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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Solar PV, Wind Generation, and Load Forecasting Dataset for ERCOT 2018: Performance-Based Energy Resource Feedback, Optimization, and Risk Management (P.E.R.F.O.R.M.)

This report describes the Advanced Research Projects Agency-Energy Performance-Based Energy Resource Feedback, Optimization, and Risk Management (PERFORM) Electric Reliability Council of Texas (ERCOT) dataset consisting of load, solar, and wind deterministic and probabilistic forecasts at three timescales. This dataset consists of 1 year of time-coincident load, wind, and solar actuals and probabilistic forecasts for a region similar to ERCOT. All the data are stored in Hierarchical Data Format 5 (HDF5) files and have been uploaded to an Amazon Web Services repository. The ERCOT data set has 2 years (2017, 2018) of actuals and 1 year (2018) of probabilistic forecasts. These data are provided at various spatial (i.e., site-level, zone-level, and system-level) and temporal scales (i.e., day-ahead, intraday, and intra-hour). Specifically, data are provided for 125 existing wind sites, 22 existing solar sites, 139 proposed wind sites, and 204 proposed solar sites.

14 SOLAR ENERGY↗
DOE OSTI2024

Sienna Modeling Framework [Slides]

NREL's Sienna modeling framework effectively builds, solves, and analyzes the scheduling problems and dynamic simulations of quasi-static infrastructure systems. It uses a modular framework to answer different questions about future energy systems, fundamentally advancing the nation's ability to model individual and integrated infrastructure systems at a range of spatial and temporal scales. This presentation will include NREL power grid researcher Clayton Barrows.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Identification of the weak-to-strong transition in Alfvénic turbulence from space plasma

Plasma turbulence is a ubiquitous dynamical process that transfers energy across many spatial and temporal scales in astrophysical and space plasma systems. Although the theory of anisotropic magnetohydrodynamic (MHD) turbulence has successfully described natural phenomena, its core prediction of an Alfvénic transition from weak to strong MHD turbulence when energy cascades from large to small scales has not been observationally confirmed. Here we report evidence for the Alfvénic weak-to-strong transition in small-amplitude, turbulent MHD fluctuations in Earth’s magnetosheath using data from four Cluster spacecraft. Our observations demonstrate the universal existence of strong turbulence accompanied by weak turbulent fluctuations on large scales. Moreover, we find that the nonlinear interactions of MHD turbulence are crucial to the energy cascade, as they broaden the cascade directions and fluctuating frequencies. The observed connection between weak and strong MHD turbulence systems may be present in many astrophysical environments, such as star formation, energetic particle transport, turbulent dynamos, and solar corona or solar wind heating.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Machine-learning based model reduction for partial differential equations

We develop a novel synergistic approach between model reduction and machine learning. The specific goal of this project is to aid in the construction of reduced order models for basis functions that are custom-made to represent the solution of partial differential equations. Partial differential equations (PDEs) are one of the main mathematical tools for describing physical phenomena. However, due to either efficiency or necessity, for many real-world problems, we are interested in constructing reduced order models (ROMs) which focus only on the explicit computation of subsets of the active spatio-temporal scales in the problem, while treating the interaction with the rest of the scales approximately. The task of accurate representation of such interactions (usually called memory terms) constitutes a vast area of research known as model reduction. PI Stinis has significant expertise in the construction of ROMs for complex systems. In addition, in recent work with the project key participant Qadeer, they have utilized machine learning to acquire custom-made basis functions (CBFs) to expand the solutions of PDEs. In the proposed work, we will merge the two concepts by constructing ROMs for subsets of the CBFs needed to represent the solution of a PDE. Specifically, we will use the Mori-Zwanzig model reduction formalism to construct ROMs for subsets of CBFs for nonlinear PDEs of various complexity, as well as investigate the usage of CBFs in the spectral vanishing viscosity method for problems that can form shocks in finite time. The outcome of the research is aimed to be proof-of-concept about a novel synergistic approach between model reduction and machine learning, thus advancing the field of scientific machine learning. Such a capability will benefit the efficient modeling of physical systems appearing in various areas of interest to the DOE.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Accelerating transients with NekRS: GPU overlapping domain implementation and multi-rate timestepping

