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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 1,261 records · Page 70
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Spin-Controllable Dynamics in Defect-Engineered Carbon Nanotubes as Single Photon Emitters: Data-Driven Modeling and Computations

Quantum technologies, such as quantum computing and sensing, require efficient single-photon emission (SPE) sources that operate at room temperature in telecom wavelengths. While several materials can serve as SPE sources, no single platform meets all the criteria for efficiency, ambient operation, and scalability. Single-walled carbon nanotubes (SWCNTs) with covalently attached molecules offer a promising solution. Their SPE can be easily tuned via modifications of the SWCNT's diameter, chirality, and bonded molecules, enabling emission across near-IR to telecom wavelengths at ambient conditions. However, to fully realize the potential of SWCNTs and unlock their quantum capabilities, a deeper understanding of how structural defects from molecular adducts affect their emission and competing photoexcited processes is essential. To address this gap in our knowledge, this project combined quantum chemistry calculations with data-driven methods of cheminformatics (QSAR) and machine learning (ML). The developed computational approaches have provided several design strategies for covalent functionalization of SWCNTs to improve their optical response. The collaboration with Los Alamos National Lab (LANL) enabled direct comparison of computational and experimental data, facilitating method validation. This partnership was enhanced through access to LANL's Center for Integrated Nanotechnologies (CINT) utilizing User Facility Program and summer internships, which provided three NDSU graduate students with hands-on experience at LANL. The outcomes of this project included (1) Advancing the current stage of computational methods in accurate modeling of non-adiabatic spin-dependent photoexcited dynamics and its applicability to nanosystems consisting of thousands of atoms, realized as open-access codes linked to existing DFT-based software; (2) Establishing the relationship between the structure of adducts and SWCNTs and intrinsic excitonic and spin properties of defect states for guiding novel synthetic strategies and experimental probes of chemically functionalized SWCNTs as near-IR emitting materials; (3) Generating virtual libraries of hypothetical functionalized SWCNTs for virtual screening of their chemical structures and optical properties, leveraging new functionalities of SWCNTs; (4) Offering a unique experience for NDSU graduate students that prepared them for future scientific careers related to materials modeling and big data processing. These results were summarized in 12 published journal papers and 3 recently submitted papers. One of a key finding is that the position of defect sites on the SWCNT surface primarily drives the emission redshift (up to 100 meV), while the polarity of the defect-inducing molecules has a much smaller effect (~10 meV). However, the electron-donating or withdrawing properties of a molecule influence selecting reactivity of defect sites. These insights important for optimizing synthetic protocols for desired emissions in SWCNTs. We also revealed that the interaction between two defects at various positions on the SWCNT enhances the redshift and optical activity of states, favoring strong near-IR emission. This suggests that manipulations in defect concentrations is a promising strategy for controlling efficient emission. Mostly important, the defect position was found controllable by the spin states of photoexcited intermediates: Excited aromatic molecules form ortho defects with SWCNTs at their singlet states in the presence of oxygen, while oxygen-free conditions favor para defects via the triplet-state mechanism. Additionally, a heat-activated [2+2] cycloaddition reaction facilitates divalent defect formation with fewer bonding positions that narrows emission bands. These groundbreaking findings have been experimentally validated and significantly advance our understanding of defect chemistry in SWCNTs. Using a novel encoding technique and 3D-MoRSE descriptors, we developed highly accurate ML/QSAR models to predict both the 3D structure and optical properties of SWCNTs with chemical defects. This model enabled the creation of a virtual library of 125,556 structures, providing new insights into the relationship between SWCNT-defect structure and emission.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Optimization of Window Positions for Wind-Driven Natural Ventilation Performance

This paper optimizes opening positions on building facades to maximize the natural ventilation’s potential for ventilation and cooling purposes. The paper demonstrates how to apply computational fluid dynamics (CFD) simulation results to architectural design processes, and how the CFD-driven decisions impact ventilation and cooling: (1) background: A CFD helps predict the natural ventilation’s potential, the integration of CFD results into design decision-making has not been actively practiced; (2) methods: Pressure data on building facades were obtained from CFD simulations and mapped into the 3D modeling environment, which were then used to identify optimal positions of two openings of a zone. The effect of the selected opening positions was validated with building energy simulations; (3) results: The cross-comparison study of different window positions based on different geographical locations quantified the impact on natural ventilation effectiveness; and (4) conclusions: The optimized window position was shown to be effective, and some optimal solutions contradicted the typical cross-ventilation strategy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Benchmarking viromics: an in silico evaluation of metagenome-enabled estimates of viral community composition and diversity

