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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Using Grover’s search protocol to select the best qubit pairs

This research represents a continuation of our investigation of the Rigetti quantum platform as part of the Quantum Leap for Fusion Energy Sciences project. We evaluate the performance of the new Aspen-11, Aspen-M-2, Aspen-M-3 quantum processing units (QPU) through the application of the Grover’s search algorithm and the validation of single gate fidelities. The performance of the new QPUs is compared to the older Aspen-7 device, and it is shown that qubit pair selection plays a key role in the optimization process. Additionally, we delve into the examination of coherent and decoherent errors associated with native gates. To optimize our approach, we have developed several relatively inexpensive hardware protocols aimed at facilitating the selection of the most suitable qubit pairs. These protocols involve running various circuits on the hardware and assessing the overall performance of the tested qubit pairs. Through these protocols, we have demonstrated that the quality of qubit pairs on a single chip can exhibit significant variations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Simulation and Optimization of Volatile Fatty Acid Upgrading Strategies for Sustainable Transportation Fuel Production

The multicomponent nature of and variability of biomass makes chemical upgrading via a single process stream to a single end use infeasible for most feedstocks. A more promising approach is to identify upgrading strategies (encompassing bioprocessing, catalysis, and separations) which valorize varied biomass fractions to distinct products to which each is best suited. A robust procedure to carry out this goal should incorporate comparison of various upgrading procedures as well as suitability of products to a slate of identified end uses. We applied this strategy to one biomass-derived feedstock, volatile fatty acids (VFAs) derived from wet waste arrested anaerobic digestion, by developing a computer program, VFA Upgrading to Liquid Transportation fUels Refinery Estimation (VULTURE) which evaluates VFA catalytic upgrading to liquid transportation fuels. VULTURE considers multiple separations, catalysis (ketonization, hydrogenation), and fuel application options, generating hundreds of candidate scenarios for a given VFA stream, then selects several promising strategies that optimize bio-content of products with properties best suited for target fuel types. We find that VFAs are upgraded most efficiently when separate light alcohol (C3-6) and heavy hydrocarbon or alcohol (C7-13) fractions are targeted to create gasoline and heavy-duty (diesel or jet) fuels or fuel blends. Surrogate property testing of VULTURE-recommended fuels reveals that most predictive models employed are robust, while rigorous process simulation shows that the simple unit operation assumptions used in VULTURE are largely accurate, especially for heavy-duty fuel synthesis. Techno-economic and life-cycle analyses show that VFA-derived fuels are profitable and have dramatically (=57%) lower carbon intensities than fossil analogs.

BIOMASS FUELS↗
DOE OSTI2018

Structure-Based Optimization of a Novel Class of Aldehyde Dehydrogenase 1A (ALDH1A) Subfamily-Selective Inhibitors as Potential Adjuncts to Ovarian Cancer Chemotherapy

Aldehyde dehydrogenase (ALDH) activity is commonly used as a marker to identify cancer stem-like cells. The three ALDH1A isoforms have all been individually implicated in cancer stem-like cells and in chemoresistance; however, which isoform is preferentially expressed varies between cell lines. In this work, we sought to explore the structural determinants of ALDH1A isoform selectivity in a series of small-molecule inhibitors in support of research into the role of ALDH1A in cancer stem cells. An SAR campaign guided by a cocrystal structure of the HTS hit CM39 (7) with ALDH1A1 afforded first-in-class inhibitors of the ALDH1A subfamily with excellent selectivity over the homologous ALDH2 isoform. We also discovered the first reported modestly selective single isoform 1A2 and 1A3 inhibitors. Two compounds, 13g and 13h, depleted the CD133+ putative cancer stem cell pool, synergized with cisplatin, and achieved efficacious concentrations in vivo following IP administration. Compound 13h additionally synergized with cisplatin in a patient-derived ovarian cancer spheroid model.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Low temperature ethanol steam reforming: Selectivity control with lithium doping of Pt/m-ZrO 2

