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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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DOE OSTI2015

On the image of AGS 3 He 2+ $\vec{n}$ 0 in the blue

This note addresses the transport of Helion spin $\vec{n}$ 0 vector, from its periodic orientation in the AGS to RHIC Blue ring injection kicker, via the AGS extraction system and the AtR line. The goal is to investigate optimal injection energy into RHIC, in the matter of Helion spin matching, in the hypothesis of equal warm and cold snake strengths in the AGS. The study uses recently computed OPERA 3-D field maps of the AGS cold snake, including possibility of independent solenoid and helix settings (as discussed in Tech. Note C-A/AP/485), together with the machinery of the AGS and AtR models developed in the stepwise ray-tracing code Zgoubi. Computing tools and methods employed are discussed as well, in order to facilitate possible further checks or investigations. They are however similar to those used in an earlier study regarding the image in RHIC Blue and Yellow of AGS $\vec{n}$ 0 via the AtR in the case of proton beam (Tech. Note C-A/AP/502), which can be referred to for additional details.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis - NLR Historical Solar PV

The U.S. Department of Energy and National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis from variable sources, hydrogen compression and storage, and hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) research platform. This dataset represents part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure nationwide. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with various energy technologies. Future datasets will demonstrate how existing hydrogen fuel cell technologies can provide controllable, dispatchable, and variable power output for artificial intelligence data centers and other variable loads. This dataset entry describes the behavior of a 1.25-MW proton exchange membrane MC250 electrolyzer system, manufactured by Nel Hydrogen , [1] when fed historical data generated by the 430-kW, fixed-axis solar photovoltaic (PV) array located at NLR’s Flatirons Campus. (While the electrolyzer balance of plant supports up to 2.5 MW of electrolysis, NLR only has a single 1.25-MW electrolysis stack.) Solar PV power output data for the 2020 calendar year were categorized on a daily basis by total energy generation and standard deviation. Each day was then ranked by these metrics, and the 25th, 50th, and 100th percentiles were selected. The 75th percentile day did not exhibit sufficient variability to make for a valuable experiment. A similar process was used for the related historical wind dataset . [2] The historical days in 2020 that represented these percentiles are Dec. 19, March 29, and May 4, respectively. The entire solar day’s power profile was then fed through the MC250 electrolyzer. Due to its length, the 100th percentile day experiment was split into two parts, and the final 3 hours of the solar day were not captured. These final 3 hours contained no spikes or dips of interest and simply represented a slow decay of input solar power. Also, a single timestamp (13:13:47 on Jan. 14, 2026) was lost in the hydrogen system supervisory control and data acquisition. Finally, during the 25th percentile experiment (solar day Dec. 19, 2020) data recording was lost from 11:00:13 to 11:14:45. The roughly 15 minutes of the solar profile were rerun at the end of the experiment and spliced into this time slot during post-processing. The electrolysis system controls hydrogen production by varying direct current applied to the stack, from a maximum of 3,000 A to a minimum safe operation of 300 A, or 10%. Because the current–voltage characteristic changes as the stack ages and efficiency degrades, the actual minimum safe operating power changes over time. The historical solar profiles were translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1-Hz frequency. For more details on the statistical analysis process, see the slide deck “Public Reference Data for Megawatt-Scale Hydrogen Electrolysis: NLR Historical Solar PV Analysis and Profile Generation” accessible with this data entry. These datasets report relevant hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. Each .zip file represents a single solar PV electrolysis experiment and is formatted as: {technology}_{percentile}_{scaling factor} For instance, “solarPV-430kW_25_2x.zip” reports the experiment using the 25th percentile solar data from the historical 2020 solar PV dataset, scaled to 200%. Scaling factors were applied to the generated solar PV power output files to more closely match the 1.25-MW capacity of the electrolyzer. Each .zip folder contains the following files: A .csv file containing raw data. An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production, electrolysis power consumption, and solar power input. A PDF file detailing the historical solar data statistical analysis used to generate the solar profile. An experiment labeled “characterization_200.zip” demonstrates the MC250 electrolyzer steady-state response with 30-minute load steps for a total duration of 5 hours. Finally, a .csv file is provided with all experiments combined into one dataset labeled "combined_solarPV_experiments.csv". [1] nelhydrogen.com/product/mc-series-electrolyser . [2] data.nlr.gov/submissions/316 .

