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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Large Divergence of Projected High Latitude Vegetation Composition and Productivity Due To Functional Trait Uncertainty

Abstract Vegetation distribution and composition are expected to change in northern high latitudes under rapid warming, which regulates ecosystem functions but remains challenging to predict. Vegetation change arises from the interplay of chronic climate trends such as warming and transient demographic processes of recruitment, growth, competition, and mortality. Most predictive models overlooked the role of demographic dynamics controlled by plant traits. Here, we simulate vegetation dynamics at the Kougarok Hillslope site in Alaska under historical and future climates using the E3SM Land Model coupled to the Functionally Assembled Terrestrial Simulator (ELM‐FATES). To evaluate the roles of plant traits, we parameterize the model with 5,265 trait configurations representing diverse physiological and demographic strategies. Results show current modeled biomass, composition, and productivity are most sensitive to traits controlling photosynthetic capacity, carbon allocation, allometry, and phenology. Among all trait configurations, ∼5% reproduce in situ biomass and plant functional type (PFT) composition measured in 2016, that are indistinguishable from these two observed ecosystem states. Notably, these same trait configurations produce diverging biomass, composition, and productivity under future climate, where the uncertainty attributable to traits is twice the change attributable to climate change. The variation of projected productivity arises from emerging PFT composition under novel climate regimes, primarily explained by traits controlling cold‐induced mortality, recruitment, and allometry. Our findings highlight the importance and uncertainty of demographic dynamics and its interaction with climate change in shaping Arctic vegetation change. Improved model predictions will likely benefit from explicit consideration of vegetation demography and better constraints of critical traits.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Enhancing the physical modeling capability of open-source MFIX-DEM software for handling particle size polydispersity: Implementation and validation

Multiphase flows are ubiquitous in many industrial processes. The inherent coupling of different phases poses many unique challenges in predicting and effectively controlling these processes. Hence, computational modeling and simulation offers a viable approach to overcome these challenges. Here in this study, we present recent development efforts for enhancing the physical modeling capabilities of an open-source computational modeling tool for real life industrial multiphase processes by enabling particle-size polydispersity and demonstrating with an associated validation study. The proposed implementation was performed in MFIX open-source framework due to its unique feature of tightly integrated computational fluid dynamics and discrete element method solvers for simulating coupled continuum fluid and granular flows. We have implemented the polydispersity feature in a minimally invasive way and provided means to allow easy specification of an arbitrary particle size distribution function, which also enables the user to easily handle an arbitrary number of solid phases, each possessing a distinct arbitrary particle-size distribution. To establish the credibility of improvements, we have carried out a preliminary verification and validation (V&V) study for the polydispersity feature by employing a hopper bin discharge problem, which is frequently encountered in industrial applications. Specifically, two types of micro-glass beads with distinct size distributions are used to fill the hopper in two possible packing arrangements, i.e., well-mixed and layered configurations, with varying mass (particle number) ratios. The experimentally obtained discharge dynamics (e.g., normalized discharge mass fraction for one of the phases versus the overall discharge mass fraction) for different systems is found to be in excellent agreement with the corresponding simulation results.

