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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Improved detection of low-frequency within-host variants from deep sequencing: A case study with human papillomavirus

Abstract High-coverage sequencing allows the study of variants occurring at low frequencies within samples, but is susceptible to false-positives caused by sequencing error. Ion Torrent has a very low single nucleotide variant (SNV) error rate and has been employed for the majority of human papillomavirus (HPV) whole genome sequences. However, benchmarking of intrahost SNVs (iSNVs) has been challenging, partly due to limitations imposed by the HPV life cycle. We address this problem by deep sequencing three replicates for each of 31 samples of HPV type 18 (HPV18). Errors, defined as iSNVs observed in only one of three replicates, are dominated by C→T (G→A) changes, independently of trinucleotide context. True iSNVs, defined as those observed in all three replicates, instead show a more diverse SNV type distribution, with particularly elevated C→T rates in CCG context (CCG→CTG; CGG→CAG) and C→A rates in ACG context (ACG→AAG; CGT→CTT). Characterization of true iSNVs allowed us to develop two methods for detecting true variants: (1) VCFgenie, a dynamic binomial filtering tool which uses each variant’s allele count and coverage instead of fixed frequency cut-offs; and (2) a machine learning binary classifier which trains eXtreme Gradient Boosting models on variant features such as quality and trinucleotide context. Each approach outperforms fixed-cut-off filtering of iSNVs, and performance is enhanced when both are used together. Our results provide improved methods for identifying true iSNVs in within-host applications across sequencing platforms, specifically using HPV18 as a case study.

Virology↗
DOE OSTI2024

Physics-informed neural networks for heterogeneous poroelastic media

This study presents a novel physics-informed neural network (PINN) framework for modeling poroelasticity in heterogeneous media with material interfaces. The approach introduces a composite neural network (CoNN) where separate neural networks predict displacement and pressure variables for each material. While sharing identical activation functions, these networks are independently trained for all other parameters. To address challenges posed by heterogeneous material interfaces, the CoNN is integrated with the Interface-PINNs (I-PINNs) framework (Sarma et al., Comput. Methods Appl. Mech. Eng. 429: 117135, 2024), allowing different activation functions across material interfaces. Further, this ensures accurate approximation of discontinuous solution fields and gradients. Performance and accuracy of this combined architecture were evaluated against the conventional PINNs approach, a single neural network (SNN) architecture, and the eXtended PINNs (XPINNs) framework through two one-dimensional benchmark examples with discontinuous material properties. The results show that the proposed CoNN with I-PINNs architecture achieves an RMSE that is two orders of magnitude better than the conventional PINNs approach and is at least 40 times faster than the SNN framework. Compared to XPINNs, the proposed method achieves an RMSE at least one order of magnitude better and is 40% faster.

42 ENGINEERING↗
DOE OSTI2024

Tree-based algorithms for weakly supervised anomaly detection

Weakly supervised methods have emerged as a powerful tool for model-agnostic anomaly detection at the Large Hadron Collider (LHC). While these methods have shown remarkable performance on specific signatures such as dijet resonances, their application in a more model-agnostic manner requires dealing with a larger number of potentially noisy input features. In this paper, we show that using boosted decision trees as classifiers in weakly supervised anomaly detection gives superior performance compared to deep neural networks. Boosted decision trees are well known for their effectiveness in tabular data analysis. Our results show that they not only offer significantly faster training and evaluation times, but they are also robust to a large number of noisy input features. By using advanced gradient boosted decision trees in combination with ensembling techniques and an extended set of features, we significantly improve the performance of weakly supervised methods for anomaly detection at the LHC. This advance is a crucial step toward a more model-agnostic search for new physics. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2023

Genomes to ecosystem function: Targeting critical knowledge gaps in methanogenesis and translation to updated global biogeochemical models