Background: Viral metagenomics (viromics) is increasingly used to obtain uncultivated viral genomes, evaluate community diversity, and assess ecological hypotheses. While viromic experimental methods are relatively mature and widely accepted by the research community, robust bioinformatics standards remain to be established. Here we used in silico mock viral communities to evaluate the viromic sequence-to-ecological-inference pipeline, including (i) read pre-processing and metagenome assembly, (ii) thresholds applied to estimate viral relative abundances based on read mapping to assembled contigs, and (iii) normalization methods applied to the matrix of viral relative abundances for alpha and beta diversity estimates. Results: Tools specifically designed for metagenomes, specifically metaSPAdes, MEGAHIT, and IDBA-UD, were the most effective at assembling viromes. Read pre-processing, such as partitioning, had virtually no impact on assembly output, but may be useful when hardware is limited. Viral populations with 2–5 × coverage typically assembled well, whereas lesser coverage led to fragmented assembly. Strain heterogeneity within populations hampered assembly, especially when strains were closely related (average nucleotide identity, or ANI ≥97%) and when the most abundant strain represented <50% of the population. Viral community composition assessments based on read recruitment were generally accurate when the following thresholds for detection were applied: (i) ≥10 kb contig lengths to define populations, (ii) coverage defined from reads mapping at ≥90% identity, and (iii) ≥75% of contig length with ≥1 × coverage. Finally, although data are limited to the most abundant viruses in a community, alpha and beta diversity patterns were robustly estimated (±10%) when comparing samples of similar sequencing depth, but more divergent (up to 80%) when sequencing depth was uneven across the dataset. In the latter cases, the use of normalization methods specifically developed for metagenomes provided the best estimates. Conclusions: These simulations provide benchmarks for selecting analysis cut-offs and establish that an optimized sample-to-ecological-inference viromics pipeline is robust for making ecological inferences from natural viral communities. Continued development to better accessing RNA, rare, and/or diverse viral populations and improved reference viral genome availability will alleviate many of viromics remaining limitations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Model Predictive Control of A Matrix-Converter Based Solid State Transformer for Utility Grid Interaction

The matrix converter solid state transformer (MC-SST), formed from the back-to-back connection of two three-to-single-phase matrix converters, is studied for use in the interconnection of two ac grids. The matrix converter topology provides a light weight and low volume single-stage bidirectional ac-ac power conversion without the need for a dc link. Thus, the lifetime limitations of dc-bus storage capacitors are avoided. However, space vector modulation of this type of MC-SST requires to compute vectors for each of the two MCs, which must be carefully coordinated to avoid commutation failure. An additional controller is also required to control power exchange between the two ac grids. In this paper, model predictive control (MPC) is proposed for an MC-SST connecting two different ac power grids. The proposed MPC predicts the circuit variables based on the discrete model of MC-SST system and the cost function is formulated so that the optimal switch vector for the next sample period is selected, thereby generating the required grid currents for the SST. Simulation and experimental studies are carried out to demonstrate the effectiveness and simplicity of the proposed MPC for such MC-SST-based grid interfacing systems.

Xue, Yaosuo↗
DOE OSTI2017

Improving the cross correlation method of indirect flow measurement by water injection and modeling - 245

This paper presents a technique to improve the Cross Correlation Function (CCF) method for indirect flow measurement. Unlike other methods, the CCF method does not need a specific instrument or tool for flow measurements. Instead, the delay time between two sensors can be derived by applying the CCF. A water flow loop was constructed to investigate the CCF method for flow measurement. Because the inherent process temperature variations were small when compared to significant amounts of measurement and process noise, inaccurate flow measurements were obtained. The performance of the CCF method could be improved by increasing the system signal-to-noise ratio, which is the process noise divided by the measurement noise. For this reason, a periodic injection of different temperature water was used to increase the process temperature variation. The results were favorable. However, several design variables needed to be selected. To assist, a CFD simulation was utilized to optimize the distance between sensors and injection point, the rate of injection flow, and other related variables. The results show that CCF optimization of these variables produces better flow measurement performance over a range of flow velocities. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Splitting Mono- and Di-Branched Alkane Isomers by a Robust Aluminum-Based Metal-Organic Framework Material with Optimal Pore Dimensions