42 ENGINEERING↗
DOE OSTI2011

SLUDGE BATCH 7B GLASS VARIABILITY STUDY

The Defense Waste Processing Facility (DWPF) is preparing to initiate processing Sludge Batch 7b (SB7b). In support of the upcoming processing, the Savannah River National Laboratory (SRNL) provided a recommendation to utilize Frits 418 with a 6% Na{sub 2}O addition (26 wt% Na{sub 2}O in sludge) and 702 with a 4% Na{sub 2}O addition (24 wt% Na{sub 2}O in sludge) to process SB7b. This recommendation was based on assessments of the compositional projections for SB7b available at the time from the Savannah River Remediation (SRR). To support qualification of SB7b, SRNL executed a variability study to assess the applicability of the current durability models for SB7b. The durability models were assessed over the expected composition range of SB7b, including potential caustic additions, combined with Frits 702 and 418 over a 32-40% waste loading (WL) range. Thirty four glasses were selected based on Frits 418 and 702 coupled with the sludge projections with an additional 4-6% Na{sub 2}O to reflect the potential caustic addition. Six of these glasses, based on average nominal sludge compositions including the appropriate caustic addition, were developed for both Frit 418 and Frit 702 at 32, 36 and 40% WL to provide coverage in the center of the anticipated SB7b glass region. All glasses were fabricated and characterized using chemical composition analysis, X-ray diffraction (XRD) and the Product Consistency Test (PCT). To comply with the DWPF Glass Product Control Program, a total of thirty four glasses were fabricated to assess the applicability of the current DWPF PCCS durability models. Based on the measured PCT response, all of the glasses were acceptable with respect to the Environmental Assessment (EA) benchmark glass regardless of thermal history. The NL[B] values of the SB7b variability study glasses were less than 1.99 g/L as compared to 16.695 g/L for EA. A small number of the D-optimally selected 'outer layer' extreme vertices (EV) glasses were not predictable using the current Product Composition Control System (PCCS) models for durability, but were acceptable compared to the EA glass when tested. These glasses fell outside of the lower 95% confidence band, which demonstrates conservatism in the model. A few of the glasses fell outside of the upper 95% confidence band; however, these particular glasses have normalized release values that were much lower than the values of EA and should be of no practical concern. Per the requirements of the DWPF Glass Product Control Program, the PCCS durability models have been shown to be applicable to the SB7b sludge system with a range of Na{sub 2}O concentrations blended with Frits 418 or 702. PCT results from the glasses fabricated as part of the variability study were shown to be predictable by the current DWPF PCCS models and/or acceptable with respect to the EA benchmark glass regardless of thermal history or compositional view.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Occupancy-Based Controls for an All-Electric Residential Community in a Cold Climate

In residential buildings, rapid improvements in sensors, communication, and information technology have enabled occupancy-based building controls. These controls utilize occupancy information and modify the operation of the heating, ventilation, and air-conditioning (HVAC) system to minimize excess HVAC energy use, especially when the building is unoccupied. This reduces the total building energy consumption and utility bills while maintaining thermal comfort. In this paper, we present two novel occupancy-driven controls - reactive control and predictive control - and compare their performance. We model an all-electric residential community based on a 27- home community in Basalt, Colorado, in the United States. We simulated various scenarios, considering different temperature setback and control algorithms, to analyze the community-scale impact of these occupancy-based controls. The results show that total HVAC energy savings in a building ranges from 1%- 20% compared to the baseline scenario without occupancy-based controls. The energy-saving potential is highly correlated with the occupancy pattern and temperature setback in the building.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗
DOE OSTI2022

Occupancy-Based Controls for an All-Electric Residential Community in a Cold Climate: Preprint

In residential buildings, rapid improvements in sensors, communication, and information technology have enabled occupancy-based building controls. These controls utilize occupancy information and modify the operation of the heating, ventilation, and air-conditioning (HVAC) system to minimize excess HVAC energy use, especially when the building is unoccupied. This reduces the total building energy consumption and utility bills while maintaining thermal comfort. In this paper, we present two novel occupancy-driven controls - reactive control and predictive control - and compare their performance. We model an all-electric residential community based on a 27- home community in Basalt, Colorado, in the United States. We simulated various scenarios, considering different temperature setback and control algorithms, to analyze the community-scale impact of these occupancy-based controls. The results show that total HVAC energy savings in a building ranges from 1%- 20% compared to the baseline scenario without occupancy-based controls. The energy-saving potential is highly correlated with the occupancy pattern and temperature setback in the building.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗
DOE OSTI2011

Research Needs and Impacts in Predictive Simulation for Internal Combustion Engines (PreSICE)