Lithium promoted 2%Pt/m-ZrO 2 catalysts previously observed to exhibit higher rates for the low temperature water-gas shift (LTS) were tested for the ethanol steam reforming with the aim of exploring the potential tuning of the selectivity. Characterization of catalysts having optimized Li content (0.5–0.75%Li) for LTS exhibited (a) weakened C—H bonding of formate, a proposed intermediate in the LTS mechanism, as shown by a shift in the ν(CH) band to lower wavenumbers, (b) a relatively low extent of blocking of Pt, as measured by the ν(CO) band intensity of Pt-CO, (c) increased basicity as measured by CO 2 temperature-programmed desorption with mass spectrometry, but not so high as to strongly inhibit CO 2 product removal, and finally (d) no evidence of electron transfer from Li to Pt. Here, for this study, the same catalysts were tested for ethanol steam reforming (ESR). Results show that Li could likewise weaken the C—C bond of the acetate intermediate, the analog of formate in LTS, and facilitate decarboxylation over decarbonylation altering the selectivity in favor of methanation. This trend was confirmed by fixed bed reaction testing, in-situ infrared spectroscopy experiments of transient ESR, and temperature-programmed ESR using MS. The Li-doped catalysts may be used to pre-reform ethanol prior to feeding to a methane steam reformer to increase the overall H 2 selectivity of the process. DRIFTS of steady state ESR revealed that deactivation occurs through losses in the Pt-support interface, thereby hindering the turnover of the acetate intermediate.

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

Application-specific optimal model weighting of global climate models: A red tide example

Global climate models (GCMs) and Earth system models (ESMs) provide many climate services with environmental relevance. The High Resolution Model Inter-comparison Project (HighResMIP) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) provides model runs of GCMs and ESMs to address regional phenomena. Developing a parsimonious ensemble of CMIP6 requires multiple ensemble methods such as independent-model subset selection, prescreening-based subset selection, and model weighting. The work presented here focuses on application-specific optimal model weighting, with prescreening-based subset selection. As such, independent ensemble members are categorized, selected, and weighted based on their ability to reproduce physically-interpretable features of interest that are problem-specific. We discuss the strengths and caveats of optimal model weighting using a case study of red tide prediction in the Gulf of Mexico along the West Florida Shelf. Red tide is a common name of specific harmful algal blooms that occur worldwide, causing adverse socioeconomic and environmental impacts. Our results indicate the importance of prescreening-based subset selection as optimal model weighting can underplay robust ensemble members by optimizing error cancellation. Prescreening-based subset selection also provides insights about the validity of the model weights. By illustrating the caveats of using non-representative models when optimal model weighting is used, the findings and discussion of this study are pertinent to many other climate services.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Multimodal parameter spaces of a complex multi-channel neuron model

One of the most common types of models that helps us to understand neuron behavior is based on the Hodgkin–Huxley ion channel formulation (HH model). A major challenge with inferring parameters in HH models is non-uniqueness: many different sets of ion channel parameter values produce similar outputs for the same input stimulus. Such phenomena result in an objective function that exhibits multiple modes (i.e., multiple local minima). This non-uniqueness of local optimality poses challenges for parameter estimation with many algorithmic optimization techniques. HH models additionally have severe non-linearities resulting in further challenges for inferring parameters in an algorithmic fashion. To address these challenges with a tractable method in high-dimensional parameter spaces, we propose using a particular Markov chain Monte Carlo (MCMC) algorithm, which has the advantage of inferring parameters in a Bayesian framework. The Bayesian approach is designed to be suitable for multimodal solutions to inverse problems. We introduce and demonstrate the method using a three-channel HH model. We then focus on the inference of nine parameters in an eight-channel HH model, which we analyze in detail. We explore how the MCMC algorithm can uncover complex relationships between inferred parameters using five injected current levels. The MCMC method provides as a result a nine-dimensional posterior distribution, which we analyze visually with solution maps or landscapes of the possible parameter sets. The visualized solution maps show new complex structures of the multimodal posteriors, and they allow for selection of locally and globally optimal value sets, and they visually expose parameter sensitivities and regions of higher model robustness. We envision these solution maps as enabling experimentalists to improve the design of future experiments, increase scientific productivity and improve on model structure and ideation when the MCMC algorithm is applied to experimental data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Capacitive Energy Extraction by Few-Layer Graphene Electrodes

