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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Optimal operation of multi-plant steam district heating systems for enhanced efficiency and sustainability

Despite their crucial role in supplying heat and power to universities, industries, and healthcare facilities, many steam-based district heating systems rely on outdated control methods. Among these, multi-central plant districts are particularly challenging due to the complexities of coordinating multiple plants, optimizing load distributions, and managing system downtime. In response, new operational strategies are developed to enhance the efficiency and sustainability of steam districts while utilizing existing resources. These strategies include reducing plant operational pressure without compromising the reliable supply to buildings and optimizing load allocation across multiple plants. The load allocation considers boiler part-load efficiency, runtime, network losses, and building pressure set points, and is compared with traditional multi-boiler controls. To support this exploration, new dynamic Modelica models are developed. In addition, methods to reduce modeling complexities are incorporated, enhancing their suitability for practical applications. A holistic district-wide analysis using a real university case study demonstrates a 4.7% fuel savings by lowering boiler operational pressure from 900 kPa to 600 kPa, along with a 13.3% reduction in condensation losses across the distribution network. Furthermore, the load allocation approach results in a 13.1% reduction in fuel consumption during peak winter periods and 15.3% during shoulder periods, with corresponding decreases in carbon emissions and fuel costs. This approach can also save maintenance costs by reducing the boiler runtime by 49.6%. In conclusion, this research underscores the benefits of retrofitting aging steam district heating systems, offering immediate operational improvements by enhancing efficiency, meeting regulatory compliance, and extending infrastructure lifespans while delaying costly overhauls.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Energy System Transitions and Low-Emission Pathways in Australia, Brazil, Canada, China, EU-28, India, Indonesia, Japan, Republic of Korea, Russia, and United States

The Paris Agreement invited Parties to submit long-term, low-emission development strategies. This study presents national low-emission scenarios to inform such strategies for Australia, Brazil, Canada, China, EU, India, Indonesia, Japan, Russia, Republic of Korea and the USA. The study uses country-level energy–economy and integrated assessment models (IAMs) that incorporate detailed and disaggregated representations of the energy demand and supply systems that are difficult to capture in global IAMs. Models were used to explore low-carbon pathways up to 2050, providing insights in their implications for country emissions, energy system restructuring, energy–economy indicators and system costs. The analysis shows that the low-carbon scenarios of most economies studied here are consistent with pathways limiting global warming to below 2 °C.

Fragkos, Panagiotis↗
DOE OSTI2021

Exploring wave propagation in heterogeneous metastructures using the relaxed micromorphic model

Metamaterials are artificial structures that can manipulate and control sound waves in ways not possible with conventional materials. While much effort has been undertaken to widen the bandgaps produced by these materials through design of heterogeneities within unit cells, comparatively little work has considered the effect of engineering heterogeneities at the structural scale by combining different types of unit cells. In this paper, we use the relaxed micromorphic model to study wave propagation in heterogeneous metastructures composed of different unit cells. We first establish the efficacy of the relaxed micromorphic model for capturing the salient characteristics of dispersive wave propagation through comparisons with direct numerical simulations for two classes of metamaterial unit cells: namely phononic crystals and locally resonant metamaterials. We then use this model to demonstrate how spatially arranging multiple unit cells into metastructures can lead to tailored and unique properties such as spatially-dependent broadband wave attenuation, rainbow trapping, and pulse shaping. In the case of the broadband wave attenuation application, we show that by building layered metastructures from different metamaterial unit cells, we can slow down or stop wave packets in an enlarged frequency range, while letting other frequencies through. In the case of the rainbow-trapping application, we show that spatial arrangements of different unit cells can be designed to progressively slow down and eventually stop waves with different frequencies at different spatial locations. Finally, in the case of the pulse-shaping application, our results show that heterogeneous metastructures can be designed to tailor the spatial profile of a propagating wave packet. Collectively, these results show the versatility of the relaxed micromorphic model for effectively and accurately simulating wave propagation in heterogeneous metastructures, and how this model can be used to design heterogeneous metastructures with tailored wave propagation functionalities.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