08 HYDROGEN↗
DOE OSTI2020

Organismic materials for beyond von Neumann machines

The elementary basis of intelligence in organisms with a central nervous system includes neurons and synapses and their complex interconnections forming neural circuits. In non-neural organisms such as slime mold with gel-like media, viscosity modulation enables adaptation to changing environments. At a larger scale, collective intelligence emerges via social interactions and feedback in animal colonies. Learning and memory are therefore multi-scale features that evolve as a result of constant interactions with the environment. There is growing interest in emulating such features of intelligence in computing machines and autonomous systems. Materials that can respond to their environment in a manner similar to organisms (referred to as "organismic materials") therefore may be of interest as hardware components in artificial intelligence machines. In this brief review, we present a class of semiconductors called correlated oxides as candidates for learning machines. The term "correlated" refers to the fact that electrons in such lattices strongly interact and the ground state is not what is predicted by classical band theory. Such materials can undergo insulator-metal transitions at near ambient conditions under external stimuli such as thermal or electrical fields, strain, and chemical doping. Depending on the mechanism driving the transition, intermediate states can be metastable with different volatilities, and the time scales of phase change can be controlled over many orders of magnitude. The change in electronic properties can be sharp or gradual, leading to digital or analog behavior. These properties enable the realization of artificial neurons and synapses and emulate the associative and non-associative learning characteristics found in various organisms. We examine microscopic properties concerning electronic and structural transitions leading to collective behavior and theoretical treatments of the ground state and dynamical response, showcasing VO 2 as a model system. Next, we briefly review algorithms designed from the plasticity demonstrated by phase changing systems. Finally, we conclude the brief review with suggestions for future research toward realizing non-von Neumann machines.

42 ENGINEERING↗
DOE OSTI2018

Ice I h revisited: No proton tunneling observed in a quasielastic neutron scattering experiment

A large broadening (${\sim}0.4\mathrm{meV}$) of quasielastic neutron scattering (QENS) signal in ${\mathrm{H}}_{2}\mathrm{O}$ ice ${I}_{h}$ at $T=5\mathrm{K}$ was observed by Bove et al. [Phys. Rev. Lett. 103, 165901 (2009)] and explained by a model of concerted proton tunneling. This result was rather unexpected, as prior studies never showed significant mobility in water at low temperatures and ambient pressure. There were a few attempts of theoretical understanding of the effect. Recent path-integral simulations as well as quantum lattice-gauge theory supported the possibility of the collective tunneling of protons in ice ${I}_{h}$, however other studies stated that concerted tunneling in ice ${I}_{h}$ should have very low frequency. In this paper, we report on QENS measurements of ${\mathrm{H}}_{2}\mathrm{O}$ ice ${I}_{h}$ at 1.8 and 5 K by using neutron scattering spectrometers with the energy resolution similar to and four times better than the energy resolution in the original experiment of Bove and co-workers. We did not observe any QENS broadening, and the measured spectra for the ice ${I}_{h}$ and the reference vanadium sample were almost identical. Therefore, we conclude that there is no proton tunneling in ice ${I}_{h}$ at temperatures down to 1.8 K measurable on an energy scale of 3.5 ${\mu}\mathrm{eV}$ and above. Finally, the literature data on low-temperature heat capacity of ice ${I}_{h}$ support this conclusion.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

MSD CoP Webinar: Representing Climate Impacts in Scenaros

Context: This webinar was hosted by the MultiSector Dynamics Community of Practice (MSD CoP; https://multisectordynamics.org). Abstract: Scenarios of future emissions and land use have been most commonly produced without accounting for the effects that climate impacts linked to those emissions and land use changes may have. The questions of how important such impacts could be, at the regional or global scale, has become an increasingly cogent one. Using the Global Change Analysis Model (GCAM), we implement climate impacts on water supply, agricultural productivity, and energy demand driven by climatic impact drivers whose evolution over the 21st century is representative of a climate consistent with GCAM's reference scenario and compare the output to the reference at both regional and global scales. We show that the impacts from the version of GCAM that uses GDP as an exogenous input are not sufficient to bend emission pathways at the global scale. However, we see effects emerge at the regional scale in terms of emissions, among other metrics linked to water, land and energy. We expect the impacts to become more significant at an aggregate, global scale in a forthcoming version of the model with endogenous GDP. Meanwhile, we document the mechanisms that drive the difference at regional scales which could still be significant in their impacts for individual economies and populations. Presenters: Dr. Claudia Tebaldi (Joint Global Change Research Institute, Pacific Northwest National Laboratory) Moderator(s): Jennifer Morris (MSD CoP SSG member), Patrick Reed (MSD CoP Facilitation Team Member, Moderator and Organizer) This webinar was held on: November 5, 2024 from 1 PM - 2:15 PM ET

