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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 OSTI2024

Carbon management technology pathways for reaching a U.S. Economy-Wide net-Zero emissions goal

The Carbon Management Study Group of the 37 th Energy Modeling Forum (EMF 37) designed seven scenarios to explore the role of three potentially key technology suites – point source carbon dioxide capture and storage (PSCCS), direct air capture of carbon dioxide (DACCS), and hydrogen systems (H 2 ) – in shaping the broader technology pathways to reaching net-zero carbon dioxide (CO 2 ) emissions in United States by 2050. Each scenario was run by up to 13 models participating in the EMF 37 study. Results show that carbon dioxide removal technologies were consistently a major part of successful pathways to net-zero U.S. CO 2 emissions in 2050. Achieving this net-zero CO 2 goal without any form of carbon dioxide capture and storage was found to be impossible for most models; some models also found it impossible to reach net-zero without DACCS. The marginal cost of achieving net-zero CO 2 emissions in 2050 was between two and 10 times higher without PSCCS and/or DACCS available. The carbon price at which DACCS was deployed as a backstop technology depended upon the assumed cost at which DACCS was available at scale. Carbon prices were between $\$$250 and $\$$500 per ton CO 2 when DACCS deployed as a backstop. The average CO 2 capture rate across all models in 2050 in the central net-zero scenario was 1.3 GtCO 2 /year, which implies a substantial upscaling of capacity to move and store CO 2 . Finally, hydrogen sensitivity scenarios showed that H 2 typically constituted a relatively small share of the overall U.S. energy system; however, H 2 deployed in applications that are considered hard to decarbonize, facilitating transition towards net-zero emissions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

A library of calcium mineral reference spectra recorded by parallel imaging using NEXAFS spectromicroscopy

Calcium minerals are ubiquitous in geology and life chemistry. Understanding the phase and chemical state of calcium minerals is important for numerous processes including materials chemistry, hard tissue biogenesis and geological processes. Photoemission spectroscopies such as near edge X-ray absorption fine structure (NEXAFS) and scanning transmission X-ray microscopy have been instrumental in identifying and characterizing calcium minerals in all these areas. In this work, we have recorded reference spectra for a range of different calcium minerals including a series of calcium carbonates, calcium oxalates and calcium phosphates. While collections of reference spectra for several calcium minerals can be found in the literature, these spectra have been reported in different contexts using a variety of instruments. We, here, report a comprehensive list of references recorded in parallel in a single experiment by imaging an array of calcium minerals using a NEXAFS microscope. We present reference NEXAFS spectra at the calcium L-, carbon K- and oxygen K-edges.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Effect of strain rate on the deformation of hollow CoS nanoboxes and doubly porous self-assembled films

Nanomaterials with multiscale porosity are attractive as lightweight structural materials because unique deformation mechanisms can be programmed at different length scales. Here, we explore this concept by investigating the mechanics of hollow nanoboxes, as well as films self-assembled from nanoboxes. Hollow CoS nanoboxes with lengths of 900 nm and <30 nm wall thickness are synthesized using colloidal methods. Individual nanoboxes are compressed inside a scanning electron microscope at strain rates of 0.001 s –1 , 0.01 s –1 , and 0.1 s –1 . Nanoboxes are found to have a first peak stress of 15 MPa and loading modulus of ~240 MPa regardless of strain rate. The nanoboxes deform through ductile, plastic bending of side walls at 0.001 s –1 strain rate, while brittle fracture occurs at the higher strain rates. Self-assembly of these nanoboxes results in films with relative density of <0.1. Nanoindentation shows strain rate independent behavior with average modulus and hardness of ~15 MPa and ~230 kPa, respectively. Furthermore, the mechanical behavior of the film is compared to that of the nanobox building blocks, cellular foams and granular materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Data-driven building energy modeling with feature selection and active learning for data predictive control

Three gaps impede the development of cost-effective and accurate data-driven building energy modeling/models (DBEM) for energy forecasting and predictive control strategies. Gap 1: data bias is common in building operation data, but this topic is hardly studied in DBEM; Gap 2: high data dimensionality is common in DBEM, but a systematic and scalable methodology is lacking to solve the problem; Gap 3: the interactions between data bias and high dimensionality have not been systematically studied for DBEM and predictive control in buildings. In this work, to address the three gaps mentioned above, we develop a framework that integrates active learning and feature selection for DBEM used for whole building data predictive control (or DPC, which is a branch of model predictive control). The framework provides a systematic methodology and automatic workflow that starts with raw data from building automation systems to the establishment of data-driven energy models for DPC controllers. The developed strategies and framework are evaluated in a virtual testbed based on EnergyPlus and BCVTB. Improved performance and reduced computational complexity are observed from the DBEM built with the developed framework, as well as the DPC controller based on that DBEM, indicating the effectiveness of the developed framework.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Envelope-driven comfort risk in residential demand response

