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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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Search for single production of a vector-like T quark decaying to a top quark and a neutral scalar boson in the lepton+jets final state in proton-proton collisions at $\sqrt{s}=13$ TeV

A search for single production of a vector-like T quark with charge 2e/3, decaying to a top quark and a neutral scalar boson is presented. The boson can be a standard model Higgs boson (H) or a new scalar boson (ϕ). In the first case, a branching fraction of 25% is assumed for the decay T → tH, while in the second case the T quark is assumed to decay exclusively to tϕ. The top quark is identified via its lepton+jets decay, and the neutral boson via its decay into a bottom quark-antiquark pair. Final states with Lorentz-boosted topologies are considered and machine-learning techniques are exploited for optimal event classification. The analysis uses data collected by the CMS experiment in proton-proton collisions at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 138 fb −1 recorded at the CERN LHC in 2016–2018. Upper limits at 95% confidence level are set on the product of cross section and branching fraction for a T quark in a narrow-width approximation. They vary between 14.7 and 0.1 fb, for T quark masses in the range 1.3–3.0 TeV and ϕ boson masses in the range 25–250 GeV. These are the first exclusion limits set on the production of a single T quark decaying into a top quark and a new neutral scalar boson. For the decay channel into a top quark and a standard model Higgs boson, the results provide the best limits on production cross sections to date, for T quark masses above 2 TeV.

hadron-hadron scattering↗
DOE OSTI2023

Scalable computations for nonstationary Gaussian processes

Nonstationary Gaussian process models can capture complex spatially varying dependence structures in spatial datasets. However, the large number of observations in modern datasets makes fitting such models computationally intractable with conventional dense linear algebra. In addition, derivative-free or even first-order optimization methods can be very slow to converge when estimating many spatially varying parameters. In this paper, we present a computational framework which couples an algebraic block diagonal plus low-rank covariance matrix approximation with stochastic trace estimation to facilitate the efficient use of second-order solvers for maximum likelihood estimation of Gaussian process models with many parameters. We demonstrate the effectiveness of these methods by simultaneously fitting 192 parameters in the popular nonstationary model of Paciorek and Schervish using 107,600 sea surface temperature anomaly measurements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Maximizing energy efficiency in wastewater treatment plants: A data-driven approach for waste heat recovery and an economic analysis using Organic Rankine Cycle and thermal energy storage

Maximizing energy efficiency through waste heat recovery (WHR) processes is crucial for sustainable and ecofriendly operations across multiple industries, notably in wastewater treatment plants (WWTPs). This work proposes a comprehensive approach for assessing the WHR feasibility in WWTPs, structured in two main objectives. Firstly, an Artificial Neural Network (ANN) model is developed to accurately predict WHR based on operational data, including biogas temperature, biogas pressure, daily production in kWh, and WHR values in kWh th . The second objective focuses on economically evaluating the WHR feasibility based on the estimated WHR values obtained by the ANN model, and then realistically assessing the economic feasibility of integrating the Organic Rankine Cycle (ORC) and Seasonal Thermal Energy Storage (STES) systems. With an application to the As-Samra WWTP located in Jordan, the developed ANN model demonstrates promising results in the validation phase, with a root mean square error (RMSE) of 2206 kWh/day, a mean absolute error (MAE) of 1674 kWh/day, and an R-squared (R 2 ) value of 68%. On the other hand, the economic analysis reveals that an optimal ORC system of 412.14 kW e capacity yields a Net Present Value (NPV) of 2.09 million US dollars, a Levelized Cost of Energy (LCOE) of 0.0749 USD/kWh, a Payback Period (PBP) of 4.8 years, and annual revenues of 428 kUSD. This work also investigates the techno-economic feasibility of integrating ORC-STES. Results indicate that the LCOE and PBP are highly affected by the ORC's capital cost, and integrating STES increases the LCOE to 0.0824 USD/kWh, rendering its integration with ORC infeasible. In conclusion, this study aims to advance the understanding and application of WHR in WWTPs, paving the way for more efficient and sustainable practices in the field.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Phenomena-based graph representations and applications to chemical process simulation

