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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 1,279 records · Page 71
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

A Comparative Study of Deformation Behaviors Between Laser-Welded Joints and Base Metal of Ti-22Al-24.5Nb-0.5Mo Alloy

The tensile deformation behaviors of laser-welded Ti-22Al-24.5Nb-0.5Mo alloy joints have been investigated at room temperature and 650 °C using in situ tensile analysis methods. The α{sub 2} phase had a significant influence on deformation behaviors of base metal at room temperature and 650 °C. The microcracks mainly nucleated in B2/α{sub 2} phase boundaries or within α{sub 2} phase and then propagated along B2/α{sub 2} phase boundaries subsequently. Compared with the plastic fracture of base metal, the fracture modes of the fully B2-phase fusion zone at room temperature and 650 °C were quasi-cleavage and intergranular fracture, respectively. While dislocation slips became foremost deformation mode in the fusion zone at room temperature, there were a great amount of slip bands on the surface of grains caused by the slip systems. The microcracks of fusion zone at 650 °C nucleated and propagated along the grain boundaries of B2 phase. Owing to the lack of grain deformation, the cross-slip bands were in small quantities on the surface of B2 phase grains.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Multiple plates subducting beneath Colombia, as illuminated by seismicity and velocity from the joint inversion of seismic and gravity data

Subduction beneath the northernmost Andes in Colombia is complex. Based on seismicity distributions, multiple segments of slab appear to be subducting, and arc volcanism ceases north of 5° N. Here, we illuminate the subduction system through hypocentral relocations and Vp and Vs models resulting from the joint inversion of local body wave arrivals, surface wave dispersion measurements, and gravity data. The simultaneous use of multiple data types takes advantage of the differing sensitivities of each data type, resulting in velocity models that have improved resolution at both shallower and deeper depths than would result from traditional travel time tomography alone. The relocated earthquake dataset and velocity model clearly indicate a tear in the Nazca slab at 5° N, corresponding to a 250-km shift in slab seismicity and the termination of arc volcanism. North of this tear, the slab is flat, and it comprises slabs of two sources: the Nazca and Caribbean plates. The Bucaramanga nest, a small region of among the most intense intermediate-depth seismicity globally, is associated with the boundary between these two plates and possibly with a zone of melting or elevated water content, based on reduced Vp and increased Vp/Vs. As a result, we also use relocated seismicity to identify two new faults in the South American plate, one related to plate convergence and one highlighted by induced seismicity.

58 GEOSCIENCES↗
DOE OSTI2024

Multifacets of lossy compression for scientific data in the Joint-Laboratory of Extreme Scale Computing

The Joint Laboratory on Extreme-Scale Computing (JLESC) was initiated at the same time lossy compression for scientific data became an important topic for the scientific communities. The teams involved in the JLESC played and are still playing an important role in developing the research, techniques, methods, and technologies making lossy compression for scientific data a key tool for scientists and engineers. Here, in this paper, we present the evolution of lossy compression for scientific data from 2015, describing the situation before the JLESC started, the evolution of this discipline in the past 8 years (until 2023) through the prism of the JLESC collaborations on this topic and some of the remaining open research questions.

Compression for AI↗
DOE OSTI2026

Joint scheduling of energy, fast and primary frequency response reserves in integrated transmission–distribution networks

Inverter-based distributed energy resources (DERs) connected to distribution networks (DNs) can provide fast frequency support, but their reserve deliverability depends on feeder constraints and differs from synchronous primary frequency response (PFR). Existing transmission–distribution coordination studies usually treat reserve generically or neglect feeder-level feasibility, while frequency-security scheduling studies rarely represent distribution feeders explicitly. This paper develops a bi-level day-ahead scheduling framework for integrated transmission–distribution networks that jointly clears energy, transmission-side PFR, and distribution-side fast frequency response (FFR) under exogenous hourly inertia and largest-loss inputs from an external unit commitment (UC) schedule. The transmission problem is modeled with DC-optimal power flow (OPF) and closed-form second-order cone (SOC) frequency-security constraints, whereas each DN is represented by a reserve-aware branch-flow AC-OPF so that scheduled fast reserves remain deliverable during activation. The bi-level problem is reformulated through Karush–Kuhn–Tucker (KKT) conditions into a mixed-integer SOC program, and a penalty term is used to tighten the distribution-network relaxation. In the reduced test system, lower exogenous inertia increased the required primary response from 179.64 MW to 191.08 MW, distribution-side fast response reduced total frequency-response procurement by up to 4.9%, and neglecting distribution constraints overstated the combined distribution-side energy and reserve award by up to 18%. In the expanded study, the largest case was solved in 2.02 s with a 0.00% optimality gap. Time-domain simulations kept the frequency nadir above 59.0 Hz in all tested hours. These results demonstrate the value of fast-response modeling and distribution-feasible reserve delivery in coordinated market clearing.

