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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Comparing non-destructive 3D X-ray computed tomography with destructive optical microscopy for microstructural characterization of fiber reinforced composites

Due to their lightweight, relatively high stiffness properties, and formability into complex shapes, discontinuous fiber composites are advantageous for producing small and medium size components. Improved characterization techniques and post-processing methodologies are required for more reliable quantification of the microstructure and defect distributions in these materials, in order to employ model-based approaches to assess their structural integrity. This work compares a non-destructive X-ray approach with a destructive optical microscopy approach for characterizing the microstructural attributes, specifically the fiber volume fraction, porosity volume fraction, fiber orientation distribution, and fiber length distribution of discontinuous glass fibers in a polypropylene matrix. Additionally, a method for destructively determining the ambiguous components of the orientation tensor (related to the sign ambiguity of the out-of-plane angle in a destructive cross-sectional cut of a fiber) over a large surface area is included. It was found that fiber volume fraction and average fiber aspect ratio matched well, while fiber orientation and porosity detection had small but notable differences. The differences in the detection capabilities of each technique are quantified and discussed shedding light on the specific advantages and disadvantages of each approach, and enabling engineers to quantify uncertainty in their microstructural characterization measurements especially as they relate to model based structural integrity activities.

Non-destructive testing↗
DOE OSTI2025

Corrosion of 316 L stainless steel under the natural circulation of molten NaCl-MgCl 2 salt

The corrosion behavior of 316 L stainless steel (SS) was studied via the natural circulation of molten eutectic NaCl-MgCl 2 salt through a microloop. The post-corrosion tested 316 L SS microloop sections were characterized with microscopy techniques to determine the microstructural and microchemical changes that occurred at the alloy/salt interface. It was found that 316 L SS showed heterogeneous dissolution at the hot-leg, whereas deposition of corrosion products occurred at the cold-leg. For the first time, experimentally obtained molten salt flow-induced corrosion of 316 L SS results were combined with computational thermodynamic-kinetic models to validate the dissolution and deposition in terms of elemental compositional changes at the alloy/salt interface. The thermodynamic-kinetic modeling predicted that the heterogeneous dissolution of Cr and Fe from the hot-leg section of 316 L SS persisted throughout the salt circulation. The model also estimated that, despite Cr deposition starting earlier than Fe, the total redeposition of Fe is expected to be significantly greater than that of Cr over the circulation of salt. Furthermore, the modeling accurately predicted the subsurface enrichment of Mo which is attributed to the reduced Cr activity and the relatively higher diffusion rate of Mo within the alloy matrix. Here, the agreement between modeling and experimental results confirms that Fe chlorides dissolve at the hot-leg and subsequently deposit at the cold-leg due to activity changes driven by the thermal gradient. By contrast, Cr was not detected in the cold-leg deposits, which is attributed to its weaker temperature dependence on activity, limiting its redeposition under these conditions.

36 - MATERIALS SCIENCE↗
DOE OSTI2022

Adaptive workflow for simulation of RF heaters

Accurate RF (Radio Frequency) simulations of fusion systems like ITER require the definition of high-fidelity analysis geometries that include detailed antenna, reactor wall, and physics regions. Here, this paper will describe a workflow for the execution of adaptive high-performance simulations of RF fusion systems. In this workflow, the simulation input consists of a CAD model attributed with the needed analysis attributes. The analysis mesh is automatically generated and the analysis steps are executed using the time-harmonic Maxwell's equations solved using high-order Nédélec finite elements. A patch recovery-based error estimator is used to drive a conforming mesh adaptation procedure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

ENDFtk: A robust tool for reading and writing ENDF-formatted nuclear data

ENDFtk is a recently developed C++ and Python interface to interact with ENDF-6 formatted nuclear data files. It provides a robust and complete interface, allowing the reading and writing of all formats currently part of the ENDF-6 formats manual, as well as some non-ENDF formats used by the NJOY processing code. It provides an interface that mimics the names in the ENDF-6 formats manual as well as an equivalent interface using human-readable attribute names. It is robust and powerful enogh for nuclear data experts to develop complex applications, while also simple enough to be used non-experts to retrieve and manipulate evaluated nuclear data. ENDFtk offers the ability to easily interrogate and manipulate data either in large-scale code projects or in simple Python scripts. Here, in this paper, a brief overview of the interface is given, as well as more substantial examples demonstrating plotting simple data, interacting with more complex data, and writing new data to files. ENDFtk is open source and available for download via GitHub (https://github.com/njoy/ENDFtk).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Timing and extent of crop damage by wild pigs (Sus scrofa Linnaeus) to corn and peanut fields