DFT-based investigation of solvation of Nd(III)/Yb(III) cations in room-temperature ionic-liquid

We report a DFT study on the solvation of bare rare-earth (RE) cations [Nd(III) and Yb(III)] in the room-temperature ionic-liquid (RTIL) based on 1,3-dimethyl-imidazolium + , PF 6 - , [MMI][PF 6 ]. In the present article, we aim to investigate whether the chosen RTIL can preferentially solvate any of the RE cations under study. First, the RTIL binding to the RE cation is studied in the gas phase, and then explored with continuum solvation model employing RTIL parameters. First, we considered the formation of the first solvation shell, where the PF 6 - anions directly bond with the Ln(III) cation. We then further surrounded the first solvation shell with [MMI]+ cations to form the second solvation shell. The stability of PF 6 - coordinated complexes is studied by calculating the binding energy values. The number of PF 6 - anions for both Ln(III) cations starts with three ligands and goes until the maximum binding energy in the RTIL medium is reached. The coordination number and binding modes of PF 6 - depend on the size of the RE cation. Comparing the binding energy values between the RE cations for the first solvation shell complexes, it is noticed that Yb-complexes have relatively greater binding energy than Nd-complexes. We have further performed a Boltzmann population analysis to find out the most dominant conformer among each category of PF 6 - coordinated complexes. A study using Natural Population Analysis shows that after complexation with PF 6 - the charge on the Yb-centers lower to a greater extent than Nd-centers, implying stronger bonding between Yb and PF 6 - , which is in harmony with the binding energy analysis. Overall, the computational investigation provides an in-depth understanding of the complexation phenomena of RE cations in RTIL, which can be useful for the practical separation of RE cations using RTIL.

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

Status of the Mu2e experiment

The Mu2e experiment at Fermilab searches for the coherent, neutrino-less conversion of a μ − to e − in the Coulomb field of Al nuclei, that represents one of the cleanest Charged Lepton Flavor Violating (CLFV) processes for exploring Beyond the Standard Model (BSM) physics. Mu2e aims to improve previous sensitivity by four orders of magnitude, with a distinctive signature provided by identifying mono-energetic electrons with energy slightly below the muon rest mass. To reach this goal, the experiment will use the highest intensity pulsed muon beam in the world, with up to 6 × 1 0 9 stopped muons/sec. This is achieved using the Fermilab proton beam and the design and realization of a unique 25 m long superconducting solenoidal system. The high beam intensity relies upon minimizing beam losses in the slow extraction region, indicating an opportunity of using bent crystals for shadowing. A high-resolution straw tracker and a fast CsI crystal calorimeter identify the conversion electron. Both detectors are inserted behind the Stopping Target in the last solenoid section. A Cosmic Ray Veto covers a large part of the solenoids to suppress background produced by cosmic rays. In this paper, we report the details of the experimental layout, the construction status of the magnetic system and detectors, and a short description of the simulation and realization of the bent crystals. Performing crystal channeling in front of the first slow extraction septa will allow beam shadowing and largely reduce beam losses. •Charge Lepton Flavor Violation processes: μ − → e − conversion.•Large Superconducting Solenoid system.•High-Intensity beams•High precision detectors.•Crystal channeling.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Uncertain quantum computing futures and potential energy and physical resource impacts at scale

