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DOE OSTI2025

Package Data for CERF-Data Centers

This dataset contains sample input 100m resolution raster files for running the CERF-DC python package (see https://github.com/IMMM-SFA/cerf_data_centers) at the state level across the CONUS. Due to data availability constraints, some of the items included in this dataset are proxies or assumptions for siting factors used in the model. These are individually noted in the item descriptions and can be exchanged with more detailed information upon availability. Data Descriptions The following raster files are included in the data download: state_siting_region.tif — State areas identified by state FIPS code composite_siting_suitability.tif — Value of 1 indicates suitable siting location, 0 otherwise. The following areas are excluded from siting: Areas within 300m of a federal airport runway Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory Protected Areas Database of the United States (PAD-US) areas Railroads, major roadways, and minor roadways Military areas and training grounds Developed lands Areas >0.8 km (0.5 miles) from developed lands land_value_dollar_per_sqft.tif — USD per square foot (sqft) derived from USDA $/acre land cost personal_property_tax_rate.tif — Personal property tax rate by state. Uses an assumed 0.0125 personal property tax rate for states with personal property tax, 0 for states without personal property tax. real_property_tax_rate.tif — Real property tax rate. Based on county level residential real estate property tax rates. sales_tax_rate.tif — Sales tax rate by state. mechanical_cooling_fraction.tif — Fraction of year (values between 0 and 1, inclusive) that the data center would be cooled through mechanical processes based on local water stress and humidity levels. water_cooling_fraction.tif — Fraction of year (values between 0 and 1, inclusive) that the data center would be cooled through evaporative (water cooled) processes based on local water stress and humidity levels. distance_to_substation.tif — Distance to nearest substation in hundreds of meters (i.e., value of 1 equals a distance of 100m). Offshore areas have a value of 0. industrial_electricity_rates_dollar_per_kwh.tif — USD/kWh industrial electricity rates. Represents the average industrial rate across all utilities that operate within a given county. Values are derived from the US Utility Rate Database. commercial_electricity_rates_dollar_per_kwh.tif — USD/kWh commercial electricity rates. Represents the average commercial rate across all utilities that operate within a given county. Values are derived from the US Utility Rate Database. data_center_market_locations.tif — Grid cells with positive values represent the centroid of existing data center market clusters. The value of non-zero grid cells represents the number of data centers in the market cluster. All other grid cells have a value of 0. Geospatial Metadata CRS: Albers Equal Area Conic (ESRI:102003) Extent: -2415585.0000000023283064,-1441981.2605773280374706 : 2384414.9999999976716936,1708018.7394226719625294 Dimensions: X: 48000 Y: 31500 Bands: 1 Origin: -2415585.0000000023283064,1708018.7394226719625294 Pixel Size: 100,-100 Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4.0 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall↗