Capacitive double-layer expansion is a promising technology to harvest energy arising from the salinity difference between freshwater and seawater. Its optimal performance requires a careful selection of the operation potentials and electrode materials. While carbonaceous materials such as graphene and various forms of activated carbons are routinely used as the electrodes, there is little knowledge on how the quantum capacitance and the electric double-layer (EDL) capacitance, which are on the same order of magnitude, affect the capacitive performance. Toward understanding that from a theoretical perspective, here we study the capacitive energy extraction with graphene electrodes as a function of the number of graphene layers. The classical density functional theory is joined with the electronic density functional theory to obtain the EDL and the quantum capacitance, respectively. The theoretical results show that the quantum capacitance contribution plays a dominant role in extracting energy using the single-layer graphene, but its effect diminishes as the number of graphene layers increases. The overall extracted energy is dominated by the EDL contribution beyond about four graphene layers. Electrodes with more graphene layers are able to extract more energy at low charging potential. Here, because many porous carbons have nanopores with stacked graphene layers, our theoretical predictions are useful to identify optimal operation parameters for capacitive energy extraction with porous electrodes of different wall thickness.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Comparison of Real-Time Pressure Rail Selection Algorithms for the Hybrid Hydraulic Electric Architecture: Case Study on a Track Loader

Abstract The hybrid hydraulic electric architecture (HHEA) seeks to combine the high power/torque/force density of hydraulics with the efficiency of electric machines. A set of common pressure rails is used to provide a majority of the power and this power is modulated by small electric machines to provide precise control for the operator. The HHEA has been studied in previous work using off-line dynamic programming optimization to determine energy efficient pressure rail selections, but this approach requires drive cycle information apriori. A Lagrange multiplier method has also been investigated where a set of gains (Lagrange multipliers) are optimized off-line with the idea the these gains, once determined, could be used for real-time operation. In this work, three new real-time pressure rail selection algorithms that do not require future drive cycle information are investigated; greedy, torque minimizing, and thresholding. The greedy control is found to only use 1% more energy than the globally optimal dynamic programming solution; but a model of energy loss is required.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Eco-driving Profile Optimization by Dynamic Programming for Battery Electric Vehicles

Although full automation has not yet been achieved, automated vehicles are a valid research area. Not only would automated vehicles provide ultimate driver convenience, but they would maximize energy efficiency by eliminating undesired human driving behaviors and optimally controlling the powertrain. From the perspective of control related to energy saving, speed profile optimization is important for improving system efficiency and satisfying passenger demands. This study employs Dynamic Programming (DP) to solve the constrained optimal problem for travel time, distance, and speed limit by exploring all possible control options. The solutions obtained by DP demonstrate consistent control patterns combining four control modes-acceleration, cruising, coasting, and braking, with cruising or coasting being selective depending on the boundary conditions. Further, this study introduces DP-based simulation results and attempts to provide comprehensive interpretations of the optimal policy by analyzing the essential factors that affect the control problem, including boundary conditions, road load, and powertrain characteristics. Based on these interpretations, the control concepts can be explained as the optimal policy selecting the best control option based on system efficiency and boundary conditions. The results of DP are compared with a human-like driver model to show that the optimal speed profiles can effectively reduce energy consumption.

