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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

First-Principles Investigations of the Temperature Dependence of Electronic Structure and Optical Properties of Rutile TiO 2

To gain additional insight into high-temperature functional material properties for applications in optical gas sensing, the temperature effects on the band gap and optical properties of rutile TiO 2 are investigated using ab initio methods. By analyzing the contributions from electron–phonon interaction and lattice thermal expansion, we show that the electron–phonon interaction is the dominant factor for temperature band-gap renormalization. As the temperature increases, the band gap increases until 300 K and then narrows above 300 K. This behavior results from the acoustic phonons, which widen the band gap, dominate below 300 K, while the optical phonons, which narrow the band gap, dominate above 300 K. Our study suggests that the band gap is narrowed by about 138 meV at 1000 K. We also investigated the temperature effects on the dielectric constants, the refractive index, as well as the extinction coefficient. Both the rate of decrease of the refractive index at 650 and 800 nm as well as the experimentally derived band gap agree with experimentally measured data as the temperature increases. Furthermore, our results and computational methods are of interest for developing high-temperature functional materials with applications toward gas sensing.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Conformational explosion: Understanding the complexity of short chain para -dialkylbenzene potential energy surfaces

The single-conformation ultraviolet and infrared spectroscopy of three short-chain para-dialkylbenzenes (para-diethylbenzene, para-dipropylbenzene, and para-dibutylbenzene) is reported for the jet-cooled, isolated molecules. The present study builds off previous work on single-chain n-alkylbenzenes, where an anharmonic local mode Hamiltonian method was developed to account for stretch-bend Fermi resonance in the alkyl CH stretch region [D. P. Tabor et al., J. Chem. Phys. 144, 224310 (2016)]. The jet-cooled molecules are interrogated using laser-induced fluorescence (LIF) excitation, fluorescence dip infrared spectroscopy, and dispersed fluorescence. The LIF spectra in the S1 ← S0 origin region show a dramatic increase in the number of resolved transitions with increasing length of the alkyl chains, reflecting an explosion in the number of unique low-energy conformations formed when two independent alkyl chains are present. Since the barriers to isomerization of the alkyl chain are similar in size, this results in an “egg carton” shaped potential energy surface. A combination of electronic frequency shift and alkyl CH stretch infrared spectra is used to generate a consistent set of conformational assignments. Using these experimental techniques in conjunction with computational methods, subsets of origin transitions in the LIF excitation spectrum can be classified into different conformational families. Two conformations are resolved in para-diethylbenzene, seven in para-dipropylbenzene, and about nineteen in para-dibutylbenzene. These chains are largely independent of each other as there are no new single-chain conformations induced by the presence of a second chain. A cursory LIF excitation scan of para-dioctylbenzene shows a broad congested spectrum at frequencies consistent with interactions of alkyl chains with the phenyl π cloud

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

Verification and Validation of the FLAG Hydrocode for Impact Cratering Simulations

Verification and validation (V&V) are necessary processes to ensure accuracy of the computational methods used to solve problems key to vast numbers of applications and industries. Simulations are essential for addressing impact cratering problems, because these problems often exceed experimental capabilities. We show that the free Lagrange (FLAG) hydrocode, developed at Los Alamos National Laboratory (Los Alamos, NM), can be used for impact cratering simulations by verifying FLAG against two analytical models of aluminum-on-aluminum impacts at different impact velocities and validating FLAG against a glass-into-water laboratory impact experiment. Our verification results show good agreement with the theoretical maximum pressures, with relative errors as low in magnitude as 1.00%. Our validation results demonstrate FLAG's ability to model various stages of impact cratering, with crater radius relative errors as low as 3.48% and crater depth relative errors as low as 0.79%. Our mesh resolution study shows that FLAG converges at resolutions low enough to reduce the required computation time from about 28 h to about 25 min. We anticipate that FLAG can be used to model larger impact cratering problems with increased accuracy and decreased computational cost on current systems relative to other hydrocodes tested by Pierazzo et al. (2008, “Validation of Numerical Codes for Impact and Explosion Cratering: Impacts on Strengthless and Metal Targets,” MAPS, 43(12), pp. 1917–1938).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Review of capture cross sections relevant for intentional nuclear forensics