The simulation of nuclear transients using Computational Fluid Dynamics (CFD) presents significant computational challenges due to the inherent complexity and the wide separation in temporal scales between various flow physical phenomena. These disparities lead to high computational costs, often making the simulation of transients impractical without advanced techniques. Consequently, multiple research initiatives are being pursued by the NEAMS thermal-hydraulic area, some driven by academic institutions and some by national laboratories. Overall, they are exploring novel methods to make transient simulations more feasible and efficient. This report delves into recent advancements within the CFD code NekRS, specifically those achieved in Fiscal Year 2024 under the CONNECT effort, aimed at improving the performance and feasibility of transient simulations. The first major advancement involves the porting of NekRS to Aurora, one of the Department of Energy’s (DOE) most powerful supercomputers. Additionally, the report discusses the implementation of an overlapping domain capability within NekRS. This novel GPU-accelerated capability allows different spatial regions of the domain to be solved independently, enhancing the code’s efficiency, particularly when running large-scale simulations in complex domains. The scalability of this approach is demonstrated, highlighting its potential to transform how transients are approached in CFD simulations. Lastly, the report focuses on how this overlapping domain capability specifically accelerates transient simulations through multi-rate timestepping. By decoupling different regions and facilitating faster computations, this method offers a promising pathway to making nuclear transient simulations more computationally feasible, addressing one of the critical bottlenecks in the field. Together, these advancements represent a significant leap forward in transient simulation technology, bringing closer the possibility of handling highly complex nuclear scenarios with greater efficiency and accuracy.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

TRACKING LIGNOCELLULOSIC BREAKDOWN BY ANAEROBIC FUNGI AND FUNGAL CELLULOSOMES

Anaerobic fungi degrade plant biomass through invasive, filamentous growth, and the secretion of multi-protein biomass-degrading complexes called fungal cellulosomes. This project developed new tools for anaerobic, non-destructive, real-time imaging of cellulosomes across spatial and temporal scales. Novel nanobody tools were synthesized and deployed to image native fungal cellulosomes. Antibodies raised against key fungal cellulosome components were also used to define the localization patterns of cellulosomes in mature fungal mats vs. fungal zoospores, and revealed direct connections between cellular life stage progression and the regulation of cellulosome production. New procedures were developed to purify native cellulosomes and study their morphology and sub-structures, and genetic engineering tools were also developed and applied to anaerobic fungi to advance in vivo labeling capabilities. A cryoEM structure of a native fungal cellulosome was achieved, as well as a proof of concept for transformation of fungi with flavin-based anaerobic reporter proteins for in vivo labeling of cellulosome components.

09 BIOMASS FUELS↗
DOE OSTI2025

Building partnerships for development of sustainable energy systems with atmospheric measurements

Atmospheric dynamics often play a critical role in the sustainability and reliability of diverse forms of energy production. This is especially true for the growing number of renewable energy deployments that harness aspects of the environment for power production. While the University of Memphis has a strong research background in energy systems, we have little experience working with the Earth and Environmental Systems Science Division (EESSD) and their associated User Facilities. Of particular interest to us is the Atmospheric Science Research and the Atmospheric Radiation Measurement (ARM) user facility to address surface-boundary layer interactions and physical phenomena. One of the major challenges for understanding and developing energy systems and management platforms is accurate modeling/forecasting of atmospheric conditions across disparate spatial and temporal scales. These conditions are often required to understand the lowest levels of the atmospheric boundary layer, but are also important to understand higher atmospheric conditions where aerosols affect cloud development. The objective of this work was to develop partnerships with national laboratories for collaboration on environmental science and its intersection with sustainable energy systems, as well as to leverage the ARM user facility data repositories to enhance our research capabilities in energy systems and their inter-dependence on environmental systems for future engagement with EESSD. Specifically, we accomplished these objectives by (1) developing collaborations with Oakridge National Laboratory ARM Data Science and Integration Group which resulted in student internships, (2) employed ARM data to develope modeling of the atmospheric boundary layer optical turbulence, and (3) optimally-sized large-scale renewable energy systems and their associated energy storage systems with ARM repository data.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Final DOE-ASR Report for the Project “Using LASSO to bridge the gap between model and observations and to learn about atmospheric convection”

