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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Engineering sensitivity and specificity of AraC-based biosensors responsive to triacetic acid lactone and orsellinic acid

Abstract We previously described the design of triacetic acid lactone (TAL) biosensor ‘AraC-TAL1’, based on the AraC regulatory protein. Although useful as a tool to screen for enhanced TAL biosynthesis, this variant shows elevated background (leaky) expression, poor sensitivity and relaxed inducer specificity, including responsiveness to orsellinic acid (OA). More sensitive biosensors specific to either TAL or OA can aid in the study and engineering of polyketide synthases that produce these and similar compounds. In this work, we employed a TetA-based dual-selection to isolate new TAL-responsive AraC variants showing reduced background expression and improved TAL sensitivity. To improve TAL specificity, OA was included as a ‘decoy’ ligand during negative selection, resulting in the isolation of a TAL biosensor that is inhibited by OA. Finally, to engineer OA-specific AraC variants, the iterative protein redesign and optimization computational framework was employed, followed by 2 rounds of directed evolution, resulting in a biosensor with 24-fold improved OA/TAL specificity, relative to AraC-TAL1.

Wang, Zhiqing↗
DOE OSTI2016

39th Lauriston S. Taylor Lecture

Since the early days of the Manhattan Engineer District, Oak Ridge National Laboratory (ORNL) has served to advance the dosimetry models used to set protection standards for radionuclides taken into the body. Throughout the years, this effort benefited significantly from ORNL staff's active participation in national and international scientific bodies. The first such interaction was in 1946 with the National Committee on Radiation Protection (NCRP), chaired by L.S. Taylor, which led to the 1949 to 1953 series of tripartite conferences of experts from Canada, the United Kingdom, and the United States. These conferences addressed the need for standardization of dosimetry models and led to the establishment of an anatomic and physiologic model called “Standard Man,” a precursor of the reference worker defined in Publication 23 of the International Commission on Radiological Protection (ICRP). Standard Man was used in setting the maximum permissible concentrations in air and water published in NBS Handbook 52 and subsequent reports by NCRP and ICRP. K.Z. Morgan, then director of the Health Physics Division at ORNL, participated in the tripartite conferences and subsequently established ORNL as a modeling and computational resource for development of radiation protection standards. ORNL’s role expanded with participation in the work of the Medical Internal Radiation Dose (MIRD) Committee of the Society of Nuclear Medicine. Results of interactions with the MIRD Committee are evident in the radiation protection guidance for internal emitters in ICRP Publication 30. The annual limit on intake and derived air concentration values tabulated in Publication 30 were computed by an ORNL-based task group of ICRP Committee 2. A few years after the appearance of Publication 30, the Chernobyl nuclear reactor accident made clear the need to develop standard dosimetry models for pre-adult ages as members of the public. In the late 1980s, ICRP began an effort to extend its reference worker concept to a reference family and develop dosimetric models for application to intake of radionuclides by members of the public. However, the modeling approach underlying the ICRP Publication 30 computational framework was not amenable to age and gender considerations. With support of U.S. federal agencies, ORNL had begun efforts in the early 1980s to develop age- and gender-specific dosimetric models, including physiologically informed biokinetic models and age-specific dosimetric phantoms. ORNL’s models and methods became the starting point for the ICRP’s series of reports on dose coefficients for radionuclide intake by the public. Currently ICRP Committee 2 is overseeing development of a second generation of post-Chernobyl models and methods, with updates of Publications 30 and 68 soon to appear and new models for members of the public in preparation. The goal of this Lauriston S. Taylor Lecture is to chronicle advancements in the dosimetry of internal emitters with some discussion of models and methods but with due deference to decisions within scientific bodies and stimulated by radiological events.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

IDAC-Bio, A Software for Internal Dosimetry Based on the New ICRP Biokinetic Models and Specific Absorbed Fractions

