density functional theory (DFT) datasets allow for the modeling of complex material properties with near-DFT accuracy while imposing a fraction of its computational cost. The curation of the DFT datasets can be extensive in size and time-consuming to train and refine. In this study, we focus on addressing these barriers by developing minimalistic and flexible datasets for many elements in the periodic table regardless of their mass, electronic configuration, and ground state lattice. These DFT datasets have on average, ~4000 different structures and 27 atoms per structure, which we found sufficient to maintain the predictive accuracy of DFT properties and notably with high transferability. We envision these highly curated training sets as starting points for the community to expand, modify, or use with other machine learning atomistic potential models, whatever may suit individual needs, further accelerating the utilization of machine learning as a tool for material design and discovery.

42 ENGINEERING↗