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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 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Davis Computational Spectroscopy Workflow—From Structure to Spectra

Here, we describe an automated workflow that connects a series of atomic simulation tools to investigate the relationship between atomic structure, lattice dynamics, materials properties, and inelastic neutron scattering (INS) spectra. Starting from the atomic simulation environment (ASE) as an interface, we demonstrate the use of a selection of calculators, including density functional theory (DFT) and density functional tight binding (DFTB), to optimize the structures and calculate interatomic force constants. We present the use of our workflow to compute the phonon frequencies and eigenvectors, which are required to accurately simulate the INS spectra in crystalline solids like diamond and graphite as well as molecular solids like rubrene. We have also implemented a machine-learning force field based on Chebyshev polynomials called the Chebyshev interaction model for efficient simulation (ChIMES) to improve the accuracy of the DFTB simulations. We then explore the transferability of our DFTB/ChIMES models by comparing simulations derived from different training sets. We show that DFTB/ChIMES demonstrates ~100× reduction in computational expense while retaining most of the accuracy of DFT as well as yielding high accuracy for different materials outside of our training sets. The DFTB/ChIMES method within the workflow expands the possibilities to use simulations to accurately predict materials properties of increasingly complex structures that would be unfeasible with ab initio methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Neutron Guide System for Ultracold and Cold Neutrons at the WWR-M Reactor

The results of calculation of fluxes of ultracold (UCNs), very cold, and cold neutrons at the output of neutron guides of the UCN source with superfluid helium at the WWR-M reactor are presented. UCN density ρ{sub 35L} = 1.3 × 10{sup 4} n/cm{sup 3} in the trap of the electric dipole moment (EDM) spectrometer was obtained by optimizing source parameters. This UCN density in the EDM spectrometer is two orders of magnitude higher than the UCN density at the output of the available UCN sources. The flux density of cold neutrons with a wavelength of 2–20 Å at the output of a neutron guide with a cross section of 30 × 200 mm{sup 2} should be as high as 1.1 × 10{sup 8} n/(cm{sup 2} s), while the flux density of very cold neutrons (50–100 Å) at the output of the same neutron guide should be 2.3 × 10{sup 5} n/(cm{sup 2} s). An extensive program of fundamental and applied physical research was mapped out for this source.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

Planning Target Volume D95 and Mean Dose Should Be Considered for Optimal Local Control for Stereotactic Ablative Radiation Therapy

Purpose: To identify the optimal dose parameters predictive for local/lobar control after stereotactic ablative radiation therapy (SABR) in early-stage non-small cell lung cancer (NSCLC). Methods and Materials: This study encompassed a total of 1092 patients (1200 lesions) with NSCLC of clinical stage T1-T2 N0M0 who were treated with SABR of 50 Gy in 4 fractions or 70 Gy in 10 fractions, depending on tumor location/size, using computed tomography-based heterogeneity corrections and a convolution superposition calculation algorithm. Patients were monitored by chest CT or positron emission tomography/CT and/or biopsy after SABR. Factors predicting local/lobar recurrence (LR) were determined by competing risk multivariate analysis. Continuous variables were divided into 2 subgroups at cutoff values identified by receiver operating characteristic curves. Results: At a median follow-up time of 31.7 months (interquartile range, 14.8-51.3 months), the 5-year time to local recurrence within the same lobe and overall survival rates were 93.8% and 44.8%, respectively. Total cumulative number of patients experiencing LR was 40 (3.7%), occurring at a median time of 14.4 months (range, 4.8-46 months). Using multivariate competing risk analysis, independent predictive factors for LR after SABR were minimum biologically effective dose (BED{sub 10}) to 95% of planning target volume (PTVD95 BED{sub 10}) ≤86 Gy (corresponding to PTV D95 physics dose of 42 Gy in 4 fractions or 55 Gy in 10 fractions) and gross tumor volume ≥8.3 cm{sup 3}. The PTVmean BED{sub 10} was highly correlated with PTVD95 BED{sub 10.} In univariate analysis, a cutoff of 130 Gy for PTVmean BED{sub 10} (corresponding to PTVmean physics dose of 55 Gy in 4 fractions or 75 Gy in 10 fractions) was also significantly associated with LR. Conclusions: In addition to gross tumor volume, higher radiation dose delivered to the PTV predicts for better local/lobar control. We recommend that both PTVD95 BED{sub 10} >86 Gy and PTVmean BED{sub 10} >130 Gy should be considered for SABR plan optimization.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Design and dynamic modeling of printed circuit heat exchangers for supercritical carbon dioxide Brayton power cycles