Atmospheric convection spans a wide range of spatial and temporal scales and involves complex interactions with the surrounding dynamic and thermodynamic environment, particularly over tropical continental regions. These processes remain a major source of uncertainty in weather and climate models, including persistent biases in the diurnal cycle of convective precipitation that directly affect estimates of climate sensitivity. Addressing these challenges requires the combined use of high-resolution observations and cloud-resolving modeling frameworks. In this context, the DOE Atmospheric Radiation Measurement (ARM) program’s Large-Eddy Simulation ARM Symbiotic Simulation and Observation (LASSO) activity provides a powerful platform that pairs comprehensive observations with numerical simulations to enable process-level understanding of atmospheric convection. Within this context, this Research and Development Partnership Pilot (RDPP) project was designed to initiate and expand DOE ARM/ASR research capacity at minority-serving institutions, while advancing scientific understanding of convective processes over the Amazon rainforest. Consistent with the RDPP mission, the project emphasized partnership development, training, and workforce capacity building alongside exploratory research activities. On the scientific side, the project produced two peer-reviewed journal articles, and one manuscript currently under review (see list in section 3.1). Together, these studies combine long-term ARM observations and cloud-resolving and convection-permitting modeling to investigate the environmental controls on the shallow-to-deep convective transition during the Amazon wet season. The results demonstrate the central role of early-day moisture preconditioning and large-scale dynamical forcing in regulating isolated deep convection, provide mechanistic insight into convective evolution, and establish physically informed modeling frameworks for future sensitivity experiments. These scientific outcomes are described in sections 2.1 to 2.3 and were disseminated in 8 conference presentations (see section 3.2) and 5 invited talks (see section 3.3), reflecting broad engagement with our community. Equally important, the project achieved its RDPP capacity-building objectives (see section 2.4). A sustained research partnership was established among the University of Maryland, Baltimore County (UMBC), Morgan State University (MSU), and Howard University (HU), and extended to include collaboration with Pacific Northwest National Laboratory (PNNL). The project organized multiple multi-day training events focused on ARM data, LASSO simulations, and quantitative analysis methods, directly engaging students, postdoctoral researchers, and faculty across institutions. These activities broadened participation in ASR research and led to independent adoption of LASSO workflows by students beyond the immediate project team. Finally, the project successfully positioned the participating institutions to pursue future DOE research. Preliminary scientific results, coupled with strengthened partnerships and technical capacity, enabled the submission of follow-on proposals to DOE ASR funding opportunities. In this way, the project fulfilled the RDPP goal of seeding durable research capacity and laying the foundation for larger-scale, sustained engagement with DOE ARM and ASR programs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Study on Application of Distributed Network of Sensors with List Mode for NMAC Literature Review

Nuclear material accounting and control (NMAC) for nuclear security detects, deters, and resolves questions related to unauthorized removal (i.e. theft) or misuse of nuclear material. NMAC also serves as a key insider threat mitigation measure and aids in recovery of nuclear material that is missing. Effective nuclear security depends on NMAC for timely and accurate information about nuclear material types, quantities, and locations. Bulk nuclear material processing facilities, however, present unique challenges for effective NMAC due to the presence of large quantities of material in-process and the accumulation of residual material holdup within process equipment. These holdup accumulations can obscure accurate physical inventory taking and complicate efforts to resolve NMAC irregularities at the facility level. Bulk material monitoring systems often rely on material balance calculations and indirect measurement techniques, which may mask protracted theft of smaller amounts of nuclear material. These monitoring limitations have generated increased interest in continuous monitoring technologies, including distributed non-destructive assay (NDA) sensor networks capable of providing real-time or near-real-time measurement of material movement and accumulation within bulk processing environments. Recent advancements in distributed networks of NDA radiation detectors and sensing technologies provide an opportunity to address these limitations. Although such distributed sensor networks have been implemented in select facilities for IAEA Safeguards applications, their potential for supporting NMAC functions specifically tailored to nuclear security objectives remains largely unexplored. Furthermore, emerging list-mode data acquisition technologies have reached high technology readiness levels, enabling time-correlated detection of nuclear events across multiple temporal scales. These capabilities provide enhanced opportunities for accurate holdup measurement, continuous process monitoring, and improved detection of material theft or misuse over time. The increasing global expansion of civil nuclear power and development of related bulk material processing facilities, including those supporting high-assay low-enriched uranium (HALEU) and other advanced reactor fuel fabrication, further increases the need for advanced measurement and monitoring strategies for NMAC.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

VIC-Global Parameter Dataset Sensitivity with the Variable Infiltration Capacity Model: Evaluating the importance of dynamic land surface parameters when using the VIC-Global parameter dataset