Residential demand response (DR) is a valuable resource for grid reliability, but remains challenging because the highly heterogeneous residential building stock leads to widely varying and hard-to-predict load and comfort responses during DR events. Although prior research has estimated the technical potential of DR-capable technologies for achieving energy demand savings, little is known about how they affect thermal comfort. In particular, it remains unclear how indoor thermal conditions due to DR depend on the thermal envelope characteristics of the housing stock. To address this gap, this study provides a systematic, location-specific assessment of indoor thermal performance during DR-events across the US housing stock using both typical DR weather data and detailed building metadata. We evaluate how envelope characteristics influence indoor temperatures during realistic simulated summer and winter DR events across 37 US locations, applying both temperature threshold and rate of temperature change criteria to estimate region-level probabilities of discomfort. Additionally, we show the impact of distinct weather patterns that intensify or abate thermal stress on comfort outcomes. Results show a near-universal overheating risk in summer DR events, where comfort outcomes are strongly influenced by rapid risk of comfort violations. In contrast, overall winter DR discomfort risk is lower, risk escalation is more gradual and shows greater sensitivity to event duration. These findings offer a data-driven quantification of comfort risk across diverse climates and building envelopes, demonstrating the need for region-specific DR scheduling and discomfort mitigation strategies tailored to local weather patterns and the performance of existing residential buildings.

Demand response↗
DOE OSTI2022

Tree-inspired lignin microrods-based composite heterogeneous nanochannels for ion transport and osmotic energy harvesting

One of the key processes of tree lignification is that lignin penetrates into the cell wall and fills in the cell wall framework, thereby increasing the hardness and hydrophobic of the tree channels, which is beneficial to consolidate and support the tree cell wall and water transport. Inspired by this natural process, we demonstrated a lignin-based nanofluidic heterogeneous membrane that closely mimics the channels in tree, which can realize ion transport function and effectively capture reverse electrodialysis. The membrane was synthesized by heating dealkaline lignin and PVA at 200 °C and this formed fusiform microrods and a closed-packed membrane. Simultaneously, this membrane composited with anodized aluminum (AAO) channels membrane at 200 °C to form asymmetric heterogeneous nanochannels membrane, which can transport counter-ions and harvest osmotic energy. This membrane implements ion current rectification in 0.1 M KCl electrolyte solution at pH 3 due to the confinement of pores and opposite surface charges in lignin-based heterogeneous nanochannel. An output power density of 0.97 W m- 2 is obtained under a 50-fold salinity gradient, which can be further improved to 1.19 W m -2 by increasing the salinity gradient from 50-fold to 500-fold. Accordingly, this nanofluidic membranes were prepared by using lignin, the key component in tree, which not only mimicked a crucial process of the water and ionic transport process of channels in tree, but also had the prospect in the field of osmotic energy harvesting.

09 BIOMASS FUELS↗
DOE OSTI2021

Numerical simulation of gas production from natural gas hydrate deposits with multi-branch wells: Influence of reservoir properties

Vast amounts of natural gas hydrate are buried in subseafloor sediments without impermeable boundaries, which is recognized as an essential energy source for the future. Previously, the multi-branch well was proposed to enhance the recovery efficiency of natural gas hydrate, and the gas production rate has been dramatically improved comparing with the vertical well. However, the multi-branch well shows a terrible performance in gas production duration. Here, as a continuation of the previous study, numerical simulations were conducted to investigate the influence of hydrate reservoir properties on the gas production potential. Results indicate that it is hard to extract hydrate commercially for hydrate accumulations without impermeable boundaries. A high initial hydrate saturation leads to a long gas production duration but a low gas production rate. An increase in the intrinsic permeability of isotropic reservoirs would shorten the gas production duration and result in a low gas recovery ratio. Permeability anisotropy shows a noticeable effect on enhancing the gas recovery ratio and the gas production duration due to the improved pressure propagation pattern. Therefore, in the upcoming field tests, reservoir reconstructions that enhance permeability anisotropy are strongly suggested to obtain better outcomes.