The global expansion of wild pigs over the last few decades has resulted in an increase in extent and distribution of damages to crops, placing a growing strain on agricultural producers and land managers. Despite the extent of wild pig damage to agriculture, there is little data regarding timing and spatial variability of damage to corn (Zea mays Linnaeus) and we found no data regarding the effect of these factors on peanuts (Arachis hypogaea Linnaeus). Our objective was to determine the timing and extent of wild pig damage to corn and peanut fields, as well as the extent to which local habitat attributes are useful predictors of damage to these crops. During 2017–2018 we performed ground-based surveys throughout the growing season for 29 corn and 41 peanut fields in South Carolina, USA to determine the most important growth stages for wild pig depredation in both crops. Damage to corn peaked shortly after planting during the seedling stage, fell to nearly zero during V4–V6 stages, and resumed during the silk and mature stages. Peanut damage was almost exclusively limited to the seedling stage. Landscape models for both crops identified the extent of forested and wetland areas surrounding crop fields as the most important attributes positively associated with wild pig damage, while the amount of adjacent agricultural area and paved roads were associated negatively. The number of wild pigs identified by remote cameras also was an important indicator of the extent of damage to peanut fields. Results suggest management efforts to limit crop depredation by wild pigs should be targeted shortly prior to planting. Further, because damage was positively associated with the availability of wetland and forest habitats, our results suggest agricultural damage by wild pigs may be most severe near areas of preferred native habitats.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Soil carbon fractions and biological activity based indices can be used to study the impact of land management and ecological successions

Soil organic carbon (SOC) is a strong indicator of soil health. Development of efficient soil quality indicators is crucial to better understand the impact of land management strategies on the recovery of degraded ecosystems. We hypothesized that SOC fractions and biological attributes can compose strong soil quality indicators to assess an ecosystem recovery following disturbance. Thus, the objective of this study was to evaluate the use of soil biological activity and SOC fractions to study the impact of different land use systems and ecological successions in ecosystem recovery. We selected six land use systems: tobacco (Nicotiana tabacum) cultivation; pastureland; reforested land with Eucalyptus sp.; and natural ecological successions with 10, 20 and 35 years of vegetation regeneration, respectively. We collected disturbed and undisturbed soil samples in triplicate at 0–5, 5–10, 10–20 and 20–40 cm depth intervals. Several fractionation approaches were used to determine SOC pools: hot water extractable organic carbon, permanganate oxidized organic carbon, particulate organic carbon, mineral associated organic carbon and total SOC. The activity of the enzyme arylsulfatase was used to represent soil biological attributes. We calculated three indices to represent the soil quality: carbon management index, soil resilience index and biological activity index. Our results suggest that the SOC fractions and the enzyme activity followed the increase of vegetation complexity of the ecological succession stages. The labile SOC pool, in addition to enzyme activity, was the most sensitive variable to assess land use changes. The biomass-C input was considered to be the main reason of SOC increase, and the gains of labile SOC fractions were directly related to the increase of SOC stocks. Both, biological and carbon management indices were efficient tools to characterize the impact of studied management systems. Also, we found that assessment of deeper soil layers (20–40 cm) was extremely important as incomplete inferences might be reached while evaluating only surface soil layers (0–20 cm). Here, we conclude that the carbon management and biological indices captured the stage of soil degradation and the influence of vegetation diversity in the soil resilience restoration, providing an advance in monitoring strategies that can be reproducible in any environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

OC5 Project Phase II: Validation of Global Loads of the DeepCwind Floating Semisubmersible Wind Turbine