Considerable attention has recently focused on the vast energy and water demands of supercomputing, namely large-scale data centers that underpin artificial intelligence (AI), one of the great disruptors of contemporary society. Looking ahead some years from now, quantum computing is poised to disrupt established computing paradigms once again. Scientists and engineers are now working intensely to bring this century-old dream of physicists to fruition. Yet, as quantum computers begin to be integrated with classical supercomputing architectures, the implications for energy and physical resource use also need to be understood, especially how they compare to today’s AI data centers. These impacts have not yet been quantified by the research community – a notable gap in the literature, even if commercial-scale deployment of Quantum-Accelerated Computing Infrastructure (QuACI) is not expected for a few more years. This study is the first to conduct such an assessment. Using publicly available information from academic sources and private industry, we characterize multiple configurations of superconducting qubit-based, fault-tolerant quantum computers (FTQC) that could plausibly be deployed at scale in the 2030s and into the 2040s. By parameterizing these FTQC systems at a process level, we conduct a prospective scenario analysis to quantify their energy and physical resource needs. While these estimates are uncertain, given the current state of quantum technologies and their unknown future trajectories, important insights can already be drawn. One key finding is that while the electricity needs for a fleet of FTQCs are within the bounds of previous modeling studies that have explored high electricity demand futures, the needs for certain physical resources, namely water and helium-3, could pose bottlenecks to QuACI scale-up.

Computing↗
DOE OSTI2025

Exploring Afro-Asian water fluxes in CMIP5 and CMIP6 models: Present-day evaluation and future projections

Accurate representation and projection of water fluxes are vital, particularly in monsoon regions where seasonal variability and changes significantly impact water resources, agriculture, hydroelectric power generation, and the economy. This study uses both CMIP5 and CMIP6 models to investigate the present-day representation of key water flux components (including precipitation, evaporation, water vapor, and moisture advection) over the Afro-Asian region during the summer monsoon (JJAS). We also examine future changes under two emission scenarios and assess the underlying physical mechanisms driving these changes while identifying sources of projection uncertainty. Our results show that both models reasonably capture the present-day characteristics of water flux components, although with noticeable biases. Across most regions, we find that CMIP6 models demonstrate slight improvements over CMIP5 in the representation of these components. In a warmer future, a robust intensification of JJAS precipitation (exceeding 0.25 mm/day) is projected across the Afro-Asian region. This increase is largely driven by the vertical thermodynamic term associated with increasing temperature. We find that model uncertainty is the primary source of uncertainty in water flux projections, accounting for over 50 % of the total variance.

Climate change↗
DOE OSTI2023

Deployment priority of public charging speeds for increasing battery electric vehicle usability

To inform charger deployment decisions, this paper aims to understand the potential utilization and deployment priority for public charging infrastructure. A data-driven Cumulative Public Recharging (CPR) model is developed to explore the travel patterns by using 2017 National Household Travel Survey data. Given the daily trip sequence, trip distance, and dwell times, the study examines the daily expected driving range and BEV feasibility under different charging speeds, battery capacity, and charging behaviors. The results suggest that more advanced public chargers increase the daily expected driving range. Home charging is sufficient for most daily short-distance trips while public chargers are still needed for medium- and long-distance trips. Extreme fast charging (xFC) may not be necessary for people with home charging but could be more useful for people without home charging and for urgent charging. In conclusion, xFC becomes even less important to drivers with longer BEV ranges, a finding that contradicts conventional thinking.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Cell transdifferentiation in ocular disease: Potential role for connexin channels

Highlights: • Cell transdifferentiation including EMT and EndMT is a pathological process in a number of ocular diseases. • Gap junction channels and connexin hemichannels play a role in cell transdifferentiation in ocular diseases. • Connexin-blocking therapies demonstrate efficacy in ocular disease models. • Future studies could explore connexin-based therapies for prevention and treatment of pathological transdifferentiation. Cell transdifferentiation is the conversion of a cell type to another without requiring passage through a pluripotent cell state, and encompasses epithelial- and endothelial-mesenchymal transition (EMT and EndMT). EMT and EndMT are well defined processes characterized by a loss of epithelial/endothelial phenotype and gain in mesenchymal spindle shaped morphology, which results in increased cell migration and decreased apoptosis and cellular senescence. Such cells often develop invasive properties. Physiologically, these processes may occur during embryonic development and can resurface, for example, to promote wound healing in later life. However, they can also be a pathological process. In the eye, EMT, EndMT and cell transdifferentiation have all been implicated in development, homeostasis, and multiple diseases affecting different parts of the eye. Connexins, constituents of connexin hemichannels and intercellular gap junctions, have been implicated in many of these processes. In this review, we firstly provide an overview of the molecular mechanisms induced by transdifferentiation (including EMT and EndMT) and its involvement in eye diseases. We then review the literature for the role of connexins in transdifferentiation in the eye and eye diseases. The evidence presented in this review supports the need for more studies into the therapeutic potential for connexin modulators in prevention and treatment of transdifferentiation related eye diseases, but does indicate that connexin channel modulation may be an upstream and unifying approach for regulating these otherwise complex processes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Toward Engineering of Solution Microenvironments for the CO 2 Reduction Reaction: Unraveling pH and Voltage Effects from a Combined Density-Functional–Continuum Theory