DOE OSTI2026

Package Data for CERF-Data Centers

This dataset contains sample input 100m resolution raster files for running the CERF-DC python package (see https://github.com/IMMM-SFA/cerf_data_centers) at the state level across the CONUS. Due to data availability constraints, some of the items included in this dataset are proxies or assumptions for siting factors used in the model. These are individually noted in the item descriptions and can be exchanged with more detailed information upon availability. Data Descriptions The following raster files are included in the data download: state_siting_region.tif — State areas identified by state FIPS code composite_siting_suitability.tif — Value of 1 indicates suitable siting location, 0 otherwise. The following areas are excluded from siting: Areas within 300 m of a federal airport runway or within an airport area boundary Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory USGS Protected Areas Database of the United States (PAD-US) GAP status 1, 2, or 3 areas US National Parks Wetlands USFWS critical habitats BIA land areas Railroads, major roadways, and minor roadways Military areas and training grounds NLCD developed lands Areas >0.8 km (0.5 miles) from NLCD developed lands land_value_dollar_per_sqft.tif — USD per square foot (sqft) derived from USDA $/acre land cost personal_property_tax_rate.tif — Personal property tax rate by state. Uses an assumed 0.0125 personal property tax rate for states with personal property tax, 0 for states without personal property tax. real_property_tax_rate.tif — Real property tax rate. Based on county level residential real estate property tax rates. sales_tax_rate.tif — Sales tax rate by state. mechanical_cooling_fraction.tif — Fraction of year (values between 0 and 1, inclusive) that the data center would be cooled through mechanical processes based on local water stress and humidity levels. water_cooling_fraction.tif — Fraction of year (values between 0 and 1, inclusive) that the data center would be cooled through evaporative (water cooled) processes based on local water stress and humidity levels. distance_to_substation.tif — Distance to nearest substation in hundreds of meters (i.e., value of 1 equals a distance of 100m). Offshore areas have a value of 0. industrial_electricity_rates_dollar_per_kwh.tif — USD/kWh industrial electricity rates. Represents the average industrial rate across all utilities that operate within a given county. Values are derived from the US Utility Rate Database. commercial_electricity_rates_dollar_per_kwh.tif — USD/kWh commercial electricity rates. Represents the average commercial rate across all utilities that operate within a given county. Values are derived from the US Utility Rate Database. data_center_market_locations.tif — Grid cells with positive values represent the centroid of existing data center market clusters. The value of non-zero grid cells represents the number of data centers in the market cluster. All other grid cells have a value of 0. Geospatial Metadata CRS: Albers Equal Area Conic (ESRI:102003) Extent: -2415585.0000000023283064,-1441981.2605773280374706 : 2384414.9999999976716936,1708018.7394226719625294 Dimensions: X: 48000 Y: 31500 Bands: 1 Origin: -2415585.0000000023283064,1708018.7394226719625294 Pixel Size: 100,-100 Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4.0 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall↗

DOE OSTI2021

Improving Data Center Energy Efficiency Through End-to-End Cooling Modeling and Optimization. Final Report

This report summarizes the development and demonstration of the three features developed for the DOE project “Improving Data Center Energy Efficiency through End-to-End Cooling Modeling and Optimization” (Award No. DE-EE0007688). The development of the three software features, Feature 1: Optimization of Cooling System Operation, Feature 2: Optimization of Airflow Management System, and Feature 3: Simultaneous Optimization of Airflow Management and Cooling Systems, are described first. Feature 1 is implemented in Phase 1 of the project and demonstrates 15% energy savings from cooling system optimization by applying the proposed retrofit solutions. Feature 2 is implemented in Phase 2 and the assessments show cooling energy-saving potential between 19.3% and 54.0%, depending on the energy-saving measures employed. In addition, investigating the energy saving potential for a hypothetical simple data center shows about 6.8% energy savings by optimizing the server load placement. Feature 3 is implemented in Phase 3 and the results show energy savings of 53.4% and 74.6% for the two partner data centers, respectively, compared to the baselines defined in Phase 1. Lastly, the project outcomes including software releases and publications are discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Mechanistic modeling of copper corrosions in data center environments

Air-side economizers are increasingly used to take advantage of “free-cooling” in data centers with the intent of reducing the carbon footprint of buildings. However, they can introduce outdoor pollutants to indoor environment of data centers and cause corrosion damage to the information technology equipment. Here, to evaluate the reliability of information technology equipment under various thermal and air-pollution conditions, a mechanistic model based on multi-ion transport and chemical reactions was developed. The model was used to predict Cu corrosion caused by Cl2-containing pollutant mixtures. It also accounted for the effects of temperature (25 °C and 28 °C), relative humidity (50%, 75%, and 95%), and synergism. It also identified higher air temperature as a corrosion barrier and higher relative humidity as a corrosion accelerator, which agreed well with the experimental results. The average root mean square error of the prediction was 13.7 Å. The model can be used to evaluate the thermal guideline for data centers design and operation when Cl2 is present based on pre-established acceptable risk of corrosion in data centers’ environment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Exploring the Limits of Cooling for Extreme Heat Flux Applications: Data Centers and Power Electronic