The NA-22 Intentional Forensics Venture is developing a system for tagging nuclear fuel using various methods of information encoding. One of the main methods under development is the insertion of isotopically enriched tracers into the fuel. In order to aid in the understanding of the neutronic performance of these taggants, we assess the quality of the nuclear data underpinning simulations, which are driven by the neutron-capture cross sections. We present these cross sections of naturally occurring isotopes of the elements provided in the neutron sublibrary of the planned ENDF/B-VIII.1 Feb. 2023 library release. We make this assessment using a rubric designed for this effort, which quantifies orthogonal features related to the overall quality. The quality metric highlights 6 aspects: experimental data, resonance evaluations, integral metrics, covariances, fission products, and documentation. We focus on energy ranges relevant for reactor applications. We also discuss additional sources for new, high-quality cross-section data that may be utilized on the time scale of the venture, including existing global data, new experiments, and computational methods. Finally, overall outlook is presented with conclusions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2012

Specification, annotation, visualization and simulation of a large rule-based model for ERBB receptor signaling

Mathematical/computational models are needed to understand cell signaling networks, which are complex. Signaling proteins contain multiple functional components and multiple sites of post-translational modification. The multiplicity of components and sites of modification ensures that interactions among signaling proteins have the potential to generate myriad protein complexes and post-translational modification states. As a result, the number of chemical species that can be populated in a cell signaling network, and hence the number of equations in an ordinary differential equation model required to capture the dynamics of these species, is prohibitively large. To overcome this problem, the rule-based modeling approach has been developed for representing interactions within signaling networks efficiently and compactly through coarse-graining of the chemical kinetics of molecular interactions. Here, we provide a demonstration that the rule-based modeling approach can be used to specify and simulate a large model for ERBB receptor signaling that accounts for site-specific details of protein-protein interactions. The model is considered large because it corresponds to a reaction network containing more reactions than can be practically enumerated. The model encompasses activation of ERK and Akt, and it can be simulated using a network-free simulator, such as NFsim, to generate time courses of phosphorylation for 55 individual serine, threonine, and tyrosine residues. The model is annotated and visualized in the form of an extended contact map. With the development of software that implements novel computational methods for calculating the dynamics of large-scale rule-based representations of cellular signaling networks, it is now possible to build and analyze models that include a significant fraction of the protein interactions that comprise a signaling network, with incorporation of the site-specific details of the interactions. Modeling at this level of detail is important for understanding cellular signaling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Deep Learning Prediction of Protein Complex Structures

Proteins interact to form protein complex to carry out biological functions such as catalytic chemical reaction. Therefore, it is important to develop computational methods to predict protein-protein interaction and the structures of protein complexes to study and enhance protein function. In this project, we successfully developed several deep learning methods to predict inter-protein contacts and the reinforcement learning and optimization methods to reconstruct protein complex structures from predicted inter-chain contacts. The methods were integrated with the MULTICOM protein complex structure prediction system and applied to predict the complex structures of biomass production-related proteins of green algae. During the two and a half years of research and development, all the specific milestones of the project were achieved successfully. 16 publications/manuscripts were produced. 10 software tools were developed. A patent application was submitted. Our MULTICOM predictors leveraging some tools developed in this project were ranked among the top predictors in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Sooting tendencies of co-optima test gasolines and their surrogates

