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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 OSTI2022

North Carolina Water Utility Builds Resilience with Distributed Energy Resources

As the frequency and duration of grid outages increase, backup power systems are becoming more important for ensuring that critical infrastructure continues to provide essential services. Most facilities rely on diesel generators, which may be ineffective during long outages owing to limited fuel supplies and high generator failure rates. Distributed energy resources such as solar, storage, and combined-heat-and-power systems, coupled with on-site biofuel production, offer an alternative source of on-site generation that can provide both cost savings and resilience (i.e., the ability to respond to catastrophic events with longer-term consequences). A mixed-integer linear program minimizes costs and maximizes resilience at a wastewater treatment plant in Wilmington, North Carolina. We find that the plant can reduce life-cycle energy costs by 3.1% through the installation of a hybrid combined-heat-and-power, photovoltaic, and storage system. When paired with existing diesel generators, this system can sustain full load for seven days while saving $664,000 over 25 years and reducing diesel fuel use by 48% compared with the diesel-only solution. This analysis informed a decision by the Cape Fear Public Utility Authority to allocate funds for the implementation of a combined-heat-and-power system at the wastewater treatment plant in fiscal year 2023. Finally, the benefits of deploying hybrid combined-heat-and-power technologies and the utilization of on-site biofuel production extend, on a national scale, to thousands of wastewater treatment facilities and other types of critical infrastructure.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Heat Pumps with Phase Change Thermal Storage: Flexible, Efficient, and Electrification Friendly

As we continue to electrify space- and water-heating, the electricity demand profile of many buildings will change significantly, and periods of high electricity demand will likely not align with renewable energy generation. We expect electricity demands will increase substantially in the winter, annual maximum electricity demands will increase, and more regions will experience annual peak electrical demands – and higher wholesale electricity prices – in the winter. This is especially important for cold climates where 60% of site energy use in buildings is for heating, and where heat pumps perform least efficiently. This paper focuses on one promising solution among the many paths to electrification: the use of phase change materials (PCM) for compact low-cost thermal energy storage (TES). We present the design and simulation of a combi heat pump and phase change thermal storage system used for space- and water-heating in a multifamily residence in a cold climate. To assess the benefits of this technology, we compare its annual performance to that of a current state-of-the-art air-to-air heat pump and separate heat pump water heater. Simulation results for IECC Climate Zone 6A reveal that the combi heat pump with phase change thermal storage can reduce the design size for heat pumps by 40-60%, reduce maximum electric demand by 30-50%, reduce electricity use during 4-12-hour load shed periods by 50%, and avoid the need for auxiliary electric resistance for both space- and water-heating. Tariff structures are highly varied between different utilities and currently reflect higher wholesale market prices for electricity during summer days. Consequently, although this system design provides large electric demand reductions during hypothetical 4-12-hour load shed periods, it does not provide energy cost reductions with current winter residential time-of-use tariffs.

Helmns, Dre↗
DOE OSTI2023

Co-Simulation of Electric Power Distribution Systems and Buildings including Ultra-Fast HVAC Models and Optimal DER Control

Smart homes and virtual power plant (VPP) controls are growing fields of research with potential for improved electric power grid operation. A novel testbed for the co-simulation of electric power distribution systems and distributed energy resources (DERs) is employed to evaluate VPP scenarios and propose an optimization procedure. DERs of specific interest include behind-the-meter (BTM) solar photovoltaic (PV) systems as well as heating, ventilation, and air-conditioning (HVAC) systems. The simulation of HVAC systems is enabled by a machine learning procedure that produces ultra-fast models for electric power and indoor temperature of associated buildings that are up to 133 times faster than typical white-box implementations. Hundreds of these models, each with different properties, are randomly populated into a modified IEEE 123-bus test system to represent a typical U.S. community. Advanced VPP controls are developed based on the Consumer Technology Association (CTA) 2045 standard to leverage HVAC systems as generalized energy storage (GES) such that BTM solar PV is better utilized locally and occurrences of distribution system power peaks are reduced, while also maintaining occupant thermal comfort. An optimization is performed to determine the best control settings for targeted peak power and total daily energy increase minimization with example peak load reductions of 25+%.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Modeling Summary of ASNF in DOE Sealed Standard Canisters