Autonomous vehicles↗
DOE OSTI2022

Concurrent Enhancement of Acetylene Uptake Capacity and Selectivity by Progressive Core Expansion and Extra–Framework Anions in Pore–Space–Partitioned Metal–Organic Frameworks

A multi-stage core-expansion method is proposed here as one component of the integrative binding-site/extender/core-expansion (BEC) strategy. The conceptual deconstruction of the partitioning ligand into three editable parts draws our focus onto progressive core expansion and allows the optimization of both acetylene uptake and selectivity. Herein the effectiveness of this strategy is shown through a family of eight cationic pore-partitioned materials containing three different partitioning ligands and various counter anions. The optimized structure, Co 3 -cpt-tph-Cl (Hcpt=4-(p-carboxyphenyl)-1,2,4-triazole, H-tph=(2,5,8-tri-(4-pyridyl)-1,3,4,6,7,9-hexaazaphenalene) with the largest surface area and highest C 2 H 2 uptake capacity (200 cm 3 /g at 298 K), also exhibits (desirably) the lowest CO 2 uptake and hence the highest C 2 H 2 /CO 2 selectivity. The successful boost in both C 2 H 2 capacity and IAST selectivity allows Co 3 -cpt-tph-Cl to rank among the best crystalline porous materials, ionic MOFs in particular, for C 2 H 2 uptake and C 2 H 2 /CO 2 experimental breakthrough separation.

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

Development of Low-Cost, High-Performance, Easy-To-Apply, Non-Flammable, Inorganic Phase Change Material (PCM) Technology (Project Final Report)

This report describes a 45-months long research program focused on the development of novel, easy-to-apply, non-flammable, and high-performance inorganic phase change materials (PCMs) for building and industrial applications. The University of Massachusetts Lowell (UML) formed a world-class team consisting of researchers form InsolCorp (only N. American manufacturer of inorganic PCM systems for building applications), and a group of industrial advisors, to develop a universal/multipurpose, simple-to-manufacture and cost-effective PCM technology. The project team expects that the results of this work will spur in the future the adoption of thermal storage materials – a key building energy saving technology as identified by DOE BTO – for a variety of building envelope applications. The main goal of this project was to demonstrate a suite of low-cost, multipurpose, and durable inorganic PCM formulations with phase transition temperatures encompassing typical building applications (between +5 o C and +55 o C). The first objective was to design, fabricate, and experimentally validate a performance of inexpensive, durable, highly efficient, non-flammable, and easy to manufacture PCMs. To allow a variety of building applications, the project team focused on formulations that exhibit repeatable phase transitions between +5 o C and +55 o C. To follow the DOE BTO cost efficiency target without compromising thermal performance, our work was based on inorganic compounds (mostly salt hydrates) and their blends, which represent a fraction of the cost of most of organic PCMs with about twice as high density as well as significantly higher thermal conductivity and phase change enthalpy. The second objective was to develop easy-to-manufacture and -install packaging/encapsulation designs that are 1) a superior barrier to current state-of-the-art macro-packaging, which significantly reduces the risk of loss of hydration water and PCM leak, and 2) optimal in enhancing the heat exchange rates with the surroundings and within the PCM core to ensure complete charging/discharging of the entire PCM within the product. Finally, the project’s intend was to scale-up the fabrication process to demonstrate installation on system-scale applications, and to validate the performance under field conditions. This work aimed at developing low-cost, high-energy storage, and reliable latent heat storage technology for building applications. This development was realized by formulating and integrating the following two technology components: 1) inorganic salt hydrate based PCMs that have high latent enthalpies and are low-cost and durable, and 2) PCM encapsulation (packaging) technology that maximizes PCM concentration and enhances heat transport characteristics in the product and with the external environment/materials. High thermal storage capacity, low cost and fire resistance are key to the building market entry for PCM technology. Therefore, the project’s focus was on salt-hydrate-based formulations which satisfy all these criteria. Packaging and/or encapsulation of PCM is a key processing step. The project team recognized that a low-cost and simple-to-manufacture salt hydrate-based PCM technology holds the best chance to be successful in the building construction market, a market which is traditionally extremely sensitive to cost and where commodity thermal insulations are the benchmark for envelope-related energy saving measures. That is why, in this project, the main intention was to minimize the production cost and maximize the product energy storage density without sacrificing the PCM performance. It was achieved through: 1. Minimizing the non-PCM components (plastics, additives, packaging/encapsulation materials, etc.) because they are significantly more expensive than salt hydrates, 2. Using highly thermally conductive and lightweight PCM carrier (packaging material) to facilitate more complete phase cycling, and 3. Optimizing the thickness and minimizing air spaces in product design (such as in pouched PCM). For this purpose, our approach was to enable an easy system design, including selection of the PCM operating temperatures, optimizing the necessary heat storage capacity (by stacking together several layers of PCM products), and if needed, a synchronized usage of PCM products of different temperatures. A specially designed, robust, highly thermally conducting and highly impermeable packaging (to retain salt hydrate water during phase transition cycles) was designed and tested to increase the overall system thermal performance and durability. All PCM products developed during this project were tested in both lab scale and in full scale field conditions. It is expected that, after further developments and commercialization, the developed PCM technologies may be also applied in space conditioning, energy storage technologies, and heat transfer applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Framework for X-ray mirror surface shape fitting