Rapid and robust simulation of chemical processes is critical to conduct process design, optimization, techno-economic analysis, and sustainability analysis. Yet, efficiently solving simulation models remains a challenge due to the highly coupled and nonlinear nature of the underlying algebraic equations that capture the physical phenomena taking place in the process (e.g., material and energy conservation, phase equilibrium, reactions). In this work, we show that graph-theoretic representations of the physical phenomena within unit operations can help navigate and decompose equations to systematically identify alternative approaches for fast and robust numerical solutions. Specifically, we present a graph-theoretic abstraction that captures the connectivity between the model variables/equations and use this abstraction to group variables/equations into fundamental phenomena. We show that phenomena-based decomposition of the underlying equations can help decouple nonlinearities and enforce material/energy conservation at the process level to accelerate convergence. The proposed decomposition approach differs from the more traditional sequential modular simulation approach, in which equations are grouped and decomposed by unit operations. We implemented the phenomena-based decomposition in BioSTEAM—an open-source process simulation platform in Python—and demonstrated that this approach can converge a variety of separation process models. Compared to sequential modular simulation, the phenomena-based approach can converge idealized systems faster, but it can be slower for (or even fail to converge) highly coupled and nonideal process systems.

Convergence↗
DOE OSTI2022

An analytical method for identifying synergies between behind-the-meter battery and thermal energy storage

Electric utilities build generation capacity to meet the highest demand period, and they often pass on the costs associated with these peaking generators to building owners through demand charges. Building owners can minimize these demand charges by shifting energy use away from peak periods with behind-the-meter storage. This storage can include batteries, which can directly shift the metered load, or thermal energy storage, which can shift thermal-driven electric loads like air conditioning. However, there is a lack of research on how best to combine battery and thermal energy storage. In this study, we develop an analytical sizing method to calculate the potential demand reduction and annualized cost savings for different combinations of thermal and battery energy storage sizes. We show that adding batteries to a thermal energy storage system can increase the total system's load shaving potential. This is particularly true when the building has onsite photovoltaic generation or electric vehicle charging, which add significant variability to the load shape. We also show that for a given total storage size, selecting a higher fraction of thermal energy storage can significantly lower the cycling of the battery, and therefore extend the battery life. This, combined with the expected lower first cost of thermal energy storage materials compared to batteries, shows that hybrid energy storage systems can outperform a standalone battery or standalone thermal storage system. Assuming the thermal storage has a capital cost 6x lower than the battery, our analysis shows that the optimal system is 71% thermal energy storage and 29% battery energy storage for a scenario with electric vehicle charging. The annualized cost savings for this system are $48.6 k/yr, whereas an equivalently sized standalone thermal energy storage system would provide annualized cost savings of $28.5 k/yr and a standalone battery would lead to savings of $8.72 k/yr. The hybrid system also reduces battery cycling by 52% compared to a standalone battery, extending battery lifetime.

25 ENERGY STORAGE↗
DOE OSTI2018

Catabolic repression in early-diverging anaerobic fungi is partially mediated by natural antisense transcripts

We report that early-diverging anaerobic fungi (order: Neocallimastigomycota), lignocelluolytic chytrid-like fungi central to fiber degradation in the digestive tracts of large herbivores, are attractive sources of cellulases and hemicellulases for biotechnology. Enzyme expression is tightly regulated and coordinated through mechanisms that remain unelucidated to optimize hydrolytic efficiency. Our analysis of anaerobic fungal transcriptomes reveals hundreds of cis-natural antisense transcripts (cis-NATs), which we hypothesize play an integral role in this regulation. Through integrated genomic and transcriptomic sequencing on a range of catabolic substrates, we validate these NATs in three species (Anaeromyces robustus, Neocallimasix californiae, and Piromyces finnis), and analyze their expression patterns and prevalence to gain insight into their function. NAT function was diverse and conserved across the three fungal genomes studied, with 10% of all metabolic process NATs associated with lignocellulose hydrolysis. Despite these similarities, however, only eleven gene targets were conserved orthologs. Several NATs were dynamically regulated by lignocellulosic substrates while their gene targets were unregulated. This observation is consistent with a hypothesized, but untested, regulatory mechanism where selected genes are exclusively regulated at the transcriptional/post-transcriptional level by NATs. However, only genes with high NAT relative expression levels displayed this phenomenon, suggesting a selection mechanism that favors larger dynamic ranges for more precise control of gene expression. In addition to this mode, we observed two other possible regulatory fates: canonical transcriptional regulation with no NAT response, and positive co-regulation of target mRNA and cognate NAT, which we hypothesize is a fine-tuning strategy to locally negate control outputs from global regulators. In conclusion, our work reveals the complex contributions of antisense RNA to the catabolic response in anaerobic fungi, highlighting its importance in understanding lignocellulolytic activity for bioenergy applications. More importantly, the relative expression of NAT to target may form a critical determinant of transcriptional vs post-transcriptional (NAT) control of gene expression in primitive anaerobic fungi.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