Due to the unique geometries and hydraulics of printed circuit heat exchangers and rapidly changing properties of supercritical carbon dioxide, the effective design and rating of printed circuit heat exchangers is an essential requirement for their use in supercritical carbon dioxide power cycles. In this study, one-dimensional design and dynamic models have been developed in Aspen Custom Modeler for printed circuit heat exchangers utilized in printed circuit heat exchangers Brayton power cycles. The design model is used to determine the optimal geometry parameters by minimizing the metal mass. The dynamic model is used to predict transient behavior and can be easily implemented into system-level models developed in Aspen Plus Dynamics for cycle performance evaluations. In these models, the heat transfer coefficient and friction factor are calculated using data reported by Heatric, a prominent printed circuit heat exchanger manufacturer. Both models are validated by comparing with the data from a small-scale exchanger used in the 100 kWe facility operated by the Naval Nuclear Laboratory, and then applied to design and simulate low- and high-temperature recuperators for a 10 MWe supercritical carbon dioxide indirect recompression closed Brayton cycle, which is of interest to the U.S. Department of Energy. The designs and dynamic responses of the printed circuit heat exchangers are compared with conventional shell-and-tube exchangers and microtube shell-and-tube exchangers for the same applications. Here, the simulation results indicate that the proposed printed circuit heat exchangers have fast dynamic responses due to their small metal masses and high heat transfer coefficients compared with the conventional shell-and-tube exchangers. Even though the metal masses of the designed PCHEs are slightly higher than those of the microtube shell-and-tube exchangers, the printed circuit heat exchangers are still promising candidates for heat recuperation because of their mature manufacturing procedures and abundant laboratory and industrial operating experience.

42 ENGINEERING↗
NASA NTRS1988

User's manual for an aerodynamic optimization scheeme that updates flow variables and design parameters simultaneously

This user's manual is presented for an aerodynamic optimization program that updates flow variables and design parameters simultaneously. The program was developed for solving constrained optimization problems in which the objective function and the constraint function are dependent on the solution of the nonlinear flow equations. The program was tested by applying it to the problem of optimizing propeller designs. Some reference to this particular application is therefore made in the manual. However, the optimization scheme is suitable for application to general aerodynamic design problems. A description of the approach used in the optimization scheme is first presented, followed by a description of the use of the program.

Rizk, Magdi H.↗
DOE OSTI2022

AI-Enabled Discovery and Physics-Based Optimization of Energy Efficient Processing Strategies for Advanced Turbine Alloys (Final Technical Report)