Here, this study characterized the sooting tendencies of a set of gasolines and their surrogates using both experimental and computational methods. Sooting tendency was defined in terms of the soot yield when 1000 ppm of the test fuel is doped into the fuel of a methane/air flame, and it provides a measure of the intrinsic chemical tendency of the fuels to form soot in a generic combustion environment. The test fuels were real gasolines containing enhanced concentrations of alkanes, aromatics, cycloalkanes, olefins, and ethanol. These compositional differences caused the experimentally measured sooting tendencies of the fuels to vary by 240%. The surrogates were 3 mixtures defined by Szybist et al. (2017) and 3 alternative formulations modified for greater experimental convenience. The sooting tendencies measured for the surrogate mixtures agreed with the real fuels to within 15%, and varied with composition in the same order. The sooting tendencies of the surrogates could be predicted to within experimental error with an empirical quantitative structure-property relationship and a linear mixing model. The experimental flames were computationally simulated with a 743-species mechanism, and sooting tendencies derived from the results agreed with the measured values to within 11%. Altogether, these results show that the sooting behavior of gasoline can vary considerably within the range of acceptable compositions, and that these variations can be accurately predicted with empirical models and computational simulations.

09 BIOMASS FUELS↗
DOE OSTI2012

PHACTS, a computational approach to classifying the lifestyle of phages

Motivation: Bacteriophages have two distinct lifestyles: virulent and temperate. The virulent lifestyle has many implications for phage therapy, genomics and microbiology. Determining which lifestyle a newly sequenced phage falls into is currently determined using standard culturing techniques. Such laboratory work is not only costly and time consuming, but also cannot be used on phage genomes constructed from environmental sequencing. Therefore, a computational method that utilizes the sequence data of phage genomes is needed. Results: Phage Classification Tool Set (PHACTS) utilizes a novel similarity algorithm and a supervised Random Forest classifier to make a prediction whether the lifestyle of a phage, described by its proteome, is virulent or temperate. The similarity algorithm creates a training set from phages with known lifestyles and along with the lifestyle annotation, trains a Random Forest to classify the lifestyle of a phage. PHACTS predictions are shown to have a 99% precision rate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Phase-Field Modeling of Materials Interfaces and Nanostructures

Nanostructured materials offer unique properties for a wide range of energy applications. Among various known processing methods, liquid metal dealloying (LMD)—the selective dissolution of a base alloy element into a metallic melt—has emerged as a powerful technique to produce a new class of nano-/meso-scale open porous and bicontinuous composite structures with ultra-high interfacial area. LMD has recently been complemented by the advent of vapor phase dealloying (VPD), a novel technique that exploits the selective evaporation of one element from a parent alloy containing elements with very different vapor pressures, thereby enabling to fabricate open nanoporous structures of various elements from less-noble metals to inorganic elements regardless of their chemical activity without requiring high LMD temperatures and chemical etching. Together LMD and VPD have greatly expanded the scope of dealloying techniques, limited by traditional electrochemical means to noble metals, and boosted the design of new functional and structural materials that combine a wide variety of elements. Topologically-connected open porous structures with ultra-high surface area allow mass transport within the structure while preserving structural integrity, enabling them to serve as catalysts, fuel cells, supercapacitors, or high-capacity battery materials. Bicontinuous composite structures in turn can display high strength and high ductility or superior radiation-damage resistance due to the ultra-high interface area between interpenetrating solid phases. This research program makes use of state-of-the-art computational methods to understand at a basic level the self-organizing dealloying process with main focus on dealloying kinetics and interfacial pattern formation at the dealloying front controlling initial structure size, topology, and phase compositions. Phase-field simulation studies of LMD focus on solid solutions, line compounds, and intermetallic systems that can form ternary composites by nucleation and growth of a new phase. Studies of VPD employ phase-field modeling and a hybrid method combining a kinetic Monte Carlo (KMC) model of evaporation and surface diffusion with molecular dynamics for vapor-phase transport inside nanopores. Simulations explore mechanisms of interface- and diffusion-controlled dealloying kinetics, both observed in VPD but not fundamentally understood. In addition, our newly developed multi-physics phase-field approach of large-volume-change phase transformations is being used to model novel 3D anode geometries based on dealloyed nanoporous structures including novel sandwiched graphene/Si/silica for highrate Li ion battery. Those studies are aimed at elucidating geometric design principles that improve mechanical stability. We expect this research to enhance the capability to tailor nano-/mesoscale structures for a wide range of energy-related materials applications and to yield further advances in computational methodologies that benefit a broad materials research community.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2016