Efficient gradient estimation using finite differencing and likelihood ratios for kinetic Monte Carlo simulations

While many optimization and control methods for stochastic processes require gradient information from the process of interest, obtaining gradient information from experiments is prohibitively expensive and time-consuming. As a result, such information is often obtained from stochastic process simulations. Computing gradients efficiently and accurately from stochastic simulations is challenging, especially for simulations involving computationally expensive models with significant inherent noise. In this work, we analyze and characterize the applicability of two gradient estimation methods for kinetic Monte Carlo simulations: finite differencing and likelihood ratio. We developed a systematic method for choosing an optimal perturbation size for finite differencing and discuss, for both methods, important implementation issues such as scaling with respect to the number of elements in the gradient vector. Through a series of numerical experiments, the methods were compared across different time and size regimes to characterize the precision and accuracy associated with each method. We determined that the likelihood ratio method is appropriate for estimating gradients at short (transient) times or for systems with small population sizes, whereas finite differencing is better-suited for gradient estimation at long times (steady state) or for systems with large population sizes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Analysis of Vector Particle-In-Cell (VPIC) memory usage optimizations on cutting-edge computer architectures

Vector Particle-In-Cell (VPIC) is one of the fastest plasma simulation codes in the world, with particle numbers ranging from one trillion on the first petascale system, Roadrunner, to ten trillion particles on the more recent Blue Waters supercomputer. As supercomputers continue to grow rapidly in size, so too does the gap between computing capability and memory capability. Current memory systems limit VPIC simulations greatly as the maximum number of particles that can be simulated directly depends on the available memory. In this study, we present a suite of VPIC memory optimizations (i.e., particle weight, half-precision, and fixed-point optimizations) that enable a significant increase in the number of particles in VPIC simulations. Here, we assess the optimizations’ impact on memory and runtime performance for a suite of cutting-edge computer architectures such has the NVIDIA V100 GPU, the IBM Power9, and the Fujitsu A64FX architectures. Our optimizations enable a 31.25% reduction in memory usage and up to 40% increase in the number of particles. This paper extends our work on developing particle storage format optimizations Tan et al.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Ethanol-fueled metal supported solid oxide fuel cells with a high entropy alloy internal reforming catalyst

High-performance metal supported solid oxide fuel cells (MS-SOFC) with an integrated high entropy alloy (HEA) internal reforming catalyst (IRC) are demonstrated for transportation applications using ethanol and methanol as fuels. Addition of the HEA IRC dramatically improves cell performance and stability when using ethanol/water blend fuel. Absence of carbon deposition predicted by thermodynamic calculations is confirmed by Raman spectroscopy analysis of posttest anodes. Optimal catalyst processing (deposition technique, loading, firing temperature) and cell operation conditions (flow rates, temperature, fuel compositions) are explored. Infiltrated HEA reforming catalyst provides a highly porous structure and low catalyst loading (6 mg cm –2 ). The designed structure and catalysts achieve small mass transport resistances in the fuel electrode (26.2 s m –1 ) and oxygen electrode (41.6 s m –1 ). The best ethanol concentration (60:40 v% ethanol: water) provides 0.83 W cm –1 at 700 °C, without carbon deposition. The ethanol-fueled MS-SOFC is operated for 500 h, including five thermal cycles. As a result, cell evolution is similar to that reported previously for hydrogen fuel; nickel aggregation and chromia deposition were the major observed changes, and carbon formation can be avoided even after long-term operation.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2015

A new method for the preparation of a Fe{sub 3}O{sub 4}/graphene hybrid material and its applications in electromagnetic wave absorption