Natural freshwater temperate wetland systems currently represent the largest natural source of atmospheric methane, but are relatively understudied using systems biology tools (e.g. meta-omics) compared to other high producing methane systems (e.g. peat, tropical, or reconstructed wetlands). Using field investigations at the NOAA operated sentinel site on Lake Erie, methane producing activities and responses to geochemical conditions will be determined along seasonal and spatial gradients (Objective 1). Here, a combination of high-throughput activity and gas measurements, combined with high-resolution systems biology and analytical methods, will provide in depth knowledge of the microbiological, chemical, and physical constraints on methane production in wetlands. Using laboratory microcosms, the formation of anoxic microsites and their capacity to facilitate methane production in wetland soils will be simulated (Objective 2). This objective will validate the findings from the field investigations, offering a more controlled environment for teasing out the role of different yet interrelated variables. Lastly, these field and laboratory data will be used for multi-scale, process-level evaluation of an ecosystem biogeochemical model that accommodates these newly identified processes and parameterizes representation of these processes along relevant environmental gradients (Objective 3).This research will identify multiple interacting geochemical, ecological, and metabolic constraints that are poorly understood, oversimplified, or missing in global biogeochemical methane models. This proposal targets the role of oxygen limitation on methane processes in soil domains, to improve reactive transport models of microbial carbon cycling across terrestrial-aquatic soils and generate data on nutrient cycling activities in Great Lake wetlands. This information could provide new insights into the microbial controllers of Lake Erie eutrophication.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A novel approach for calculating galaxy rotation curves using spaxel cross-correlation and iterative smoothing

ABSTRACT Precise measurements of the internal dynamics of galaxies have proven of great importance for understanding the internal dark matter distribution of galaxies. We present a novel method for measuring the line-of-sight (LOS) velocities across the face of galaxies by cross-correlation of spectral pixels (spaxels) and an iterative method of smoothing. On simulated data the method can accurately recover the input LOS velocities for different types of spectra (absorption-line dominated, emission-line dominated, and differing shapes of the continuum), and can handle stellar population radial gradients. Most important of all, it continues to provide reliable measurements of LOS velocities with reasonable uncertainties even when the spectra are very low signal-to-noise ratio (approaching ∼1), which is a challenge for traditional template-fitting approaches. We apply our method to data from a real MaNGA galaxy as a demonstration and find promising results with good precision. This novel approach can be complementary to existing methods primarily based on template fitting.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Investigation of Scanning Droplet Cell Technology for Electrochemical Deposition of Custom Three-Dimensional Alloys

The goal of this project was to assess the feasibility of a new method of electroplating, which we have termed “electroprinting”, for fabricating millimeter to centimeter metal parts with full density, and eventually, with bespoke 3D internal density patterns. Alloys, gradients and varying density is beyond the scope of this work, and efforts were focused on developing the technology to be capable of printing solid parts, beyond the lines and columns previously reported in the literature. Enabling this technology would expand the design space possible for the WPD program, allowing for smooth and complex density gradients in parts rather than discrete density steps between multilayers. We successfully designed and built an electroprinting apparatus, capable of printing copper in customizable 1-D patterns, which can be printed in stacked layers to form 3D parts. We successfully characterized the flat printed patterns, however, have encountered difficulty in characterizing multilayer prints. We have partially addressed the feasibility question, by developing the method for electroprinting 3D parts, however, some questions about the internal porosity and density of these parts still remain.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Gradient Amine Sorbents for Low Vacuum Swing CO 2 Capture at Ambient Temperature

The growing concerns over CO 2 emissions have led to the development of various methods for CO 2 capture. CO 2 capture by amine-based sorbents has been achieved by temperature (thermal) swing adsorption (TSA) process, pressure swing adsorption (PSA) process, and temperature pressure or vacuuming swing adsorption (TPSA or TVSA) process for CO 2 capture. Amine sorbents for energy-efficient TSA, PSA, VSA, TPSA or TVSA CO 2 capture should possess a capability which allow adsorbed CO 2 to desorb and amine sorbent to be regenerated at low temperatures or low vacuums. Development of such amine sorbents would significantly decrease energy consumption and sorbent degradation during the regeneration step. The objective of this project is to develop a low vacuum swing adsorption (VSA) process for the capture of CO 2 from air. The focus is on the development of amine sorbent which allows CO 2 to adsorb in the form of weakly adsorbed CO 2 . The weakly adsorbed CO 2 species can be collected from the sorbent by applying a low vacuum at ambient temperature. Specifically, no heating (i.e., thermal energy) is needed for regeneration of amine sorbents. This novel sorbent allows VSA to be operated at ambient temperature without a significant energy demand. This process eliminates the energy-intensive heating and cooling process in temperature swing adsorption (TSA) process. Ambient temperature operation could prolong the lifetime of sorbent and minimize the maintenance cost. Extensive sorbent studies with in-situ infrared spectroscopy have revealed the modifications of conventional amine sorbents with additives can increase the fraction of weakly adsorbed CO 2 . The amine sorbents with loaded CO 2 can be regenerated in part by the following approaches: (i) mild heating to temperatures below 70 °C, (ii) flowing purging gas over modified amine sorbents at temperatures below 40 °C, (iii) vacuuming at pressure below 0.2 atm and temperatures below 40 °C. The performance of these modified amine sorbents has been tested in a 60 grams VSA unit at ambient temperatures. The results show that weakly adsorbed CO 2 which can be desorbed from modified amine sorbents with purging gas at 100 cc/min can also be evacuated at a vacuum of 1 psia at room temperature. A preliminary techno- economic analysis indicates the major cost of the VSA process with modified amine sorbents stems from the operation of vacuum pumps. Thus, developing an excellent sealing with minimum leakage for the VSA unit is the most critical task for further the development of this VSA technology.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Molecular Geometry and Electronic Structure of Copper Corroles