Engineering the electrolyte microenvironment represents an attractive route to tuning the selectivity of electrocatalytic reactions beyond catalyst composition and morphology. However, harnessing the full potential of this approach requires understanding the interplay between voltage, electrolyte composition, and adsorbate binding within the electrical double layer, which is absent from the usual theoretical approaches. In this study, we apply a recently developed density functional theory (DFT)–continuum approach based on the effective screening medium method and reference interaction site model (ESM–RISM) to explore electrolyte effects with an enhanced description of the electrochemical interface. Furthermore, applying this method to the binding of CO adsorbates in potassium-containing electrolytes on copper, a problem of direct relevance to CO 2 electroreduction to value-added products, we show that the interdependence of voltage and pH leads to an unexpected change in adsorption site preference on Cu(001) terraces. Our findings highlight the often-overlooked importance of the electrical double-layer structure for predicting catalyst operation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Connecting Hindered Transport in Porous Media across Length Scales: From Single-Pore to Macroscopic

Hindered mass transport is widely observed in various porous media, including filtration membranes and porous rocks. However, there is no universal model capable of predicting transport in porous media due to the heterogeneity of porous structures and the complexity of the underlying microscopic mechanisms, including electrostatic and hydrodynamic effects. Here, we used a highly ordered porous medium (i.e. a silica inverse opal film) as a model system to directly explore the effects of geometric parameters (i.e. pore size, pore throat size and tracer particle size) and microscopic interaction parameters (e.g. controlled by ionic strength) on nanoparticle transport in porous environments using three-dimensional single-particle tracking. We found a linear scaling relation between the macroscopic diffusion coefficient and microscopic diffusion behaviors and a combination of parameters associated with microscopic features and phenomena, including both geometric effects and particle-level interactions. The proportionality coefficient relating micro and macro behaviors was complex and related to the connectivity of the matrix and pore-size variation, which could lead to tortuous diffusion pathways, hindering macroscopic transport. Furthermore, these findings provide important insights into nanoparticles and macromolecules transport in various porous media.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Analyzing Production Rate and Carbon Utilization Trade-offs in CO 2 RR Electrolyzers

Carbon utilization is a crucially important performance characteristic of CO 2 electrolyzers; however, applications of simple and accurate descriptive models to experimental data on this topic are currently lacking. Here, we apply a simple analytical reactor model to parameterize single-pass conversion as a function of feed-gas flow rate and show that it captures a wide body of experimental data in the literature exceptionally well. In doing so, we demonstrate that this simple conceptual approach can characterize progress and capture recent innovations in reactor design. To validate the extracted parameters, we employ a well-established comprehensive model to confirm that the results agree with physical expectations. Lastly, we explore the implications of the descriptive model by examining the inherent trade-off between single-pass conversion efficiency and reactor productivity. Understanding such trade-offs is crucially important for advancing this electrolyzer technology toward commercial applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Heuristic Computational Model for Predicting Lignin Solubility in Tailored Organic Solvents

