Search NASA⌕ Search

SEARCH · Search NASA

Results for “CHARTS - INTERCOOLERS”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Computation-guided discovery of coating materials to stabilize the interface between lithium garnet solid electrolyte and high-energy cathodes for all-solid-state lithium batteries

All-solid-state batteries with a lithium metal anode, enabled by lithium garnet solid electrolytes such as Li 7 La 3 Zr 2 O 12 (LLZO), are a promising next-generation energy-storage technology. The further development of all-solid-state battery requires the integration of high-energy cathodes such as LiNi 1-x-y Mn x Co y O 2 (NMC) with the garnet solid electrolyte with stable and low-resistance interfaces, which requires a coating layer to stabilize the interface during high-temperature sintering and electrochemical cycling. In order to guide the future development of interfacial coatings, we perform high-throughput thermodynamic analyses based on first-principles computation to investigate the stability of LLZO garnet and high-energy NMC cathodes with a wide range of materials chemistries. Here our study reveals the factors governing the materials stability with LLZO garnet and NMC cathodes, and identifies the mechanisms of good coating layers stable with LLZO and NMC. In addition to classifying known coating layers, our study provides detailed guiding charts and multiple new materials systems as promising coatings for stabilizing LLZO—NMC interfaces to enable high-energy-density garnet-based all-solid-state batteries. Our demonstrated computation scheme and high-throughput analyses are generally applicable to investigate and screen coating materials for stabilizing interfaces in energy-related applications.

25 ENERGY STORAGE↗
DOE OSTI2023

Suspending democratic (dis)belief: Nonliberal energy polities of solar power in Morocco and Tanzania

This paper proposes the concept of technodemocratic imaginaries (TDIMs) to supplement the widely-used framework of sociotechnical imaginaries (STIMs) for analyzing nonliberal political contexts in energy social science research. Taking the power of coproductionist analysis of STIMs seriously, TDIMs highlight the inherent instabilities within overarching STIMs by arguing that not only are particular groups marginalized and excluded but that they continue to practice and mobilize their own imaginaries of collective governance and justice vis-à-vis energy systems. In contrast, the existing idea of contested STIMs, in which a shared mode of knowledge politics undergirds different imaginaries, casts aside subaltern modes of political engagement and knowledge-making. TDIMs expand conceptions of democracy to include shared practices of credibility in nonliberal political orders. The intent is not to promote democratic relativism but rather to ask scholars and international energy-access practitioners to suspend their democratic disbelief when studying energy matters in so-called nonliberal contexts. We develop the concept of TDIMs by comparing two African nation-states–Morocco and Tanzania–to show how the states and subaltern groups do (or do not) develop TDIMs related to solar power. While international governance organizations often portray Morocco as authoritarian and Tanzania corrupt, each state differently experienced colonization and decolonization and practices different relationships with domestic subaltern groups. Whereas low-income citizens and indigenous groups seek integration into the Moroccan state’s STIM, the Maasai in Tanzania chart their own TDIM separate from the state and international development groups.

14 SOLAR ENERGY↗
DOE OSTI2026

Holistic energy analysis method for thermal management architectures of data centers

