Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “consumption”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,207 records · Page 67

Current inequality and future potential of US urban tree cover for reducing heat-related health impacts

Excessive heat is a major and growing risk for urban residents. Here, we estimate the inequality in summertime heat-related mortality, morbidity, and electricity consumption across 5723 US municipalities and other places, housing 180 million people during the 2020 census. On average, trees in majority non-Hispanic white neighborhoods cool the air by 0.19 ± 0.05 °C more than in POC neighborhoods, leading annually to trees in white neighborhoods helping prevent 190 ± 139 more deaths, 30,131 ± 10,406 more doctors’ visits, and 1.4 ± 0.5 terawatt-hours (TWhr) more electricity consumption than in POC neighborhoods. We estimate that an ambitious reforestation program would require 1.2 billion trees and reduce population-weighted average summer temperatures by an additional 0.38 ± 0.01 °C. This temperature reduction would reduce annual heat-related mortality by an additional 464 ± 89 people, annual heat-related morbidity by 80,785 ± 6110 cases, and annual electricity consumption by 4.3 ± 0.2 TWhr, while increasing annual carbon sequestration in trees by 23.7 ± 1.2 MtCO 2 e yr -1 and decreasing annual electricity-related GHG emissions by 2.1 ± 0.2 MtCO 2 e yr -1 . The total economic value of these benefits, including the value of carbon sequestration and avoided emissions, would be USD 9.6 ± 0.5 billion, although in many neighborhoods the cost of planting and maintaining trees to achieve this increased tree cover would exceed these benefits. The exception is areas that currently have less tree cover, often the majority POC, which tend to have a relatively high return on investment from tree planting.

54 ENVIRONMENTAL SCIENCES↗

Comparison of multi-stage air treatment process divided by the same temperature and enthalpy difference

The multi-stage air treatment system has been proposed recently, and lower grade chilled/hot water could be used and energy efficiency could be improved. However, it has not been studied which division method of air treatment processes has higher energy efficiency. In this study, the model to calculate the energy consumption of multi-stage air treatment process is introduced, and the effects of two division methods, i.e. multi-stage air treatment process divided by the same temperature difference (ST method) or same enthalpy difference (SE method) between inlet and outlet at each stage, under 9 different air inlet parameters in the 2-stage and 3-stage air treatment processes are analysed and compared. The results show that (1) the system energy consumption of the SE method is generally lower than that of the ST method; (2) there is generally a larger energy consumption reduction rate of SE method when the air relative humidity is 70% compared to relative humidity of 50% and 90%; (3) the difference between ST method and SE method is not great, so both methods can be used for the design of multi-stage treatment system although SE method is normally recommended.

Wang, Wentao↗

Small-Scale Irrigation: Improving Food Security under Changing Climate and Water Resource Conditions in Ethiopia

We develop a new systems modeling tool that integrates knowledge from hydrology, agriculture, and economics to understand the effect of small-scale irrigation on food security and groundwater sustainability in Ethiopia. Irrigation is an effective tool to mitigate climate impacts and improve agricultural yields. Small-scale irrigation, such as decentralized groundwater irrigation, is well suited for developing countries where smallholder farming communities are widely dispersed and can only afford small infrastructure investment. We study the underlying interdependencies between food and water systems in Ethiopia, where small-holder agriculture is the foundation of the nation’s economy and climate variability has led to great challenges to its food security. Our coupled market and crop model with groundwater module captures the interdependencies of climate, water availability (including irrigation), crop yield, farmland allocation, crop production, transport and consumption based on a system approach across multiple spatial scales. We study the implication of small-scale irrigation to Ethiopia’s food security and water resource conditions as a “what-if” question by comparing an irrigation scenario to the calibrated baseline in 2015, a year of significant drought and crop failure over a large portion of Ethiopia. Our model offers fresh insights into geographic disparities in outcomes that are driven by baseline climate variability, soil fertility, and market conditions. In general, we find that small-scale irrigation can potentially improve food security through increases in food consumption, but it requires policy support to direct the increases of production to domestic consumption while maintaining a sustainable groundwater condition. By using Ethiopia as an example, we show the strength of our model to study how water infrastructure resources support critical functions and service in water and food systems.