A pathway for road-ready and final disposition packaging configurations for the aluminum-clad spent nuclear fuel (ANSF) fuel dictates storage within helium backfilled sealed DOE standard canisters. The typical packaging configuration for the 15-foot DOE canisters places 10 advance test reactor (ATR) elements within a Type 1a basket, and three baskets are loaded within each DOE canister. During in-reactor operations and cooling pond storage conditions, oxyhydroxide layers form on the surface of the aluminum clad fuel. These layers produce hydrogen gas over time due to the fuel’s radiation field. As part of the packing procedure, the ATR fuel should be dried to remove any residual physio-/chemi- sorbed water bound to the surface. The results of this modeling will include results at fully saturated and fully dried conditions. In addition, fuels that are currently stored at the Savannah River Site were also studied for their potential for hydrogen and pressure build up. These two additional fuels modeled were the Missouri University Research Reactor (MURR) fuel, which is packaged in the same configuration as the ATR, but with a 10-foot-tall DOE standard canister. This was selected due to its relatively high decay heat compared to other DOE-managed ASNF. The second additional fuel studied with the modeling effort was the High Flux Isotope Reactor (HFIR) fuel. This fuel is modeled as two separate DOE canisters with the inner and outer annulus split for storage. The HFIR was selected for study due to its high aluminum cladding surface area. In the associated experimental work, Task 2 - Oxyhydroxide Layer Radiolytic Gas Generation Resolution, additional tests were completed in a helium environment, and updated G-values for the radiolytic production of hydrogen from the oxyhydroxide layers were provided. These values were 2.92 ×10 -4 µmol/J at 50% relative humidity and 4.12 ×10 -4 µmol/J at 100% relative humidity. These values are lower than the value for Argon that was used in prior modeling results. In addition, prior modeling results have been completed with the G-value applied to just the mass of the corrosion layer, and this has been updated to apply the G-value for hydrogen generation to the full mass of the fuel. These two effects combine to show much smaller pressure and hydrogen build up for the sealed canister model. For a nominal scenario of stored ATR fuel, after 50 years the model results give a 1.36 atm total pressure, 7% mole percent hydrogen, for the upper decay heat, 1.51 atm total pressure, 16% mole percent hydrogen, and for upper decay heat with undried fuel 2.6 atm total pressure, 15% mole percent hydrogen. For the MURR nominal case, the model results give 1.34 atm and 6% hydrogen, for upper decay heat this gives 1.41 atm total pressure with 10.8 % hydrogen, and for upper decay heat with undried fuel, this gives 2.38 atm total pressure with 9.9% hydrogen. The nominal scenario for HFIR fuel gives 1.39 atm total pressure with 9.9% hydrogen, the upper decay heat case gives 1.43 atm with 12.1% hydrogen, and the upper decay heat with undried fuel gives 2.17 atm total pressure with 11.9% hydrogen. These results confirm the ATR scenario bounds the other intact ASNF modeled here. No case modeled yields significant oxygen, and the lower decay heat cases for all fuels modeled have hydrogen concentrations that are under the 4% flammability limit after 50 years of storage. In addition, the modeled pressures for all cases are all significantly below the 500-psi limit for the DOE standard sealed canister.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

COVID-19 pandemic ramifications on residential Smart homes energy use load profiles

The COVID-19 pandemic has significantly affected people’s behavioral patterns and schedules because of stay-at-home orders and a reduction of social interactions. Therefore, the shape of electrical loads associated with residential buildings has also changed. In this paper, we quantify the changes and perform a detailed analysis on how the load shapes have changed, and we make potential recommendations for utilities to handle peak load and demand response. Our analysis incorporates data from before and after the onset of the COVID-19 pandemic, from an Alabama Power Smart Neighborhood with energy-efficient/smart devices, using around 40 advanced metering infrastructure data points. This paper highlights the energy usage pattern changes between weekdays and weekends pre– and post–COVID-19 pandemic times. The weekend usage patterns look similar pre– and post–COVID-19 pandemic, but weekday patterns show significant changes. We also compare energy use of the Smart Neighborhood with a traditional neighborhood to better understand how energy-efficient/smart devices can provide energy savings, especially because of increased work-from-home situations. HVAC and water heating remain the largest consumers of electricity in residential homes, and our findings indicate an even further increase in energy use by these systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Hardware-Based Emulator with Deep Learning Model for Building Energy Control and Prediction Based on Occupancy Sensors’ Data