Copper corroles are known for their unique multiconfigurational electronic structures in the ground state, which arise from the transfer of electrons from the π orbitals of the corrole to the d-orbital of copper. While density functional theory (DFT) provides reasonably good molecular geometries, the determination of the ground spin state and the associated energetics is heavily influenced by functional choice, particularly the percentage of the Hartree–Fock exchange. Using extended multireference perturbation theory methods (XMS-CASPT2), the functional choice can be assessed. The molecular geometries and electronic structures of both the unsubstituted and the meso-triphenyl copper corroles were investigated. A minimal active space was employed for structural characterization, while larger active spaces are required to examine the electronic structure. The XMS-CASPT2 investigations conclusively identify the ground electronic state as a multiconfigurational singlet (S o ) with three dominant electronic configurations in its lowest energy and characteristic saddled structure. In contrast, the planar geometry corresponds to the triplet state (T o ), which is approximately 5 kcal/mol higher in energy compared to the S o state for both the bare and substituted copper corroles. Notably, the planarity of the T o geometry is reduced in the substituted corrole compared with that in the unsubstituted one. By analyzing the potential energy surface (PES) between the S o and T o geometries using XMS-CASPT2, the multiconfigurational electronic structure is shown to transition toward a single electron configuration as the saddling angle decreases (i.e., as one approaches the planar geometry). Despite the ability of the functionals to reproduce the minimum energy structures, only the TPSSh-D 3 PES is reasonably close to the XMS-CASPT2 surface. Significant deviations along the PES are observed with other functionals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

COLUMBUS─An Efficient and General Program Package for Ground and Excited State Computations Including Spin–Orbit Couplings and Dynamics

The COLUMBUS program system provides the tools for performing high-level multireference (MR) computations, including the multireference configuration interaction (MRCI) method and its multireference averaged quadratic coupled cluster (MR-AQCC) extension, allowing computations on a wide range of fascinating atomic and molecular systems, including the treatment of open-shells and complicated excited state phenomena. The inclusion of spin−orbit coupling (SOC) directly within the MRCI step enables the description of systems containing heavy elements, such as lanthanides and actinides, whose properties are strongly influenced by SOC. Analytic energy gradients and nonadiabatic couplings at the correlated MRCI level provide the foundation for a variety of dynamics studies, giving insight into ultrafast photochemistry. New and ongoing method developments in COLUMBUS include the computation of spin densities, improved descriptions of ionic states, enhancements to the AQCC method, and the porting of COLUMBUS to graphical processing units (GPUs). New external interfaces enable an enhanced description of electronic resonances and molecules in strong laser fields. This work highlights these new developments while providing a detailed account of the diverse applications of COLUMBUS in recent years.

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

Nonlinear model predictive control for mode‐switching operation of reversible solid oxide cell systems

Abstract Solid oxide cells (SOCs) are a promising dual‐mode technology for the production of hydrogen through high‐temperature water electrolysis, and the generation of power through a fuel cell reaction that consumes hydrogen. Switching between these two modes as the price of electricity fluctuates requires reversible SOC operation and accurate tracking of hydrogen and power production set points. Moreover, a well‐functioning control system is important to avoid cell degradation during mode‐switching operation. In this article, we apply nonlinear model predictive control (NMPC) to an SOC module and supporting equipment and compare NMPC performance to classical proportional‐integral (PI) control strategies, while switching between the modes of hydrogen and power production. While both control methods provide similar performance across various metrics during mode switching, NMPC demonstrates a significant advantage in reducing cell thermal gradients and curvatures (mixed spatial‐temporal partial derivatives), thereby helping to mitigate long‐term degradation.