In this project, the multi-organizational team of academic and industrial researchers from the University of Kentucky an aerospace and energy generation OEM partner has leveraged novel Digital Process Twin (DPT) models of process/structure interactions (i.e., process-induced surface integrity) to advance a paradigm of fully integrated computational materials engineering (ICME). Using efficient process models as the core of a digital process simulator for a reinforcement learning algorithm, the team has integrated industrial data and metrics of structure/performance/energy relationships and manufacturing-related energy metrics to optimize dynamic processing parameters for significantly improved life-cycle energy efficiency of advanced γ-TiAl low-pressure turbine (LPT) alloys, as indicated by a set of design relevant parameters (e.g., residual stresses and scrap rate). The key objective and anticipated outcome of the project was at least a 10% reduction in life-cycle embodied energy for a recently developed, γ-TiAl low-pressure turbine (LPT) alloy and nickel-based superalloy Inconel 718, through the adoption of the proposed AI-enabled process optimization approach. The final project outcomes significantly exceeded this original target, realizing manufacturing-related energy efficiency improvements of more than 130% for TiAl and up to 80% for Inconel 718. Rather than following the prevailing and highly inefficient empirical paradigm, the proposed study demonstrated the feasibility of adopting a digital, physics-based process design and optimization paradigm. The recurring need for manual intervention, rework, reinspection causes significant production bottlenecks and unnecessary expense associated with delivering the requisite component quality. The OEM partner, and turbine industry in general, expect to reap significant cost and resource savings if an AI-optimized set of parameters can be applied to specific machining operations. The technical scope of the proposed project involved the paving of a realistic path towards model-based and AI-enabled Integrated Computational Materials Engineering (ICME), and away from inefficient empirical process optimization and legacy manufacturing practices, which are no longer able to efficiently process novel high-performance turbine alloy materials. The project team will address the fundamental knowledge gap that currently exists within the ICME paradigm with respect to the process/structure/performance/energy impacts of finishing processes. While significant resources have been devoted to the ‘early stages’ of manufacturing, such as alloy design, primary and secondary processing, finishing processes have not been adequately integrated within ICME. To provide an actionable path towards model-based finishing process design (e.g., machining, burnishing, grinding, polishing), we will employ a novel AI-enabled process optimization paradigm, based on a computationally efficient, physics-based process simulator. Through limited experimental work to calibrate and validate our process simulator model via an advanced in-situ characterization technique and process optimization via reinforcement learning, the project will seek to demonstrate a viable alternative to the inefficient ‘legacy’ processing strategies, empirical testing and broad scope machining learning approaches, all of which fail to adequately consider complex process physics. The project team has identified an intermetallic γ-TiAl LPT alloy, which is currently being used as part of the OEM partner’s advanced gas turbine designs. This particular alloy poses significant manufacturing challenges during finishing operations, which limit the degree to which the current turbine design can be manufactured in an energy- and cost-efficient manner. Empirical testing and numerical modeling efforts to optimize processing parameters for γ-TiAl have not been able to resolve these manufacturing challenges, so the proposed physics-based AI-enabled optimization technology would offer a truly novel and transformative capability. The multi-organizational team of academic and industry experts from the UKY and the OEM partner will work together closely to demonstrate the analytical and experimental critical function and characteristic proof of concept of this novel approach.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Digital Tuning of the Transition Temperature of Epitaxial VO 2 Thin Films on MgF 2 Substrates by Strain Engineering

Abstract Straining the vanadium dimers along the rutile c ‐axis can be used to tune the metal‐to‐insulator transition (MIT) of VO 2 but has thus far been limited to TiO 2 substrates. In this work VO 2 /MgF 2 epitaxial films are grown via molecular beam epitaxy (MBE) to strain engineer the transition temperature ( T MIT ). First, growth parameters are optimized by varying the synthesis temperature of the MgF 2 (001) substrate ( T S ) using a combination of X‐ray diffraction techniques, temperature dependent transport, and soft X‐ray photoelectron spectroscopy. It is determined that T S values greater than 350 °C induce Mg and F interdiffusion and ultimately the relaxation of the VO 2 layer. Using the optimized growth temperature, VO 2 /MgF 2 (101) and (110) films are then synthesized. The three film orientations display MITs with transition temperatures in the range of 15–60 °C through precise strain engineering.

Howard, Sebastian A.↗
DOE OSTI2026

From Pyrolysis Oil to Advanced Biographite Anode: Unravelling Biocoke Structural Evolution and Delayed Coking Effects