Heating, ventilation, and air conditioning (HVAC) is the largest source of residential energy consumption. Occupancy sensors’ data can be used for HVAC control since it indicates the number of people in the building. HVAC and sensors form a typical cyber-physical system (CPS). In this paper, we aim to build a hardware-based emulation platform to study the occupancy data’s features, which can be further extracted by using machine learning models. In particular, we propose two hardware-based emulators to investigate the use of wired/wireless communication interfaces for occupancy sensor-based building CPS control, and the use of deep learning to predict the building energy consumption with the sensor data. We hypothesize is that the building energy consumption may be predicted by using the occupancy data collected by the sensors, and question what type of prediction model should be used to accurately predict the energy load. Another hypothesis is that an in-lab hardware/software platform could be built to emulate the occupancy sensing process. The machine learning algorithms can then be used to analyze the energy load based on the sensing data. To test the emulator, the occupancy data from the sensors is used to predict energy consumption. The synchronization scheme between sensors and the HVAC server will be discussed. We have built two hardware/software emulation platforms to investigate the sensor/HVAC integration strategies, and used an enhanced deep learning model—which has sequence-to-sequence long short-term memory (Seq2Seq LSTM)—with an attention model to predict the building energy consumption with the preservation of the intrinsic patterns. Because the long-range temporal dependencies are captured, the Seq2Seq models may provide a higher accuracy by using LSTM architectures with encoder and decoder. Meanwhile, LSTMs can capture the temporal and spatial patterns of time series data. The attention model can highlight the most relevant input information in the energy prediction by allocating the attention weights. The communication overhead between the sensors and the HVAC control server can also be alleviated via the attention mechanism, which can automatically ignore the irrelevant information and amplify the relevant information during CNN training. Our experiments and performance analysis show that, compared with the traditional LSTM neural network, the performance of the proposed method has a 30% higher prediction accuracy.

Ye, Zhijing↗
DOE OSTI2020

Making Better Use of On-Site PV Generation: Direct Distribution of DC Power in Buildings

With the increasing availability of Direct Current (DC) power from on-site solar systems, building designers now have the option of direct distribution of DC power to energy storage systems and end-use loads such as lighting, electronics, and motors. Research shows that direct-DC can reduce building distribution losses by 5-15%, while improving power reliability. A readily available way to implement direct-DC distribution is Power over Ethernet (PoE), which typically has provided low-voltage DC to power office equipment such as WiFi access points. With recent updates to the PoE standard, equipment such as network-controlled LED lighting, thin client and computer workstations, HVAC controllers, fans, motors, and actuators can now be powered by PoE and are commercially available. This paper presents results from a DOE-funded project that is demonstrating PoE technologies and describes a DC modeling and design tool that is intended to help building designers properly evaluate and design DC distribution systems in buildings. The tool provides designers of DC power systems with estimates of both power system losses and indirect effects on building heating and cooling. The results of this research demonstrate that the design methods and products are available to implement DC power in buildings today.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Performance evaluation of underground thermal storage integrated dual-source heat pump systems