Modern high-performance computing (HPC) data centers (DCs), particularly those supporting energy-intensive artificial intelligence (AI) workloads, face escalating thermal management challenges that degrade performance through thermal throttling and drive up cooling power consumption and operational costs. To address this challenge, many have developed a wide variety of thermal management solutions (single-phase, two-phase, direct, indirect, hybrid, and more) which attempt to cool HPC DCs effectively while attempting to minimize overall system power consumption. However, the analysis of these solutions and methods to effectively compare one with another is lacking. Overall power usage effectiveness (PUE) and total-power usage effectiveness (TUE) provide a metric to quantify power consumption but fail to identify components in the system which require further optimization. To address this, we propose a holistic analytical framework – the waterfall diagram (WFD) – which leverages a waterfall chart methodology, offering a comprehensive visualization of both the thermal management system loop and heat flow pathways from individual server components to the outdoor ambient. Use of the WFD enables graphical estimations of power efficiency and cooling performance across each component of a DC cooling system and complements Sankey-style energy flow visualizations by additionally resolving stage-wise temperature changes and incremental TUE contributions. The framework is used in conjunction with simulation-based approaches, to conduct a detailed pressure drop and flow distribution analysis aimed at identifying the optimal coolant distribution architecture for a single-phase direct-to-chip water-cooled DC, which serves as the baseline for subsequent WFD analysis. Among the evaluated architectures, the 3 U modular coolant distribution architecture is found to demonstrate the best performance, considering minimal pressure drop and uniform flow distribution. In addition, TUE is calculated for each cooling loop component based on its associated pressure drop and corresponding pumping power, which are integrated into the WFD. This correlation between TUE and local temperature offers immediate insight into the power efficiency and thermal performance contributions of individual components, facilitating further development and optimization. Examples of WFD applications are presented under varying thermal loads and ambient conditions, demonstrating reasonable cooling strategies. Notably, the 3 U modular architecture maintains a consistent chip case temperature of 85°C, achieving a TUE of 1.016 at ambient temperature of 47°C, and a TUE of 1.026 at ambient temperature of 52°C. The WFD methodology provides an efficient, holistic, and streamlined framework for DC thermal management architecture assessment and enables design optimization which is important for addressing the thermal-fluidic energy challenges of current and next-generation DCs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

EPIsembleVis: A geo-visual analysis and comparison of the prediction ensembles of multiple COVID-19 models

In this work, we present EPIsembleVis, a web-based comparative visual analysis tool for evaluating the consistency of multiple COVID-19 prediction models. Our approach analyzes a collection of COVID-19 predictions from different epidemiological models as an ensemble and utilizes two metrics to quantify model performance. These metrics include (a) prediction uncertainty (represented as the dispersion of predictions in each ensemble) and (b) prediction error (calculated by comparing individual model predictions with the recorded data). Through an interactive visual interface, our approach provides a data-driven workflow for (a) selecting and constructing the COVID-19 model prediction ensemble based on the spatiotemporal overlap of available predictions of multiple epidemiological models, (b) quantifying the model performance using both the uncertainty of each model prediction ensemble, and the error of each ensemble member that represents individual model predictions, and (c) visualizing the spatiotemporal variability in the projection performance of individual models using a suite of novel ensemble visualization techniques, such as the data availability map, a spatiotemporal textured-tile calendar, multivariate rose chart, and time-series leaflet glyph. We demonstrate the capability of our ensemble visual interface through a case study that investigates the performance of weekly COVID-19 predictions, which are provided through the COVID-19 Forecast Hub UMass-Amherst Influenza Forecasting Center of Excellence [47] for the United States and United States Territories. The EPIsembleVis tool is implemented using open-source web technologies and adaptive system design, rendering it interoperable with Elasticsearch and Kibana for automatically ingesting COVID-19 predictions from online repositories, and it is generalizable for analyzing worldwide projections from more epidemiological models.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

In-situ imaging of nucleation and growth of superlattices from nanoscale colloidal nanoparticles

Nanoparticle-based superlattices have attracted extensive research efforts due to their versatile composition and packing-dependent optical, magnetic, electronic, catalytic, mechanical properties. Yet the experimental understanding of the thermodynamics and pathways of the crystallization processes for these systems, which govern their ultimate morphology, size, and packing structure, has for long remained underexplored. Here, this minireview highlights recent integration of liquid-phase transmission electron microscopy, which allows for real-space, real-time direct imaging of the crystallization processes at the single-particle level, with simulation and the conceptual framework established by pioneers such as Dr. A. A. Chernov. Different aspects of superlattice formation were revealed by direct imaging for the first time, such as the existence of prenucleation precursor in nonclassical crystallization, surface energy-dependent growth, and coalescence, allowing for the charting of phase coordinates and thermodynamic quantities at the nanoscale. We discuss the similarities and differences of crystal growth behaviors in atomic and nanoparticle systems, as well as engineering opportunities to fashion, shape, and achieve quality control in superlattice formation from nanoparticles, for next-generation optical and mechanical metamaterials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Manufacturing scalability implications of materials choice in inorganic solid-state batteries