This paper summarizes the findings from Phase II of the Offshore Code Comparison, Collaboration, Continued, with Correlation project. The project is run under the International Energy Agency Wind Research Task 30, and is focused on validating the tools used for modeling offshore wind systems through the comparison of simulated responses of select system designs to physical test data. Validation activities such as these lead to improvement of offshore wind modeling tools, which will enable the development of more innovative and cost-effective offshore wind designs. For Phase II of the project, numerical models of the DeepCwind floating semisubmersible wind system were validated using measurement data from a 1/50th-scale validation campaign performed at the Maritime Research Institute Netherlands offshore wave basin. Validation of the models was performed by comparing the calculated ultimate and fatigue loads for eight different wave-only and combined wind/wave test cases against the measured data, after calibration was performed using free-decay, wind-only, and wave-only tests. The results show a decent estimation of both the ultimate and fatigue loads for the simulated results, but with a fairly consistent underestimation in the tower and upwind mooring line loads that can be attributed to an underestimation of wave-excitation forces outside the linear wave-excitation region, and the presence of broadband frequency excitation in the experimental measurements from wind. Participant results showed varied agreement with the experimental measurements based on the modeling approach used. Modeling attributes that enabled better agreement included: the use of a dynamic mooring model; wave stretching, or some other hydrodynamic modeling approach that excites frequencies outside the linear wave region; nonlinear wave kinematics models; and unsteady aerodynamics models. Also, it was observed that a Morison-only hydrodynamic modeling approach could create excessive pitch excitation and resulting tower loads in some frequency bands.

17 WIND ENERGY↗
DOE OSTI2016

Hollow Silicon Nanospheres Encapsulated with a Thin Carbon Shell: An Electrochemical Study

In this study we have investigated the electrochemical properties of hollow silicon nanospheres encapsulated with a thin carbon shell, HSi@C, as a potential candidate for lithium-ion battery anodes. Hollow Si nanospheres are formed using a templating method which is followed by carbon coating via carbonization of a pyrrole precursor to form HSi@C. The synthesis conditions and the resulting structure of HSi@C have been studied in detail to obtain the target design of hollow Si nanospheres encapsulated with a carbon shell. The HSi@C obtained exhibits much better electrochemical cycle stability than both micro-and nano-size silicon anodes and deliver a stable specific capacity of 700 mA h g(-1) after 100 cycles at a current density of 2 A g(-1) and 800 mA h g(-1) after 120 cycles at a current density of 1 A g(-1). The superior performance of HSi@C is attributed to the synergistic combination of the nanostructured material, the enhanced conductivity, and the presence of the central void space for Si expansion with little or no change in the volume of the entire HSi@C particle. This study is the first detailed investigation of the synthesis conditions to attain the desired structure of a hollow Si core with a conductive carbon shell. This study also offers guidelines to further enhance the specific capacity of HSi@C anodes in the future. (C) 2016 Elsevier Ltd. All rights reserved.

Anode↗
DOE OSTI2017

Simulations of Lithium-Magnetite Electrodes Incorporating Phase Change

In this work, the phase changes occurring in magnetite (Fe 3 O 4 ) during lithiation and voltage recovery experiments are modeled using a model that simulates the electrochemical performance of a Fe 3 O 4 electrode by coupling the lithium transport in the agglomerate and nano-crystal length-scales to thermodynamic and kinetic expressions. Phase changes are described using kinetic expressions based on the Avrami theory for nucleation and growth. Also, simulated results indicate that the slow, linear voltage change observed at long times during the voltage recovery experiments can be attributed to a slow phase change from α-Li x Fe 3 O 4 to β-Li 4 Fe 3 O 4 . In addition, the long voltage plateau at ~1.2 V observed during lithiation of electrodes is attributed to conversion from α-Li x Fe 3 O 4 to γ-(4 Li 2 O + 3 Fe). Simulations for the lithiation of 6 and 32 nm Fe 3 O 4 suggest the rate of conversion to γ-(4 Li 2 O + 3 Fe) decreases with decreasing crystal size.

25 ENERGY STORAGE↗
DOE OSTI2018

Improvement of Li-S battery electrochemical performance with 2D TiS 2 additive

As a multifunctional cathode additive, TiS 2 has been used in Li-S batteries to promote sulfur utilization and improve cell cycle life. The improvement of Li-S cell electrochemical performance in the presence of TiS 2 has been attributed to the ability of TiS 2 in adsorbing polysulfide. To maximize the interaction between polysulfides and TiS 2 , the 2-dimensional (2D) TiS 2 nano-sheets with highly exposed TiS 2 surface area has been successfully synthesized by a two-step sonication synthetic procedure. The 2D TiS 2 particles are micron size in the basal plane direction and are nano-sized along the c-axis direction. 2D TiS 2 nano-sheets may adsorb polysulfides more effectively than the raw TiS 2 powder. Li-S cells with 2D TiS 2 nano-sheets additive exhibit better discharge rate capability and significantly reduced capacity-fading after a long term cell cycling. Here, this improvement can be partially attributed to the uniform distribution of 2D TiS 2 nano-sheets particles within the sulfur cathode matrix, which induces more uniform sulfur and lithium sulfide deposition during the cell charge and discharge.