TH-CD-BRA-02: 3D Remote Dosimetry for MRI-Guided Radiation Therapy: A Hybrid Approach

Purpose: To validate the dosimetric accuracy of a commercially available MR-IGRT system using a combination of 3D dosimetry measurements (with PRESAGE(R) radiochromic plastic and optical-CT readout) and an in-house developed GPU-accelerated PENELOPE Monte-Carlo dose calculation system. Methods: {sup 60}Co IMRT subject to a 0.35T lateral magnetic field has recently been commissioned in our institution following AAPM’s TG-119 recommendations. We performed PRESAGE(R) sensitivity studies in 4ml cuvettes to verify linearity, MR-compatibility, and energy-independence. Using 10cm diameter PRESAGE(R), we delivered an open calibration field to examine the percent depth dose and a symmetrical 3-field plan with three adjacent regions of varying dose to determine uniformity within the dosimeter under a magnetic field. After initial testing, TG-119 plans were created in the TPS and then delivered to 14.5cm 2kg PRESAGE(R) dosimeters. Dose readout was performed via optical-CT at a second institution specializing in remote 3D dosimetry. Absolute dose was measured using an IBA CC01 ion chamber and the institution standard patient-specific QA methods were used to validate plan delivery. Calculated TG-119 plans were then compared with an independent Monte Carlo dose calculation (gPENELOPE). Results: PRESAGE(R) responds linearly (R{sup 2}=0.9996) to {sup 60}Co irradiation, in the presence of a 0.35T magnetic field, with a sensitivity of 0.0305(±0.003)cm{sup −1}Gy{sup −1}, within 1% of a 6MV non-MR linac irradiation (R{sup 2}=0.9991) with a sensitivity of 0.0302(±0.003)cm{sup −1}Gy{sup −1}. Analysis of TG-119 clinical plans using 3D-gamma (3%/3mm, 10% threshold) give passing rates of: HN 99.1%, prostate 98.0%, C-shape 90.8%, and multi-target 98.5%. The TPS agreed with gPENELOPE with a mean gamma passing rate of 98.4±1.5% (2%/2mm) with the z-score distributions following a standard normal distribution. Conclusion: We demonstrate for the first time that 3D remote dosimetry using both experimental and computational methods is a feasible and reliable approach to commissioning MR-IMRT, which is particularly useful for less specialized clinics in adopting this new treatment modality.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

AI-accelerated protein-ligand docking for SARS-CoV-2 is 100-fold faster with no significant change in detection

Protein-ligand docking is a computational method for identifying drug leads. The method is capable of narrowing a vast library of compounds down to a tractable size for downstream simulation or experimental testing and is widely used in drug discovery. While there has been progress in accelerating scoring of compounds with artificial intelligence, few works have bridged these successes back to the virtual screening community in terms of utility and forward-looking development. We demonstrate the power of high-speed ML models by scoring 1 billion molecules in under a day (50 k predictions per GPU seconds). We showcase a workflow for docking utilizing surrogate AI-based models as a pre-filter to a standard docking workflow. Our workflow is ten times faster at screening a library of compounds than the standard technique, with an error rate less than 0.01% of detecting the underlying best scoring 0.1% of compounds. Our analysis of the speedup explains that another order of magnitude speedup must come from model accuracy rather than computing speed. In order to drive another order of magnitude of acceleration, we share a benchmark dataset consisting of 200 million 3D complex structures and 2D structure scores across a consistent set of 13 million “in-stock” molecules over 15 receptors, or binding sites, across the SARS-CoV-2 proteome. We believe this is strong evidence for the community to begin focusing on improving the accuracy of surrogate models to improve the ability to screen massive compound libraries 100 × or even 1000 × faster than current techniques and reduce missing top hits. The technique outlined aims to be a fast drop-in replacement for docking for screening billion-scale molecular libraries.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

An Integrated Tool for Functional Requirements Analysis, Function Allocation and Task Analysis