The separation of alkanes with different degrees of branching, particularly mono- and dibranched isomers, represents a challenging yet important industrial process for the production of premium gasoline blending components with high octane number. We report here the separation of linear/monobranched and dibranched alkanes through complete molecular sieving by a robust aluminum-based MOF material, Al-bttotb (H 3 bttotb = 4,4',4"-(benzene-1,3,5-triyltris(oxy))tribenzoicacid). Single- and multicomponent adsorption experiments reveal that the material adsorbs linear and monobranched alkanes, but fully excludes their dibranched isomers. Adsorption sites of alkanes within the MOF channels have been identified by single-crystal X-ray diffraction studies, and the adsorption mechanism was explored through computational calculations and modeling. The highly selective adsorption of the MOF should be attributed to its optimal pore dimensions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Invertible Neural Networks for Aerodynamic Design of Wind Turbine Blades

The state-of-the-practice methods for aerodynamic design of wind turbine blads use linearized blade element momentum theory (BEM) to optimize the twist and chord profiles from a pre-selected set of 2D airfoil shapes. In this work, we apply invertible neural network (INN) tools to enable the rapid inverse aerodynamic design of wind turbine blades including component airfoils. The INN is trained on data obtained through the use of robust automated mesh generation and the HAMSTR computational fluid dynamics solver with advanced turbulence and transition models validated for turbine applications. Our design technique is a significant improvement over the state-of-the-practice linearized blade element momentum (BEM) techniques in capturing 3D nonlinear aerodynamic effects that are critical for optimal design of the rotors. This is made possible by developing sparse, invertible neural networks (INNs) for inverse design and optimization that realize a 100x cost reduction compared to adjoint-based computational fluid dynamics (CFD) approaches, while enabling increased robustness of the final design. We demonstrate the INN tool for design of a section of the NREL 5-MW blade. All generated shapes satisfy the desired aerodynamic characteristics, demonstrating the success of the INN approach for inverse design of wind turbine blades.

aerodynamic design↗
DOE OSTI2022

Uncertainty-aware mixed-variable machine learning for materials design

Abstract Data-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching the vast design space of chemistry, structure, and synthesis methods. Bayesian optimization (BO) employs uncertainty-aware machine learning models to select promising designs to evaluate, hence reducing the cost. However, BO with mixed numerical and categorical variables, which is of particular interest in materials design, has not been well studied. In this work, we survey frequentist and Bayesian approaches to uncertainty quantification of machine learning with mixed variables. We then conduct a systematic comparative study of their performances in BO using a popular representative model from each group, the random forest-based Lolo model (frequentist) and the latent variable Gaussian process model (Bayesian). We examine the efficacy of the two models in the optimization of mathematical functions, as well as properties of structural and functional materials, where we observe performance differences as related to problem dimensionality and complexity. By investigating the machine learning models’ predictive and uncertainty estimation capabilities, we provide interpretations of the observed performance differences. Our results provide practical guidance on choosing between frequentist and Bayesian uncertainty-aware machine learning models for mixed-variable BO in materials design.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Production of Gaseous Olefins from Syngas over a Cobalt-HZSM-5 Catalyst

Light olefins (C2–C5) are high-value platform chemicals used to produce plastics, lubricants, coating material, and surfactants, and can be intermediates to produce transportation fuels. In this study we report on the development of a Co-based catalyst for direct conversion of fossil as well as biomass derived syngas into light olefins that enables high conversion and olefin/paraffin (O/P) product ratio, minimal selectivity to CO2 (<?1%) and wax formation (C14+ selectivity?<?1%). Over a Co/HZSM-5 bi-functional catalyst 38% CO conversion with C2–C5 olefin selectivity of 48%, and O/P ratio of 4.0 was achieved at 240 °C, 17 bar, and 1.5 L/(g–h). In addition to a gaseous olefin-rich stream a C6–C13 mixed olefin/paraffin liquid byproduct was also produced with 25% selectivity. Both Co loading and the HZSM-5 acidity levels were optimized to maintain the balance between the hydrogenation and the cracking activity to generate high levels of C2–C4 olefins. A suite of catalyst characterization tools were used to correlate the physical and chemical property of the catalyst to the performance of the catalyst towards syngas conversion and C2–C4 olefin selectivity.