2D TiS2 nano-sheets↗
DOE OSTI2019

Statistical change detection of building energy consumption: Applications to savings estimation

The surge in interval meter data availability and associated activity in energy data analytics has inspired new interest in advanced methods for building efficiency savings estimation. Statistical and machine learning approaches are being explored to improve the energy baseline models used to measure and verify savings. One outstanding challenge is the ability to identify and account for operational changes that may confound savings estimates. In the measurement and verification (M&V) context, ‘non-routine events’ (NREs) cause changes in building energy use that are not attributable to installed efficiency measures, and not accounted for in the baseline model’s independent variables. In the M&V process NREs must be accounted for as ‘adjustments’ to appropriately attribute the estimated energy savings to the specific efficiency interventions that were implemented. Currently this is a manual and custom process, conducted using professional judgment and engineering expertise. As such it remains a barrier in scaling and standardizing meter-based savings estimation. Here in this work, a data driven methodology was developed to (partially) automate, and therefore streamline the process of detecting NREs in the post-retrofit period and making associated savings adjustments. The proposed NRE detection algorithm is based on a statistical change point detection method and a dissimilarity metric. The dissimilarity metric measures the proximity between the actual time series of the post-retrofit energy consumption and the projected baseline, which is generated using a statistical baseline model. The suggested approach for NRE adjustment involves the NRE detection algorithm, the M&V practitioner, and a regression modeling algorithm. The performance of the detection and adjustment algorithm was evaluated using a simulation-generated test data set, and two benchmark algorithms. Results show a high true positive detection rate (75%-100% across the test cases), higher than ideal false positive detection rates (20%-70%), and low errors in energy adjustment (<0.7%). These results indicate that the algorithm holds for helping M&V practitioners to streamline the process of handling NREs. Moreover, the change point algorithm and underlying statistical principles could prove valuable for other building analytics applications such as anomaly detection and fault diagnostics.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Conditional distribution estimation of building characteristics with diffusion models for urban energy modeling

Understanding current energy consumption behavior in communities is critical for informing future energy use decisions and enabling efficient energy management. Urban energy models, which are used to simulate these energy use patterns, require large datasets with detailed building characteristics for accurate outcomes. However, such detailed characteristics at the individual building level are often unknown and costly to acquire, or unavailable. Through this work, we propose using a generative modeling approach to generate realistic building attributes to fill in the data gaps and finally provide complete characteristics as inputs to energy models. Our model learns complex, building-level patterns from training on a large-scale residential building stock model containing 2.2 million buildings. We employ a tabular diffusion-based framework that is designed to handle heterogeneous (discrete and continuous) features in tabular building data, such as occupancy, floor area, heating, cooling, and other equipment details. We develop a capability for conditional diffusion, enabling the imputation of missing building characteristics conditioned on known attributes. We conduct a comprehensive validation of our conditional diffusion model, firstly by comparing the generated conditional distributions against the underlying data distribution, and secondly, by performing a case study for a Baltimore residential region, showing the practical utility of our approach. Our work is one of the first to demonstrate the potential of generative modeling to accelerate building energy modeling workflows.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

AI-Based Analytics and Energy Modeling Framework for Characterizing Urban Energy Systems