This report is based on a SC/EERE Workshop to Identify Research Needs and Impacts in Predictive Simulation for Internal Combustion Engines (PreSICE), held March 3, 2011, to determine strategic focus areas that will accelerate innovation in engine design to meet national goals in transportation efficiency. The U.S. has reached a pivotal moment when pressures of energy security, climate change, and economic competitiveness converge. Oil prices remain volatile and have exceeded $100 per barrel twice in five years. At these prices, the U.S. spends $1 billion per day on imported oil to meet our energy demands. Because the transportation sector accounts for two-thirds of our petroleum use, energy security is deeply entangled with our transportation needs. At the same time, transportation produces one-quarter of the nation’s carbon dioxide output. Increasing the efficiency of internal combustion engines is a technologically proven and cost-effective approach to dramatically improving the fuel economy of the nation’s fleet of vehicles in the near- to mid-term, with the corresponding benefits of reducing our dependence on foreign oil and reducing carbon emissions. Because of their relatively low cost, high performance, and ability to utilize renewable fuels, internal combustion engines—including those in hybrid vehicles—will continue to be critical to our transportation infrastructure for decades. Achievable advances in engine technology can improve the fuel economy of automobiles by over 50% and trucks by over 30%. Achieving these goals will require the transportation sector to compress its product development cycle for cleaner, more efficient engine technologies by 50% while simultaneously exploring innovative design space. Concurrently, fuels will also be evolving, adding another layer of complexity and further highlighting the need for efficient product development cycles. Current design processes, using “build and test” prototype engineering, will not suffice. Current market penetration of new engine technologies is simply too slow—it must be dramatically accelerated. These challenges present a unique opportunity to marshal U.S. leadership in science-based simulation to develop predictive computational design tools for use by the transportation industry. The use of predictive simulation tools for enhancing combustion engine performance will shrink engine development timescales, accelerate time to market, and reduce development costs, while ensuring the timely achievement of energy security and emissions targets and enhancing U.S. industrial competitiveness. In 2007 Cummins achieved a milestone in engine design by bringing a diesel engine to market solely with computer modeling and analysis tools. The only testing was after the fact to confirm performance. Cummins achieved a reduction in development time and cost. As important, they realized a more robust design, improved fuel economy, and met all environmental and customer constraints. This important first step demonstrates the potential for computational engine design. But, the daunting complexity of engine combustion and the revolutionary increases in efficiency needed require the development of simulation codes and computation platforms far more advanced than those available today. Based on these needs, a Workshop to Identify Research Needs and Impacts in Predictive Simulation for Internal Combustion Engines (PreSICE) convened over 60 U.S. leaders in the engine combustion field from industry, academia, and national laboratories to focus on two critical areas of advanced simulation, as identified by the U.S. automotive and engine industries. First, modern engines require precise control of the injection of a broad variety of fuels that is far more subtle than achievable to date and that can be obtained only through predictive modeling and simulation. Second, the simulation, understanding, and control of these stochastic in-cylinder combustion processes lie on the critical path to realizing more efficient engines with greater power density. Fuel sprays set the initial conditions for combustion in essentially all future transportation engines; yet today designers primarily use empirical methods that limit the efficiency achievable. Three primary spray topics were identified as focus areas in the workshop: The fuel delivery system, which includes fuel manifolds and internal injector flow, The multi-phase fuel–air mixing in the combustion chamber of the engine, and The heat transfer and fluid interactions with cylinder walls. Current understanding and modeling capability of stochastic processes in engines remains limited and prevents designers from achieving significantly higher fuel economy. To improve this situation, the workshop participants identified three focus areas for stochastic processes: Improve fundamental understanding that will help to establish and characterize the physical causes of stochastic events, Develop physics-based simulation models that are accurate and sensitive enough to capture performance-limiting variability, and Quantify and manage uncertainty in model parameters and boundary conditions. Improved models and understanding in these areas will allow designers to develop engines with reduced design margins and that operate reliably in more efficient regimes. All of these areas require improved basic understanding, high-fidelity model development, and rigorous model validation. These advances will greatly reduce the uncertainties in current models and improve understanding of sprays and fuel–air mixture preparation that limit the investigation and development of advanced combustion technologies. The two strategic focus areas have distinctive characteristics but are inherently coupled. Coordinated activities in basic experiments, fundamental simulations, and engineering-level model development and validation can be used to successfully address all of the topics identified in the PreSICE workshop. The outcome will be: New and deeper understanding of the relevant fundamental physical and chemical processes in advanced combustion technologies, Implementation of this understanding into models and simulation tools appropriate for both exploration and design, and Sufficient validation with uncertainty quantification to provide confidence in the simulation results. These outcomes will provide the design tools for industry to reduce development time by up to 30% and improve engine efficiencies by 30% to 50%. The improved efficiencies applied to the national mix of transportation applications have the potential to save over 5 million barrels of oil per day, a current cost savings of $500 million per day.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2013