The software application is called "HFE-Trace". This is an integrated method and tool for the management of Human Factors Engineering analyses and related data. Its primary purpose is to support the coherent and consistent application of the nuclear industry's best practices for human factors engineering work. The software is a custom Microsoft® Access® application. The application is used (in conjunction with other tools such as spreadsheets, checklists and normal documents where necessary) to collect data on the design of a new nuclear power plant from subject matter experts and other sources. This information is then used to identify potential system and functional breakdowns of the intended power plant design. This information is expanded by developing extensive descriptions of all functions, as well as system performance parameters, operating limits and constraints, and operational conditions. Once these have been verified, the human factors elements are added to each function, including intended operator role, function allocation considerations, prohibited actions, primary task categories, and primary work station. In addition, the application includes a computational method to assess a number of factors such as system and process complexity, workload, environmental conditions, procedures, regulations, etc.) that may shape operator performance. This is a unique methodology based upon principles described in NUREG/CR-3331 ("A methodology for allocating nuclear power plant control functions to human or automatic control") and it results in a semi-quantified allocation of functions to three or more levels of automation for a conceptual automation system. The aggregate of all this information is then linked to the Task Analysis section of the application where the existing information on all operator functions is transformed into task information and ultimately into design requirements for Human-System Interfaces and Control Rooms. This final step includes assessment of methods to prevent potential operator errors.

Hugo, Jacques↗
DOE OSTI2023

Bayesian sequential optimal experimental design for nonlinear models using policy gradient reinforcement learning

We present a mathematical framework and computational methods for optimally designing a finite sequence of experiments. This sequential optimal experimental design (sOED) problem is formulated as a finite-horizon partially observable Markov decision process (POMDP) under a Bayesian setting and with information-theoretic utilities. The formulation is general and may accommodate continuous random variables, non-Gaussian posteriors, and nonlinear forward models. The sOED design policy incorporates elements of feedback and lookahead simultaneously, and we show it to generalize the commonly-used batch and greedy design strategies. We solve for the sOED policy using the policy gradient (PG) method from reinforcement learning, and provide a derivation for the PG expression in the sOED context. Adopting an actor-critic approach, the policy and value functions are parameterized using deep neural networks and improved via PG estimates produced from simulated episodes of designs and observations. The new PG-sOED algorithm is first validated on a linear-Gaussian benchmark, and then compared against other design baselines on a sensor movement problem for contaminant source inversion in a convection-diffusion field. As a result, we provide explanation for the policy behaviors using knowledge of the underlying physical process.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Efficient Reformulation and Optimization for SC-ACOPF with Line Switching

This project aims to develop efficient and robust computational methods for solving the security-constrained alternating current optimal power flow problem (SC-ACOPF). The SC-ACOPF problem is a central problem in operating the electric power grids in the United States. It determines the most economically efficient way to operate the generation and transmission system to meet daily electricity demand. The solution found by solving an SC-ACOPF problem must satisfy the physics of the alternating current (AC) power flows, various generator and network operational constraints, and must maintain secure operation under various contingency scenarios, where a generator, a transmission branch, or a transformer may unexpectedly trip offline.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Rapid Multiscale Computational Screening for OLED Host Materials