Noh, Seung-Gil↗
DOE OSTI2012

Prognostic Impact of the 6th and 7th American Joint Committee on Cancer TNM Staging Systems on Esophageal Cancer Patients Treated With Chemoradiotherapy

Purpose: The new 7th edition of the American Joint Committee on Cancer TNM staging system is based on pathologic data from esophageal cancers treated by surgery alone. There is no information available on evaluation of the new staging system with regard to prognosis of patients treated with chemoradiotherapy (CRT). The objective of this study was to evaluate the prognostic impact of the new staging system on esophageal cancer patients treated with CRT. Methods and Materials: A retrospective review was performed on 301 consecutive esophageal squamous cell carcinoma patients treated with CRT. Comparisons were made of the prognostic impacts of the 6th and 7th staging systems and the prognostic impacts of stage and prognostic groups, which were newly defined in the 7th edition. Results: There were significant differences between Stages I and III (p < 0.01) according to both editions. However, the 7th edition poorly distinguishes the prognoses of Stages III and IV (p = 0.36 by multivariate analysis) in comparison to the 6th edition (p = 0.08 by multivariate analysis), although these differences were not significant. For all patients, T, M, and gender were independent prognostic factors by multivariate analysis (p < 0.05). For the Stage I and II prognostic groups, survival curves showed a stepwise decrease with increase in stage, except for Stage IIA. However, there were no significant differences seen between each prognostic stage. Conclusions: Our study indicates there are several problems with the 7th TNM staging system regarding prognostic factors in patients undergoing CRT.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Joint Estimation of Cardiac Toxicity and Recurrence Risks After Comprehensive Nodal Photon Versus Proton Therapy for Breast Cancer

Purpose: The study aims to perform joint estimation of the risk of recurrence caused by inadequate radiation dose coverage of lymph node targets and the risk of cardiac toxicity caused by radiation exposure to the heart. Delivered photon plans are compared with realistic proton plans, thereby providing evidence-based estimates of the heterogeneity of treatment effects in consecutive cases for the 2 radiation treatment modalities. Methods and Materials: Forty-one patients referred for postlumpectomy comprehensive nodal photon irradiation for left-sided breast cancer were included. Comparative proton plans were optimized by a spot scanning technique with single-field optimization from 2 en face beams. Cardiotoxicity risk was estimated with the model of Darby et al, and risk of recurrence following a compromise of lymph node coverage was estimated by a linear dose-response model fitted to the recurrence data from the recently published EORTC (European Organisation for Research and Treatment of Cancer) 22922/10925 and NCIC-CTG (National Cancer Institute of Canada Clinical Trials Group) MA.20 randomized controlled trials. Results: Excess absolute risk of cardiac morbidity was small with photon therapy at an attained age of 80 years, with median values of 1.0% (range, 0.2%-2.9%) and 0.5% (range, 0.03%-1.0%) with and without cardiac risk factors, respectively, but even lower with proton therapy (0.13% [range, 0.02%-0.5%] and 0.06% [range, 0.004%-0.3%], respectively). The median estimated excess absolute risk of breast cancer recurrence after 10 years was 0.10% (range, 0.0%-0.9%) with photons and 0.02% (range, 0.0%-0.07%) with protons. The association between age of the patient and benefit from proton therapy was weak, almost non-existing (Spearman rank correlations of −0.15 and −0.30 with and without cardiac risk factors, respectively). Conclusions: Modern photon therapy yields limited risk of cardiac toxicity in most patients, but proton therapy can reduce the predicted risk of cardiac toxicity by up to 2.9% and the risk of breast cancer recurrence by 0.9% in individual patients. Predicted benefit correlates weakly with age. Combined assessment of the risk from cardiac exposure and inadequate target coverage is desirable for rational consideration of competing photon and proton therapy plans.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Integrating Multiomics Information in Deep Learning Architectures for Joint Actuarial Outcome Prediction in Non-Small Cell Lung Cancer Patients After Radiation Therapy