We developed an Extreme Heat Flux Micro-cooler (EHFµ-C) capable of removing heat flux of ~1 kW/cm 2 with a small temperature rise ~10°C. This represents greater than 10× reduction in thermal resistance compared to the state-of-the-art single-phase cooling technologies (~0.5 cm 2 -°C/W) that greatly increases the efficiency, resulting in improved performance and reliability for microprocessors and power electronics. The EHFµ-Cooler utilizes liquid wicking (no pump) and thin-film evaporation in a 30 µm thick wick porous microstructure such as copper inverse opal (CIO), silicon and AlN pin fins along with a 3D manifold (copper wiremesh and silicon) with liquid routing and vapor extraction channels achieving an unprecedented vapor quality ~ >0.9. The proposed EHFµ-Cooler significantly reduces device temperature, resulting in 25-50% improvement in reliability and device performance. Based on very conservative estimates, the total energy saving for data centers due to improved performance can be 6 billion kWh/year (>$300M). Better cooling, leading to more efficient, higher performance and more reliable power electronics, would save the nation 3 Quad of primary energy in 2030. Just for automotive application, a 10% increase in fuel efficiency for SiC-based power electronics equates to >20 million gallons of fuel savings annually. The proposed EHFµ-Cooler technology ensures the United States maintains its technological lead in high-performance computing and high-power electronics, while at the same time improving the energy efficiency, and as a result reducing energy-related emissions such as greenhouse gases.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

A review of challenges, barriers, and opportunities for large-scale deployment of cool surfaces

Major urban centers are warming due to a combination of global and local phenomena. City governments are increasingly adopting strategies to mitigate the causes and impacts of extreme heat on their populations. Among these strategies are high solar-reflectance (cool) surfaces installed on building roofs and walls. Use of cool surfaces is a cost-effective and simple strategy that replaces conventional darker surfaces with surfaces that have a high reflectance to shortwave (solar) energy. This report reviews the recent history of cool-surface deployment efforts. This includes peer-reviewed literature, conference proceedings, and grey literature to identify challenges and barriers to wide-scale deployment of cool surfaces. We have also researched heat action plans and programs from cities and different codes and standards, as well as available incentive and rebate programs. The review identifies challenges, barriers, and opportunities associated with large-scale deployment of cool surfaces and categorize them broadly as being related to product development & performance or policies & mandates. It provides a foundation upon which we intend to build a roadmap for rapidly accelerating future deployments of cool surfaces. Finally, this roadmap will address identified challenges and incorporate lessons learned from historical efforts to generate a practical and actionable plan.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Prototype energy models for data centers

Data centers in the United States consume about two percent of the nation’s electricity. Because heat gains from IT equipment drive cooling demand, data centers offer unique opportunities for energy savings. However, no prototype energy model for data centers is available in the suite of existing U.S. Department of Energy’s Commercial Prototype Building Models. Here we present the development of two new data center prototype models and their implementation in OpenStudio and EnergyPlus. The small-size data center model represents a computer room in a building served by computer room air conditioners (CRACs); while the large-sized model represents stand-alone data centers served by computer room air handlers (CRAHs) with a central chiller plant. For each data center model, two levels of IT equipment (ITE) load density were considered, to cover the wide range of IT power density of data centers: 40 and 100 W/ft 2 (430 and 1076 W/m 2 ) for the computer room, and 100 and 500 W/ft 2 (1076 and 5382 W/m 2 ) for the stand-alone data center. All other assumptions, such as building envelope, lighting, HVAC efficiencies and schedules, were based on the minimal requirements of ASHRAE Standard 90.1 at various vintages. We introduced a novel concept of supply and return air approach temperatures to capture the essential effects of non-uniform airflow and temperature distribution in data centers. The approach temperatures were pre-computed by computational fluid dynamics (CFD) simulations for various configurations of ITE loads and airflow containment management in data centers. A new feature was developed in EnergyPlus to implement the approach temperature method. A case study was conducted to demonstrate the use of the data center models. The two data center models cover all U.S. climate zones and can be used to evaluate energy saving measures for data centers, as well as to support development of data center energy efficiency codes and standards.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

The Installation of Direct Water-Cooling Systems to Reduce Cooling Energy Requirements for High-Performance Computing Centers