Accurate prediction of runoff is essential to water resources management, flood risk assessment, and ecosystem protection. However, many hydrological models still have relatively substantial limitations when representing the influence of land use and land cover (LULC) on runoff generation and routing. Changes in LULC, such as deforestation, urban expansion, agricultural intensification, and wetland loss, have been shown to alter the water balance at the land surface through fundamental hydrologic processes (e.g., interception, infiltration, evapotranspiration, and soil storage). However, it remains an open question what the exact magnitude and timing of these impacts are for the spatial and temporal scales commonly used in engineering applications. In this analysis we focus on one aspect of recent LULC change for assessing human impacts, which is urbanization. Specifically we seek to determine the impacts of urbanization on the magnitude and timing of surface runoff and baseflow in HUC-12 basins in Clark County, Nevada which has experienced rapid urbanization. We use the Variable Infiltration Capacity (VIC) hydrology model with a widely used off-the-shelf dataset of land surface parameters, VIC-Global, both of which have been commonly used in the past for water and energy balance modeling for large scale hydrologic studies. We examine two scenarios where the first scenario removes all urbanized land cover and parameterizes those areas of the basins as barren or open shrubland. The second scenario tests the opposite case where all areas of the basins are classified as urban regardless of their present classification. The results from the VIC model show there is a low sensitivity for daily surface runoff between scenarios. The daily baseflow values indicate similar low sensitivity to the classification change during specific periods, but then have substantial differences during other period when large precipitation events are occurring. This is likely due to the assumed parameter values for the urban land cover classification made by the VIC-Global dataset. Using a static land cover parameterization is reasonable for large domain hydrology models that are being used for near-term planning horizons (<30 years). However, longer planning horizons where feedbacks between the atmosphere and land surface are important, especially in transient climate situations, considerations for how to update land surface parameters should be incorporated.

42 ENGINEERING↗
DOE OSTI2020

A global DSCOVR/EPIC-based hourly/daily shortwave radiation/PAR dataset

Accurate characterization of the global downward shortwave (SW) and photosynthetically active radiation (PAR) is fundamental for Earth system modeling and global change research. Combined with a machine-learning method, we used the Earth Polychromatic Imaging Camera (EPIC) data onboard the Deep Space Climate Observatory (DSCOVR) to generate the global SW/PAR dataset at both hourly and daily temporal scales and 0.1degree×0.1degree spatial resolution. Covering a period from June, 2015 to June (about 4 years), 2019, the dataset consists of total SW/PAR and their direct and diffuse components. Our dataset will be valuable for understanding the diurnal cycles and seasonal patterns of ecosystem processes, as well as the solar energy utilizations

14 SOLAR ENERGY↗
DOE OSTI2022

Editorial: Particulate Matter Emissions From Conventional and Reformulated Fuel Combustion: Advances in Experiments and Simulations

Particulate matter (PM or soot) emissions generated by combustion of conventional and reformulated fuels adversely impact the environment and human health (Bond et al., 2013; Landrigan et al., 2018; World Health Organization (WHO), 2016), generating public awareness and motivating efforts towards the mitigation of their harmful effects. Soot formation is one of the most complex phenomena in combustion, involving interactions between combustion chemistry, fluid mechanics, mass/heat transport, and particle dynamics, spanning different spatial and temporal scales. A fundamental understanding of soot formation process is thus necessary to achieve a strong reduction of PM emissions and design cleaner and more efficient combustion systems. This has motivated a longstanding and ongoing research activity aimed at improving our understanding of the physical and chemical processes involved in soot formation, well reviewed in these recent works (Wang and Chung, 2019; Michelsen et al., 2020; Martin et al., 2022). Despite the wide interest, the transition from gas-phase molecules to incipient soot particles is still elusive and the successive particle growth and oxidation processes are far from being fully understood, especially in conditions relevant to real-world applications. Recent advances in combustion PM emission diagnostic and computational capabilities helped in improving the predictability of fundamental chemical and aerosol models for practical applications, thus tackling some of the above-mentioned challenges. The aim of this research topic is to display the ongoing research efforts in addressing the existing gaps on particulate formation from various fuel sources and in conditions typical of practical combustion applications (e.g. flames, engines, pool fires), through both experimental and numerical approaches.