Climate Scenarios↗
DOE OSTI2021

Sensing and arresting metal corrosion in molten chloride salts at 800 degrees Celsius

Originally this was planned as a 5 task, 3 year project, starting in May 2019. Task 1 Detecting oxidants and metal corrosion in molten chloride salt at 800°C. Task 2 Effectiveness of Zr getter using head gas sensors of inlet and outlet gases. Task 3 Cathodic protection of metal in molten salt at reducing potential. Task 4 Find reduction potential of metals in molten chloride salts & quantum modeling. Task 5 Develop corrosion test sections integrated into molten salt flow loop. After Q4, the project was scaled back and the reorganized SOPO was reduced to 3 tasks to be completed at the end of Q7 on 01/05/2021. The 3 SOPO tasks are: Task 1 Sense oxidants and the metal corrosion in molten Na-K-Mg-Cl salt at 800°C. Task 3 Cathodically protect H230 alloy in molten Na-K-Mg-Cl salt at 800C by making H230 alloy the cathode of a power supply with a chromium-rich-metal as the anode. Task 4 Find the reduction potential of Cr metal in molten Na-K-Mg-Cl salt at 800°C. The total timeline was reduced from 12 to 7 quarters with the end date being, January 5, 2021. This amounted to task 1,3 and 4 being done in Q5,6 and 7 of an extended year 1, ending on January 5, 2021. Task 1, 3 and 4 were completed. For task1) the increase of oxidants in molten salt is detected by two things: i) a positive shift of the electrode potential for a Haynes 230 alloy (H230) metal coupon versus a silver/silver-chloride reference electrode (SSE) and ii) an increase in the corrosion rate of a H230 electrode at 800C. For task 3) applying a cathodic potential (negative overpotential) to a H230 electrode versus SSE arrested the corrosion of the cathodically protected H230 compared to an unprotected “spectator H230 electrode” in the same molten chloride salt. Lastly, for task 4) the reduction potential of chrome and chrome rich, and nickel metal electrodes were measured. The most valuable outcome of this work is that a power supply can arrest metal corrosion for metal in molten chloride salt when the power supply applies a cathodic potential to bare metal, like H230, in molten chloride salts, like ternary Mg-K-Na-chloride salt. H230 is a high strength metal for containing molten chloride salt and ternary Mg-K-Na-chloride salt is used as heat transfer fluids in concentrating solar power plants. When to use cathodic protection for protecting metal in molten chloride salts? Two scenarios are envisioned: 1) for protecting high-valued components, like solar collectors and heat exchangers and 2) for protecting the bare surface in holidays (point defects) of corrosion inhibitors that are to intended to cover the entire surface of metal.

14 SOLAR ENERGY↗
DOE OSTI2016

Recommended Best Practices for the Characterization of Storage Properties of Hydrogen Storage Materials

These reports, written by H2 Technology Consulting under contract with the National Renewable Energy Laboratory, provides an introduction to and overview of the recommended best practices in making measurements of the hydrogen storage properties of materials. Sections 1-5 (published in February 2012), Section 6 (published in February 2013), and Section 7 (published in January 2016). The goal in developing this report was to create a reference guide of common methodologies and protocols for measuring critical performance properties of advanced hydrogen storage materials and to serve as a resource to the hydrogen storage materials development community to aid in clearly communicating the relevant performance properties of new materials as they are discovered and tested. Section 1: Introduction Section 2: Kinetics Measurements Section 3: Capacity Measurements Section 4: Thermodynamic Measurements Section 5: Cycle-Life Measurements Section 6: Thermal Properties Measurements Section 7: Mechanical Properties Measurements

08 HYDROGEN↗
DOE OSTI2018

Genetic techniques for studies of methyl-coenzyme M reductase from Methanosarcina acetivorans C2A.