A large cluster of High-Performance Computing (HPC) equipment at the Lawrence Livermore National Laboratory in California was retrofitted with an Asetek cooling system. The Asetek system is a hybrid scheme with water-cooled cold plates on high-heat-producing components in the information technology (IT) equipment, and with the remainder of the heat being removed by conventional air-cooling systems. In order to determine energy savings of the Asetek system, data were gathered and analyzed two ways: using top-down statistical models, and bottom-up engineering models. The cluster, “Cabernet”, rejected its heat into a facilities cooling water loop which in turn rejected the heat into the same chilled water system serving the computer-room air handlers (CRAHs) that provided the air-based cooling for the room. Because the “before” and “after” cases both reject their heat into the chilled water system, the only savings is due to reduction in CRAH fan power. The top-down analysis showed a 4% overall energy savings for the data center (power usage effectiveness (PUE) —the ratio of total data center energy to IT energy— dropped from 1.60 to 1.53, lower is better); the bottom-up analysis showed a 3% overall energy savings (PUE from 1.70 to 1.66) and an 11% savings for the Cabernet system by itself (partial PUE of 1.51). Greater savings, on the order of 15-20%, would be possible if the chilled water system was not used for rejecting the heat from the Asetek system. About 37% of the heat from the Cab system was rejected to the cooling water, lower than at other installations.

Earni, Shankar↗

DOE OSTI2024

Best Practices to Design, Retrofit, and Operate Efficient Data Centers

Presentation provides an overview of energy efficient data center strategies, including thermal (environmental) guidelines, air management, free cooling, performance metrics, and liquid-based cooling.

air management↗

DOE OSTI2024

Experimental Investigation on Cooling performance of A Thermoelectric Freezer

Thermoelectric heat pumps (TEHPs) have found widespread use in the electronics cooling industry and portable refrigerators. However, there has been a lack of extensive research on the application of TEHPs in low-temperature refrigeration settings. To address this gap, various configurations of TEHPs were fabricated to assess their suitability for freezer applications. Key parameters such as cooling capacity and system performance of the TEHPs were crucial in evaluating these configurations. Three configurations, each with different numbers of cooling units and fan arrangements, were tested using a 300-liter freezer prototype under typical room conditions (21°C). A cooling unit is comprised of two-stage thermoelectric modules, an aluminum plate fin heat exchanger sink with fans positioned either on top or directing airflow through the center, and a cooling block with circulating icy water for heat dissipation. Across all configurations, the minimum temperature inside the freezer cabinet reached -16.0°C. The cooling capacity peaked at 74.7 W, with the thermoelectric coefficient of performance (COP) reaching a maximum of 0.45. System COP ranged from 0.23 to 0.28. Minimum TE power consumption was recorded at 138.8 W, with TE system power consumption at 174.4 W, indicating feasibility for practical residential freezer applications. This investigation lays the foundation for integrating TE freezers with ice thermal storage systems.

Hu, Yifeng↗

DOE OSTI2023

Geospatial Characterization of Low-Temperature Heating and Cooling Demand in the United States: Preprint

Geothermal resources at temperatures below 150 degrees C have great potential as energy sources for various direct-use applications including heating and cooling in residential and commercial buildings. This study geospatially quantifies U.S. heating and cooling demand in residential, commercial, and manufacturing sectors; heating demand in the agricultural sector; and cooling demand in data centers at the county level through end-use energy consumption, expenditure, and commissioned power analyses. Heating and cooling demand in the residential sector was estimated using energy consumption data obtained from the U.S. Energy Information Administration accounting for different U.S. climate zones. For commercial sector analysis, the end-use major fuel energy intensity at the census division level was disaggregated to the county level with respect to principal building activities. Heating and cooling demand analysis for the manufacturing sector was based on end-use energy consumption for direct-use total process categorized by the North America Industry Classification System. Fuel expenditures in the U.S. Department of Agriculture Farm Production Expenditures were examined for heating demand analysis in the agricultural sector, particularly for the greenhouse, nursery, and floriculture production category. Lastly, commissioned power for data centers in the United States were explored for cooling demand analysis. Results indicated a significant fraction of U.S. primary energy consumption is used for low-temperature heating and cooling applications. Heating and cooling demand in residential and commercial sectors is significantly affected by the number of housing units and climate zone designations, while heating and cooling demand in manufacturing and agricultural sectors and data centers are mainly dependent on the number of facilities and their locations. Maps were generated visualizing where heating and cooling demand is high and, overlain with geothermal resource maps, can indicate locations where geothermal energy can supply this heating and cooling demand.