Zhang, Ying↗

Note: Effect of hot liner in producing {sup 40,48}Ca beam from RIKEN 18-GHz electron cyclotron resonance ion source

In order to produce a high-intensity and stable {sup 48}Ca beam from the RIKEN 18-GHz electron cyclotron resonance ion source, we have begun testing the production of a calcium beam using a micro-oven. To minimize the consumption rate of the material ({sup 48}Ca), we introduced the “hot liner” method and investigated the effect of the liner on the material consumption rate. The micro-oven was first used to produce the {sup 48}Ca beam for experiments in the RIKEN radioisotope beam factory, and a stable beam could be supplied for a long time with low consumption rate.

07 ISOTOPE AND RADIATION SOURCES↗

A numerical study of the effect of various reactions, pressure and gas mixture ratio on the density distribution of etchant species (H, Br, Br{sup +}, and HBr{sup +}) in HBr/He plasma

In this study, a fluid model has been used to study the effect of gas mixing ratio and pressure on the density distribution of important etchant species, i.e., hydrogen (H), bromine (Br), Br{sup +}, and HBr{sup +} in HBr/He plasma. Our simulation results show that the densities of active etchant species H, Br, and HBr{sup +} increase with the increase in pressure as well as the HBr fraction in HBr/He mixture. On the contrary, the density of Br{sup +} decreases with the increase in He percentage in HBr/He mixture and with the increase in the pressure. Time averaged reaction rates (of the reactions involved in the production and consumption of these species) have been calculated to study the effect of these reactions on the density distribution of these species. The spatial distribution of these species is explained with the help of the time averaged reaction rates. Important reactions have been identified that contribute considerably to the production and consumption of these active species. The code has been optimized by identifying 26 reactions (out of 40 reactions which contribute in the production and consumption of these species) that have insignificant effect on the densities of H, Br, Br{sup +}, and HBr{sup +}. This shows that out of 40 reactions, only 14 reactions can be used to calculate the density and distribution of the important species in HBr/He plasma discharge.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Climate change effects on agriculture: Economic responses to biophysical shocks

Agricultural production is sensitive to weather and will thus be directly affected by climate change. Plausible estimates of these climate change impacts require combined use of climate, crop, and economic models. Results from previous studies vary substantially due to differences in models, scenarios, and data. This paper is part of a collective effort to systematically integrate these three types of models. We focus on the economic component of the assessment, investigating how nine global economic models of agriculture represent endogenous responses to seven standardized climate change scenarios produced by two climate and five crop models. These responses include adjustments in yields, area, consumption, and international trade. We apply biophysical shocks derived from the IPCC’s Representative Concentration Pathway that result in end-of-century radiative forcing of 8.5 watts per square meter. The mean biophysical impact on crop yield with no incremental CO2 fertilization is a 17 percent reduction globally by 2050 relative to a scenario with unchanging climate. Endogenous economic responses reduce yield loss to 11 percent, increase area of major crops by 12 percent, and reduce consumption by 2 percent. Agricultural production, cropland area, trade, and prices show the greatest degree of variability in response to climate change, and consumption the lowest. The sources of these differences includes model structure and specification; in particular, model assumptions about ease of land use conversion, intensification, and trade. This study identifies where models disagree on the relative responses to climate shocks and highlights research activities needed to improve the representation of agricultural adaptation responses to climate change.

climate change adaptation model intercomparison in↗

Climate change is projected to have severe impacts on the frequency and intensity of peak electricity demand across the United States

Significance The existing empirical literature on the impacts of climate change on the electricity sector has focused on changing electricity consumption patterns. In this paper, we show that incorporating impacts on the frequency and intensity of peak load consumption during hot days implies sizable required investments in peak generating capacity (or major advances in storage technology or the structure of electricity prices), which results in substantially larger impacts than those from just changes in overall consumption.

Auffhammer, Maximilian↗

AstroPix4 — a novel HV-CMOS sensor developed for space based experiments

For the proposed space based gamma-ray observatory All-sky Medium-Energy Gamma-ray Observatory eXplorer (AMEGO-X), a silicon tracker based on a novel High Voltage-CMOS (HV-CMOS) sensor called AstroPix, is currently being developed. Preliminary measurements with the first full reticle prototype AstroPix3 show that the power target of 1.5 mW/cm 2 can currently not be reached due to the digital consumption of 3.08 mW/cm 2 , while the analog power consumption of 1.04 mW/cm 2 and a break down voltage of over 350 V look promising. Based on these results, the design changes in AstroPix4, submitted in May 2023, are presented, containing changes to the time stamp generation and readout architecture. A digital power consumption below 0.25 mW/cm 2 is expected by removing the fast 200 MHz clock used to measure the time-over-threshold (ToT) and an LVDS receiver. A maximum resolution of 3.125 ns for time-of-arrival (ToA) and ToT is reached by adding per-pixel Flash-Time-to-Digital Converter (TDCs) controlled by a global delay-locked loop (DLL).