Maddi, Balakrishna↗
DOE OSTI2022

Comparative Study of Wind Energy Potential Estimation Methods for Wind Sites in Togo and Benin (West Sub-Saharan Africa)

The characterization of wind speed distribution and the optimal assessment of wind energy potential are critical factors in selecting a suitable site for wind power plants (WPP). The Weibull distribution law has been used extensively to analyze the wind characteristics of candidate WPP sites, and to estimate the available and deliverable energy. This paper presents a comparative study of five wind energy resource assessment methods as they applied to the context of wind sites in West Sub-Saharan Africa. We investigated three numerical approaches, namely, the adaptive neuro-fuzzy inference system (ANFIS), the multilayer perceptron method (MLP), and support vector regression (SVR), to derive the distribution law of wind speeds and to optimally quantify the corresponding wind energy potential. Next, we compared these three approaches to two well-known Weibull distribution law-based methods: the empirical method of Justus (EMJ) and the maximum likelihood method (MLM). Case study results indicated that the neural network-based methods, ANFIS and MLP, yielded the most accurate distribution fits and wind energy potential estimates, and consequently, are the most recommended methods for the wind sites in Togo and Benin. The orders of magnitude of the root mean squared error (RMSE) in estimating the recoverable energy using ANFIS were, respectively, 10-4 and 10-5 for Lomé and Cotonou, while MLP achieved an RMSE order of magnitude of 10-3 for both sites.

17 WIND ENERGY↗
DOE OSTI2020

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

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

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Polybenzimidazole-derived carbon molecular sieve hollow fiber membranes with tailored oxygen selective transport

Polymer membrane carbonization is a promising strategy to obtain carbon molecular sieve (CMS) membrane materials with tailorable cavity size distributions for high gas selectivities beyond that attained from traditional polymer membrane materials. Despite their already demonstrated exceptional separation performance characteristics for several gas separation applications (e.g. CO 2 /CH 4 and H 2 /CO 2 ), further development of CMS membranes having high O 2 /N 2 perm-selectivity for energy-efficient high-purity O 2 production has proven challenging. Herein, we explore CMS hollow fiber membranes (HFMs) derived from highly rigid and tightly-packed polybenzimidazole (PBI) materials. Nearly defect-free PBI-derived CMS HFMs were fabricated and evaluated for O 2 /N 2 separation performance for the first time. The micro-structural and O 2 selective gas separation characteristics of PBI-CMS HFMs pyrolyzed at different pyrolysis conditions were intensively studied. With rigorous carbonization protocol optimization, we obtained highly O 2 permselective PBI-CMS HFMs having O 2 /N 2 selectivities approaching 14, enabling high purity O 2 production. The PBI-CMS HFM O 2 /N 2 separation performance dependence on operating conditions (e.g. temperature and feed pressure) are reported.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Selection, Evaluation, and Rating of Compact Heat Exchangers v. 1.006

SEARCH determines and optimizes the design of a compact heat exchanger for specified process conditions. The user specifies process boundary conditions including the fluid state and flow rate and SEARCH will determine the optimum flow arrangement, channel geometry, and mechanical design for the unit. Fluids are modeled using NIST Refprop or tabulated values. A variety of thermal-hydraulic correlations are available including user-defined equations to accurately capture the heat transfer and pressure drop behavior of the process flows.

Carlson, Matthew D.↗
DOE OSTI2023

HBMax: Optimizing Memory Efficiency for Parallel Influence Maximization on Multicore Architectures

The goal of influence maximization is to select k most-influential vertices or seeds in a network, where influence is defined by a given diffusion process. The problem has a number of important applications such as viral marketing, information spread, and epidemic control. Although computing optimal seed set is NP-Hard, due to the submodular nature of the problem efficient approximation algorithms exist. However, even state-of-the-art parallel implementations are limited by a sampling step that incurs large memory footprints. This in turn limits the problem size reach and approximation quality. In this work, we study the memory footprint of the sampling process collecting reverse reachability information in the IMM algorithm over large real-world social networks. We present an adaptive and memory-efficient optimization approach for a state-of-the-art multi-threaded parallel influence maximization algorithm. Our approach,HuffMax, uses a portion of the reverse reachable (RR) sets collected by the algorithm to learn the characteristics of the graph. Then, it compresses the intermediate reverse reachability information with Huffman coding, and queries directly on the compressed data to preserve the memory savings obtained through compression. We also propose an efficient sampling strategy based on the distribution of RR sets, which can further reduce the computation time for typical social networks with long-tail distributions. Considering a NUMA architecture, we scale up our solution on 128-core CPUs and reduce the memory footprint by up to 45.7% with negligible time overhead (or even faster) and without perceivable loss of accuracy.