Methanogenic archaea generate methane as a by-product of anaerobic respiration using CO 2 , C 1 compounds (like methanol or methylated amines), or acetate as terminal electron acceptors. Methanogens are an untapped resource for biotechnological advances related to methane production as well as methane consumption. Yet, key biological features of these organisms remain poorly understood. One such feature is the enzyme methyl-coenzyme M reductase (referred to as MCR), which catalyzes the last step in the methanogenic pathway and results in methane formation. Gene essentiality has limited genetic analyses of MCR thus far. Therefore, studies of this important enzyme have been limited to biochemical and biophysical techniques that are especially laborious and often reliant on sophisticated instrumentation that is not commonly available. In this chapter, we outline our recently developed CRISPR–Cas9-based genome editing tools and describe how these tools have been used for the introduction of a tandem affinity purification tag at the chromosomal mcr locus in the model methanogen, Methanosarcina acetivorans C2A. Moreover, we report a protocol for rapid affinity purification of MCR from M. acetivorans C2A that will enable high-throughput studies of this enzyme in the future.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Patterning via optical-saturable transformations: A review and simple simulation model

Most of the nanoscale fabrication in the semiconductor industry is based on patterning with scanning-electron beam lithography (SEBL). Although this approach is very versatile and has very high resolution, it is intrinsically a serial writing process, and therefore, relatively slow. Our group has been investigating alternative nano-fabrication techniques, adapted from ideas of saturating optical transitions such as those used in stimulated emission-depletion microscopy and related methods, and optical interference lithography. Linewidths and resolutions on the scale of a few tens of nanometers and below are highly desirable for various applications in nanotechnology. However, the spatial resolution of optical lithography is restricted by diffraction. In the past, we developed absorbance modulation to overcome this limit. This approach utilizes photochromic molecules that can be optically switched between two thermally stable states, one opaque and the other transparent. However, absorbance modulation is limited to surface (2-D) patterning. Here, we report on an alternative approach that exploits unique combinations of spectrally selective reversible and irreversible photochemical transitions to achieve deep subwavelength resolution with potential extension to 3-dimensions. This approach, which we refer to as patterning via optical-saturable transformations have the potential for massive parallelism, enabling the creation of nanostructures and devices at a speed far surpassing what is possible with SEBL. The aim of our research is to translate the success in circumventing Abbe's diffraction limit in optical microscopy to optical lithography.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

DNABERT-S: pioneering species differentiation with species-aware DNA embeddings

SUMMARY: We introduce DNABERT-S, a tailored genome model that develops species-aware embeddings to naturally cluster and segregate DNA sequences of different species in the embedding space. Differentiating species from genomic sequences (i.e. DNA and RNA) is vital yet challenging, since many real-world species remain uncharacterized, lacking known genomes for reference. Embedding-based methods are therefore used to differentiate species in an unsupervised manner. DNABERT-S builds upon a pre-trained genome foundation model named DNABERT-2. To encourage effective embeddings to error-prone long-read DNA sequences, we introduce Manifold Instance Mixup (MI-Mix), a contrastive objective that mixes the hidden representations of DNA sequences at randomly selected layers and trains the model to recognize and differentiate these mixed proportions at the output layer. We further enhance it with the proposed Curriculum Contrastive Learning (C2LR) strategy. Empirical results on 28 diverse datasets show DNABERT-S's effectiveness, especially in realistic label-scarce scenarios. For example, it identifies twice more species from a mixture of unlabeled genomic sequences, doubles the Adjusted Rand Index (ARI) in species clustering, and outperforms the top baseline's performance in 10-shot species classification with just a 2-shot training. AVAILABILITY AND IMPLEMENTATION: Model, codes, and data are publically available at https://github.com/MAGICS-LAB/DNABERT_S.