Novel actuarial deep learning neural network (ADNN) architectures are proposed for joint prediction of radiation therapy outcomes—radiation pneumonitis (RP) and local control (LC)—in stage III non-small cell lung cancer (NSCLC) patients. Unlike normal tissue complication probability/tumor control probability models that use dosimetric information solely, our proposed models consider complex interactions among multiomics information including positron emission tomography (PET) radiomics, cytokines, and miRNAs. Additional time-to-event information is also used in the actuarial prediction.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Strategy of the Joint Center for Energy Storage Research to Influence Laboratory Safety Among its Funded Researchers

In 2011, a team at Argonne National Laboratory (Argonne) was preparing a proposal for a Department of Energy (DOE)-funded project that called for the creation of an “Energy Innovation Hub.” The proposal team, well engrained in Argonne's and DOE's safety culture, was also aware of the high profile university laboratory accidents from 2009 and 2010 and many other high profile research laboratory safety incidents. They knew they wanted to influence safety by setting an expectation of safe science across the Hub they proposed. In 2012, the DOE awarded Argonne the contract to create the Hub, now known as the Joint Center for Energy Research (JCESR). JCESR promptly hired and appointed a Safety Lead charged with fulfilling JCESR's vision of influencing safety across their newly created Hub. This article discusses JCESR's implementation of a strategy to influence laboratory safety among its funded researchers.

Influencing Safety↗
DOE OSTI2017

Bayesian seismic inversion based on rock-physics prior modeling for the joint estimation of acoustic impedance, porosity and lithofacies

We propose a Bayesian approach for seismic inversion to estimate acoustic impedance, porosity and lithofacies within the reservoir conditioned to post-stack seismic and well data. The link between elastic and petrophysical properties is given by a joint prior distribution for the logarithm of impedance and porosity, based on a rock-physics model. The well conditioning is performed through a background model obtained by well log interpolation. Two different approaches are presented: in the first approach, the prior is defined by a single Gaussian distribution, whereas in the second approach it is defined by a Gaussian mixture to represent the well data multimodal distribution and link the Gaussian components to different geological lithofacies. The forward model is based on a linearized convolutional model. For the single Gaussian case, we obtain an analytical expression for the posterior distribution, resulting in a fast algorithm to compute the solution of the inverse problem, i.e. the posterior distribution of acoustic impedance and porosity as well as the facies probability given the observed data. For the Gaussian mixture prior, it is not possible to obtain the distributions analytically, hence we propose a Gibbs algorithm to perform the posterior sampling and obtain several reservoir model realizations, allowing an uncertainty analysis of the estimated properties and lithofacies. Both methodologies are applied to a real seismic dataset with three wells to obtain 3D models of acoustic impedance, porosity and lithofacies. The methodologies are validated through a blind well test and compared to a standard Bayesian inversion approach. Using the probability of the reservoir lithofacies, we also compute a 3D isosurface probability model of the main oil reservoir in the studied field.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Even spheres as joint spectra of matrix models

The Clifford spectrum is a form of joint spectrum for noncommuting matrices. This theory has been applied in photonics, condensed matter and string theory. In applications, the Clifford spectrum can be efficiently approximated using numerical methods, but this only is possible in low dimensional example. In this paper we examine the higher-dimensional spheres that can arise from theoretical examples. We also describe a constructive method to generate five real symmetric almost commuting matrices that have a K-theoretical obstruction to being close to commuting matrices. For this, we look to matrix models of topological electric circuits.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Effect of post-weld heat treatment on microstructure and mechanical properties of rotary inertia friction welded dissimilar 422-4140 martensitic steel piston joints