47 OTHER INSTRUMENTATION↗

ECON-T and ECON-D: Endcap Concentrator ASICs for the CMS HGCAL

With over 6 million channels, the High Granularity Calorimeter (HGCAL) for the CMS HL-LHC Upgrade presents a unique data challenge. The ECON ASICs provide critical on-detector data reduction for the 40 MHz trigger path (ECON-T) and 750 kHz data acquisition path (ECON-D) of the HGCAL. The ASICs, fabricated in 65 nm CMOS, are rad-tolerant (600 Mrad) with low power consumption (<2.5 mW/channel). This presentation is the first comprehensive description of the ECON designs, first functionality and radiation tests for the ECON-T ASIC, and first results from the full production of 75k ECON-D and ECON-T ASICs.

Bergamin, G. [CERN] (ORCID:0000000285758704)↗

Modifications to JLab 12 GeV Refrigerator and Wide Range Mix Mode Performance Testing Results

Analysis of data obtained during the spring 2013 commissioning of the new 4.5-K refrigeration system at Jefferson Lab (JLab) for the 12 GeV upgrade indicated a wide capacity range with good efficiency and minimal operator interaction. Testing also showed that the refrigerator required a higher liquid nitrogen (LN) consumption for its pre-cooler than anticipated by the design. This does not affect the capacity of the refrigerator, but it does result in an increased LN utility cost. During the summer of 2015 the modifications were implemented by the cold box manufacturer, according to a design similar to the JLab 12 GeV cold box specification. Subsequently, JLab recommissioned the cold box and performed extensive performance testing, ranging from 20% to 100% of the maximum capacity, and in various modes of operation, ranging from pure refrigeration, pure liquefaction, half-and-half mix mode and at selected design modes. This testing demonstrated the modifications resulted in a LN consumption significantly lower than the expected design for the nominal and maximum capacity modes and equal to the expected design for the pure refrigeration mode (which is the most demanding). The testing also demonstrated that the refrigerator system has a good and fairly constant performance over this wide capacity range and different modes of operation. Also, a pulsed-load test, similar to what is expected for cryogenic systems supporting fusion experiments, was conducted to observe the response using the Floating Pressure ? Ganni Cycle, which was stable and robust. This paper will discuss the results and analysis of this testing pertaining to the LN consumption, the system efficiency over a wide range of capacity and different modes and the behavior of the system to a pulsed load.

Knudsen, P.↗

Latent demand for electricity in sub-Saharan Africa: A review

Universal access to electricity is an essential part of sub-Saharan Africa's path to development. With the United Nations setting Goal 7 of its sustainable development goals to be universal access to clean, reliable and affordable electricity, substantial research efforts have been made to optimize electricity supply based on projected demand in sub-Saharan African (SSA) countries. Our study reviews the literature on electricity demand, with a specific focus on latent demand (i.e., electricity demand that would exist if the necessary techno-economic conditions were met) in SSA. We found that out of 57 electricity demand papers reviewed, only 3 (5%) incorporated latent demand in their electricity demand projections. Furthermore, majority of the literature on electricity consumption and demand estimation in SSA use econometric models to identify determinants of electricity consumption and project future demand. We find that population density, urbanization, household income, electricity price, market value of crops and availability of natural resources to be significant determinants of electricity consumption in SSA. We conclude the review by proposing a methodology, and providing an initial proof of concept, for more accurately projecting latent demand in sub-Saharan Africa. Incorporating latent demand in electrification models would help inform energy sector stakeholders (e.g., investors and policymakers) about which sectors and geographic locations hold potential for wealth creation via electricity access.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