Catalytic graphitization of biomass-derived carbon offers a promising route to produce biographite as a sustainable alternative to petroleum-based synthetic graphite for lithium-ion battery (LIB) anodes. This study investigates the physicochemical properties of biocokes produced from pyrolysis oil at carbonization temperatures ranging from 150 degrees C to 500 degrees C. Using an iron (Fe) catalyst, graphitization was performed at 1500 degrees C, significantly lower than the ~3000 degrees C required for conventional synthetic graphite. The effects of introducing an intermediate-temperature hold (400 degrees C-600 degrees C) prior to graphitization were evaluated, simulating a "delayed coking" process to enable the coproduction of sustainable aviation fuels (SAFs). Chemical structure evolution during biocoke formation was analyzed, and proposed mechanisms are presented. Biographites produced via the delayed coking pathway exhibited high crystallinity and excellent electrochemical performance in both half-cell and full-cell LIB configurations. The full cells exhibited an initial discharge capacity close to the theoretical capacity of the NMC622 cathode (175 mAh/g at 4.2 V), and high capacity retention (~88%) after 150 cycles. Notably, the graphitic and electrochemical properties remained stable across the range of intermediate hold temperatures. These findings provide a foundation for optimizing temperature parameters in delayed coking systems to enable scalable, integrated production of biographite and SAFs from pyrolysis oil.

09 BIOMASS FUELS↗
DOE OSTI2023

Frequency‐Scanning Considerations in Axionlike Dark Matter Spin‐Precession Experiments

Abstract Galactic dark matter may consist of axionlike particles (ALPs) that can be described as an “ultralight bosonic field” oscillating at the ALP Compton frequency. The ALP field can be searched for using nuclear magnetic resonance (NMR), where resonant precession of spins of a polarized sample can be sensitively detected. The ALP mass to which the experiment is sensitive is scanned by sweeping the bias magnetic field. The scanning either results in detection of ALP dark matter or rules out ALP dark matter with sufficiently strong couplings to nuclear spins over the range of ALP masses corresponding to the covered span of Larmor frequencies. In this work, scanning strategies are analyzed with the goal of optimizing the parameter‐space coverage via a proper choice of experimental parameters (e.g., the effective transverse relaxation time).

Zhang, Yuzhe (ORCID:0009000660829066)↗
DOE OSTI2018

Harnessing a methane‐fueled, sediment‐free mixed microbial community for utilization of distributed sources of natural gas

Abstract Harnessing the metabolic potential of uncultured microbial communities is a compelling opportunity for the biotechnology industry, an approach that would vastly expand the portfolio of usable feedstocks. Methane is particularly promising because it is abundant and energy‐rich, yet the most efficient methane‐activating metabolic pathways involve mixed communities of anaerobic methanotrophic archaea and sulfate reducing bacteria. These communities oxidize methane at high catabolic efficiency and produce chemically reduced by‐products at a comparable rate and in near‐stoichiometric proportion to methane consumption. These reduced compounds can be used for feedstock and downstream chemical production, and at the production rates observed in situ they are an appealing, cost‐effective prospect. Notably, the microbial constituents responsible for this bioconversion are most prominent in select deep‐sea sediments, and while they can be kept active at surface pressures, they have not yet been cultured in the lab. In an industrial capacity, deep‐sea sediments could be periodically recovered and replenished, but the associated technical challenges and substantial costs make this an untenable approach for full‐scale operations. In this study, we present a novel method for incorporating methanotrophic communities into bioindustrial processes through abstraction onto low mass, easily transportable carbon cloth artificial substrates. Using Gulf of Mexico methane seep sediment as inoculum, optimal physicochemical parameters were established for methane‐oxidizing, sulfide‐generating mesocosm incubations. Metabolic activity required >∼40% seawater salinity, peaking at 100% salinity and 35 °C. Microbial communities were successfully transferred to a carbon cloth substrate, and rates of methane‐dependent sulfide production increased more than threefold per unit volume. Phylogenetic analyses indicated that carbon cloth‐based communities were substantially streamlined and were dominated by Desulfotomaculum geothermicum . Fluorescence in situ hybridization microscopy with carbon cloth fibers revealed a novel spatial arrangement of anaerobic methanotrophs and sulfate reducing bacteria suggestive of an electronic coupling enabled by the artificial substrate. This system: 1) enables a more targeted manipulation of methane‐activating microbial communities using a low‐mass and sediment‐free substrate; 2) holds promise for the simultaneous consumption of a strong greenhouse gas and the generation of usable downstream products; and 3) furthers the broader adoption of uncultured, mixed microbial communities for biotechnological use.