cooling demand↗

DOE OSTI2023

Geospatial Characterization of Low-Temperature Heating and Cooling Demand in the United States

cooling demand↗

DOE OSTI2025

NLR HPC Facility Power Usage Effectiveness (PUE) Data

Timeseries of Energy Systems Integration Facility (ESIF) Data Center Power Usage Effectiveness (PUE) Data provided in Parquet and compressed CSV formats Power Metrics Timeseries Fields: ts: Timestamp cooling_kw: Cooling (kilowatts) - Captures the power used by fans and pipe trace heaters associated with outdoor cooling equipment. The dedicated tower filter pump power is also captured as cooling load. energy_reuse: Energy Reuse Effectiveness hvac_kw: Heating, ventilation, and air conditioning (kilowatts) - Captures fan walls, fan coils that support the data center electrical rooms, and the make-up air unit. it_power_kw: IT equipment (kilowatts) - Captures power used by the IT equipment on the data center floor. plug_and_light_kw: Lights and utility plugs (kilowatts) - Captures power associated with the data center and dedicated mechanical room. The crank-case heater for the emergency standby generator is also captured as light and plug load. pue: Power Usage Effectiveness pump_kw: Pumps (kilowatts) - Captures power from pumps that move water in the data center Energy Recover Water loop and the Tower Water loops, and also captures power used by the boost pumps that circulate water through the fan walls. Note: The tower filter pump runs constantly to filter water from the data center cooling tower system, so 2.67 kilowatts are attributed to this pump and that is not reflected in this data field. day: Day of month Outside Weather Station Timeseries Fields: ts: Timestamp outside_air_humidity: Outside air humidity - Relative humidity percent outside_air_temp: Outside air temperature - Degrees Fahrenheit day: Day of month More detail: High-Performance Computing Data Center Power Usage Effectiveness

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Direct Evidence of Photoinduced Charge Transport Mechanism in 2D Conductive Metal Organic Frameworks

Conductive metal organic frameworks (MOFs) represent a promising class of porous crystalline materials that have demon-strated potential in photo-electronics and photocatalytic applications. However, the lack of fundamental understanding on charge transport (CT) mechanism as well as the correlation of CT mechanism with their structure hampered their further development. Herein, we report the direct evidence of CT mechanism in 2D Cu-THQ MOFs and the correlation of temporal and spatial behaviors of charge carriers with their photoconductivity by combining three advanced spectroscopic methods including time resolved optical and X-ray absorption spectroscopy and terahertz spectroscopy. In addition to Cu-THQ, the CT in Cu/Zn-THQ after incorporating Zn2+ guest metal was also examined to uncover the contribution of through space pathway as the presence of the redox inactive 3d10 Zn2+ is expected to perturb the long range in-plane CT. We show that the hot carriers in Cu-THQ generated after photoexcitation are highly mobile and undergo fast localization to a lower energy state (cool carriers) with electrons occupying Cu center and holes in ligands. The cool carriers, which have super long lifetime (> 17 ns) are responsible for the long-term photoconductivity in Cu-THQ and transport through O-Cu-O motif with negligible contribution from interlayer ligand π-π stacking as incorporation of Zn2+ in Cu-THQ significantly reduced photoconductivi-ty. These unprecedent results not only demonstrate the capability to experimentally probe CT mechanism but also provide important insight in the rational design of 2D MOFs for photoelectronic and photocatalytic applications.

14 SOLAR ENERGY↗

DOE OSTI2026

From Bricks to Clicks: Mapping the White Space in Building Innovation

It is a critical national imperative to transform the buildings sector, yet innovation is impeded by deployment failures that leave promising technologies stranded. Conventional market reports and techno-economic analysis provide an insufficient understanding of markets and resource allocation for emerging building technologies. They omit crucial commercialization factors such as ecosystem maturity and adoption friction, where the coordinated participation of a network of suppliers, contractors, financiers, regulators, and integrators is required to scale solutions. This study addresses these gaps by introducing an evaluation framework grounded in front-line data from six years of the DOE's IMPEL incubator, comprising experience from 300 building-sector innovators and the adjacent, complex ecosystem. Our methodology synthesizes top-down market analysis with bottom-up, practitioner-level data across five megatrends: (M1) Affordable materials and industrialized construction; (M2) Healthy and efficient mechanical systems; (M3) Intelligent building operations; (M4) Buildings as grid assets; and (M5) High-density power and cooling for data centers and therein identify twelve "white space" technology opportunities. Next, we develop a multi-criteria scoring rubric to rank these opportunities based on parameters, i.e., Affordability, Quality of Life, Reliability, and Security, yielding composite ‘Demand’ and ‘Maturity’ indices. Our results indicate that the most significant white spaces may not be incremental products but a new class of ‘Ecosystem Enablers’, such as logistics platforms, orchestration layers, and automated compliance software that solve structural deployment gaps. This paper summarizes this transparent, evidence-based, practitioner-informed evaluation framework for policymakers and investors to re-evaluate policy and resource allocation and unlock scalable market transformation.