Zhou, Zhihan↗
DOE OSTI2020

Global rules for translating land-use change (LUH2) to land-cover change for CMIP6 using GLM2

Anthropogenic land-use and land-cover change activities play a critical role in Earth system dynamics through significant alterations to biogeophysical and biogeochemical properties at local to global scales. To quantify the magnitude of these impacts, climate models need consistent land-cover change time series at a global scale, based on land-use information from observations or dedicated land-use change models. However, a specific land-use change cannot be unambiguously mapped to a specific land-cover change. Here, nine translation rules are evaluated based on assumptions about the way land-use change could potentially impact land cover. Utilizing the Global Land-use Model 2 (GLM2), the model underlying the latest Land-Use Harmonization dataset (LUH2), the land-cover dynamics resulting from land-use change were simulated based on multiple alternative translation rules from 850 to 2015 globally. For each rule, the resulting forest cover, carbon density and carbon emissions were compared with independent estimates from remote sensing observations, U.N. Food and Agricultural Organization reports, and other studies. The translation rule previously suggested by the authors of the HYDE 3.2 dataset, that underlies LUH2, is consistent with the results of our examinations at global, country and grid scales. This rule recommends that for CMIP6 simulations, models should (1) completely clear vegetation in land-use changes from primary and secondary land (including both forested and non-forested) to cropland, urban land and managed pasture; (2) completely clear vegetation in land-use changes from primary forest and/or secondary forest to rangeland; (3) keep vegetation inland-use changes from primary non-forest and/or secondary non-forest to rangeland. Our analysis shows that this rule is one of three (out of nine)rules that produce comparable estimates of forest cover, vegetation carbon and emissions to independent estimates and also mitigate the anomalously high carbon emissions from land-use change observed in previous studies in the 1950s. According to the three translation rules, contemporary global forest area is estimated to be 37.42×106 km2, within the range derived from remote sensing products. Likewise, the estimated carbon stock is in close agreement with reference biomass datasets, particularly over regions with more than 50 % forest cover

58 GEOSCIENCES↗
DOE OSTI2019

Description of the ARM Large-Scale Forcing Data from the Constrained Variational Analysis (VARANAL) Version 2

This technical report represents an update of the previous technical report written by Zhang et al., which described the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility constrained variational analysis (VARANAL) that is used to develop the large-scale forcing data for driving single-column models (SCMs), cloud-resolving models (CRMs), and large-eddy simulation models (LES). The VARANAL algorithm was originally developed by Zhang and Lin and Zhang et al. at Stony Brook University and was migrated to the Lawrence Livermore National Laboratory (LLNL) as the ARM operational objective analysis system in May 1999. Since then, the algorithm has been evolved with time along with the availability of new observations and techniques to meet various modeling needs. Major updates include: 1. The method used to develop multi-year continuous forcing data, 2. The incorporation of eddy correlation flux measurement system (ECOR) turbulent fluxes into the analysis, and 3. Improvements to the workflow (e.g., implementing part of the code into the ARM Data Integrator [ADI]) to increase efficiency. The ARM large-scale forcing data have been widely used for SCM/CRM/LES to understand and improve physical processes in models. Zhang et al. has provided a comprehensive review of the SCM concept, early efforts to derive forcing data for SCM studies, efforts of the ARM constrained variational analysis, and previous SCM studies using ARM cases. This technical report focuses on the constrained variational analysis algorithm and the introduction of the ARM VARANAL products. We also extended the VARANAL algorithm into a three-dimensional constrained variational analysis (3DCVA) and designed an ensemble framework to address the forcing uncertainty. The 3D large-scale forcing data are released as another datastream named “varanal3d”. Please refer to the ARM varanal3d technical report for more information.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Intelligent Experiments through Real-Time AI: Fast Data Processing and Autonomous Detector Control for High-Energy Nuclear Experiments