For accurate characterization of grazing-incidence X-ray mirrors, we present a comprehensive framework to fit measured surface shapes (either slope or height) of X-ray mirrors used in synchrotron radiation and free-electron laser facilities. We summarize the closed-form expressions of some typical surface shapes of X-ray mirrors including elliptic cylinders, hyperbolic cylinders, ellipsoids, hyperboloids, and diaboloids. This framework is composed of four layers: definition of standard shapes with closed-form expressions, generation of theoretical surface with pose parameters (six degrees of freedom defining an object's position and orientation relative to a coordinate system), parameter optimization with the ability to select which parameters are fit and which are held constant, and the development of user-friendly fitting function wrappers for particular fitting tasks. A few practical fitting examples are demonstrated to verify the effectiveness of the proposed fitting framework. We discuss the physical meanings of the fitting parameters, and provide several examples using the elliptic cylinder and ellipsoid shapes to highlight some features of the framework. Moreover, we provide the presented framework as open-source codes (MATLAB and Python codes available at https://github.com/nsls2omf/xmf) to the community to encourage academic collaboration and further improvements.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Combined Effects of Carbon and Nitrogen Source to Optimize Growth of Proteobacterial Methanotrophs

Methane, a potent greenhouse gas, and methanol, commonly called wood alcohol, are common by-products of modern industrial processes. They can, however, be consumed as a feedstock by bacteria known as methanotrophs, which can serve as useful vectors for biotransformation and bioproduction. Successful implementation in industrial settings relies upon efficient growth and bioconversion, and the optimization of culturing conditions for these bacteria remains an ongoing effort, complicated by the wide variety of characteristics present in the methanotroph culture collection. Here, we demonstrate the variable growth outcomes of five diverse methanotrophic strains - Methylocystis sp. Rockwell, Methylocystis sp. WRRC1, Methylosinus trichosporium OB3b, Methylomicrobium album BG8, and Methylomonas denitrificans FJG1 - grown on either methane or methanol, at three different concentrations, with either ammonium or nitrate provided as nitrogen source. Maximum optical density (OD), growth rate, and biomass yield were assessed for each condition. Further metabolite and fatty acid methyl ester (FAME) analyses were completed for Methylocystis sp. Rockwell and M. album BG8. The results indicate differential response to these growth conditions, with a general preference for ammonium-based growth over nitrate, except for M. denitrificans FJG1. Methane is also preferred by most strains, with methanol resulting in unreliable or inhibited growth in all but M. album BG8. Metabolite analysis points to monitoring of excreted formic acid as a potential indicator of adverse growth conditions, while the magnitude of FAME variation between conditions may point to strains with broader substrate tolerance. These findings suggest that methanotroph strains must be carefully evaluated before use in industry, both to identify optimal conditions and to ensure the strain selected is appropriate for the process of interest. As a result, much work remains in addressing the optimization of growth strategies for these promising microorganisms since disregarding these important steps in process development could ultimately lead to inefficient or failed bioprocesses.