The pursuit of scalable and manufacturable all-solid-state batteries continues to intensify, motivated by the rapidly increasing demand for safe, dense electrical energy storage. Here, we describe the numerous, often conflicting implications of materials choices that have been made in the search for effective mitigations to the interfacial instabilities plaguing solid-state batteries. Specifically, we show that the manufacturing scalability of solid-state batteries can be governed by at least three principal consequences of materials selection: (1) the availability, scaling capacity, and price volatility of the chosen materials’ constituents, (2) the manufacturing processes needed to integrate the chosen materials into full cells, and (3) the cell performance that may be practically achieved with the chosen materials and processes. While each of these factors is, in isolation, a pivotal determinant of manufacturing scalability, we show that consideration and optimization of their collective effects and trade-offs is necessary to more completely chart a scalable pathway to manufacturing.

25 ENERGY STORAGE↗
DOE OSTI2022

Controlling dendrite propagation in solid-state batteries with engineered stress

Metal-dendrite penetration is a mode of electrolyte failure that threatens the viability of metal-anode-based solid-state batteries. Whether dendrites are driven by mechanical failure or electrochemical degradation of solid electrolytes remains an open question. If internal mechanical forces drive failure, superimposing a compressive load that counters internal stress may mitigate dendrite penetration. Here, we investigate this hypothesis by dynamically applying mechanical loads to growing dendrites in Li 6.6 La 3 Zr 1.6 Ta 0.4 O 12 solid electrolytes. Operando microscopy reveals marked deflection in the dendrite growth trajectory at the onset of compressive loading. For sufficient loading, this deflection averts cell failure. Using fracture mechanics, we quantify the impact of stack pressure and in-plane stresses on dendrite trajectory, chart the residual stresses required to prevent short-circuit failure, and propose design approaches to achieve such stresses. For the materials studied here, we show that dendrite propagation is dictated by electrolyte fracture, with electronic leakage playing a negligible role.

25 ENERGY STORAGE↗
DOE OSTI2023

Rational designs to enable 10-min fast charging and long cycle life in lithium-ion batteries

A daunting challenge in the design of lithium ion batteries (LiBs) is enabling 10-min extreme fast charging (XFC) while achieving appreciable charge acceptance and cycle life. This desirable outcome requires both a comprehensive understanding of LiB operation and aging behavior at different length scales and careful optimization. Lithium plating has been a critical bottleneck because, at XFC rates, it consumes cyclable lithium causing distinct aging and safety concerns even in moderate-loading LiBs. Here, we propose combining multiple solutions, including materials-to-electrode design-to-charging protocols, that are intended to overcome limitations in lithium-ion transport in the electrolyte phase, thus enabling 10-min XFC in LiBs. Some implemented strategies include cathode chemistry, optimized carbon binder domain in the cathode, dual layer anode design, improved separator and advanced electrolyte. Further, innovative charging protocols in moderately loading (~3 mAh/cm 2 anode/2.7 mAh/cm 2 cathode) single-layer pouch cells are proposed, together with demonstrated 10-min XFC with higher charge acceptance between 87.3 and 92.1% (or 2–2.1 mAh/cm 2 ) for 600 cycles without lithium plating. This methodical study with well-defined cells shows promise in combining multiple solution strategies to enable 10-min XFC, charting a pathway to achieve XFC in higher-loading energy-optimized LiBs.