The study investigated how post‐weld heat treatment (PWHT) temperature affects the microstructure and localized deformation/fracture during bend testing of rotary inertia friction welds (RIFW) between AISI 422 stainless steel and AISI 4140 steel. RIFW produced a fully martensitic interface with approximately 550 HV hardness in both the thermo-mechanically affected (TMAZ) and heat‐affected zones (HAZ). Due to differences in temper resistance, the 4140 TMAZ/HAZ softened progressively under PWHT temperatures from 525 °C to 700 °C, while the 422 TMAZ unexpectedly maintained about 550 HV up to 600 °C before significantly softening at temperatures ≥625 °C. This asymmetric softening generated steep hardness gradients across the interface at temperatures ≤600 °C. Furthermore, carbon migration across the interface was minimal up to 600 °C, moderate at 625 °C, and by 700 °C produced a carbide‐rich eutectoid layer in the 422 TMAZ alongside a carbon‐depleted soft ferrite layer in the 4140 TMAZ. Strain during bending was PWHT‐dependent, concentrating on the 4140 side; in as-welded joints, the high hardness led to deformation and crack initiation in the base metals, whereas in PWHT samples, cracking initiated in the softened 4140 TMAZ near the interface. The intermediate PWHT temperature of 625 °C offered the best balance of limited carbon diffusion across the interface, relatively low peak weld hardness and minimized hardness gradients across the interface, more homogenous deformation, and good bend test performance.

4140 low-alloy steel↗
DOE OSTI2020

Optimal planning of the joint placement of photovoltaic panels and green roofs under climate change uncertainty

Photovoltaic (PV) panels directly convert sunlight into electricity; but, sunlight also heats the panels, negatively impacting their efficiency. Green roofs are vegetative layers grown on rooftops, mainly to provide added insulation on the roof to save energy. Green roofs also cool near-surface air temperature. Hence, the joint installation of PV panels and green roofs may potentially lead to higher efficiency of PV panels in certain climates. We develop a two-stage stochastic programming model to optimally place PV panels and green roofs under climate change uncertainty to maximize the overall profit from energy generated and saved. We calibrate the model using the literature, industry reports, and the data from different, at times conflicting, climate projections. We then conduct a case study for a mid-size city in the U.S., perform extensive sensitivity and robustness analyses and provide insights.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

JDFTx: Software for joint density-functional theory

Density-functional theory (DFT) has revolutionized computational prediction of atomic-scale properties from first principles in physics, chemistry and materials science. Continuing development of new methods is necessary for accurate predictions of new classes of materials and properties, and for connecting to nano- and mesoscale properties using coarse-grained theories. JDFTx is a fully-featured open-source electronic DFT software designed specifically to facilitate rapid development of new theories, models and algorithms. Using an algebraic formulation as an abstraction layer, compact C++11 code automatically performs well on diverse hardware including GPUs (Graphics Processing Units). This code hosts the development of joint density-functional theory (JDFT) that combines electronic DFT with classical DFT and continuum models of liquids for first-principles calculations of solvated and electrochemical systems. In addition, the modular nature of the code makes it easy to extend and interface with, facilitating the development of multi-scale toolkits that connect to ab initio calculations, e.g. photo-excited carrier dynamics combining electron and phonon calculations with electromagnetic simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Joint Optimization of Multimodal Transit Frequency and Shared Autonomous Vehicle Fleet Size with Hybrid Metaheuristic and Nonlinear Programming

Shared autonomous vehicles (SAVs) bring competition to traditional transit services but redesigning multimodal transit network can utilize SAVs as feeders to enhance service efficiency and coverage. This paper presents an optimization framework for the joint multimodal transit frequency and SAV fleet size problem, a variant of the transit network frequency setting problem. The objective is to maximize total transit ridership (including SAV-fed trips and subtracting boarding rejections) across multiple time periods under budget constraints, considering endogenous mode choice (transit, point-to-point SAVs, driving) and route selection, while allowing for strategic route removal by setting frequencies to zero. Due to the problem’s non-linear, non-convex nature and the computational challenges of large-scale networks, we develop a hybrid solution approach that combines a metaheuristic approach (particle swarm optimization) with nonlinear programming for local solution refinement. To ensure computational tractability, the framework integrates analytical approximation models for SAV waiting times based on fleet utilization, multimodal network assignment for route choice, and multinomial logit mode choice behavior, bypassing the need for computationally intensive simulations within the main optimization loop. Applied to the Chicago metropolitan area’s multimodal network, our method illustrates a 33.3% increase in transit ridership through optimized transit route frequencies and SAV integration, particularly enhancing off-peak service accessibility and strategically reallocating resources.