42 ENGINEERING↗
DOE OSTI2024

Advances in statistical methods for cancer surveillance research: an age-period-cohort perspective

Background: Analysis of Lexis diagrams (population-based cancer incidence and mortality rates indexed by age group and calendar period) requires specialized statistical methods. However, existing methods have limitations that can now be overcome using new approaches. Methods: We assembled a “toolbox” of novel methods to identify trends and patterns by age group, calendar period, and birth cohort. We evaluated operating characteristics across 152 cancer incidence Lexis diagrams compiled from United States (US) Surveillance, Epidemiology and End Results Program data for 21 leading cancers in men and women in four race and ethnicity groups (the “cancer incidence panel”). Results: Nonparametric singular values adaptive kernel filtration (SIFT) decreased the estimated root mean squared error by 90% across the cancer incidence panel. A novel method for semi-parametric age-period-cohort analysis (SAGE) provided optimally smoothed estimates of age-period-cohort (APC) estimable functions and stabilized estimates of lack-of-fit (LOF). SAGE identified statistically significant birth cohort effects across the entire cancer panel; LOF had little impact. As illustrated for colon cancer, newly developed methods for comparative age-period-cohort analysis can elucidate cancer heterogeneity that would otherwise be difficult or impossible to discern using standard methods. Conclusions: Cancer surveillance researchers can now identify fine-scale temporal signals with unprecedented accuracy and elucidate cancer heterogeneity with unprecedented specificity. Birth cohort effects are ubiquitous modulators of cancer incidence in the US. The novel methods described here can advance cancer surveillance research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Skill of Mesoscale Models in Forecasting Springtime Macrophysical Cloud Properties at the Savannah River Site in the Southeastern US

Predicting boundary layer clouds is important for the accurate modeling of pollutant dispersion. Higher resolution mesoscale models would be expected to produce better forecasts of cloud properties that affect dispersion. Using ceilometer observations, we assess the skill of two operational mesoscale models (RAMS and WRF) to forecast cloud base altitude and cloud fraction at the Savannah River Site in the southeastern US during the springtime. Verifications were performed at small spatial and temporal scales necessary for dispersion modeling. Both models were unreliable with a 50% (RAMS) and a 46% (WRF) rate of predicting clouds observed by the ceilometer which led to low cloud fraction predictions. Results indicated that WRF better predicted daytime cloud bases from convection that occurred frequently later in the period and RAMS better predicted nighttime cloud bases. Using root mean squared error (RMSE) to score the forecast periods also highlighted this diurnal dichotomy, with WRF scores better during the day and RAMS scores better at night. Analysis of forecast errors revealed divergent model cloud base biases—WRF low and RAMS high. A hybrid solution which weighs more heavily the RAMS nighttime forecasts and WRF daytime forecasts will likely provide the best prediction of cloud properties for dispersion.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Volumetric Rendering on Wavelet-Based Adaptive Grid

Numerical modeling of physical phenomena frequently involves processes across a wide range of spatial and temporal scales. In the last two decades, the advancements in wavelet-based numerical methodologies to solve partial differential equations, combined with the unique properties of wavelet analysis to resolve localized structures of the solution on dynamically adaptive computational meshes, make it feasible to perform large-scale numerical simulations of a variety of physical systems on a dynamically adaptive computational mesh that changes both in space and time. Volumetric visualization of the solution is an essential part of scientific computing, yet the existing volumetric visualization techniques do not take full advantage of multi-resolution wavelet analysis and are not fully tailored for visualization of a compressed solution on the wavelet-based adaptive computational mesh. Our objective is to explore the alternatives for the visualization of time-dependent data on space-time varying adaptive mesh using volume rendering while capitalizing on the available sparse data representation. Two alternative formulations are explored. The first one is based on volumetric ray casting of multi-scale datasets in wavelet space. Rather than working with the wavelets at the finest possible resolution, a partial inverse wavelet transform is performed as a preprocessing step to obtain scaling functions on a uniform grid at a user-prescribed resolution. As a result, a solution in physical space is represented by a superposition of scaling functions on a coarse regular grid and wavelets on an adaptive mesh. An efficient and accurate ray casting algorithm is based just on these coarse scaling functions. Additional details are added during the ray tracing by taking an appropriate number of wavelets into account based on support overlap with the interpolation point, wavelet coefficient magnitude, and other characteristics, such as opacity accumulation (front to back ordering) and deviation from frontal viewing direction. The second approach is based on complementing of wavelet-based adaptive mesh to the traditional Adaptive Mesh Refinement (AMR) mesh. Both algorithms are illustrated and compared to the existing volume visualization software for Rayleigh-Benard thermal convection and electron density data sets in terms of rendering time and visual quality for different data compression of both wavelet-based and AMR adaptive meshes.