03 NATURAL GAS↗
DOE OSTI2026

Physics-informed machine learning exploration of Na storage mechanisms in disordered carbon

Sodium-ion batteries are a cost-effective, sustainable alternative to lithium-ion systems for large-scale energy storage. However, optimizing sodium storage in carbon-based anodes with microstructural complexity and atomic disorder remains a major challenge. The intrinsic inhomogeneity of these materials produces diverse local environments, making it difficult for conventional methods to predict and control ion dynamics. Hard carbon (HC) anodes, composed of ranges of ordered-to-disordered graphitic and amorphous nanodomains, offer tunable ion storage and rate capacity, yet rationale design remains a challenge due to poorly understood correlation between local atomic feature and ion transport mechanism. Here, to address this challenge, we introduce a data-driven framework that integrates validated machine-learned interatomic potentials, large-scale molecular dynamics simulations, and machine learning to elucidate sodium transport mechanisms as a function of carbon and sodium loading densities. By computing per-ion structural descriptors and applying unsupervised learning, we identify distinct diffusion modes governed by microscopic features. Supervised analysis and correlation mapping then establish quantitative links between these transport regimes and processing variables such as bulk carbon density and sodium content. This physics-informed approach establishes quantitative structure–transport relationships and offers actionable design principles for engineering high-performance HC anodes.

Data-driven framework↗
DOE OSTI2025

Probing Cretaceous-Paleogene crustal thickness in southern Tibet using quartz-zircon chronobarometry

Knowledge of the crustal thickness history of southern Tibet during the India-Asia collision is key to understanding what deformation mechanisms accommodated northward propagating crustal shortening. Thermoisotopic models suggest that a relatively thin margin of southern Asia persisted from ca. 200 to 45 Ma whereas trace element paleodepth proxies are interpreted to indicate a thinning phase from ca. 100 to 65 Ma when the crust reached ∼30 km-thickness. As neither of these methods directly measures crustal thickness, resolution of this conflict awaits development of a method that can. Here, in this study, we place bounds on the thickening history of southern Tibet using coupled quartz-zircon thermobarometry and U-Pb geochronology of granitoid plutons in the Gangdese batholith. We find that crustal thicknesses were as high (or higher) as 60–75 km at 65 Ma, or 10–15 Ma prior to the onset of hard continental collision. Magmatic inflation in the lower crust likely contributed to crustal thickening and thermal weakening of the lower crust, suggesting that pure shear in the lower crust was likely the primary accommodation mechanism for N-S shortening during collision. These data are in partial agreement with results of thermoisotopic models of crustal thickness but contradict thickness histories derived from empirical trace element proxies.

Chronobarometry↗
DOE OSTI2020

Toward a scalable robust security-constrained optimal power flow using a proximal projection bundle method

Robust security-constrained optimal power flow (rSCOPF) aims to find the worst-case contingencies of alternating current optimal power flow (ACOPF) in power systems. With the rise of GPU architectures on the upcoming supercomputer architectures, optimization algorithms that rely on sparse linear algebra and indefinite linear systems are becoming increasingly hard to solve efficiently (e.g. interior-point method). To address this we revisit a maximin optimization formulation of the rSCOPF and the single-level mixed-integer semidefinite programming (MISDP) reformulation, which is obtained by taking the Lagrangian relaxation of the inner minimization ACOPF problem. In this paper, we focus on the development of a proximal projection bundle method (PPBM) for solving continuous relaxation node subproblems of the MISDP problem, based primarily on the well-known alternating direction method of multipliers. Cutting planes reminiscent of bundle method ideas are also applied in coordination with updates of the proximal parameter. The cutting-plane method can generate a large number of linear inequalities, leading to a large scale but decomposable quadratic programming (QP) subproblem that is amenable to GPUs. We present the numerical results on the IEEE 30, 57, 118, and 300-bus systems by using our PBMM method. We discuss the main computational bottleneck of our method, which is the time taken to solve each iteration of a QP subproblem instance of the PPBM, and how GPU architectures can accelerate this solution process.

bundle method↗
DOE OSTI2022

Power quality disturbances diagnosis: A 2D densely connected convolutional network framework

The fast and accurate diagnosis of power quality disturbances (PQD) aids in avoiding shutdowns and unnecessary procedures, concerning electric energy distribution systems. As such, a number of techniques have been tested and applied in order to reach this objective. Majority of the techniques applied are two-step based. On the first step, power quality disturbances features are extracted. Second step, considering features extracted, disturbance classification is implemented. Recently, relevant literature has presented data-driven signal processing-based approaches, as deep convolutional neural networks (DCNN), which can implement both processing steps while providing automated recognition of patterns and outliers in data. However, not considered by state-of-art, power quality disturbances are evolving in nature, while all possible regularities might not be represented in the dataset. In this work a 2 Dimension Densely Connected Convolutional Network (2D-DenseNet) framework is presented. Further, a case study with synthetic disturbance events are analyzed. Easy-to-implement formulation, built on the 2D-DenseNet, without hard-to-design parameters, highlight potential aspects for real-life implementation.