The increasing demand for electricity stresses the existing electric grids. Buildings consume 73% of all U.S. electricity and are responsible for 30% of U.S. greenhouse gas emissions. Integrating thermal energy storage (TES) in building heating/cooling systems, which consume considerable electricity, can mitigate the challenges to electric grids. Here, this study reports on a novel thermal energy storage device integrated heat pump system to reshape the building electricity demand profile while maintaining thermal comfort. The annual performance of the proposed system has been evaluated through a dynamic system simulation with high fidelity in the Modelica platform. The dynamic model of the novel hybrid component named ‘dual purpose underground thermal battery’ was developed and validated. It was then incorporated into the system model. Given a time-of-use tariff, a rule-based control strategy was designed to shift the electric demand and switch the heat pump source for a typical single-family house in different climate zones of the United States. The system performance of the new TES-integrated dual-source heat pump was compared with that of a conventional air-source heat pump system. The results indicate that the proposed system can reduce the annual HVAC electricity cost by up to 52% while saving 45.2% on electricity consumption. In the Northern areas, the annual peak load of the HVAC system can be reduced by 64.9%. However, this reduction is less in the Southern areas as the system’s higher efficiency in winter dominates the overall energy-saving potential.

25 ENERGY STORAGE↗
DOE OSTI2026

NG-CT Peaker Upgrades with Bottoming Cycle: Preliminary Analysis

The National Energy Technology Laboratory (NETL) has conducted a preliminary analysis exploring a significant opportunity to increase the nation's power grid capacity and reliability. The study focuses on retrofitting under-utilized natural gas combustion turbines (NG-CTs), often called "peaker" plants, with a bottoming cycle to capture waste heat and generate additional electricity. This analysis shows that upgrading these existing assets could add approximately 26,250 MW of new power capacity across the United States. A detailed study of the PJM Interconnection, the nation's largest grid operator, confirmed the benefits of these upgrades. Key findings for the PJM region include: - A median increase in the plants' capacity factor by 17 percentage points. - A projected 36.2% median reduction in the levelized cost of electricity (LCOE) for the upgraded plants, with nearly 88% of them expected to break even. - An average reduction in the regional wholesale price of electricity by approximately 3.0% in the year 2030. - A significant boost to grid reliability, with a 46% reduction in projected Loss of Load Hours (LOLH). These findings suggest that retrofitting peaker plants is a promising and economically viable strategy to enhance grid performance, reduce electricity costs, and meet future demand without the challenges associated with building entirely new facilities.

bottoming cycle↗
DOE OSTI2026

EASY-SHIFT v Alpha

The software is a generic, price- and load-responsive control algorithm integrating heat pumps with thermal energy storage. The algorithm leverages simple models of the system and easily accessible data to schedule operation of heat pumps and thermal energy storage in ways that minimize the cost of operating the heating/cooling system. This tool is specifically designed to be easy to interact with, and something that industry partners are able to adopt. There are two current state of the art approaches. Industry tends to develop very simple algorithms, with predetermined schedules that are not capable of changing operation in response to changes in operating environment. For example, a control designed to avoid high-price electricity from 5-8 PM will not be able to adapt if the high-price period changes to 4-9 PM. Academia commonly develops algorithms called Model predictive control (MPC). MPC requires extensive data and highly trained staff to develop a specific type of simulation model of the building, connect the building to optimization algorithms, and leverage powerful computers. Industry, with limited time/finance budgets for any project, is resistant to adopting MPC due to the associated high complexity and cost.

Grant, Peter [Lawrence Berkeley National Laborator↗
DOE OSTI2021

Evaluating the energy impact potential of energy efficiency measures for retrofit applications: A case study with U.S. medium office buildings

Quantifying the energy savings of various energy efficiency measures (EEMs) for an energy retrofit project often necessitates an energy audit and detailed whole building energy modeling to evaluate the EEMs; however, this is often cost-prohibitive for small and medium buildings. In order to provide a defined guideline for projects with assumed common baseline characteristics, this paper applies a sensitivity analysis method to evaluate the impact of individual EEMs and groups these into packages to produce deep energy savings for a sample prototype medium office building across 15 climate zones in the United States. We start with one baseline model for each climate zone and nine candidate EEMs with a range of efficiency levels for each EEM. Three energy performance indicators (EPIs) are defined, which are annual electricity use intensity, annual natural gas use intensity, and annual energy cost. Then, a Standard Regression Coefficient (SRC) sensitivity analysis method is applied to determine the sensitivity of each EEM with respect to the three EPIs, and the relative sensitivity of all EEMs are calculated to evaluate their energy impacts. For the selected range of efficiency levels, the results indicate that the EEMs with higher energy impacts (i.e., higher sensitivity) in most climate zones are high-performance windows, reduced interior lighting power, and reduced interior plug and process loads. However, the sensitivity of the EEMs also vary by climate zone and EPI; for example, improved opaque envelope insulation and efficiency of cooling and heating systems are found to have a high energy impact in cold and hot climates.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Novel Modular Heat Engines with sCO 2 Bottoming Cycle Utilizing Advanced Oil-Free Turbomachinery