A model for selecting the best sustainable airport technology alternatives

Air transport is a continually expanding industry, a fact that has become even more evident with the recovery of the aviation industry post-COVID-19. This expansion amplified energy consumption at airports, which was already significantly high. Airport decision-makers are increasingly focusing on improving sustainability and addressing social, economic, and environmental criteria across airports worldwide. In this article, we propose a decision-making model for identifying a sustainable airport technology solution that minimizes the energy consumption of airport lighting while considering economic, emission, and life-cycle criteria. The proposed model combines data envelopment analysis and multi-criteria decision making techniques. We applied the model to the Dallas/Fort Worth International Airport (DFW) as a case study, considering eight lighting technology solutions, each with five luminous flux alternatives. Our model identified the best lighting technology solution for DFW outdoor and indoor environments based on the following criteria: luminous flux, capital costs, life-cycle costs, energy consumption, and emissions (CO2e, NOx, SO2, and PM2.5). The designed model is customizable to any airport and is applicable to a wide range of airport lighting technologies. In our analysis, Light-Emitting Diode lighting emerged as the most sustainable technology option. It ranked first in most cases due to its balance of high efficacy, long lifespan, low life-cycle cost, low capital cost, and lower emissions across all pollutants.

Tchivwila, Moise B↗

Empirically-calibrated H100 node power models for accurate AI training energy estimation

Accurately quantifying the energy use of artificial intelligence (AI) training is critical for infrastructure planning, carbon accounting, and sustainable data center operation, but few studies have directly measured the power consumption of production workloads on contemporary hardware. By combining empirical measurements from Brookhaven National Laboratory during AI training on 8-graphics-processing-unit H100 systems with open-source benchmarking data, we develop statistical models relating computational intensity to node-level power consumption. We measure the gap between manufacturer-rated thermal design power (TDP) and actual power demand during AI training. Our analysis reveals that even computationally intensive workloads operate at only 76% of the 10.2 kW TDP rating. Our architecture-specific model, calibrated to floating-point operations, predicts energy consumption with 11.4% mean absolute percentage error, significantly outperforming TDP-based approaches (27%–37% error). We identified distinct power signatures between transformer and convolutional neural network architectures, with transformers showing characteristic fluctuations that may impact grid stability. These results provide a measurement-grounded basis for improving AI training energy estimates, enabling more reliable infrastructure sizing, cost projections, and environmental impact assessments.

Newkirk, Alex C↗

Hemerythrins enhance aerobic respiration in Methylomicrobium alcaliphilum 20ZR, a methane-consuming bacterium

ABSTRACT Numerous hemerythrins, di-iron proteins, have been identified in prokaryote genomes, but in most cases their function remains elusive. Bacterial hemerythrin homologs (bacteriohemerythrins, Bhrs) may contribute to various cellular functions, including oxygen sensing, metal binding and antibiotic resistance. It has been proposed that methanotrophic Bhrs support methane oxidation by supplying oxygen to a core enzyme, particulate methane monooxygenase. In this study, the consequences of the overexpression or deletion of the Bhr gene (bhr) in Methylomicrobiam alcaliphillum 20ZR were investigated. We found that the bhrknockout (20ZRΔbhr) displays growth kinetics and methane consumption rates similar to wild type. However, the 20ZRΔbhr accumulates elevated concentrations of acetate at aerobic conditions, indicating slowed respiration. The methanotrophic strain overproducing Bhr shows increased oxygen consumption and reduced carbon-conversion efficiency, while its methane consumption rates remain unchanged. These results suggest that the methanotrophic Bhr proteins specifically contribute to oxygen-dependent respiration, while they have minimal, if any, input of oxygen for the methane oxidation machinery.

Nariya, Snehal↗

Smoking, Alcohol, and Biliary Tract Cancer Risk: A Pooling Project of 26 Prospective Studies

Abstract Background Tobacco and alcohol are well-established risk factors for numerous cancers, yet their relationship to biliary tract cancers remains unclear. Methods We pooled data from 26 prospective studies to evaluate associations of cigarette smoking and alcohol consumption with biliary tract cancer risk. Study-specific hazard ratios (HRs) and 95% confidence intervals (CIs) for associations with smoking and alcohol consumption were calculated. Random-effects meta-analysis produced summary estimates. All statistical tests were two-sided. Results Over a period of 38 369 156 person-years of follow-up, 1391 gallbladder, 758 intrahepatic bile duct, 1208 extrahepatic bile duct, and 623 ampulla of Vater cancer cases were identified. Ever, former, and current smoking were associated with increased extrahepatic bile duct and ampulla of Vater cancers risk (eg, current vs never smokers HR = 1.69, 95% CI = 1.34 to 2.13 and 2.22, 95% CI = 1.69 to 2.92, respectively), with dose-response effects for smoking pack-years, duration, and intensity (all Ptrend < .01). Current smoking and smoking intensity were also associated with intrahepatic bile duct cancer (eg, >40 cigarettes per day vs never smokers HR = 2.15, 95 % CI = 1.15 to 4.00; Ptrend = .001). No convincing association was observed between smoking and gallbladder cancer. Alcohol consumption was only associated with intrahepatic bile duct cancer, with increased risk for individuals consuming five or more vs zero drinks per day (HR = 2.35, 95%CI = 1.46 to 3.78; Ptrend = .04). There was evidence of statistical heterogeneity among several cancer sites, particularly between gallbladder cancer and the other biliary tract cancers. Conclusions Smoking appears to increase the risk of developing all biliary tract cancers except gallbladder cancer. Alcohol may increase the risk of intrahepatic bile duct cancer. Findings highlight etiologic heterogeneity across the biliary tract.