42 ENGINEERING↗
DOE OSTI2021

Multi-spatio-temporal scales PIV in a turbulent buoyant jet discharging in a linearly stratified environment

Time-resolved particle image velocimetry is implemented with a camera array and several laser sheets; this results in a multi-spatio-temporal scale system that is modular and flexible. The setup is optimized to capture global flow features, while locally resolving in space and time near the Kolmogorov scale. The apparatus relies extensively on machine vision CMOS cameras; they are inexpensive and stream to computer hard drive with virtually continuous time-resolved records (up to one hour for the current system). This leads to statistically converged data and also helps in minimizing down time between experimental runs. Details of the implementation and design of experiment are reported. The system instruments a vertical buoyant jet discharging in a linearly stratified environment. Refractive index matched aqueous solutions enable precise optical deployment. The density difference is 3% and the fluids have similar dynamic viscosity. The jet Reynolds number is 2.00 x 10 4 and is above the mixing transition. Such flows are typically challenging to instrument and few velocity field data are available to date. The velocity statistics are reported as well as first insights gained from the campaign.

42 ENGINEERING↗
DOE OSTI2025

Adaptive Interface-PINNs (AdaI-PINNs) for transient diffusion: Applications to forward and inverse problems in heterogeneous media

We model transient diffusion in heterogeneous materials using a novel physics-informed neural networks framework (PINNs) termed Adaptive interface physics-informed neural networks or AdaI-PINNs (Roy et al. arXiv preprint arXiv:2406.04626, 2024). AdaI-PINNs utilize different activation functions with trainable slopes tailored to each material region within the computational domain, allowing for a fully automated and adaptive PINNs approach to model interface problems with strongly and weakly discontinuous solutions. To enhance its performance in highly heterogeneous transient diffusion systems, we prescribe a suite of robust practices, including appropriate non-dimensionalization of equations, a biased sampling method, Glorot initialization, and the hard enforcement of boundary and initial conditions. Here we evaluate the efficacy of the proposed method on several benchmark forward and inverse problems. Comparative studies on one-dimensional and two-dimensional benchmark problems reveal that the modified AdaI-PINNs outperform its unmodified counterpart, achieving root-mean-square errors that are at least two orders of magnitude better in forward problems. For inverse problems, the maximum errors in the approximated diffusion coefficients by modified AdaI-PINNs are four orders of magnitude better than those of the unmodified version. Additionally, modified AdaI-PINNs demonstrate improved stability in problems with large material mismatches.

42 ENGINEERING↗
DOE OSTI2020

Reexamining supercritical gas adsorption theories in nano-porous shales under geological conditions

Adsorption is an important phenomenon in surface chemistry, especially for nano-porous shales. In the shale gas-in-place, adsorbed gas could contribute up to 85%. However, adsorption is hard to quantitatively characterize due to different adsorption mechanisms, patterns, surfaces, and pore sizes. Moreover, key thermodynamic parameters, such as the enthalpy of adsorption, are challenging to determine due to uncertainties in adsorbed gas densities used in constructing absolute isotherms. In this paper, we revisit the Brunauer, Emmett, and Teller (BET) model, analytically simplify Ono-Kondo (OK) models for subsurface shales, and compare commonly used mono- and multilayer adsorption models (e.g., Langmuir, supercritical Dubinin-Radushkevich (SDR), supercritical BET (SBET), and simplified OK (OKs) models) with recently proposed pressure-dependent adsorption densities to develop a practical and reliable methodology that can be used in the supercritical state, typical for subsurface black shale conditions.Three independent data sets were used for nitrogen and methane adsorption isotherms at different temperatures. Furthermore, we demonstrate that adsorption predicted by the SDR model is comparable or lower than that predicted by the SBET model, but higher than the amounts predicted by Langmuir and OK models. The nitrogen BET method tends to underestimate the accessible SSA for methane. Measurement of isosteric heat of adsorption is suggested instead of the experimental fitting method, due to the significant difference between the calculated results by two commonly used methods. Experimental fitting and simulation methods are also briefly reviewed to guide future research on shale gas adsorption.