Developing location-specific district energy models is essential for understanding energy patterns and supporting efficient management and planning decisions. However, accurately characterizing these models remains challenging due to gaps in building characteristics and labor-intensive traditional modeling workflows. To address these challenges, we develop an AI-based framework that integrates top-down and bottom-up building energy data to automate urban energy model characterization. The framework trains multimodal deep learning models using heterogeneous ResStockTM datasets to infer missing building characteristics from varying levels of known information and generate simulation-ready inputs for district-scale energy modeling. It also employs a conditioning-based injection approach to generate ”what-if” scenarios, enabling users to explore retrofit, efficiency, and technology-upgrade pathways. Integrated within URBANoptTM, a bottom-up district energy modeling platform for simulating co-located buildings, the framework infers detailed building-level inputs required for bottom-up simulations. Both localized and generalized AI models are developed to learn relationships across categorical, numerical, and time-series data, enabling reconstruction of missing attributes and generation of targeted upgrade scenarios. We demonstrate this methodology on a residential neighborhood in Baltimore, MD, assessing internal consistency against ResStock reference data and URBANopt simulation, and comparing selected attributes against real-world building characteristics. Results show strong overall predictive accuracy in data completion and scenario generation, with localized and generalized models offering complementary trade-offs between precision and scalability. Overall, our automated framework streamlines energy modeling and provides a reliable framework for urban building energy characterization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Uncertainty quantification and propagation in lithium-ion battery electrodes using bayesian convolutional neural networks

The complex nature of manufacturing processes stipulates electrodes to possess high variability with increased heterogeneity during production. X-ray computed tomography imaging has proved to be critical in visualizing the complicated stochastic particle distribution of as-manufactured electrodes in lithium-ion batteries. However, accurate prediction of their electrochemical performance necessitates precise evaluation of kinetic and transport properties from real electrodes. Image segmentation that characterizes voxels to particle/pore phase is often meticulous and fraught with subjectivity owing to a myriad of unconstrained choices and filter algorithms. Here we utilize a Bayesian convolutional neural network to tackle segmentation subjectivity and quantify its pertinent uncertainties. Otsu inter-variance and Blind/Referenceless Imaging Spatial Quality Evaluator are used to assess the relative image quality of grayscale tomograms, thus evaluating the uncertainty in the derived microstructural attributes. We analyze how image uncertainty is correlated with the uncertainties and magnitude of kinetic and transport properties of an electrode, further identifying pathways of uncertainty propagation within microstructural attributes. The coupled effect of spatial heterogeneity and microstructural anisotropy on the uncertainty quantification of transport parameters is also understood. This work demonstrates a novel methodology to extract microstructural descriptors from real electrode images through quantification of associated uncertainties and discerning the relative strength of their propagation, thus facilitating feedback to manufacturing processes from accurate image based electrochemical simulations.

25 ENERGY STORAGE↗
DOE OSTI2017

The acute effects of ultraviolet radiation on the blood transcriptome are independent of plasma 25OHD{sub 3}

The molecular basis of many health outcomes attributed to solar ultraviolet radiation (UVR) is unknown. We tested the hypothesis that they may originate from transcriptional changes in blood cells. This was determined by assessing the effect of fluorescent solar simulated radiation (FSSR) on the transcriptional profile of peripheral blood pre- and 6 h, 24 h and 48 h post-exposure in nine healthy volunteers. Expression of 20 genes was down-regulated and one was up-regulated at 6 h after FSSR. All recovered to baseline expression at 24 h or 48 h. These genes have been associated with immune regulation, cancer and blood pressure; health effects attributed to vitamin D via solar UVR exposure. Plasma 25-hydroxyvitamin D{sub 3} [25OHD{sub 3}] levels increased over time after FSSR and were maximal at 48 h. The increase was more pronounced in participants with low basal 25OHD{sub 3} levels. Mediation analyses suggested that changes in gene expression due to FSSR were independent of 25OHD{sub 3} and blood cell subpopulations. - Highlights: • Exposure to UVR induced transient changes in gene expression in blood cells. • Affected genes are related to immune regulation, cancer and blood pressure. • UVR increased 25OHD{sub 3} over time, especially in participants with low baseline levels. • The acute effects of UVR on the blood transcriptome are independent of 25OHD{sub 3}.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Maternal and paternal serum concentrations of persistent organic pollutants and the secondary sex ratio: A population-based preconception cohort study