Vezolainen, Alexei V.↗
DOE OSTI2020

Image Collection Simulation Using High-Resolution Atmospheric Modeling

A new method is described for simulating the passive remote sensing image collection of ground targets that includes effects from atmospheric physics and dynamics at fine spatial and temporal scales. The innovation in this research is the process of combining a high-resolution weather model with image collection simulation to attempt to account for heterogeneous and high-resolution atmospheric effects on image products. The atmosphere was modeled on a 3D voxel grid by a Large-Eddy Simulation (LES) driven by forcing data constrained by local ground-based and air-based observations. The spatial scale of the atmospheric model (10–100 m) came closer than conventional weather forecast scales (10–100 km) to approaching the scale of typical commercial multispectral imagery (2 m). This approach was demonstrated through a ground truth experiment conducted at the Department of Energy Atmospheric Radiation Measurement Southern Great Plains site. In this experiment, calibrated targets (colored spectral tarps) were placed on the ground, and the scene was imaged with WorldView-3 multispectral imagery at a resolution enabling the tarps to be visible in at least 9–12 image pixels. The image collection was simulated with Digital Imaging and Remote Sensing Image Generation (DIRSIG) software, using the 3D atmosphere from the LES model to generate a high-resolution cloud mask. The high-resolution atmospheric model-predicted cloud coverage was usually within 23% of the measured cloud cover. The simulated image products were comparable to the WorldView-3 satellite imagery in terms of the variations of cloud distributions and spectral properties of the ground targets in clear-sky regions, suggesting the potential utility of the proposed modeling framework in improving simulation capabilities, as well as testing and improving the operation of image collection processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The Concept of a Quantum Edge Simulator: Edge Computing and Sensing in the Quantum Era

Sensors, enabling observations across vast spatial, spectral, and temporal scales, are major data generators for information technology (IT). Processing, storing, and communicating this ever-growing amount of data pose challenges for the current IT infrastructure. Edge computing—an emerging paradigm to overcome the shortcomings of cloud-based computing—could address these challenges. Furthermore, emerging technologies such as quantum computing, quantum sensing, and quantum communications have the potential to fill the performance gaps left by their classical counterparts. Here, we present the concept of an edge quantum computing (EQC) simulator—a platform for designing the next generation of edge computing applications. An EQC simulator is envisioned to integrate elements from both quantum technologies and edge computing to allow studies of quantum edge applications. The presented concept is motivated by the increasing demand for more sensitive and precise sensors that can operate faster at lower power consumption, generating both larger and denser datasets. These demands may be fulfilled with edge quantum sensor networks. Envisioning the EQC era, we present our view on how such a scenario may be amenable to quantification and design. Given the cost and complexity of quantum systems, constructing physical prototypes to explore design and optimization spaces is not sustainable, necessitating EQC infrastructure and component simulators to aid in co-design. We discuss what such a simulator may entail and possible use cases that invoke quantum computing at the edge integrated with new sensor infrastructures.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Can Cooling and Heating Functions Be Modeled with Homogeneous Radiation Fields?

Abstract Cooling and heating functions describe how radiative processes impact the thermal state of a gas as a function of its temperature and other physical properties. In a most general case the functions depend on the detailed distributions of ionic species and on the radiation spectrum. Hence, these functions may vary on a very wide range of spatial and temporal scales. In this paper, we explore cooling and heating functions between 5 ≤ z ≤ 10 in simulated galaxies from the Cosmic Reionization On Computers project. We compare three functions: (1) the actual cooling and heating rates of hydrodynamic cells as a function of cell temperature, (2) the median cooling and heating functions computed using median interstellar medium (ISM) properties (median ISM), and (3) the median of the cooling and heating functions of all gas cells (instantaneous). We find that the median ISM and instantaneous approaches to finding a median cooling and heating function give identical results within the spread due to cell-to-cell variation. However, the actual cooling (heating) rates experienced by the gas at different temperatures in the simulations do not correspond to either summarized cooling (heating) functions. In other words, the thermodynamics of the gas in the simulations cannot be described by a single set of a cooling plus a heating function with a spatially constant radiation field that could be computed with common tools, such as CLOUDY.

79 ASTRONOMY AND ASTROPHYSICS↗