McGee, Emma E.↗

Energy-Centric Cooperative Onramp Merging Strategy: An Analytical Solution

Cooperative highway onramp merging plays an important role in mitigating highway traffic congestion. A crucial component of a cooperative merging system is the merging sequence strategy, which determines each merging participant's order to reach the merging point. Existing merging sequence strategies can be classified into rule-based and optimization-based approaches. The rule-based strategies can be effortlessly implemented with a light online computational burden. However, they may not achieve the optimal energy efficiency. In contrast, the optimization-based strategies can yield the optimal merging sequence to minimize fuel consumption, but typically involve computationally expensive numerical optimization. To leverage the advantages from both sides, we propose a novel merging sequence strategy that can minimize fuel consumption while avoiding online numerical optimization. The key idea is to analytically formulate the expected fuel consumption of each merging participant. Using a realistic highway onramp scenario based on the NGSIM dataset, we validate the performance and the computational efficiency of the proposed merging sequence strategy via SUMO/SIMULINK joint simulation.

Wang, Zejiang↗

Management of Large-Scale Wireless Sensor Networks Utilizing Multi-Parent Recursive Area Hierarchies

Autonomously configuring and self-healing a largescale wireless sensor network requires a light-weight maintenance protocol that is scalable. Further, in a battery powered wireless sensor network duty-cycling a node’s radio can reduce the power consumption of a device and extend the lifetime of a network. With duty-cycled nodes the power consumption of a node’s radio depends on the amount of communication is must perform and by reducing the communication the power consumption can also be reduced. Multi-parent hierarchies can be used to reduce the communication cost when constructing a recursive area clustering hierarchy when compared to singleparent solutions that utilize inefficient communication methods such as flooding and information propagation via single-hop broadcasts. The multi-parent hierarchies remain scalable and provides a level of redundancy for the hierarchy.

Environmental Molecular Sciences Laboratory↗

PowerMappeR: Power-Optimized Mapping of SNNs onto ReRAM Crossbars coupled via Packet-Switched NoCs

Many recent efforts in developing hardware-accelerated spiking neural networks (SNNs) are characterized by deep co-design between algorithms, architectures, and devices. Architectural advances overcome device constraints by coupling together many small resistive-RAM (ReRAM) crossbars via a network-on-chip (NoC) for neuromorphic component operation. Concurrently, improved SNN training methods increase accuracy and structural sparsity in networks despite growing problem sizes. Finally, compilers leverage these attributes to minimize area and inter-crossbar communication while mapping large SNNs to sophisticated architectures. However, for compiler-driven co-design to realize increasingly complex and profitable optimizations, a compile-time view of power consumption is critical. We present PowerMappeR to express and optimize over mapping-, architecture-, and device-specific power consumption information. By modeling the dynamic power of well-established components, we develop an integer linear programming (ILP)-based, encoding-agnostic, parametric power estimation model. Using this model, we demonstrate practical improvements in area and inter-crossbar communication by 0%–9.5% and 1.4%–5.1%, respectively. We also limit hotspot formation during optimization, achieving comparable or better results in targeted metrics with up to 96.4%–97.1% restriction of hotspot magnitude. Finally, we introduce profile-guided formulations to reduce worst-case and expected-case hotspot magnitude by 40.7%–69.5% and 40.6%–56.3%, respectively. Optimizing worst-case hotspot magnitude incidentally improves expected-case magnitude by 10.85%–33.45%. Reciprocally, optimizing expected-case magnitude incidentally improves worst-case magnitude by 4.33%–39.87%. Validation against hardware simulators confirms that PowerMappeR can decrease dynamic power consumption by 12.6%–27.3%.

Pohl, Devin [ORNL] (ORCID:0009000040149027)↗