This report presents the thermal, mechanical, and electric machine design of a 27krpm rotating test rig aimed at reducing technical risks with hermetic oil-free super-critical carbon dioxide (sCO 2 ) turbomachinery. The test rig rotor system is based on a conceptual turbomachine design discussed that evaluates a sCO 2 waste heat recovery (WHR) unit for land-based gas turbines at natural gas (NG) compressor stations. The key novelty of the hermetic sCO 2 turbomachine concept is the utilization of additively manufactured CO 2 gas bearings and a CO 2 -immersed direct-drive permanent magnet (PM) electric machine. The previous effort on conceptual design identified 3 main technical risks, which included thrust bearing load capacity, rotordynamics, and thermal performance of the system. Therefore, key requirements of the test vehicle include the ability to test the rotor system in a 400psi (27.6 bar) hermetic CO 2 operating environment, apply up to 1,500 lbs (6.7 kN) of rotor thrust loads, capability to assess rotordynamics with radial gas bearings, inclusion of instrumentation to assess thrust bearing load capability, and flexibility for varying secondary cooling flows to confirm thermal model predictions. Design topics such as rotordynamics, bearing design, and electric machine design are addressed, while highlighting critical elements. In addition, additive build trials for Inconel 718 radial and thrust bearings were performed to prove the manufacturability of the bearing design concepts. The results of the design efforts yield a test rig concept with ability to operate at 27,000 rpm, apply thrust loads to 1,500 lbs, deliver CO 2 up to 800psi, and modulate cooling flows.

03 NATURAL GAS↗
DOE OSTI2025

Glauber’s Salt Composites for HVAC Applications: A Study on the Use of the T-History Method with a Modified Data Evaluation Methodology

Phase change materials (PCMs) can be utilized in buildings for peak load shifting in air conditioning systems, and the use of salt hydrate-based PCMs can reduce the cost of thermal energy storage devices. Glauber’s salt is an economical salt hydrate PCM with a melting point of around 32 °C. However, the desired melting range typically falls between 18 and 22 °C for building air conditioning applications. Although many researchers have characterized Glauber’s salt and its composites with modified melting points, enthalpy–temperature curves for composites of Glauber’s salt and NaCl are unavailable. In this study, we report the melting and solidification enthalpy–temperature curves for two different composites of Glauber’s salt and NaCl with a melting point of 21 °C obtained by the T-history method. Both composites contain NaCl to suppress the melting point, borax to reduce supercooling, and sodium polyacrylate as a thickener to enhance cyclic stability. The first composite with 12 wt.% NaCl demonstrated 139 kJ·kg−1 of latent heat of fusion, and the second composite with 9 wt.% NaCl demonstrated 171 kJ·kg−1. Both the composites have high volumetric energy densities compared to their organic counterparts with similar melting points.

Chemistry↗
DOE OSTI2024

Design And Control of Thermal Storage for Ventilative Cooling in Multifamily Buildings (Final Report)