03 NATURAL GAS↗
DOE OSTI2020

Surface Design for Immobilization of an Antimicrobial Peptide Mimic for Efficient Anti-Biofouling

Microbial surface attachment negatively impacts a wide range of devices from water purification membranes to biomedical implants. Mimics of antimicrobial peptides (AMPs) constituted from poly(N-substituted glycine) „peptoids“ are of great interest as they resist proteolysis and can inhibit a wide spectrum of microbes. We investigate how terminal modification of a peptoid AMPmimic and its surface immobilization affect antimicrobial activity. We also demonstrate a convenient surface modification strategy for enabling alkyne–azide „click“ coupling on amino-functionalized surfaces. Our results verified that the N- and C-terminal peptoid structures are not required for antimicrobial activity. Moreover, our peptoid immobilization density and choice of PEG tether resulted in a „volumetric“ spatial separation between AMPs that, compared to past studies, enabled the highest AMP surface activity relative to bacterial attachment. Our analysis suggests the importance of spatial flexibility for membrane activity and that AMP separation may be a controlling parameter for optimizing surface anti-biofouling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Nanographene Aerogels: Size Effect of the Precursor Graphene Oxide on Gelation Process and Electrochemical Properties

Improving the electrochemical properties of graphene aerogels (GAs) without doping or making composites is an attractive synthetic strategy. Here in this work we report on the effects of graphene sheet dimensions on the properties of GAs. Nanographene aerogels (nG-AGs) were synthesized using nanographene oxide (nGO) powder with a mean platelet diameter of 90 nm. In-situ Fourier transform infrared spectroscopy (FTIR) during gelation revealed a longer fast-gelation regime for nG-AGs than for stdG-AGs. The surface-area-normalized capacitance of nG-AGs calculated from cyclic voltammetry is 16% higher than that of stdG-AGs, and the onset of hydrogen evolution is observed at a lower over-potential. These observations can be attributed to increased density of edge sites and defects in nanographene sheets. Our results imply that the diameter of the precursor graphene sheets can be used as a parameter to optimize the electrochemical properties of graphene AGs depending on the application.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Illumination correction of dyed fabric based on extreme learning machine with improved ant lion optimizer

Abstract In order to eliminate the influence of scene illumination on the evaluation of the color difference of dyed fabrics, this paper proposes a dyed fabric illumination correction algorithm based on the extreme learning machine (ELM) with grey wolf optimizer (GWO)‐optimized ant lion optimizer (ALO). Firstly, the Grey Edge framework is used to extract the features of the dyed fabric image as the input vector. Then, to improve the optimization ability of the ALO algorithm, the GWO algorithm is used to provide a set of optimized initial populations to the ALO algorithm, and then the improved ALO algorithm is used to optimize the parameters of the ELM. Finally, the proposed GWO‐ALO‐ELM algorithm is used to correct the illumination of the dyed fabric, and restore the graphics to the effect display under standard illumination through the diagonal reduction model. Compared with the experimental results of GWO‐ELM, ALO‐ELM, backpropagation (BP), ELM, random vector function link (RVFL), and other algorithms, it can be seen that the GWO‐ALO‐ELM algorithm proposed in this paper has good predictive value and quasi‐bias effect, and good stability.

Zhou, Zhiyu↗
DOE OSTI2019

High‐throughput screening of aqueous biphasic systems with ionic liquids as additives for extraction and purification of enveloped virus‐like particles