Lignin is a random heteropolymer that has been extensively studied as a renewable source of aromatic precursors for high-value chemicals, biofuels, and bioplastics. A key challenge in lignin valorization is the structural and compositional heterogeneity of lignin feedstocks. Solvent-based approaches are commonly used to fractionate lignin to reduce this heterogeneity, but solvent selection can be challenging due to variability in lignin composition. In this work, we developed computational methods to predict good and poor organic solvents as a function of lignin composition. We analyzed 28 different linear pentamer structures, 18 from known libraries and 10 hypothetical polymers, and calculated their activity coefficients in 50 different organic solvents by using the conductor-like screening model for realistic solvents. We used these data to train a regression model that enabled the extensive investigation of the impact of solvent and monolignol compositions on predicted lignin solubility. The exhaustive exploration of solubility trends using model predictions revealed sets of solvents, identified using Kamlet–Taft parameters, that are predicted to promote lignin dissolution regardless of lignin composition. We further identified solvents expected to selectively isolate lignin fractions enriched in certain subunits. Furthermore, these results establish heuristic guidelines for solvent selection that can be used to tailor fractionation processes for lignin feedstocks of distinct composition or to design new processes that isolate fractions with higher proportions of selected subunits.

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

Bayesian Model for HOPE Mass Spectrometers on Van Allen Probes

Space instruments rely heavily on modeling to predict and understand the instrument response, enabling a determination of the capabilities and resolution. The Bayesian approach provides a framework to incorporate prior knowledge and propagate uncertainty to predict the instrument response. We present an empirical Bayes model for the end-to-end performance of the Helium, Oxygen, Proton, and Electron mass spectrometers aboard the Van Allen Probes mission. In this model, we use a combination of external modeling, laboratory calibration, and expert opinion to construct the time-of-flight spectra and demonstrate good agreement with on-orbit data. The empirical Bayes model is applied to explore doubly charged ions and carbon, nitrogen, oxygen discrimination during the Van Allen Probes mission.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Leakage From Coexisting Geologic Forcing and Injection‐Induced Pressurization: A Semi‐Analytical Solution for Multilayered Aquifers With Multiple Wells

Abstract Abnormal fluid pressures (above or below hydrostatic pressure) can develop and persist in sedimentary basins. The common occurrence of abnormal pressures may cause challenges for project permitting of geological carbon sequestration (GCS), particularly in reservoirs with pre‐injection overpressure. The leaky wells that may exist in some sedimentary basins can provide flow paths between deep brine aquifers and shallower freshwater aquifers. Pre‐injection relative overpressures can cause brine leakage through leaky wells even before any injection occurs. The tendency for flow through leaky wells is coupled with the process of pressure dissipation that occurs through aquitards. Specifically, with non‐zero permeability, aquitards can dissipate pressure over large areal extents and thereby reduce leakage rates through leaky wells. This study presents development of a semi‐analytical solution for hydraulic head and brine leakage in multilayered aquifer–aquitard systems with geologic pressure forcing. The geologic forcing that causes abnormal pressures in the multilayered system can coexist with any number of injection, extraction, and leaky wells that also affect fluid pressure. The semi‐analytical model is applied to explore how leakage through leaky wells varies as functions of pressurization rate, along with aquitard and leaky well properties in an overpressured multilayered system. The results show that although injection‐induced pressures can dissipate rapidly through suitably permeable aquitards, coexisting geologic forcing may create sustained rates of brine leakage into freshwater aquifers through leaky wells. In GCS, a very low‐permeability aquitard with high capillary entry pressure to free‐phase CO 2 is desired to serve as the caprock to prevent leakage of CO 2 from the storage reservoir. Nevertheless, the results from this study show that the brine leakage impact to shallow freshwater aquifers through leaky wells might substantially decrease with increasing aquitard permeability values, as long as small aquitard permeability and high capillary entry pressure serve to prevent CO 2 leakage.