The concept of natural ventilation is to provide a heat sink for cooling the building during the occupancy periods at night and improve indoor thermal comfort during the daytime without or with minimal need for mechanical cooling. Commonly, natural ventilation through the window or mechanical circulation of the air through the ventilation ducts are two key methods used to achieve night ventilation. Due to the increasing electric need for space heating and cooling, renewable energies combined with energy storage systems are receiving attention worldwide nowadays. When designed and controlled properly, this strategy can reduce energy use for cooling, reduce the size of mechanical cooling equipment, reduce peak electrical demand for cooling, and better enable demand flexibility for mechanical cooling equipment. In some cases, ventilative cooling can eliminate the need for mechanical cooling altogether. For these reasons, California’s Building Energy Efficiency Standards have recently added prescriptive requirements that all new single-family residences (in most California climates) must include ventilative cooling systems (aka: “whole house fans”). However, the current standards do not address multifamily buildings because market-available ventilative cooling products are designed for single-family residences and are physically incompatible with many multifamily building archetypes. The results from the study show that the combination of thermal energy storage on building walls along with nighttime ventilation cooling assisted with nighttime ventilation can save total building energy usage by up to 9% and peak load by 16% in moderate climate zones. The controlled nighttime ventilation cooling (activate ventilation only when outdoor conditions are favorable) can also provide total electricity savings of up to 5% and 7% in hot and cold climates, respectively. In the case of moderate climates, kitchen exhaust-assisted nighttime ventilation cooling can eliminate the need for cooling during overnight hours. This can also significantly assist in reducing carbon emissions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

High-temperature seals for supercritical carbon-dioxide (sCO 2 ) turbines (Final Report)

This is the final report for project DE-FE0031924 titled “High-temperature seals for supercritical carbon-dioxide (sCO 2 ) turbines.” The report provides a summary of the entire project efforts from October 2020 through December 2024 including the high-temperature commercial dry gas seal (DGS) tests and thermal modeling of Task 2, as well as the high-temperature, large-diameter seal design and high-temperature tests of large-diameter seals in Task 3. A key outcome of Task 2 was the testing completion of specially instrumented commercial DGS in the GE-SwRI Apollo sCO 2 compressor (27,000 rpm). Test data from the DGS showed elevated temperatures upwards of 350 o F, which are close to the higher operating temperature limit of the DGS. The temperature measurements provide insight into the expected thermal loads on DGS operating in high-speed sCO 2 compressor and provided test data for validation of an in-house thermal model of the compressor/seal. Under Task 2.0, this report also presents the development of a steady-state conjugate heat-transfer model of the DGS operating in the sCO 2 compressor – a first of its kind model for modeling heat transfer of sCO 2 in an actual operating compressor. The findings of the thermal model show a reasonable match between temperature predictions of the model and the measured temperature data, also pointing out the validity of the approach and assumptions made in modeling the flows, heat transfer coefficients and windage modeling in the rig. Under Task 3.0, this report presents the preliminary design of a large-diameter hybrid face seal (14 inch and 26-inch diameter) for field testing in a land-based GE turbine. The preliminary seal design effort presented in this report under project DE-FE0031924 builds on the development and successful laboratory testing for such large diameter hybrid face seal under the prior DE-FE0024007 project. Key aspects of seal fluid analyses with CFD, mechanical design considerations and assembly considerations in a land-based turbine are presented. Finally, under Task 3.0, this report also presents the continued high-temperature testing of the 14-inch diameter hybrid face seal developed previously under the DE-FE0024007 program. Specifically, test data demonstrating successful non-contact seal operation and seal effective leakage of 0.001-inch with seal inlet temperatures above 700 o F are presented in this report. Successful hybrid seal operation in a laboratory environment for a large diameter (14-inch) seal at temperatures above 700 o F is a major technological milestone for this technology.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

PANDEMIC: Occupancy driven predictive ventilation control to minimize energy consumption and infection risk