Due to their unique properties, virus‐like particles (VLPs) have been portrayed as a promising high‐value biopharmaceutical in VLP‐based vaccination and cancer therapy. Nevertheless, due to limited physical and economical capabilities of the current downstream processing of VLPs, their production is still difficult and seen as a major problem that needs to be tackled. In this work, high‐throughput screening on a liquid handling station (LHS) has been implemented for efficient selection of adequate polymer‐salt aqueous biphasic systems (ABS) for extraction and purification of enveloped Hepatitis C virus pseudoparticles (HCVpp). The effect of polyethylene glycol (PEG) molecular weight and salt type (citrate, sulfate, and phosphate) was first evaluated. Furthermore, to optimize extraction parameters, the effect of pH and tie‐line lengths (TLL) was also addressed. For the most promising ABS and extraction conditions, the addition of ten ionic liquids (ILs) as adjuvants was investigated. Insights on the chemical features of ILs that impact HCV‐VLPs partitioning are highlighted. Finally, the potential of studied ABS containing ILs as adjuvants in the extraction and purification of VLPs from cell culture supernatants was addressed. The 100% extraction efficiency of VLPs in the PEG‐rich phase was achieved, and VLP purity was increased compared to the system without IL. In conclusion, these promising results show that ILs can be very effective in modulating the phase properties of polymer‐salt ABS, achieving high HCV‐VLP purification.

Marchel, Mateusz↗
DOE OSTI2025

BCARS Simulated Phantom Dataset for Evaluation of Processing Pipelines

Broadband coherent anti-Stokes Raman scattering (BCARS) microscopy is a powerful label-free biological imaging technique, but the raw signal requires careful processing. The vibrationally resonant (Raman) fingerprint signal is usually small compared with instrumental noise sources and the nonresonant background (NRB) inherent in the BCARS signal. Fortunately, the NRB exhibits a systematic phase relationship with the coherent Raman response, acting as a heterodyne amplifier for the weak fingerprint signal. Due to this heterodyne effect, the Raman response can be recovered quantitatively and invariantly across different instruments, provided the NRB shape is known. Even with heterodyne amplification, the amplitudes of fingerprint signal components are often comparable to system noise. Singular value decomposition (SVD), which utilizes spatial information, is often employed for additional noise filtering. Consequently, finding optimal processing parameters to properly distinguish the NRB and Raman responses and suppress noise in the complex BCARS signal requires a reference system that realistically represents the spectral and spatial properties of BCARS signals obtained from biological samples. We present a digital tissue phantom that meets these criteria as a tool for testing candidate signal processing pipelines. The digital phantom is generated with simulated hyperspectral Raman images having system-specific noise and background characteristics. Here, we analyze phantom datasets with differing background and signal-to-noise conditions to evaluate their impact on the performance of multiple signal processing pipelines. Specifically, we investigate the application of a Butterworth filter-based routine to directly estimate the NRB from the BCARS signal. Additionally, we evaluate a Lorentzian wavelet transform as an alternative to the Hilbert transform for extracting the Raman spectrum from the BCARS signal. While we demonstrate this phantom for BCARS, it can be used for any spectroscopic Raman imaging approach.

Dixon, Jessica Z. [Georgia Institute of Technology↗
DOE OSTI2022

RDPM: An Extensible Tool for Resilience Design Patterns Modelling

Resilience to faults, errors, and failures in extreme-scale high-performance computing (HPC) systems is a critical challenge. Resilience design patterns offer a new, structured hardware and software design approach for improving resilience. While prior work focused on developing performance, reliability, and availability models for resilience design patterns, this paper extends it by providing a Resilience Design Patterns Modeling (RDPM) tool which allows (1) exploring performance, reliability, and availability of each resilience design pattern, (2) offering customization of parameters to optimize performance, reliability, and availability, and (3) allowing investigation of trade-off models for combining multiple patterns for practical resilience solutions.

Kumar, Mohit↗
DOE OSTI2022

Pure quark and gluon observables in collinear drop

We construct a class of pure quark and gluon observables by using the collinear drop grooming technique. The construction is based on linear combinations of multiple cumulative distributions of the jet mass in collinear drop, whose specific weights are fully predicted perturbatively. This yields observables which obtain their values purely from quarks (or purely from gluons) in a wide region of phase space. We demonstrate this by showing that these observables are effective in two phase space regions, one dominated by perturbative resummation and one dominated by nonperturbative effects. The nonperturbative effects are included using shape functions which only appear as a common factor in the linear combinations constructed. We test this construction using a numerical analysis with next-to-leading logarithmic resummation and various shape function models, as well as analyzing these observables with PYTHIA and VINCIA. Choices for the collinear drop parameters are optimized for experimental use.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