Radiation dosimetry is central to virtually all radiation safety applications, optimization, and research. It relates to various individuals and population groups and to miscellaneous exposure situations—including planned, existing, and emergency situations. The International Commission on Radiological Protection (ICRP) has developed a new computational framework for internal dose estimations. Important components are more detailed and improved anatomical models and more realistic biokinetic models than before. The ICRP is currently producing new organ dose and effective dose coefficients for occupational intakes of radionuclides (OIR) and environmental intakes of radionuclides (EIR), which supersede the earlier dose coefficients in Publication 68 and the Publication 72 series, respectively. However, the ICRP only publishes dose coefficients for a single acute intake of a radionuclide and for an integration period of 50 years for intake by adults and to age 70 years for intakes by pre-adults. The new software, IDAC-Bio, performs committed absorbed dose and effective dose calculations for a selectable intake scenario, e.g., for a continuous intake or an intake during x hours per day and y days per week, and for any selected integration time. The software uses the primary data and models of the ICRP biokinetic models and numerically solves the biokinetic model and calculates the absorbed doses to organs and tissues in the ICRP reference human phantoms. The software calculates absorbed dose using the nuclear decay data in ICRP publication 107. IDAC-Bio is a further development and an important addition to the internal dosimetry program IDAC-Dose2.1. Here, the results generated by the software were validated against published ICRP dose coefficients. The potential of the software is illustrated by dose calculations for a nuclear power plant worker who had been exposed to varying levels of 60 Co and who had undergone repeated whole-body measurements, and for a hypothetical member of the public subject to future releases of 148 Gd from neutron spallation in tungsten at the European Spallation Source.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2021

Philympics 2021: Prophage Predictions Perplex Programs

Most bacterial genomes contain integrated bacteriophages—prophages—in various states of decay. Many are active and able to excise from the genome and replicate, while others are cryptic prophages, remnants of their former selves. Over the last two decades, many computational tools have been developed to identify the prophage components of bacterial genomes, and it is a particularly active area for the application of machine learning approaches. However, progress is hindered and comparisons thwarted because there are no manually curated bacterial genomes that can be used to test new prophage prediction algorithms. Here, we present a library of gold-standard bacterial genome annotations that include manually curated prophage annotations, and a computational framework to compare the predictions from different algorithms. We use this suite to compare all extant stand-alone prophage prediction algorithms to identify their strengths and weaknesses. We provide a FAIR dataset for prophage identification, and demonstrate the accuracy, precision, recall, and f 1 score from the analysis of seven different algorithms for the prediction of prophages. We discuss caveats and concerns in this analysis and how those concerns may be mitigated.

Roach, Michael J.↗
DOE OSTI2019

Orbital angular momentum constraints in the variational optimization of the two-electron reduced-density matrix

The direct variational determination of the two-electron reduced-density matrix (2-RDM) corresponding to an atomic or molecular system is usually carried out in a basis of real-valued atom-centered Gaussian basis functions, under the assumption that the 2-RDM is a real-valued quantity. Furthermore, for systems that possess orbital angular momentum symmetry, the description of states with a well-defined, nonzero z projection of the orbital angular momentum requires a computational framework generalized to include either complex basis functions or a complex-valued 2-RDM. We consider a semidefinite program suitable for the direct optimization of a complex-valued 2-RDM and explore the role of orbital angular momentum constraints in systems that possess the relevant symmetries. For atomic systems, constraints on the expectation values of the square and z projection of the orbital angular momentum operator allow one to optimize 2-RDMs for multiple orbital angular momentum states. Similarly, in linear molecules, orbital angular momentum projection constraints enable the description of multiple electronic states and, moreover, the application of such constraints is essential for a qualitatively correct description of the electronic structure. For example, in the case of molecular oxygen, we demonstrate that orbital angular momentum constraints are necessary to recover the correct energy ordering of the lowest-energy singlet and triplet states near the equilibrium geometry. However, care must still be taken in the description of the dissociation limit, because the 2-RDM-based approach is not size consistent, and the size-consistency error varies dramatically, depending on the z projections of the spin and orbital angular momenta.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2019

Transport of hot carriers in plasmonic nanostructures

Plasmonic hot carrier devices extract excited carriers from metal nanostructures before equilibration and have the potential to surpass semiconductor light absorbers. However their efficiencies have so far remained well below theoretical limits, which necessitates quantitative prediction of carrier transport and energy loss in plasmonic structures to identify and overcome bottlenecks in carrier harvesting. Here, we present a theoretical and computational framework, nonequilibrium scattering in space and energy (NESSE), to predict the spatial evolution of carrier energy distributions that combines the best features of phase-space (Boltzmann) and particle-based (Monte Carlo) methods. Within the NESSE framework, we bridge first-principles electronic structure predictions of plasmon decay and carrier collision integrals at the atomic scale, with electromagnetic field simulations at the nano- to mesoscale. Finally, we apply NESSE to predict spatially-resolved energy distributions of photoexcited carriers that impact the surface of experimentally realizable plasmonic nanostructures at length scales ranging from tens to several hundreds of nanometers, enabling first-principles design of hot carrier devices.