The aim of this project is to develop software and hardware for fast real-time data processing and autonomous detector control and calibration for the sPHENIX and the future EIC experiments. Below summarizes Georgia Tech team efforts in the past year: 1. We developed a real-time clustering algorithm and FPGA-based pipeline architecture for processing fired pixel data from ALPIDE sensors in sPHENIX experiments. Our Columnar Clustering Co-Design introduces a hardware-aware, stream-friendly approach that segments pixel data by column pairs using a Column Pair Clustering (CPC) strategy, followed by Cluster Stitching to merge adjacent subclusters. Implemented in Vitis HLS, the pipeline comprises five stages—read-in, subclustering, stitching, analysis, and write-out—connected by tagged HLS streams with custom end-of-event signaling for robust synchronization. We designed a pipelined dataflow model optimized for throughput, low latency, and minimal buffering, enabling scalable clustering across events of arbitrary size. Our system maintains spatial precision via center-of-mass and shape key extraction and efficiently handles edge cases such as fragmented or nested clusters. Compared against DBSCAN in both software and hardware, our approach demonstrates competitive performance under FPGA constraints. 2. We also conducted a comprehensive algorithm-to-hardware co-design of connected component analysis tailored for sPHENIX experiments, focusing on real-time, low-latency processing using FPGAs and High-Level Synthesis (HLS). Starting from a Python-based particle tracking pipeline, the team translated the core logic—graph traversal via DFS and Union-Find—into an HLS-compatible C++ model, replacing dynamic memory and recursion with static arrays and pipelined control flow. The final design includes a fully streamed and dataflow-compatible Union-Find kernel optimized across five iterations, incorporating loop pipelining, array partitioning, AXI/FIFO interface tuning, and function flattening. Experimental results show up to 14.8× speedup over the CPU baseline, reducing per-graph latency to 1.58 μs and demonstrating strong resource efficiency with only ~7k LUTs and zero BRAM usage. The design maintains functional correctness against the Python reference using a Python-based C-simulation framework and Mean Squared Error metrics. This work validates the potential of HLS-driven FPGA designs for edge-level HEP data acquisition, laying a scalable foundation for future integration with real-time detector pipelines and multi-graph processing systems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Expansion of high-latitude deciduous forests driven by interactions between climate warming and fire: Modeling Archive

This Modeling Archive is in support of an NGEE Arctic publication. More information in the related references. The dataset is a model (ecosys) output used in a study that examined the potential future shifts in high-latitude vegetation types (PFTs) driven by changes in 21st century climate and fire. The study highlights the importance of mechanistically representing differences in plant functional traits on current and possible 21st century trajectories of Alaskan PFTs. This dataset package includes 13 files in pdf, csv, and nc formats.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The short-term comprehensive impact of the phase-out of global coal combustion on air pollution and climate change

With the continuous intensification of global warming, the reduction and ultimate phase-out of coal combustion is an inevitable trend in the future global energy transformation. This study comprehensively analyzed the impact of phasing out coal combustion on global emissions and concentrations of air pollutants, radiative fluxes, meteorology and climate using Community Earth System Model 2 (CESM2). The results indicate that after the global phase-out of coal combustion, there is a marked decrease in the concentrations of sulfur dioxide (SO 2 ), nitrogen oxides (NO x ) and fine particulate matter (PM 2.5 ), with some regions experiencing a reduction of exceeding 50%. There is no significant change in global ozone (O 3 ) concentration. There are decreasing AOD and positive radiative fluxes globally in the short term, though the cloud contributes minor negative radiative fluxes. The global air temperature may increase by approximately (0.02 ± 0.15) °C on average with regional and seasonal variations, and the precipitation may potentially increase by approximately (2.7 ± 40.6) mm yr −1 globally and over 20% in equatorial regions in the short term. But combined with the decreasing trend of cloud water content in the Northern Hemisphere, it indicates a potential increase in the extremity of precipitation events. In conclusion, this study provides references for global control of air pollution, mitigation strategies of climate change, and transformation of energy structures under the objective of “carbon neutrality”, such as focusing on the negative climate impacts of exacerbating regional warming and increasing extreme precipitation resulting from the rapid reduction of aerosols in the short term.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2016

Rationalizing the suitability of rhodamines as chromophores in dye-sensitized solar cells: A systematic molecular design study