The design of new host materials for phosphorescent OLEDs is challenging because several physical property requirements must be met simultaneously. A triplet energy (E T ) higher than that of the chosen emitting dopant, appropriate HOMO/LUMO energy levels, good charge carrier transport, and high stability are all required. Here, computational methods were used to screen structures to find the most promising candidates for OLED hosts. The screening was carried out in three Tiers. The Tier 1 selection, based on Density Functional Theory calculations identified a set of eight molecular structures with E T > 2.9 eV, suitable for hosting blue phosphorescent dopants such as iridium(III)bis((4,6-di-fluorophenyl)-pyridinato-N,C2’)picolinate (FIrpic). Phenanthro[9,10 d]imidazole was chosen as the starting point for the Tier 2 selection. Thirty seven unique molecular structures were enumerated by isoelectronic nitrogen transmutation of up to two CH fragments of the phenanthrene. Three molecules, i.e. imidazo[4,5 f] phenanthrolines with nitrogens at the 1,10 , 3,8 and 4,7 positions, were selected for Tier 3, which involved the use of molecular dynamics simulations and electron coupling calculations to predict differences in charge transport between the three materials. The three were explored experimentally through synthesis and device fabrication. The singlet, triplet, and frontier orbital energies computed using single molecule DFT calculations (tiers 1 and 2) were consistent with the experimental values in fluid solution, and the multi scale modeling scheme (tier 3) correctly predicted the poor device performance of one material. Here, we conclude that screening host materials using only single molecule quantum mechanical data was not sufficient to predict whether a given material would make a good OLED host with certainty, however they can be used to screen out materials that are destined to fail due to low singlet/triplet energies or a poor match of the frontier orbital energies to the dopant or transport materials.

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

Optimization of Conditions for Photoluminescence-Based Sensing of Critical Minerals: Strategies and Outlook

Economically critical minerals and metals are vital to America’s security, with application areas in energy, advanced technologies, and defense systems. The growing implementation of renewable energy sources such as electric vehicles is expected to significantly increase demand for these minerals, while the global supply is monopolistic, with most of the production occurring in a few countries outside of the US. To mitigate potential supply chain vulnerabilities, the domestic production of economically critical metals has become a bipartisan priority of the US government, and unconventional sources such as coal and its utilization byproducts such as fly ash and acid mine drainage are being actively explored as an alternative method for meeting domestic demand. Slow and expensive characterization methods for critical metals present a significant pain point for domestic production, which has led to the exploration of alternative low-cost, portable platforms capable of detecting target metals during resource prospecting and downstream process monitoring. The National Energy Technology Laboratory has developed a portable fiber optic-based luminescence spectrometer that utilizes a metal-organic framework (MOF) material to detect and distinguish parts-per-billion levels of the visible-emitting rare earth elements terbium, dysprosium, samarium, and europium, which are all economically critical metals. Although preliminary results with this system are promising, a crucial barrier to practical deployment is the ability to operate in low pH, high ionic strength environments, as acidic conditions and the presence of other metal ions can significantly reduce luminescence signal. Here, current strategies related to sample treatment, material design, and sensor configuration are discussed in the context of improving sensor performance in application-relevant conditions. Additionally, future opportunities, such as the optimization of the sensing binding environment through computational methods and specific metals to be removed for treatment will be discussed, presenting a forward-looking view for innovation in luminescent sensing of critical metals.

36 MATERIALS SCIENCE↗
DOE OSTI2011

Structure Effects on the Energetics of the Electrochemical Reduction of CO2 by Copper Surfaces

Polycrystalline copper electrocatalysts have been experimentally shown to be capable of reducing CO{sub 2} into CH{sub 4} and C{sub 2}H{sub 4} with relatively high selectivity, and a mechanism has recently been proposed for this reduction on the fcc(211) surface of copper, which was assumed to be the most active facet. In the current work, we use computational methods to explore the effects of the nanostructure of the copper surface and compare the effects of the fcc(111), fcc(100) and fcc(211) facets of copper on the energetics of the electroreduction of CO{sub 2}. The calculations performed in this study generally show that the intermediates in CO{sub 2} reduction are most stabilized by the (211) facet, followed by the (100) facet, with the (111) surface binding the adsorbates most weakly. This leads to the prediction that the (211) facet is the most active surface among the three in producing CH{sub 4} from CO{sub 2}, as well as the by-products H{sub 2} and CO. HCOOH production may be mildly enhanced on the more close-packed surfaces ((111) and (100)) as compared to the (211) facet, due to a change in mechanism from a carboxyl intermediate to a formate intermediate. The results are compared to experimental data on these same surfaces; the predicted trends in voltage requirements are consistent between the experimental and computational data.

77 NANOSCIENCE AND NANOTECHNOLOGY↗