58 GEOSCIENCES↗
DOE OSTI2021

Technological aspects in blanket design: Effects of micro-alloying and thermo-mechanical treatments of EUROFER97 type steels after neutron irradiation

Presently available data on neutron irradiation damage raise doubts on the feasibility of using EUROFER97 steel for a water-cooled starter blanket in a DEMO reactor, since the ductile-to-brittle transition temperature (DBTT) increases significantly for irradiation temperatures below 350°C. The additional DBTT shift caused by H and He transmutation can only be estimated based on very few results with isotopically tailored EUROFER97 steel. Conservative calculations show that the DBTT of EUROFER97 steel could exceed the operating temperature in water-cooled starter blankets within a relatively short time period. This paper presents results from a EUROfusion funded irradiation campaign that was performed in the High Flux Isotope Reactor at Oak Ridge National Laboratory. The paper compares ten newly developed reduced activation ferritic-martensitic (RAFM) steels irradiated to a nominal dose of 2.5 dpa at 300°C. The post-irradiation experiments using Small Specimen Test Technology included hardness, tensile, and fracture mechanics tests combined with fractography and microstructure analysis are presented. Results show that micro-alloying EUROFER97-type steels influenced the mechanical properties but a dominating impact on irradiation damage resistance could not be identified. In contrast, specific thermo-mechanical treatments lead to better DBTT behavior. Discussion about irradiation response to heat treatment conditions is also given. Here, despite requiring data also at high dpa values, the results indicate that with these modified materials an increased lifetime and potentially also an increased operating temperature window can be achieved compared to EUROFER97.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Adaptive elasticity policies for staging-based in situ visualization

In situ processing aims to alleviate the growing gap between computation and I/O capabilities by performing data processing close to the data source. In situ processing is widely used to process data generated by multiple data sources, including observation data from edge devices or scientific observational facilities and the simulation data generated by scientific computation on a high-performance computing (HPC) platform. For a scientific workflow that is run on an HPC platform and composed of a simulation program and an in situ data analytics or visualization (abbreviated as ana/vis) task, there is an implicit assumption that the computing resources assigned to the workflow keep static during the workflow execution. However, with the converging trend between the HPC and cloud computing platform, running the in situ ana/vis task in an elastic way is promising to decrease its overhead and improve its resource utilization rate. Resource elasticity represents the ability to change resource configurations such as the number of computing nodes/processes during workflow execution. An elastic job may dynamically adjust resource configurations; it may use a few resources at the beginning and more resources toward the end of the job when interesting data appear. However, it is hard to predict a priori how many computing nodes/processes need to be added/removed during the workflow execution to adapt to changing workflow needs. How to efficiently guide elasticity operations, such as growing or shrinking the number of processes used for in situ analysis during workflow execution, is an open-ended research question. In this article, we present adaptive elasticity policies that adopt workflow runtime information collected during workflow execution to predict how to trigger the addition/removal of processes in order to minimize in situ processing overhead. Taking in situ visualization tasks as an example, we integrate the presented elasticity policies into a staging-based elastic workflow and evaluate its efficiency in multiple elasticity scenarios. Compared with the situation without elasticity or with a static elasticity policy that uses a fixed number of processes for each rescaling operation, the adaptive elasticity policy can save overhead in finding a proper resource configuration and improve resource utilization efficiency. Furthermore, one experiment illustrates that the adaptive elasticity policy saves 41% of core-hours compared with the situation without the resource elasticity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

QoS-aware edge AI placement and scheduling with multiple implementations in FaaS-based edge computing

Resource constraints on the computing continuum require that we make smart decisions for serving AI-based services at the network edge. AI-based services typically have multiple implementations (e.g., image classification implementations include SqueezeNet, DenseNet, and others) with varying trade-offs (e.g., latency and accuracy). The question then is how should AI-based services be placed across Function-as-a-Service (FaaS) based edge computing systems in order to maximize total Quality-of-Service (QoS). To address this question, we propose a problem that jointly aims to solve (i) edge AI service placement and (ii) request scheduling. These are done across two time-scales (one for placement and one for scheduling). Here we first cast the problem as an integer linear program. We then decompose the problem into separate placement and scheduling subproblems and prove that both are NP-hard. We then propose a novel placement algorithm that places services while considering device-to-device communication across edge clouds to offload requests to one another. Our results show that the proposed placement algorithm is able to outperform a state-of-the-art placement algorithm for AI-based services, and other baseline heuristics, with regard to maximizing total QoS. Additionally, we present a federated learning-based framework, FLIES, to predict the future incoming service requests and their QoS requirements. Our results also show that our FLIES algorithm is able to outperform a standard decentralized learning baseline for predicting incoming requests and show comparable predictive performance when compared to centralized training.

97 MATHEMATICS AND COMPUTING↗