Cihan, A.↗
DOE OSTI2024

Reduced‐Order Probabilistic Emulation of Physics‐Based Ring Current Models: Application to RAM‐SCB Particle Flux

Abstract In this work, we address the computational challenge of large‐scale physics‐based simulation models for the ring current. Reduced computational cost allows for significantly faster than real‐time forecasting, enhancing our ability to predict and respond to dynamic changes in the ring current, valuable for space weather monitoring and mitigation efforts. Additionally, it can also be used for a comprehensive investigation of the system. Thus, we aim to create an emulator for the Ring current‐Atmosphere interactions Model with Self‐Consistent magnetic field (RAM‐SCB) particle flux that not only improves efficiency but also facilitates forecasting with reliable estimates of prediction uncertainties. The probabilistic emulator is built upon the methodology developed by Licata and Mehta (2023), https://doi.org/10.1029/2022sw003345 . A novel discrete sampling is used to identify 30 simulation periods over 20 years of solar and geomagnetic activity. Focusing on a subset of particle flux, we use Principal Component Analysis for dimensionality reduction and Long Short‐Term Memory (LSTM) neural networks to perform dynamic modeling. Hyperparameter space was explored extensively resulting in about 5% median symmetric accuracy across all data sets for one‐step dynamic prediction. Using a hierarchical ensemble of LSTMs, we have developed a reduced‐order probabilistic emulator (ROPE) tailored for time‐series forecasting of particle flux in the ring current. This ROPE offers accurate predictions of omnidirectional flux at a single energy with no pitch angle information, providing robust predictions on the test set with an error score below 11% and calibration scores under 8% with bias under 2% providing a significant speed up as compared to the full RAM‐SCB run.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Imaging local discharge cascades for correlated electrons in WS 2 /WSe 2 moiré superlattices

Transition metal dichalcogenide moiré heterostructures provide an ideal platform to explore the extended Hubbard model, where long-range Coulomb interactions play a critical role in determining strongly correlated electron states. This has led to experimental observations of Mott insulator states at half filling as well as a variety of extended Wigner crystal states at different fractional fillings. However, a microscopic understanding of these emerging quantum phases is still lacking. Here we describe a scanning tunnelling microscopy (STM) technique for the local sensing and manipulation of correlated electrons in a gated WS 2 /WSe 2 moiré superlattice, which enables the experimental extraction of fundamental extended Hubbard model parameters. We demonstrate that the charge state of the local moiré sites can be imaged by their influence on the STM tunnelling current. In addition to imaging, we are also able to manipulate the charge state of correlated electrons. When we ramp the bias on the STM tip, there is a local discharge cascade of correlated electrons in the moiré superlattice, which allows us to estimate the nearest-neighbour Coulomb interaction. Two-dimensional mapping of the moiré electron charge states also enables us to determine the on-site energy fluctuations at different moiré sites. Furthermore, our technique should be broadly applicable to many semiconductor moiré systems, offering a powerful tool for the microscopic characterization and control of strongly correlated states in moiré superlattices.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Training calibration-based counterfactual explainers for deep learning models in medical image analysis

The rapid adoption of artificial intelligence methods in healthcare is coupled with the critical need for techniques to rigorously introspect models and thereby ensure that they behave reliably. This has led to the design of explainable AI techniques that uncover the relationships between discernible data signatures and model predictions. In this context, counterfactual explanations that synthesize small, interpretable changes to a given query while producing desired changes in model predictions have become popular. This under-constrained, inverse problem is vulnerable to introducing irrelevant feature manipulations, particularly when the model’s predictions are not well-calibrated. Hence, in this paper, we propose the TraCE (training calibration-based explainers) technique, which utilizes a novel uncertainty-based interval calibration strategy for reliably synthesizing counterfactuals. Given the wide-spread adoption of machine-learned solutions in radiology, our study focuses on deep models used for identifying anomalies in chest X-ray images. Using rigorous empirical studies, we demonstrate the superiority of TraCE explanations over several state-of-the-art baseline approaches, in terms of several widely adopted evaluation metrics. Our findings show that TraCE can be used to obtain a holistic understanding of deep models by enabling progressive exploration of decision boundaries, to detect shortcuts, and to infer relationships between patient attributes and disease severity.

62 RADIOLOGY AND NUCLEAR MEDICINE↗