25 ENERGY STORAGE↗
DOE OSTI2022

Monitoring and flaw detection during wire-based directed energy deposition using in-situ acoustic sensing and wavelet graph signal analysis

The goal of this work is to detect flaw formation in the wire-based directed energy deposition (W-DED) process using in-situ sensor data. The W-DED studied in this work is analogous to metal inert gas electric arc welding. The adoption of W-DED in industry is limited because the process is susceptible to stochastic and environmental disturbances that cause instabilities in the electric arc, eventually leading to flaw formation, such as porosity and suboptimal geometric integrity. Moreover, due to the large size of W-DED parts, it is difficult to detect flaws post-process using non-destructive techniques, such as X-ray computed tomography. Accordingly, the objective of this work is to detect flaw formation in W-DED parts using data acquired from an acoustic (sound) sensor installed near the electric arc. To realize this objective, we develop and apply a novel wavelet integrated graph theory approach. The approach extracts a single feature called graph Laplacian Fiedler number from the noise-contaminated acoustic sensor data, which is subsequently tracked in a statistical control chart. Using this approach, the onset of various types of flaws are detected with a false alarm rate less-than 2%. This work demonstrates the potential of using advanced data analytics for in-situ monitoring of W-DED.

42 ENGINEERING↗
DOE OSTI2022

Nuclear Data Sheets for A=267,271,275,279,283,287,291,295,299

Information on the production, identification, half-lives and decay modes for experimentally known nuclides with mass numbers of 267, 271, 275, 279, 283, 287, and 291, along with attempts to observe nuclides with mass numbers of 295 and 299, are presented along with the recommended values. Data here supersede data from the previous evaluation in this region, 2005Gu33. Since the previous evaluation many new and conclusive experiments have explored the region of superheavy nuclei, thus significantly expanding our knowledge of the nuclear chart in this area.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Nuclear Data Sheets for A=269,273,277,281,285,289, and 293

Herein, information on the production, identification, half-lives and decay modes for experimentally known nuclides with mass numbers of 269, 273, 277, 281, 285, 289, and 293 are presented along with the recommended values. Data here supersede data from the previous evaluation in this region, 2005Gu33. Since the previous evaluation many new and conclusive experiments have explored the region of superheavy nuclei, thus significantly expanding our knowledge of the nuclear chart in this area.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Systematic trends of 0 2 + , 1 1 - , 3 1 - and 2 1 + excited states in even-even nuclei

Here, the spin and parity ( J π ) assignments in even-even nuclei were reviewed across the nuclear chart using the Evaluated Nuclear Structure Data File (ENSDF). The prevalence of 2$+\atop{1}$ first or lowest excited states is confirmed. The properties of 0$+\atop{2}$, 1$-\atop{1}$, and 3$-\atop{1}$ lowest excited states were reexamined using the ENSDF data evaluation procedures. The J π systematic trends and correlations between level quantum numbers and nuclear physics phenomena are discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Towards next-generation optical potentials for nuclear reactions and structure calculations

Optical-model potentials (OMPs) are critical ingredients for basic and applied nuclear physics. Present-day computational capabilities allow us to generate data-driven nucleon-nucleus OMPs that are non-local and exactly dispersive (as theoretically required to be), include statistically-sound uncertainty quantification, and are trained on both scattering and bound-state data from a wide area of the nuclear chart. Combined together, these features allow for significant improvement in fidelity and extrapolative power of the model. Here, we present preliminary work toward the development and training of such an OMP. The capability of the model to describe data at this first stage is encouraging.

Perrotta, Salvatore Simone [Lawrence Livermore Nat↗
DOE OSTI2024

Mixed polyester recycling can enable a circular plastic economy with environmental benefits

The mixed and varied nature of fossil-based and bio-based plastic waste requires complex and costly separations to enable compatibility with recycling technologies. A circular plastic economy based on mixed polyesters through cleaving ester bonds to produce monomers, while re-utilizing bio-based monomers to produce high-quality sustainable plastics, charts an exciting solution. However, the feasibility of such a circular economy solution remains underexplored. Here, in this study, we conducted a techno-economic analysis and life-cycle assessment of three polyester depolymerization recycling processes-methanolysis, glycolysis, and acid hydrolysis-for a mixed feedstock (polyethylene terephthalate [PET], polylactic acid [PLA], and polybutylene adipate terephthalate [PBAT]). Methanolysis outperforms glycolysis and hydrolysis economically and environmentally due to more efficient downstream separations, generating products with a 31% decrease in selling price and 21%-46% reduction in acidification, carcinogenic toxicity, fossil-fuel depletion, global warming potential, particulate formation, and smog formation compared to conventional polyester manufacturing. This study highlights the viability of a circular plastic economy for mixed polyesters via a single chemical recycling process.