During the SARS-CoV-2 (COVID-19) pandemic, governments around the world have formulated policies requiring ventilation systems to operate at a higher outdoor fresh air flow rate for a sufficient time, which has led to a sharp increase in building energy consumption. Therefore, it is necessary to identify an energy-efficient ventilation strategy to reduce the risk of infection. In this study, we developed an occupant-number-based model predictive control (OBMPC) algorithm for building ventilation systems. First, we collected the occupancy and Heating, ventilation, and air conditioning system (HVAC) data from March to July 2021. Then, four different models (Auto regression moving average-based multilayer perceptron (ARMA_MLP), Recurrent neural networks (RNN), Long short-term memory networks (LSTM), and Nonhomogeneous Markov with change points detection (NH_Markov)) were used to predict the number of room occupants from 15 min to 24 h ahead with an interval output. We found that each model could predict the number of occupants with 85% accuracy using a one-person offset. Furthermore, the accuracy of 15 min of the ahead prediction could reach 95% with a one-person offset, but none of them could track abrupt changes. The occupancy prediction results were used to calculate the ventilation demand using the Wells-Riley equation, and the upper bound can maintain an infection risk lower than 2% for 93% of the day. This OBMPC model could reduce the coil load by 52.44% and shift the peak load by 3 h up to 5 kW compared with 24 × 7 h full outdoor air (OA) system when people wear masks in the space. The occupancy prediction uncertainty could cause a 9% to 26% difference in demand ventilation, a 0.3°C to 2.4°C difference in zone temperature, a 28.5% to 44.5% difference in outdoor airflow rate, and a 10.7% to 28.2% difference in coil load.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

helios: An R package to process heating and cooling degrees for GCAM

helios is an open-source R package that estimates population-weighted heating and cooling degree-hours (HDH and CDH) and degree-days (HDD and CDD) at various temporal (e.g., energy dispatch segments, monthly, yearly) and spatial scales (e.g., U.S. states, global political regions, countries). The degree hour and degree day outputs from helios are used to inform electricity demand load in the Global Change Analysis Model (GCAM) as well as in GCAM-USA (which is the version of GCAM with U.S. state-level details). helios uses a workflow with four steps: processing raw data; calculating heating and cooling degrees; visualizing performance diagnostics; and outputing results in various formats. There are two sources of widely-used climate data compatible with helios: (1) hourly climate data with 12-km resolution that are dynamically downscaled with the Weather Research and Forecasting (WRF) model and projected using a thermal global warming (TGW) approach; and (2) daily climate data with 0.5-degree resolution from the Coupled Model Intercomparison Project (CMIP) that is bias-adjusted and statistical downscaled by the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). In summary, helios is a model that standardizes methodology of heating and cooling degrees-hours and degree-days using publicly available data and advance the understanding of the impact of spatial and temporal temperature variability on building energy services.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Effects of Accident Thermal Loading on In-Plane Shear Behavior of Steel-Plate Composite Walls

Structural walls in safety-related nuclear facilities are required to be designed for seismic and accident thermal (due to postulated high-energy pipe break events) loading combination. Current U.S. and international codes provide limited guidance for analysis and design of walls for this loading combination. This paper describes the experimental results and observations from tests conducted on a laboratory-scale (1:4 to 1:5) test unit representing steel-plate composite (SC) walls subjected to combined in-plane (seismic) and accident thermal loading. The test unit was subjected to surface temperatures of up to 450°F in combination with cyclic in-plane loading. Results of similar experiments recently conducted in Japan are also summarized (with surface temperatures up to 570°F). Surface heating combined with the low thermal conductivity and high specific heat of concrete resulted in nonlinear thermal gradients through the thickness of the specimens. These nonlinear thermal gradients and the associated self- or internal restraint led to extensive concrete cracking. This concrete cracking reduced the initial and secant stiffness of the specimens. The initial stiffness of the heated specimens reduced to 30 to 40% of the initial stiffness of the control (unheated) specimen. The secant stiffness of the heated specimens reduced up to 50% of the secant stiffness of the control (unheated) specimen. However, the in-plane shear strength of the heated SC specimens was still approximately 10 to 30% greater than the nominal in-plane shear strength calculated, for the limit state of steel plate von Mises yielding, using AISC N690 equations and measured material properties. Evaluation of the experimental results and observations suggests that the in-plane shear strength of SC walls subjected to typical accident surface temperatures (up to 570°F) can be estimated conservatively using the current provisions of AISC N690. The stiffness for accident thermal loading combinations can be considered to reduce from cracked composite stiffness at ambient temperature to fully cracked—that is, steel-only stiffness as the surface temperature increases up to typical accident value.

Construction & Building Technology↗