08 HYDROGEN↗
DOE OSTI2022

Single-stage gradient-based stellarator coil design: Optimization for near-axis quasi-symmetry

Here we present a new coil design paradigm for magnetic confinement in stellarators. Our approach directly optimizes coil shapes and coil currents to produce a vacuum quasi-symmetric magnetic field with a target rotational transform on the magnetic axis. This approach differs from the traditional two-stage approach in which first a magnetic configuration with desirable physics properties is found, and then coils to approximately realize this magnetic configuration are designed. The proposed single-stage approach allows us to find a compromise between confinement and engineering requirements, i.e., find easy-to-build coils with good confinement properties. Using forward and adjoint sensitivities, we derive derivatives of the physical quantities in the objective, which is constrained by a nonlinear periodic differential equation. In two numerical examples, we compare different gradient-based descent algorithms and find that incorporating approximate second-order derivative information through a quasi-Newton method is crucial for convergence. We also explore the optimization landscape in the neighborhood of a minimizer and find many directions in which the objective is mostly flat, indicating ample freedom to find simple and thus easy-to-build coils.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

3D printed transparent ceramic YAG laser rods: Matching the core-clad refractive index

Yttrium Aluminum Garnet (YAG) solid state laser gain media rods with an active Neodymium-doped core and an optically-clear cladding region were additively manufactured via direct-ink-writing (DIW), followed by sintering and hot isostatic pressing to form fully dense optical ceramics. Lutetium and Gadolinium were chosen as optically-inert co-doping ions in the clad to match the increase in refractive index caused by the Neodymium in the core. Additionally, either 11.6% Lutetium or 3.8% Gadolinium can be used to match the index change from 2% neodymium; however, differences in diffusion distances across the core-clad interface lead to large fluctuations in index in that region. These index fluctuations can be minimized either by matching dopants with similar diffusion distances, or by implementing a gradual gradient in the doping profile, possible through DIW, rather than a sharp compositional interface attainable via more standard fabrication methods. This improvement in index homogeneity resulted in a 40% improvement in lasing performance compared with that of a core-clad rod fabricated with a sharp interface between the doped and undoped regions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Advanced Data Science Model for Detecting Intelligent Malware

This study focused on developing a robust artificial intelligence (AI) model capable of detecting and characterizing advanced malware in Internet of Things (IoT) devices using network data. By analyzing network traffic with various machine learning (ML) models, our AI model can identify and characterize malicious activities to significantly improve malware detection accuracy and reliability as compared to traditional methods. The developed AI/ML model was trained using network data from IoT devices, leveraging classifiers such as Random Forest, Gradient Boosting, AdaBoost, and others to optimize detection performance. This project demonstrates a scalable framework for real-time malware detection and characterization in IoT networks, capable of identifying infected devices and facilitating the necessary steps to remove or isolate them, thereby preventing further infections. Although digital twin (DT) integration is not yet implemented in the current model, it represents a promising future enhancement. By creating a virtual replica of physical IoT devices, DT technology would allow for real-time monitoring and analysis without directly accessing operational technology, thus reducing the risk of compromising or reducing the performance of actual devices. This integration would further enhance the security of IoT ecosystems, combining AI technology to better flag and detect indications of malware-infected devices within a nuclear system environment.

42 ENGINEERING↗
DOE OSTI2020

Estimating total attenuation using Rayleigh targets at cloud top: applications in multilayer and mixed-phase clouds observed by ground-based multifrequency radars

At millimeter wavelengths, attenuation by hydrometeors, such as liquid droplets or large snowflakes, is generally not negligible. When using multifrequency ground-based radar measurements, it is common practice to use the Rayleigh targets at cloud top as a reference in order to derive attenuation-corrected reflectivities and meaningful dual-frequency ratios (DFRs). By capitalizing on this idea, this study describes a new quality-controlled approach that aims at identifying regions of cloud where particle growth is negligible. The core of the method is the identification of a “Rayleigh plateau”, i.e., a large enough region near cloud top where the vertical gradient of DFR remains small. By analyzing co-located Ka–W band radar and microwave radiometer (MWR) observations taken at two European sites under various meteorological conditions, it is shown how the resulting estimates of differential path-integrated attenuation (ΔPIA) can be used to characterize hydrometeor properties. When the ΔPIA is predominantly produced by cloud liquid droplets, this technique alone can provide accurate estimates of the liquid water path. When combined with MWR observations, this methodology paves the way towards profiling the cloud liquid water, quality-flagging the MWR retrieval for rain and drizzle contamination, and/or estimating the snow differential attenuation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