Singh, Reshma↗

DOE OSTI2026

Best Practices for Resilience Hub Development and Management

The Carbon League and its community partners in East St. Louis, Illinois, have identified five facilities to serve as resilience hubs. These hubs are intended to support the local community through a range of services and resources during blue-sky (everyday), gray-sky (pre-event), and black-sky (emergency) conditions. Transforming these facilities into fully functional resilience hubs requires a broad operational improvement and programmatic planning roadmap. This memo outlines best practices to guide the development of these resilience hubs in East St. Louis, including recommendations for infrastructure services; safety and physical protection; community services; operational protocols; and a phased implementation strategy aligned with realistic funding and capacity constraints. Infrastructure recommendations include strengthening electric power, communications, water, sanitation, and transportation/logistics capabilities, all of which are essential for hubs that may serve as cooling and warming centers, distribution points, and information hubs during emergencies. Safety recommendations focus on accessibility, emergency action planning, indoor air quality, and secure storage of critical equipment. A phased roadmap provides guidance from immediate, low-cost readiness actions to long-term optimization and community integration. Performance metrics and maintenance protocols ensure continuous improvement and operational readiness. This guidance draws on best practices that can be used to support the development of resilient, community-centered hubs capable of enhancing public safety, health, and well-being during everyday operations and emergencies alike.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2026

Cooling Matters: Benchmarking Large Language Models and Vision-Language Models on Liquid-Cooled Versus Air-Cooled H100 GPU Systems

The unprecedented growth in artificial intelligence (AI) workloads, recently dominated by large language models (LLMs) and vision-language models (VLMs), has intensified power and cooling demands in data centers. This study benchmarks LLMs and VLMs on two HGX nodes, each with 8× NVIDIA H100 graphics processing units (GPUs), using liquid and air cooling. Leveraging GPU Burn, Weights & Biases, and IPMItool, we collect detailed thermal, power, and computation data. Results show that the liquid-cooled systems maintain GPU temperatures between 41-50$^\circ$C, while the air-cooled counterparts fluctuate between 54-72$^\circ$C under load. This thermal stability of liquid-cooled systems yields 17% higher performance (54 TFLOPs/ GPU vs. 46 TFLOPs/GPU), performance-per-watt, reduced energy overhead, and greater system efficiency than the air-cooled counterparts. These findings underscore the energy and sustainability benefits of liquid cooling, offering a compelling path forward for hyperscale data centers seeking to optimize AI infrastructure. https://github.com/iscaas/Cooling-Matters.

Latif, Imran↗

DOE OSTI2023

Temperature-Controlled Reversible Formation and Phase Transformation of 3D Nanocrystal Superlattices Through In Situ Small-Angle X-ray Scattering

For decades, the spontaneous organization of nanocrystals into superlattices has captivated the scientific community. However, achieving direct control over the formation of the superlattice and its phase transformations has proven to be a grand challenge, often resulting in the generation of multiple symmetries under the same experimental conditions. Here, we achieve direct control over the formation of the superlattice and its phase transformations by modulating the thermal energy of a nanocrystal dispersion without relying on solvent evaporation. In this study, we follow the temperature-dependent dynamics of the self-assembly process using synchrotron-based small-angle X-ray scattering. When cooled below –24.5 °C, lead sulfide nanocrystals form micrometer-sized three-dimensional phase-pure body-centered cubic superlattices. When cooled below –35.1 °C, these superlattices undergo a collective diffusionless phase transformation that yields denser body-centered tetragonal phases. These structural changes can be reversed by increasing the temperature of the dispersion and may lead to the direct modulation of the optical properties of these artificial solids.

36 MATERIALS SCIENCE↗