Graphical abstract: A microwave-assisted solvothermal process was used to prepare Fe{sub 3}O{sub 4} nanoparticles/graphene hybrids, which could be applied as an electromagnetic (EM) radiation absorbent. The absorber, composed of 20 wt% Fe{sub 3}O{sub 4}/graphene–epoxy, exhibited a dual-frequency reflection characteristic covering the C and Ku bands with maximum reflection losses of less than −20 dB at thicknesses of 4 and 5 mm. - Highlights: • Fe{sub 3}O{sub 4}/graphene composites were prepared by a microwave-assisted solvothermal route. • Uniform loading of Fe{sub 3}O{sub 4} nanoparticles on graphene was obtained. • The products as-synthesized show great promise as a microwave absorption material. • Synergistic effects of Fe{sub 3}O{sub 4} and graphene caused improved absorption efficiency. • The Fe{sub 3}O{sub 4}/graphene product possessed a dual-frequency reflection characteristic. - Abstract: A rapid, simple, and inexpensive process combining a microwave-assisted technique and a solvothermal method has been developed using graphene sheets and FeCl{sub 3}·6H{sub 2}O as the reactant to prepare graphene/Fe{sub 3}O{sub 4} nanoparticle hybrids, which can be applied as an electromagnetic radiation absorbent. The experimental factors (i.e., composition ratio, microwave power, and irradiation time) on the products’ characteristics were examined. Under optimal conditions, the morphological analysis revealed that the graphene sheet was homogeneously covered with Fe{sub 3}O{sub 4} nanoparticles (∼50 nm). The electromagnetic parameters of the composites made from 20 wt% Fe{sub 3}O{sub 4}/graphene–epoxy were measured by a vector network analyzer. It was found that the 4- and 5 mm-thick composites could attain a reflection loss below −20 dB in the dual-ranges of 4–8 and 12–18 GHz.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Microstructure and mechanical properties of high Mn-containing ferritic-martensitic alloys exposed to cyclical thermal treatment

Substantial residual tensile stress tends to accumulate in currently available high-Cr ferritic martensitic steels that are subjected to cyclical heat treatment, which leads to premature brittle fracture. By tailoring the alloy composition, this thermal cycling can be exploited to induce a high number density of nanoprecipitates and phase transformations countering residual tensile stresses. In this work, three new variants of ferritic-martensitic steels have been designed with computational thermodynamics to meet the goals of mitigating residual tensile stresses by lowering martensite start temperatures and of enhancing mechanical strength and irradiation sink strength by increasing the number density of nanoprecipitates. Furthermore, ctenast materials were subjected to cyclical heat treatment. The thermally cycled samples were evaluated with mechanical testing and microstructural analysis to identify the optimal composition in which figures of merit include low residual stress and a high density of nanoscale MX (M = metal, X = C/N) precipitates, leading to high yield strength with reasonable ductility. The noticeably higher density of nanoprecipitates in the optimal alloy favor its higher yield strength, which is supported by the microstructure-derived yield strength calculation and precipitation kinetics simulation.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Enhancing Neutron Beam Production with a Convoluted Moderator

We describe a new concept for a neutron moderating assembly resulting in the more efficient production of slow neutron beams. The Convoluted Moderator, a heterogeneous stack of interleaved moderating material and nearly transparent single-crystal spacers, is a directionally-enhanced neutron beam source, improving beam effectiveness over an angular range comparable to the range accepted by neutron beam lines and guides. We have demonstrated gains of 50% in slow neutron intensity for a given fast neutron production rate while simultaneously reducing the wavelength-dependent emission time dispersion by 25%, both coming from a geometric effect in which the neutron beam lines view a large surface area of moderating material in a relatively small volume. Additionally, we have confirmed a Bragg-enhancement effect arising from coherent scattering within the single-crystal spacers. We have not observed hypothesized refractive effects leading to additional gains at long wavelength. In addition to confirmation of the validity of the Convoluted Moderator concept, our measurements provide a series of benchmark experiments suitable for developing simulation and analysis techniques for practical optimization and eventual implementation at slow neutron source facilities.