36 MATERIALS SCIENCE↗
DOE OSTI2019

On the Traffic Impacts of Optimally Controlled Connected and Automated Vehicles

The implementation of connected and automated vehicle (CAV) technologies enables a novel computational framework for real-time control actions aimed at optimizing energy consumption and associated benefits. Several research efforts reported in the literature to date have proposed decentralized control algorithms to coordinate CAVs in various traffic scenarios, e.g., highway on-ramps, intersections, and roundabouts. However, the impact of optimally coordinating CAVs on the performance of a transportation network has not been thoroughly analyzed yet. In this paper, we apply a decentralized optimal control framework in a transportation network and compare its performance to a baseline scenario consisting of human-driven vehicles. We show that introducing of CAVs yields radically improved roadway capacity and network performance.

Zhao, Liuhui↗
DOE OSTI2017

Optimal Power Flow of Radial Networks and Its Variations: A Sequential Convex Optimization Approach

This paper proposes a sequential convex optimization method to solve broader classes of optimal power flow (OPF) problems over radial networks. The non-convex branch power flow equation is decomposed as a second-order cone inequality and a non-convex constraint involving the difference of two convex functions. Provided with an initial solution offered by an inexact second-order cone programming relaxation model, this approach solves a sequence of convexified penalization problems, where concave terms are approximated by linear functions and updated in each iteration. It could recover a feasible power flow solution, which usually appears to be very close, if not equal, to the global optimal one. Two variations of the OPF problem, in which non-cost related objectives are optimized subject to power flow constraints and the convex relaxation is generally inexact, are elaborated in detail. One is the maximum loadability problem, which is formulated as a special OPF problem that seeks the maximal distance to the boundary of power flow insolvability. The proposed method is shown to outperform commercial nonlinear solvers in terms of robustness and efficiency. The other is the bi-objective OPF problem. A non-parametric scalarization model is suggested, and is further reformulated as an extended OPF problem by convexifying the objective function. It provides a single trade-off solution without any subjective preference. The proposed computation framework also helps retrieve the Pareto front of the bi-objective OPF via the e-constraint method or the normal boundary intersection method. This paper also discusses extensions for OPF problems over meshed networks based on the semidefinite programming relaxation method.

bi-objective optimization↗
DOE OSTI2022

Computational Elucidation of Mechanical Degradation in NMC Cathodes: Impact on Cell Performance

Next-generation lithium ion batteries are expected to demonstrate superior energy and power density with longer cycle life for successful electrification of the automobile, aviation, and marine industries. Adoption of lithium metal anodes with solid electrolytes can help to achieve that goal given that the dendrite-related issues are solved eventually. Another possibility is to use Ni-rich high-capacity NMC cathode materials with liquid and/or solid electrolytes, which presently experiences rapid capacity fade while charged to higher voltages. Several mechanical and chemical degradation mechanisms are active within these NMC-based cathode particles. Recent experimental research activities attempted to correlate the mechanical damage with the capacity fade experienced by Ni-rich LiNi x Mn y Co z O 2 (x+y+z = 1) (NMC) cathodes. Here, a computational framework is developed in this study capable of quantifying the evolution of inter primary particle and cathode/electrolyte interfacial fracture experienced by the poly- and single-crystalline NMC cathodes during charge/discharge operation. Influences of mechanical degradation on the overall cell capacity, while operating with liquid and/or solid electrolytes, are successfully characterized. Decreasing the size of the cathode primary particles, or the size of the single-crystalline cathodes, can mitigate the overall mechanical degradation, and subsequent capacity fade, experienced by NMC cathodes. The developed theoretical methodology can help the engineers and scientists to better understand the mechanical degradation mechanism prevalent in Ni-rich NMC cathodes and build superior lithium ion-based energy storage devices for the application in next-generation devices.

25 ENERGY STORAGE↗
DOE OSTI2022

Feasibility of Vascular Parameter Estimation for Assessing Hypertensive Pregnancy Disorders

Abstract Hypertensive pregnancy disorders (HPDs), such as pre-eclampsia, are leading sources of both maternal and fetal morbidity in pregnancy. Noninvasive imaging, such as ultrasound (US) and magnetic resonance imaging (MRI), is an important tool for predicting and monitoring these high risk pregnancies. While imaging can measure hemodynamic parameters, such as uterine artery pulsatility and resistivity indices (PI and RI), the interpretation of such metrics for disease assessment relies on ad hoc standards, which provide limited insight to the physical mechanisms underlying the emergence of hypertensive pregnancy disorders. To provide meaningful interpretation of measured hemodynamic data in patients, advances in computational fluid dynamics can be brought to bear. In this work, we develop a patient-specific computational framework that combines Bayesian inference with a reduced-order fluid dynamics model to infer parameters, such as vascular resistance, compliance, and vessel cross-sectional area, known to be related to the development of hypertension. The proposed framework enables the prediction of hemodynamic quantities of interest, such as pressure and velocity, directly from sparse and noisy MRI measurements. We illustrate the effectiveness of this approach in two systemic arterial network geometries: an aorta with branching carotid artery and a maternal pelvic arterial network. For both cases, the model can reconstruct the provided measurements and infer parameters of interest. In the case of the maternal pelvic arteries, the model can make a distinction between the pregnancies destined to develop hypertension and those that remain normotensive, expressed through the value range of the predicted absolute pressure.