Metering Best Practices Applied in the National Renewable Energy Laboratory's Research Support Facility: A Primer to the 2011 Measured and Modeled Energy Consumption Datasets

Modern buildings are complex energy systems that must be controlled for energy efficiency. The Research Support Facility (RSF) at the National Renewable Energy Laboratory (NREL) has hundreds of controllers -- computers that communicate with the building's various control systems -- to control the building based on tens of thousands of variables and sensor points. These control strategies were designed for the RSF's systems to efficiently support research activities. Many events that affect energy use cannot be reliably predicted, but certain decisions (such as control strategies) must be made ahead of time. NREL researchers modeled the RSF systems to predict how they might perform. They then monitor these systems to understand how they are actually performing and reacting to the dynamic conditions of weather, occupancy, and maintenance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Metering Best Practices Applied in the National Renewable Energy Laboratory's Research Support Facility: A Primer to the 2011 Measured and Modeled Energy Consumption Datasets

Modern buildings are complex energy systems that must be controlled for energy efficiency. The Research Support Facility (RSF) at the National Renewable Energy Laboratory (NREL) has hundreds of controllers -- computers that communicate with the building's various control systems -- to control the building based on tens of thousands of variables and sensor points. These control strategies were designed for the RSF's systems to efficiently support research activities. Many events that affect energy use cannot be reliably predicted, but certain decisions (such as control strategies) must be made ahead of time. NREL researchers modeled the RSF systems to predict how they might perform. They then monitor these systems to understand how they are actually performing and reacting to the dynamic conditions of weather, occupancy, and maintenance.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Modelling Heterogeneity in Cellulose Properties Predicts the Slowdown Phenomenon during Enzymatic Hydrolysis

Fine control of the enzymatic hydrolysis of cellulose is challenging due to complex process dynamics. Conversion rate from cellulose to valuable monomeric products is often significantly slowed down after an initial rapid but short-lived phase. Underlying mechanisms for this process have yet to be fully understood and as a result poorly represented in existing models. Here, we propose a new modelling platform termed Multi-Layered Population Balance Model (ML-PBM), which enables various key aspects of cellulose enzymatic hydrolysis occurring over the entire breakdown process to be captured. As a core component for predicting the slowdown phenomenon, the ML-PBM properly accounts for heterogeneity in cellulose crystallinity and chain lengths across the structural layers of cellulose particles with different morphologies. Beyond a decent quantitative fit to highly nonlinear dynamic experimental data collected across different conditions, the ML-PBM reveals that the rate slowdown phenomenon is potentially due to heterogeneity in cellulose properties coupled with cellulose morphology. Equipped with the unification of various process fundamentals, the ML-PBM is a rational framework with the potential to facilitate sound cellulose engineering.

Ahamed, Firnaaz↗
DOE OSTI2018

Natural and Hydraulic Fracture Density Prediction and Identification of Controllers

Discrete Fracture Networks (DFN’s) incorporated into hydraulic fracture modeling and reservoir simulation are typically constructed and calibrated to all available high-quality natural fracture data from image logs and core, which generally results in an extremely limited calibration data set. To extrapolate these data over large areas, more broadly sampled data sets, such as discontinuity-related 3-D seismic attributes are often used. Broad spatial trending methodologies can potentially misrepresent natural fracture systems through over-reliance on seismic attributes that are commonly influenced by noise. The Hydraulic Fracture Test Site (HFTS) provides a rare insight of the subsurface natural fracture network and controlling factors on fracture distribution from a mechanical and lithological standpoint. The physical occurrence of hydraulic fractures and their interaction and relationship to preexisting natural fractures can be predicted using analytical models. Such model outputs can be applied to provide higher confidence when developing DFN’s.