09 BIOMASS FUELS↗
DOE OSTI2025

Machine Learning-Driven Optimization of Building Enclosures for Moisture Durability and Thermal Performance

The design of moisture-durable building enclosures with low embodied carbon often involves an iterative process of selecting the materials for the specific exposure conditions to meet the performance requirements. While hygrothermal simulations are commonly used to evaluate moisture durability, they often require advanced expertise for proper implementation. Machine learning (ML) provides a promising alternative by streamlining the design process and minimizing the reliance on complex simulations. This study presents a machine learning-based approach for predicting moisture durability in residential wall assemblies. The ML model was trained to estimate the mold index and maximum moisture content of various layers under typical exposure conditions. The model achieved a high predictive accuracy, with a coefficient of determination (R²) exceeding 0.90 when compared to traditional hygrothermal simulations on materials that were not part of training the ML model. Building on these results, the ML model was developed into a practical tool for optimizing wall assembly designs. This tool allows users to automatically optimize material selections based on energy, moisture, and carbon performance criteria. By incorporating multi-objective optimization, the tool identifies configurations that minimize embodied carbon while maintaining moisture safety and code-compliant thermal performance. Additionally, it provides insights into how material choices influence assembly durability, energy efficiency, and carbon reduction. The tool will be implemented in the Building Science Advisor (BSA) to enhance its performance and provide more granularity on the results. This research highlights the potential for ML-driven tools to simplify the design of high-performance building enclosures, offering architects and engineers a faster, more efficient way to balance critical performance factors.

Salonvaara, Mikael [ORNL] (ORCID:0000000318991554)↗
DOE OSTI2017

Optimizing the configuration of transport and storage systems

A method is proposed for selecting the configuration of transport and storage systems. The method permits optimization of the configuration of transport hubs and storage systems so as to facilitate loading, unloading, storage, and handling of loads, with minimal capital and operating costs.

42 ENGINEERING↗
DOE OSTI2018

Understanding the Role of Functional Groups in Polymeric Binder for Electrochemical Carbon Dioxide Reduction on Gold Nanoparticles

Electrochemical CO 2 reduction reaction (CO 2 RR) is one of the promising strategies for converting CO 2 to value-added chemicals. Gold (Au) catalysts are considered to be the best benchmarking materials for CO 2 RR to produce CO. In this work, the role of different functional groups of polymeric binders on CO 2 RR over Au catalysts is systematically investigated by combined experimental measurements and density functional theory (DFT) calculations. Especially, it is revealed that the functional groups can play a role in suppressing the undesired hydrogen evolution reaction, the main competing reaction against CO 2 RR, thus enabling more catalytic active sites to be available for CO 2 RR and enhancing the CO 2 RR activity. Consistent with the DFT prediction, fluorine (F)-containing functional groups in the F-rich polytetrafluoroethylene binder lead to a high Faradaic efficiency (≈94.7%) of CO production. Finally, this study suggests a new strategy by optimizing polymeric binders for the selective CO 2 RR.

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

Coordination Stoichiometry Effects on the Binding Hierarchy of Histamine and Imidazole–M 2+ Complexes

Histidine–M 2+ coordination bonds are a recognized bond motif in biogenic materials with high hardness and extensibility, which has led to growing interest in their use in soft materials for mechanical function. However, the effect of different metal ions on the stability of the coordination complex remains poorly understood, complicating their implementation in metal-coordinated polymer materials. Herein, rheology experiments and density functional theory calculations are used to characterize the stability of coordination complexes and establish the binding hierarchy of histamine and imidazole with Ni 2+ , Cu 2+ , and Zn 2+ . It is found that the binding hierarchy is driven by the specific affinity of the metal ions to different coordination states, which can be macroscopically tuned by changing the metal-to-ligand stoichiometry of the metal-coordinated network. These findings facilitate the rational selection of metal ions for optimizing the mechanical properties of metal-coordinated materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