Ng, Max↗
DOE OSTI2016

Joint optimization of logistics infrastructure investments and subsidies in a regional logistics network with CO 2 emission reduction targets

This study proposes an optimization model that simultaneously incorporates the selection of logistics infrastructure investments and subsidies for green transport modes to achieve specific CO 2 emission targets in a regional logistics network. The proposed model is formulated as a bi-level formulation, in which the upper level determines the optimal selection of logistics infrastructure investments and subsidies for green transport modes such that the benefit-cost ratio of the entire logistics system is maximized. The lower level describes the selected service routes of logistics users. A genetic and Frank-Wolfe hybrid algorithm is introduced to solve the proposed model. The proposed model is applied to the regional logistics network of Changsha City, China. Findings show that using the joint scheme of the selection of logistics infrastructure investments and green subsidies is more effective than using them solely. In conclusion, carbon emission reduction targets can significantly affect logistics infrastructure investments and subsidy levels.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

The Tribomechadynamics Research Challenge: Confronting blind predictions for the linear and nonlinear dynamics of a thin-walled jointed structure with measurement results

The present article summarizes the submissions to the Tribomechadynamics Research Challenge announced in 2021. The task was a blind prediction of the vibration behavior of a system comprising a thin plate clamped on two sides via bolted joints. Both geometric and frictional contact nonlinearities are expected to be relevant. Provided were the CAD models and technical drawings of all parts as well as assembly instructions. The main objective was to predict the frequency and damping ratio of the lowest-frequency mode as function of the amplitude. Many different prediction approaches were pursued, ranging from well-known methods to very recently developed ones. After the submission deadline, the system has been fabricated and tested. The aim of this article is to evaluate the current state of the art in modeling and vibration prediction, and to provide directions for future methodological advancements.

42 ENGINEERING↗
DOE OSTI2022

An End-To-End Earthquake Detection Method for Joint Phase Picking and Association Using Deep Learning

Earthquake monitoring by seismic networks typically involves a workflow consisting of phase detection/picking, association, and location tasks. In recent years, the accuracy of these individual stages has been improved through the use of machine learning techniques. Here, in this study, we introduce a new end-to-end approach that improves overall earthquake detection accuracy by jointly optimizing each stage of the detection pipeline. We propose a neural network architecture for the task of multi-station processing of seismic waveforms recorded over a seismic network. This end-to-end architecture consists of three sub-networks: a backbone network that extracts features from raw waveforms, a phase picking sub-network that picks P- and S-wave arrivals based on these features, and an event detection sub-network that aggregates the features from multiple stations to associate and detect earthquakes across a seismic network. We use these sub-networks together with a shift-and-stack module based on back-projection that introduces kinematic constraints on arrival times, allowing the neural network model to generalize to different velocity models and to variable station geometry in seismic networks. We evaluate our proposed method on the STanford EArthquake Dataset (STEAD) and on the 2019 Ridgecrest, CA earthquake sequence. The results demonstrate that our end-to-end approach can effectively pick P- and S-wave arrivals and achieve earthquake detection accuracy rivaling that of other state-of-the-art approaches. Because our approach preserves information across tasks in the detection pipeline, it has the potential to outperform approaches that do not.

58 GEOSCIENCES↗
DOE OSTI2022

Modeling the Joint Effects of Vegetation Characteristics and Soil Properties on Ecosystem Dynamics in a Panama Tropical Forest

Abstract In tropical forests, both vegetation characteristics and soil properties are important not only for controlling energy, water, and gas exchanges directly but also determining the competition among species, successional dynamics, forest structure and composition. However, the joint effects of the two factors have received limited attention in Earth system model development. Here we use a vegetation demographic model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM), ELM‐FATES, to explore how plant traits and soil properties affect tropical forest growth and composition concurrently. A large ensemble of simulations with perturbed vegetation and soil hydrological parameters is conducted at the Barro Colorado Island, Panama. The simulations are compared against observed carbon, energy, and water fluxes. We find that soil hydrological parameters, particularly the scaling exponent of the soil retention curve ( B sw ), play crucial roles in controlling forest diversity, with higher B sw values (>7) favoring late successional species in competition, and lower B sw values (1 ∼ 7) promoting the coexistence of early and late successional plants. Considering the additional impact of soil properties resolves a systematic bias of FATES in simulating sensible/latent heat partitioning with repercussion on water budget and plant coexistence. A greater fraction of deeper tree roots can help maintain the dry‐season soil moisture and plant gas exchange. As soil properties are as important as vegetation parameters in predicting tropical forest dynamics, more efforts are needed to improve parameterizations of soil functions and belowground processes and their interactions with aboveground vegetation dynamics.

54 ENVIRONMENTAL SCIENCES↗