Campbell, Whitney↗
DOE OSTI2019

Enhancing the Implementation of a First-order Equivalent Thermal Parameter Model to Enable Accurate and Robust Building Thermal Response Prediction

Predicting indoor temperature conditions are often necessary for advanced building controls. Gray-box models are preferable to do so because they can over- come limitations of white-box and black-box models; however, their current implementation methods can be problematic due to their difficulties in capturing non-linear behaviors and time-varying disturbances. This paper proposes a novel method to implement a popular gray-box model, the 1st-order equivalent thermal parameter (ETP) model. To evaluate performance, we applied the proposed method to predict in- door temperatures for a medium-office building, over the course of one day. Predictions are compared against two existing methods: the ETP model implemented through a common approach and a black-box model. Results suggest that the proposed method can yield more accurate results compared to the other methods; additionally, the proposed method demonstrated similar capabilities in capturing non-linear behaviors as the black-box model.

Hinkelman, Kathryn↗
DOE OSTI2016

Metering Best Practices Applied in the National Renewable Energy Laboratory's Research Support Facility: A Primer to the 2011 Measured and Modeled Energy Consumption Datasets

Modern buildings are complex energy systems that must be controlled for energy efficiency. The Research Support Facility (RSF) at the National Renewable Energy Laboratory (NREL) has hundreds of controllers -- computers that communicate with the building's various control systems -- to control the building based on tens of thousands of variables and sensor points. These control strategies were designed for the RSF's systems to efficiently support research activities. Many events that affect energy use cannot be reliably predicted, but certain decisions (such as control strategies) must be made ahead of time. NREL researchers modeled the RSF systems to predict how they might perform. They then monitor these systems to understand how they are actually performing and reacting to the dynamic conditions of weather, occupancy, and maintenance.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Extending Classification Algorithms to Case-Control Studies

Classification is a common technique applied to ’omics data to build predictive models and identify potential markers of biomedical outcomes. Despite the prevalence of case-control studies, the number of classification methods available to analyze data generated by such studies is extremely limited. Conditional logistic regression is the most commonly used technique, but the associated modeling assumptions limit its ability to identify a large class of sufficiently complicated ’omic signatures. We propose a data preprocessing step which generalizes and makes any linear or nonlinear classification algorithm, even those typically not appropriate for matched design data, available to be used to model case-control data and identify relevant biomarkers in these study designs. We demonstrate on simulated case-control data that both the classification and variable selection accuracy of each method is improved after applying this processing step and that the proposed methods are comparable to or outperform existing variable selection methods. Finally, we demonstrate the impact of conditional classification algorithms on a large cohort study of children with islet autoimmunity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Reduction Kinetics of Perovskite Oxides for Selective Hydrogen Combustion in the Context of Olefin Production

Chemical looping represents a novel approach for generating light olefins in which thermal cracking or catalytic dehydrogenation is coupled with selective hydrogen combustion (SHC) by a metal oxide redox catalyst, which enables autothermal operation, increased per-pass conversion, and greater-than-equilibrium yields. Recent studies indicate that Na 2 WO 4 -promoted perovskite oxides are effective redox catalysts with high olefin selectivity. Herein, kinetic parameters, rates, and reaction models for the reduction of unpromoted and Na 2 WO 4 -promoted CaMnO 3 redox catalysts by H 2 , C 2 H 4 , and C 2 H 6 , is reported. Reduction rates of CaMnO 3 under ethylene and ethane are significantly lower than under H 2 . Additionally, model fitting of reduction kinetics show good agreement with reaction order–controlled models for CaMnO 3 reduction and predict greater oxygen site dependence and higher activation energy for CaMnO 3 reduction by C 2 H 4 as compared with H 2 . Avrami–Erofe'ev nucleation and growth models provide the best fit to the reduction of Na 2 WO 4 /CaMnO 3 in H 2 and in C 2 H 4 . After Na 2 WO 4 promotion, the reduction rate of CaMnO 3 is three orders of magnitude lower in ethylene in comparison to hydrogen, consistent with its superior selectivity to hydrogen combustion. The models developed can be applied toward reactor design and optimization in the context of enhanced olefin production via SHC under a cyclic redox scheme.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Long distance effects in inclusive rare B decays and phenomenology of $ \overline{B} $→ Xdℓ + ℓ -