Biophysics↗
DOE OSTI2023

A Versatile Fiber Coating Process for Efficient Fabrication of Multifunctional Composites

The objective of this research is to demonstrate the versatility of a dip coating process for the efficient integration of piezoelectric barium titanate (BaTiO3) microparticles on a wide variety of fibers to design passive self-sensing composites. The microparticles were deposited on glass, aramid, and basalt fiber weaves through the proposed dip coating technique. A computational framework is established to predict the deposition thickness on the fiber surfaces from the given microparticle concentration, size, coating velocity, and coating fluid viscosity. The deposition quality assessment was performed through scanning electron microscope imaging and subsequent image analysis. BaTiO3-coated fibers were directly used in composite preparation. After fabrication, the BaTiO3-enhanced composites were subjected to high-voltage poling. Finally, their passive self-sensing properties were characterized through experimental studies. The results show the adaptability of the proposed coating process to integrate BaTiO3 microparticles within different types of fiber-reinforced composites enabling passive self-sensing to attain subsurface damage characterization.

Gupta, Sumit↗
DOE OSTI2024

MVP: a modular viromics pipeline to identify, filter, cluster, annotate, and bin viruses from metagenomes

While numerous computational frameworks and workflows are available for recovering prokaryote and eukaryote genomes from metagenome data, only a limited number of pipelines are designed specifically for viromics analysis. With many viromics tools developed in the last few years alone, it can be challenging for scientists with limited bioinformatics experience to easily recover, evaluate quality, annotate genes, dereplicate, assign taxonomy, and calculate relative abundance and coverage of viral genomes using state-of-the-art methods and standards. Here, we describe Modular Viromics Pipeline (MVP) v.1.0, a user-friendly pipeline written in Python and providing a simple framework to perform standard viromics analyses. MVP combines multiple tools to enable viral genome identification, characterization of genome quality, filtering, clustering, taxonomic and functional annotation, genome binning, and comprehensive summaries of results that can be used for downstream ecological analyses. Overall, MVP provides a standardized and reproducible pipeline for both extensive and robust characterization of viruses from large-scale sequencing data including metagenomes, metatranscriptomes, viromes, and isolate genomes. As a typical use case, we show how the entire MVP pipeline can be applied to a set of 20 metagenomes from wetland sediments using only 10 modules executed via command lines, leading to the identification of 11,656 viral contigs and 8,145 viral operational taxonomic units (vOTUs) displaying a clear beta-diversity pattern. Further, acting as a dynamic wrapper, MVP is designed to continuously incorporate updates and integrate new tools, ensuring its ongoing relevance in the rapidly evolving field of viromics. MVP is available at https://gitlab.com/ccoclet/mvp and as versioned packages in PyPi and Conda.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Two-Level Sketching Alternating Anderson Acceleration for Complex Physics Applications

We present a novel two-level sketching extension of the Alternating Anderson–Picard (AAP) method for accelerating fixed-point iterations in challenging single- and multiphysics simulations governed by discretized PDEs. Our approach combines a static, physics-based projection that reduces the least-squares (LS) problem to the most informative field (e.g., via Schur-complement insight) with a dynamic, algebraic sketching stage driven by a backward stability analysis under Lipschitz continuity. We introduce inexpensive estimators for stability thresholds and cache-aware randomized selection strategies to balance computational cost against memory access overhead. The resulting algorithm solves reduced LS systems in place, minimizes memory footprints, and seamlessly alternates between low-cost Picard updates and Anderson mixing. Implemented in Julia, our two-level sketching AAP achieves up to 50% time-to-solution reductions compared to standard Anderson acceleration—without degrading convergence rates—on benchmark problems including Stokes, 𝑝-Laplacian, bidomain, and Navier–Stokes formulations at varying problem sizes. These results demonstrate the method’s robustness, scalability, and potential for integration into high-performance scientific computing frameworks. Our implementation is available open source in the AAP.jl library.