The Generality of the GUGA MRCI Approach in COLUMBUS for Treating Complex Quantum Chemistry

The core part of the program system COLUMBUS allows highly efficient calculations using variational multireference (MR) methods in the framework of configuration interaction with single and double excitations (MR-CISD) and averaged quadratic coupled-cluster calculations (MR-AQCC), based on uncontracted sets of configurations and the graphical unitary group approach (GUGA). The availability of analytic MR-CISD and MR-AQCC energy gradients and analytic nonadiabatic couplings for MR-CISD enables exciting applications including, e.g., investigations of π-conjugated biradicaloid compounds, calculations of multitudes of excited states, development of diabatization procedures, and furnishing the electronic structure information for on-the-fly surface nonadiabatic dynamics. With fully variational uncontracted spin-orbit MRCI, COLUMBUS provides a unique possibility of performing high-level calculations on compounds containing heavy atoms up to lanthanides and actinides. Crucial for carrying out all of these calculations effectively is the availability of an efficient parallel code for the CI step. Configuration spaces of several billion in size now can be treated quite routinely on standard parallel computer clusters. Emerging developments in COLUMBUS, including the all configuration mean energy multiconfiguration self-consistent field method and the graphically contracted function method, promise to allow practically unlimited configuration space dimensions. Spin density based on the GUGA approach, analytic spin-orbit energy gradients, possibilities for local electron correlation MR calculations, development of general interfaces for nonadiabatic dynamics, and MRCI linear vibronic coupling models conclude this overview.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Method and apparatus for constructing informative outcomes to guide multi-policy decision making

In Multi-Policy Decision-Making (MPDM), many computationally-expensive forward simulations are performed in order to predict the performance of a set of candidate policies. In risk-aware formulations of MPDM, only the worst outcomes affect the decision making process, and efficiently finding these influential outcomes becomes the core challenge. Recently, stochastic gradient optimization algorithms, using a heuristic function, were shown to be significantly superior to random sampling. In this disclosure, it was shown that accurate gradients can be computed-even through a complex forward simulation—using approaches similar to those in dep networks. The proposed approach finds influential outcomes more reliably, and is faster than earlier methods, allowing one to evaluate more policies while simultaneously eliminating the need to design an easily-differentiable heuristic function.

Olson, Edwin↗
DOE OSTI2022

Leveraging gradient weighted class activation mapping to improve classification effectiveness: Case study in transportation infrastructure characterization

Roadway “corners†are common for pedestrian use, whether designated with markings or not. Different types of markings have been deployed, ranging from simple parallel lines to more complex designs. Understanding the impact of different types of crosswalks is important for public safety. In this work we explore methods to improve the logging of marked crosswalk types. We used the Roadway Information Database from the Second Strategic Highway Research Project and used active learning methods with transfer learning to identify the crosswalk types (marked or unmarked). Upon completion we found our classifiers were unable to perform above roughly 94% correct classifications. To improve their efficacy, we separated the crosswalks into their “fine grained†types and used Gradient-Weighted Class Activation Mapping to isolate and study the features that classified the crosswalks. We compared this with sampled manually marked crosswalks and present findings. We believe this use case can represent a process to improve the active learning method for some visual machine learning applications.

Karnowski, Thomas↗
DOE OSTI2020

Primal interface debonding formulation for finite strain isotropic plasticity

In this work, a framework is developed for modeling ductile damage of nonlinear materials whose plastic deformation is characterized using rate independent classical plasticity. This method relies on the assumption that the free energy can be decomposed into elastic, plastic and damage parts. A thermodynamically consistent method is derived which satisfies the second law of thermodynamics in the Clausius–Duhem inequality form. The dissipation associated with plasticity takes place in the domain only, while damage dissipation is localized to the interface. The method is developed using Variational Multiscale ideas to obtain definitions of the interface fluxes within a primal formulation analogous to the Discontinuous Galerkin method, which ensures weakly vanishing interface gap prior to reaching a damage initiation criterion. The local nonlinear problem to calculate both plastic deformation gradient and damage variable follows an incremental approach similar to classical plasticity return mapping algorithm. This elastoplastic damage formulation is developed for material undergoing finite strain, and it naturally accommodates a trapezoidal traction separation law (TSL) whose shape can be varied to model either ductile interface behavior or brittle interface behavior. The formulation's performance is assessed through modeling a patch test and a compact tension specimen.

42 ENGINEERING↗