Chen, Xinyu↗
DOE OSTI2022

Selective Thermal Emission with Radiation and Adsorption in Annuli (STERADIAN): An Advanced Heat Exchanger Concept for Supercritical CO 2 Power Cycles

One of the key limitations affecting ability to achieve the efficiency and cost saving advantages of a supercritical CO 2 (scCO 2 ) power cycle resides with the primary heat exchanger where costs can exceed 50% of the plant capital budget depending on design. This project established the foundational groundwork for a new type of heat exchanger optimized to take advantage of radiative heat transfer to scCO 2 . Selective thermal emission coatings were designed and tested that radiated more light tuned to the 4.2 µm infrared absorption band of CO 2 . Coating design utilized principles of nanophotonic bandgap theory to design a selective emitter that has: 1) low emissivity in the visible band, 2) near 100% effective emissivity in the mid-IR wavelength between 4 and 4.5 µm wavelength, and 3) strong suppression of far-IR light emission that will maximize radiative heat transfer efficiency to scCO 2 . Computational fluid dynamics simulations show that an advanced heat exchanger implementing this selective thermal emitter coating is 25 to as much as 40% smaller than conventional heat exchanger designs.

14 SOLAR ENERGY↗
DOE OSTI2017

Wireless Chemical Sensor Systems (Optimization of Field-Structured Chemiresistors for in situ Quantification of Propellant Stabilizer Concentration)

This project is a Cooperative Research and Development Agreement (CRADA) with International Electronic Machines Corp. (IEM, Troy, NY), who is contracted through the U.S. Naval Air Systems Command (NAVAIR) (under SBIR N111-010) to develop an in situ wireless chemical sensor system called the Stabilizer Level Accurate Measurement System (SLAMS). The SLAMS device is specified to include a miniature, very thin, self-contained chemical-sensor node to detect and track the level of stabilizer present in various PADs and CADs. IEM is focused on the wireless sensor platform, node communication, and electronics; whereas Sandia National Laboratories’ (SNL’s) efforts are focused on the chemical sensor development and integration. The sensor technology being employed is known as field-structured chemiresistors (FSCRs), which was invented by James Martin, et al. (SNL) and refined by Martin and Read. These sensors are small, require very little power, and have tunable sensitivity and selectivity. In the following, we discuss SNL’s efforts to optimize the FSCRs for the demanding chemical and environmental conditions that will be encountered in this application.

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

Effect of fiber reinforcement on the process parameters of injection molding manufacturing process

Use of fillers with the resin is common in injection molding manufacturing technique to enhance the stiffness and strength of the manufactured components. However, the addition of fillers changes the rheological and mechanical properties of the material, required adjustments in the process parameters used for the manufacturing. The present study aims to investigate this impact of fillers on the process parameters of the injection molding manufacturing process. Neat Acrylonitrile Butadiene Styrene (ABS) and ABS reinforced with short glass fibers are used as the materials to perform the comprehensive numerical analysis including complete process sequence, namely filling, packing, cooling and warpage. Parametric studies are conducted to optimize the manufacturing process for minimal warpage (or shrinkage) in the produced part. The selected process parameters for the parametric study include cooling time, packing pressure, and mold temperature, all of which directly impact the quality of the manufactured parts. The optimal values obtained for these parameters are compared to examine the effect fiber reinforcement has on the manufacturing process.

Garg, Nikhil↗
DOE OSTI2015

Additive Manufacturing for Cost Efficient Production of Compact Ceramic Heat Exchangers and Recuperators

An additive manufacture technique known as laminated object manufacturing (LOM) was used to fabricate compact ceramic heat exchanger prototypes. LOM uses precision CO2 laser cutting of ceramic green tapes, which are then precision stacked to build a 3D object with fine internal features. Modeling was used to develop prototype designs and predict the thermal response, stress, and efficiency in the ceramic heat exchangers. Build testing and materials analyses were used to provide feedback for the design selection. During this development process, laminated object manufacturing protocols were established. This included laser optimization, strategies for fine feature integrity, lamination fluid control, green handling, and firing profile. Three full size prototypes were fabricated using two different designs. One prototype was selected for performance testing. During testing, cross talk leakage prevented the application of a high pressure differential, however, the prototype was successful at withstanding the high temperature operating conditions (1300 °F). In addition, analysis showed that the bulk of the part did not have cracks or leakage issues. This led to the development of a module method for next generation LOM heat exchangers. A scale-up cost analysis showed that given a purpose built LOM system, these ceramic heat exchangers would be affordable for the applications.

01 COAL, LIGNITE, AND PEAT↗