Barnafi, Nicolas [University of Chile, Santiago]↗
DOE OSTI2020

Library for Evolutionary Algorithms in Python (LEAP)

There are generally three types of scientific software users: users that solve problems using existing science software tools, researchers that explore new approaches by extending existing code, and educators that teach students scientific concepts. Python is a general-purpose programming language that is accessible to beginners, such as students, but also as a language that has a rich scientific programming ecosystem that facilitates writing research software. Additionally, as high-performance computing (HPC) resources become more readily available, software support for parallel processing becomes more relevant to scientific software.There currently are no Python-based evolutionary computation frameworks that support all three types of scientific software users. Moreover, some support synchronous concurrent fitness evaluation that do not efficiently use HPC resources. We pose here a new Python-based EC framework that uses an established generalized unified approach to EA concepts to provide an easy to use toolkit for users wishing to use an EA to solve a problem, for researchers to implement novel approaches, and for providing a low-bar to entry to EA concepts for students. Additionally, this toolkit provides a scalable asynchronous fitness evaluation implementation friendly to HPC that has been vetted on hardware ranging from laptops to the world’s fastest supercomputer, Summit.

Coletti, Mark↗
DOE OSTI2016

A Computational Study of Optically Concentrating, Solar-Fuels Generators from Annual Thermal- and Fuel-Production Efficiency Perspectives

The commercial deployment of wireless photoelectrochemical cells (PECs) may provide a viable means to close the anthropogenic carbon cycle associated with the global transportation sector. The growing body of research on PECs has largely focused on developing and integrating the materials necessary for robust, efficient solar-fuel production on the laboratory benchtop. While these efforts are a prerequisite for the commercialization of PECs, deployed PECs will have to contend with extreme heat, cold, and insolation variations in the outdoor environment. They will also have to operate efficiently throughout their lifetime and in multiple locations. This paper reports a computational framework for estimating the hourly profiles of time-varying temperature and solar-to-hydrogen efficiency that optically concentrating, wireless PECs will attain over the course of a typical year. It is found that annual weighted average solar-to-hydrogen efficiencies in excess of 9 to 11% can be achieved in extremely cloudy and sunny locations, respectively. Additionally, typical PECs will likely incur damage due to overheating or freezing; measures to protect PECs from extreme heat and cold are outlined. Here, the findings also help to bring into focus issues regarding real-world deployment of energy-generation technologies and methodologies towards tackling them.

25 ENERGY STORAGE↗
DOE OSTI2015

Reduced order modeling of mechanical degradation induced performance decay in lithium-ion battery porous electrodes

In this paper, a one-dimensional computational framework is developed that can solve for the evolution of voltage and current in a lithium-ion battery electrode under different operating conditions. A reduced order model is specifically constructed to predict the growth of mechanical degradation within the active particles of the carbon anode as a function of particle size and C-rate. Using an effective diffusivity relation, the impact of microcracks on the diffusivity of the active particles has been captured. Reduction in capacity due to formation of microcracks within the negative electrode under different operating conditions (constant current discharge and constant current constant voltage charge) has been investigated. At the beginning of constant current discharge, mechanical damage to electrode particles predominantly occurs near the separator. As the reaction front shifts, mechanical damage spreads across the thickness of the negative electrode and becomes relatively uniform under multiple discharge/charge cycles. Mechanical degradation under different drive cycle conditions has been explored. It is observed that electrodes with larger particle sizes are prone to capacity fade due to microcrack formation. Finally, under drive cycle conditions, small particles close to the separator and large particles close to the current collector can help in reducing the capacity fade due to mechanical degradation.

25 ENERGY STORAGE↗
DOE OSTI2019

Ion Association Constants for Lithium Ion Battery Electrolytes from First-Principles Quantum Chemistry

We provide a quantum chemical computational framework to calculate ion association constants relevant to lithium ion battery electrolytes. We compare our method to reported experimental values as the solvent, cation, and anion are varied. For solvent, anion, and cation variations, the standard errors are respectively 0.2 eV, 0.12 eV, and 0.11 eV for the chosen data set, where Pearson correlation values are all above 0.92.

25 ENERGY STORAGE↗
DOE OSTI2019

Atom Modeling PipeLine

This repository contains code for the computational framework to build ML models for drug discovery purposes. Our end-to-end pipeline extracts features from model-ready datasets and trains and saves the model to our model zoo or to disk. Our pipeline generates a variety of molecular features and both shallow and deep ML models. The HPC-specific module we have developed conducts efficient parallelized search of the model hyperparameter space and reports the best-performing hyperparameters for each of these feature/model combinations.

Minnich, AmandaJean↗