Highlights: • Recent declines in the SSR in some countries may be attributed to exposure to POPs. • Maternal PCB 128 and paternal HCB were associated with a female excess. • Maternal mirex and paternal PCB 128 and p,p′-DDE were associated with a male excess. • These findings await corroboration given the exploratory design of this study. Recent declines in the secondary sex ratio (SSR), defined as the ratio of males to females at birth, in some industrialized countries may be attributed to exposure to environmental toxicants such as persistent organic pollutants (POPs). This study aimed to evaluate the association of couples’ preconception exposure to POPs with the SSR. The study cohort comprised 235 couples who were enrolled in the Longitudinal Investigation of Fertility and the Environment (LIFE) Study between 2005 and 2009 prior to conception and prospectively followed through delivery of a singleton birth. Upon enrollment, couples’ serum concentrations (ng/g) were measured for 9 organochlorine pesticides, 1 polybrominated biphenyl, 10 polybrominated diphenyl ethers, and 36 polychlorinated biphenyls (PCBs). Birth outcome data including infant sex were collected upon delivery. Modified Poisson regression models were used to estimate the relative risks (RRs) and 95% confidence intervals (CIs) of a male birth for each chemical. Of the 56 POPs examined, maternal PCB 128 and paternal hexachlorobenzene were significantly associated with a female excess (RRs, 0.75 [95% CI, 0.60–0.94] and 0.81 [95% CI, 0.68–0.97] per 1 SD increase in log-transformed serum chemical concentrations, respectively), whereas maternal mirex and paternal PCB 128 and p,p′-dichlorodiphenyldichloroethylene were significantly associated with a male excess (RR range, 1.10–1.22 per 1 SD increase in log-transformed serum chemical concentrations). After adjusting for multiple comparisons, only maternal mirex remained significantly associated with the SSR. This exploratory study on multiple classes of POPs demonstrated no conclusive evidence on the association between parental preconception exposure to POPs and the SSR.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

The association between ambient temperature and mortality in South Africa: A time-series analysis

Highlights: • Country-wide epidemiology study of temperature-mortality association in S. Africa. • Strongest associations were in children/elderly and from cardiorespiratory causes. • Total attributable mortality was 3.4%, mostly from cold (3.0%) rather than heat (0.4%). • Attributable mortality of 3.4% is on the low end of what is reported in other areas. • Results can help estimate future burdens from climate change and inform adaptation. There is an extensive literature describing temperature-mortality associations in developed regions, but research from developing countries, and Africa in particular, is limited.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Projecting future climate change impacts on heat-related mortality in large urban areas in China

Highlights: • The study projects future heat-related mortality risk in 51 major cities in China. • The projections are based on high resolution climate and population projections. • Future heat mortality risk is likely to be considerable under all emission scenarios. • Climate mitigation and heat risk management are needed to reduce such risk. Global climate change is anticipated to raise overall temperatures and has the potential to increase future mortality attributable to heat. Urban areas are particularly vulnerable to heat because of high concentrations of susceptible people. As the world's largest developing country, China has experienced noticeable changes in climate, partially evidenced by frequent occurrence of extreme heat in urban areas, which could expose millions of residents to summer heat stress that may result in increased health risk, including mortality. While there is a growing literature on future impacts of extreme temperatures on public health, projecting changes in future health outcomes associated with climate warming remains challenging and underexplored, particularly in developing countries. This is an exploratory study aimed at projecting future heat-related mortality risk in major urban areas in China. We focus on the 51 largest Chinese cities that include about one third of the total population in China, and project the potential changes in heat-related mortality based on 19 different global-scale climate models and three Representative Concentration Pathways (RCPs). City-specific risk estimates for high temperature and all-cause mortality were used to estimate annual heat-related mortality over two future twenty-year time periods. We estimated that for the 20-year period in Mid-21st century (2041–2060) relative to 1970–2000, incidence of excess heat-related mortality in the 51 cities to be approximately 37,800 (95% CI: 31,300–43,500), 31,700 (95% CI: 26,200–36,600) and 25,800 (95% CI: 21,300–29,800) deaths per year under RCP8.5, RCP4.5 and RCP2.6, respectively. Slowing climate change through the most stringent emission control scenario RCP2.6, relative to RCP8.5, was estimated to avoid 12,900 (95% CI: 10,800–14,800) deaths per year in the 51 cities in the 2050s, and 35,100 (95% CI: 29,200–40,100) deaths per year in the 2070s. The highest mortality risk is primarily in cities located in the North, East and Central regions of China. Population adaptation to heat is likely to reduce excess heat mortality, but the extent of adaptation is still unclear. Future heat mortality risk attributable to exposure to elevated warm season temperature is likely to be considerable in China's urban centers, with substantial geographic variations. Climate mitigation and heat risk management are needed to reduce such risk and produce substantial public health benefits.

54 ENVIRONMENTAL SCIENCES↗