Here, rhodamines are chromophores that are employed in many dye applications. Their strong optical absorption in the visible region of the electromagnetic spectrum renders them attractive dye candidates for dye-sensitized solar cells (DSCs). However, they have not yet been systematically tested in DSCs as single- or co-sensitizers. Recent advances in concerted experimental and computational workflows involving molecular design protocols can afford a better understanding of the molecular origins of the optoelectronic properties in these sensitizers. Herein, we examine the suitability of rhodamines R560 (1), R575 (2), R590 (3), R610 (4), R620 (5), R640 (6), and R3B (7) as chromophores in co-sensitized DSCs. Our study follows a stepwise approach. Initially, structural and optical properties of the dyes are investigated by experimental and computational methods to reveal structure-property relationships and other useful features for DSC applications. Subsequently, 1-7 are investigated at the dye···TiO 2 interface, both by calculations of dye-adsorption onto the surface of a modeled (TiO 2 ) 9 cluster, and by experimental studies of dye-adsorption on TiO 2 . For that purpose, a selection of rhodamine dyes are paired together (1 and 5) and (1 and 7) for co-sensitization, among which 5 is also co-adsorbed with a fluorescein dye in order to explore chemical compatibility factors. The best dye candidates are identified from the findings of these adsorption studies in terms of dye aggregation, anchoring modes, and panchromatic response. Despite their promising dye…TiO 2 adsorption and optical prospects, our results show that rhodamines lack a suitable intramolecular charge transfer pathway for dye-to-TiO 2 electron injection to occur, thus precluding their photovoltaic function as DSC dyes. Our results are then assessed against ostensibly disparate reports of rhodamines performing successfully in DSC devices; this comparison necessitated the internal reproduction of previously reported co-sensitization experiments on 2 with the industrial standard reference dye, N3. We achieve reconciliation between our results and those in the literature by reasoning that while rhodamines cannot deliver photovoltaic function in DSCs in their own right, they can either act to facilitate or deplete the photovoltaic output of a DSC indirectly by affecting the TiO 2 adsorption prospects of a photovoltaic-active dye with which a rhodamine is co-sensitized.

14 SOLAR ENERGY↗
DOE OSTI2019

An Accurate Fast Fluid Dynamics Model for Data Center Applications

Traditional CFD is broadly useful in the design and operation of reliable and efficient data centers. It is nevertheless computationally expensive, particularly when employed for design optimization, which usually requires multiple simulations. To speed-up calculations while retaining essential airflow physics, several researchers have turned to an alternative CFD methodology, namely, fast fluid dynamics (FFD). FFD has been reported to be much faster than traditional CFD, but at an assumed (acceptable) trade-off of reduced accuracy. However, a recent comparison of FFD and traditional CFD for data center plenum applications produced nearly indistinguishable results and suggested that previously-reported FFD/traditional-CFD differences were due primarily to inconsistent: 1) advection schemes, 2) computational grids, and 3) turbulence models. The present paper extends this FFD/traditional-CFD comparison to the data center whitespace and confirms the finding that a true like-for-like comparison produces nearly identical predictions. Our FFD implementation utilizes a first-order upwind finite-volume scheme for advection like traditional CFD, so alternative advection schemes (e.g., semi-Lagrangian) are not considered further here. Likewise, the effect of grid choice on traditional-CFD predictions is well known. However, the effect of turbulence model for data center applications has not been reported extensively so we do consider this topic further here. We compare the standard k-e and a simpler algebraic model to benchmark experimental data from a real data center. We find that the algebraic turbulence model predicts rack-inlet temperatures at a similar level-of-accuracy as the k-e model for our fairly-simple-airflow reference data center.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Comparing simulated demand flexibility against actual performance in commercial office buildings

Commercial building energy benchmarking has been used as a mechanism to evaluate energy use of a single building over time, relative to other similar buildings, or to simulations of a reference building conforming to various energy standards. Lack of empirical demand flexibility data and consistent flexibility metrics has limited the ability to compare demand flexibility performance with estimated demand flexibility in buildings. In this study, we collected demand response performance data for a total of 831 demand response events from 192 sites as a first step to build such a building demand flexibility dataset, and propose a standard core data schema to consolidate field data from different sources. We also performed parametric simulations of a control strategy called “global temperature adjustment” using commercial office prototype building models. We then compared the simulated demand flexibility performance against the actual data for offices with global temperature adjustment strategy implemented. During demand response events with an average outside air temperature of 34 °C (range 23 °C–42 °C), the measured demand decrease intensity of the demand flexibility metrics were 6.1 watts per square meter (W/m 2 ), 10.0 W/m 2 , 11.1 W/m 2 , 7.1 W/m 2 , and 4.7 W/m 2 for small, small–medium, medium, medium–large, and large office buildings, respectively. Compared to the measured data in medium- and large-size buildings, the simulated demand decrease intensity was 0.7 W/m 2 (17%) lower on average. The discrepancy between simulated and measured peak demand intensities fell within one standard deviation of the mean measured data. Here, the comparison results validate the credibility of simulations in capturing real building data for assessing the technical potential of building demand flexibility.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