Rare inclusive B decays such as $ \overline{B} $ → Xs(d)ℓ + ℓ - are interesting probes for physics beyond the Standard Model. Due to the complementarity to their exclusive counterparts, they might shed light on the anomalies currently seen in exclusive b → s transitions. Distinguishing new-physics effects from the Standard Model requires precise predictions and necessitates the control of long distance effects. In the present work we revisit and improve the description of various long distance effects in inclusive decays such as charmonium and light-quark resonances, nonfactorizable power corrections, and cascade decays. We then apply these results to a state-of-the-art phenomenological study of $ \overline{B} $ → Xdℓ + ℓ - , including also logarithmically enhanced QED corrections and the recently calculated fivebody contributions. To fully exploit the new-physics potential of inclusive flavour-changing neutral current decays, the $ \overline{B} $ → Xdℓ + ℓ - observables should be measured in a dedicated Belle II analysis.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

National soil organic carbon estimates can improve global estimates

Global inventories of spatial and vertical distribution of soil organic carbon (SOC) stocks are being used in national and global initiatives targeted to mitigate climate change and land degradation impacts. Yet, national level high-resolution estimates of SOC stocks can be useful for improving the accuracy of global SOC inventories. We estimated spatially resolved SOC stocks of surface 0–30 cm and subsurface 30–100 cm layers at a spatial resolution of 30m in tropical Island, Sri Lanka using a legacy harmonized soil database of 122 soil profiles. The national estimates were compared with two global estimates derived from WISE30sec and SoilGrids250m. The tropical Island (land area=64,610 km2) occupying 0.03% of global land area showed a considerable heterogeneity in SOC stocks ranging from 2.0-342.5 Mg ha -1 and 2.7-391.7 Mg ha -1 in the surface and subsurface soil layers, respectively. We found, elevation, precipitation and slope angle as main environmental controllers of the spatial distribution of SOC stocks under tropical climate. Incorporating the pedogenic information (derived from soil series level legacy map, soil orders and suborders) with environmental controllers resulted in better regression models of predicting surface (R 2 =0.61) and subsurface (R 2 =0.81) SOC stocks. Geographically weighted regression kriging derived maps of SOC stocks revealed that 0–100 cm soil layer of the tropical Island stored 500 Tg C contributing for 0.04% of the global SOC stocks. The validation results of our estimates showed low Mean Estimation Error (MEE: surface -1.6 and subsurface -1.6 Mg ha -1 ) and Root Mean Square Error (RMSE: surface 29.5 and subsurface 24.9 Mg ha -1 ) indicating a low bias and satisfactory predictions. The relative improvement of the prediction accuracy of the SOC stocks of our geospatial estimates in the 0–30 cm layer in comparison to SoilGrids250m and WISE30sec data derived SOC stocks were 51.7% and 35.2%, respectively. The SOC stocks predictions of the 30-100 cm soil layer showed even better relative improvement compared to SoilGrids250m (78.4%) and WISE30sec (57.4%) SOC estimates. Compared to estimates of total SOC stocks resulted in this study, WISE30sec data derived SOC stock maps showed 30% over estimation of the C stock in surface 0-30 cm (332 Tg C) and 41% overestimation in 30-100 cm layer (343 Tg C). The over estimation of total SOC stocks by the SoilGrids250 SOC stocks map for the surface 0-30 cm layer was 122% (567 Tg C) and for the 30-100 cm layer it was 209% (750 Tg C). We conclude that the fusion of legacy soil information of SOC stocks with appropriate environmental covariates and pedogenic information derived from legacy area-class soil maps at national level can produce more accurate inventories of spatial and vertical distribution of SOC stocks. As a result, these national inventories have a great potential of upgrading global inventories of SOC stocks.

54 ENVIRONMENTAL SCIENCES↗