09 BIOMASS FUELS↗
DOE OSTI2022

Emergence of an island of extreme nuclear isomerism at high excitation near 208 Pb

Metastable states with $T_{1/2}$ = 8(2) ms in 205Bi and $T_{1/2}$ = 0.22(2) ms in 204 Pb, with ≈ 8 MeV excitation energy and angular momentum ≥ 22 $\hbar$, have been established. These represent, by up to two orders of magnitude, the longest-lived nuclear states above an excitation energy of 7 MeV, ever identified in the nuclear chart. Additionally, the half-life of the 10.17 MeV state in 206 Bi has been determined to be 0.027(2) ms, the next highest value in this highly excited regime. These observations indicate the emergence of an island of extreme nuclear isomerism arising from core-excited configurations at high excitation in the vicinity of the doubly closed-shell nucleus 208 Pb. These results are expected to provide discriminating tests of the effective interactions used in current large-scale shell-model calculations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Controlling extrapolations of nuclear properties with feature selection

Predictions of nuclear properties far from measured data are inherently inaccurate because of uncertainties in our knowledge of nuclear forces and in our treatment of quantum many-body effects in strongly-interacting systems. While the model bias can be directly calculated when experimental data is available, only an estimate can be made in the absence of such measurements. Current approaches to compute the estimated bias quickly lose predictive power when their input variables are taken far from the training region, resulting in uncontrolled uncertainties in applications such as nucleosynthesis simulations. In this letter, we present a novel technique to identify the input variables of machine learning algorithms that can provide robust estimates of model bias. Our process is based on selecting input variables, or features, based on their probability distribution functions across the entire nuclear chart. We illustrate our approach on the problem of quantifying the model bias in nuclear binding energies calculated with Density Functional Theory (DFT). We prove that building model biases with only Z and N as features leads to highly unreliable extrapolations. Conversely, we show that proper feature selection can systematically improve theoretical predictions without increasing uncertainties.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Electroweak nuclear radii constrain the isospin breaking correction to V ud

We lay out a novel formalism to connect the isospin-symmetry breaking correction to the rates of superallowed nuclear beta decays, δ C , to the isospin-breaking sensitive combinations of electroweak nuclear radii that can be accessed experimentally. We individuate transitions in the superallowed decay chart where a measurement of the neutron skin of a stable daughter even at a moderate precision could already help discriminating between models used to compute δ C . We review the existing experimental situation and make connection to the existing and future experimental programs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Atomic masses with machine learning for the astrophysical r process

The astrophysical r process plays a vital role in the production of heavy elements. Modeling of the r process is sensitive to masses and further requires knowledge of masses beyond current experimental reach. Therefore, simulations of the r process offer a unique test bed for predicting mass extrapolations. We take a Machine-Learning (ML) approach to model the masses across the entire chart of nuclides. For the first time, we simulate r-process nucleosynthesis with a physics-based ML mass model. We compare simulated abundances to solar data in order to evaluate the model's performance far from stability. The resulting r-process abundances up to thorium and uranium qualitatively match those of the observed solar system abundance pattern, with the characteristic peaks well positioned. We propagate the mass uncertainties obtained from the ML model to r-process abundance yields to estimate an uncertainty band associated with our approach. The size of the uncertainty band is approximately one order of magnitude which aligns with the uncertainty reported using alternative techniques.

79 ASTRONOMY AND ASTROPHYSICS↗