Grooved moderator↗
DOE OSTI2016

A convex penalty for switching control of partial differential equations

A convex penalty for promoting switching controls for partial differential equations is introduced; such controls consist of an arbitrary number of components of which at most one should be simultaneously active. Using a Moreau–Yosida approximation, a family of approximating problems is obtained that is amenable to solution by a semismooth Newton method. In conclusion, the efficiency of this approach and the structure of the obtained controls are demonstrated by numerical examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Smartphone-based Dual-channel Immunochromatographic Test Strip with Polymer Quantum Dot Labels for Simultaneous Detection of Cypermethrin and 3-phenoxybenzoic acid

Currently, gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) are the primary methods used to detect pesticides and their metabolites for biomonitoring of exposure. Although GC-MS and LC-MS can provide accurate and sensitive measurements, these techniques are not suitable for point-of-care or in-field biomonitoring applications. The objective of this work is to develop a smartphone-based dual-channel immunochromatographic test strip (ICTS) for on-site biomonitoring of exposure to cypermethrin by simultaneous detection of cypermethrin and its metabolite, 3-phenoxybenzoic acid (3-PBA). Polymer carbon dots (PCDs) with ultra-high fluorescent brightness were synthesized and used as signal amplifier in ICTS assay. Cypermethrin (a representative pyrethroid pesticide) and its major metabolite 3-PBA were simultaneously detected to provide more comprehensive analysis of cypermethrin exposure. After competitive immunoreactions between the target sample and the coating antigens preloaded on the test line, the tracer antibody (PCDs conjugated antibody) was quantitatively captured on the test lines. The captured PCDs were inversely proportional to the amount of the target compound in sample. The red fluorescence on the test line then was recorded by a smartphone-based device capable of conducting image analysis and recording. Under optimal conditions, the sensor showed excellent linear responses for detecting cypermethrin and 3-PBA ranging from 1 ng/mL to 100 ng/mL and from 0.1 ng/mL to 100 ng/mL, the limits of detection were calculated to be ~0.35 ng/mL for cypermethrin and ~0.04 ng/mL for 3-PBA, respectively. The results demonstrate that the ICTS device is promising for accurate point-of-care biomonitoring of pesticide exposure.

Zhao, Yuting↗
DOE OSTI2024

Adaptive Data-Driven Deep-Learning Surrogate Model for Frontal Polymerization in Dicyclopentadiene

Frontal polymerization (FP) is a self-sustaining curing process that enables rapid and energy-efficient manufacturing of thermoset polymers and composites. Computational methods conventionally used to simulate the FP process are time-consuming, and repeating simulations are required for sensitivity analysis, uncertainty quantification, or optimization of the manufacturing process. Here, in this work, we develop an adaptive surrogate deep-learning model for FP of dicyclopentadiene (DCPD), which predicts the evolution of temperature and degree of cure orders of magnitude faster than the finite-element method (FEM). The adaptive algorithm provides a strategy to select training samples efficiently and save computational costs by reducing the redundancy of FEM-based training samples. The adaptive algorithm calculates the residual error of the FP governing equations using automatic differentiation of the deep neural network. A probability density function expressed in terms of the residual error is used to select training samples from the Sobol sequence space. The temperature and degree of cure evolution of each training sample are obtained by a 2D FEM simulation. The adaptive method is more efficient and has a better prediction accuracy than the random sampling method. With the well-trained surrogate neural network, the FP characteristics (front speed, shape, and temperature) can be extracted quickly from the predicted temperature and degree-of-cure fields.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Understanding the Impacts of Biomass Blending on the Uncertainty of Hydrolyzed Sugar Yield from a Stochastic Perspective

Feedstock price and availability are key challenges for biorefinery development and biomass blending has been suggested as a route to overcome these limitations. However, the impacts of feedstock blending on the variability of yields of hydrolyzed sugar remained unclear. This study quantifies the uncertainties in the sugar yields from the blends of corn stover, switchgrass, and grass clippings by considering both variable and model uncertainties. The results indicate that feedstock blending reduces the uncertainties in sugar yields and delivers feedstock of more uniform quality. A further investigation on xxx demonstrates that the reduction in overall uncertainty is achieved by reduced compositional uncertainties of blended feedstock. Based on these results, the optimization of blending ratios was performed with respect to three objectives: (1) probability of meeting the sugar yield target; (2) expected sugar yields; and (3) sugar yield per unit feedstock expense, while satisfying constraints of feedstock availability and cost. In conclusion, the optimization method developed in this study, is readily applied to other combinations of feedstocks, biofuels production processes, and constraints.

09 BIOMASS FUELS↗