Vertical processes and resolution impact ice shelf basal melting: A multi-model study
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The poor thermal properties of Aluminum–Gallium Nitride (AlGaN) channel High Electron Mobility Transistors (HEMTs) on sapphire limit the device’s maximum switching frequency due to high channel temperatures. Extracting the correct thermal time constants can provide guidelines for the device’s safe frequency operation. This study experimentally quantifies the impact of pulse width and duty cycle on the transient thermal dynamics of multi-finger AlGaN channel HEMTs. In most transient thermal metrology, lock-in averaging approaches are leveraged to increase signal-to-noise ratio and measure the device’s relative temperature rise. Assuming no heat accumulation, the temperature rise is used to quantify the device’s thermal resistance under continued pulsed biasing. For devices grown on sapphire substrates, however, heat accumulation leads to elevated reference temperatures and causes the differential measurement to underestimate the peak temperature. This study demonstrates the capability of transient Gate Resistance Thermometry (tGRT) to measure the absolute temperature, which is required to precisely evaluate the device’s thermal resistance. When an absolute temperature measurement is not feasible (such as thermoreflectance imaging), a solution is proposed to derive the peak temperature under pulsed biasing based on the differential temperature under pulsing and the steady-state thermal resistance (which is typically easier to obtain). Finally, additional tGRT (without averaging) is performed to demonstrate the temperature-dependent thermal time constants required to minimize heat accumulation effects.
Sulfurized polyacrylonitrile (SPAN) is considered one of the most promising cathode materials to overcome the operational challenges that plague lithium-sulfur (Li-S) batteries. However, material properties and electrochemical performance implications of SPAN prepared under different synthesis conditions are not yet fully investigated. In this study, we show the impacts of different synthesis conditions on the formation of sulfur to PAN bonds and redox reaction mechanisms of multi-phase SPAN via comprehensive material and electrochemical characterizations. In-situ Raman analysis was first applied to study the multi-phase SPAN-based Li-S cells. We found that both elemental sulfur and chemically bonded sulfur are present under the synthesis condition of 300 degree C/3 h along with unreacted PAN. The incompletely sulfurized, multi-phase SPAN exhibited an unusually rapid capacity degradation in the resultant Li-S cells, which is attributed to polysulfide formation and continuously growing interfacial impedance in the Li-S cells. On the other hand, SPAN samples prepared under the synthesis condition of 350 degree C/3 h are found completely sulfurized with chemically bonded sulfur to the PAN backbone without the presence of free elemental sulfur. Complete sulfurization of SPAN led to exceptionally stable cycle performance due to excellent reversible redox processes of chemically bonded sulfur with Li+ in the Li-S cells.
Simulations of rock damage and gas transport following underground explosions that omit preexisting fracture networks in the subsurface cannot fully characterize the influence of geo-structural variability on gas transport. Previous studies do not consider the impact that fracture network structure and variability have on gas seepage. In this study, we develop a sequentially coupled, axi-symmetric model to look at the damage pattern and resulting gas breakthrough curves following an underground explosion given different fracture network realizations. We simulate 0.327 and 0.164 kT chemical explosives with burial depths of 100 m for 90 stochastically generated fracture networks. Gases quickly reach the surface in 30% of the higher yield simulations and 5% of the lower yield simulations. The fast breakthrough can be attributed to the formation of connected pathways between fractures to the surface. The formation of a connected damage pathway to the surface is not clearly correlated with the fracture intensity (P32) in our simulations. Breakthrough curves with slower transport are highly variable depending on the fracture network sample. The variability in the breakthrough behavior indicates that ignoring the influence of fracture networks on rock damage, which strongly influences the hydraulic properties following an underground explosion, will likely lead to a large underestimation of the uncertainty in the gas transport to the surface. This work highlights the need for incorporation of fracture networks into models for accurately predicting gas seepage following underground explosions.
This study investigated the fast-charging infrastructure needed by 2030 to enable seamless electrified road trips to and from national parks and monuments in seven western states: Washington, Oregon, Idaho, Wyoming, Utah, Nevada, and Arizona. It also estimated impacts to the electric grid. The research team investigated how on-route charging infrastructure projections change with different parameters or assumptions, as do related charging loads and grid impacts. NREL conducted the study in partnership with utility service provider PacifiCorp and Utah State University as part of the Western Smart Regional EV Adoption and Infrastructure at Scale project.
The prediction of boiling incipience is a critical issue for a reactor. Vapor lock causes operating instability and undesirable temperature rise, especially for small channels. This study uses the Eulerian multiphase flow boiling model to predict the onset of nucleate boiling in a millimeter scale rectangular channel. Two wall boiling models, namely the RPI (Rensselaer Polytechnic Institute) and the non-equilibrium sub cooled model are applied. Computational fluid dynamics is employed for this study to investigate the impact of operating conditions like liquid flow rate, operating pressure, heat flux, velocity profile, turbulence model, and inlet liquid temperature. Here, the effect of fundamental boiling parameters on the surface wall temperature and vapor volume fraction is also studied. The parameters under consideration are the bubble departure diameter, bubble departure frequency, nucleate site density, quenching time period, and interface heat transfer coefficients. A series of CFD calculations is conducted by varying the considered variables systematically in a wide range of flow conditions covering laminar, transition, and turbulent flows. The sub-component heat fluxes like the liquid convective, the vapor convective, the evaporative, and the quenching terms are monitored to infer the boiling dynamics in the transition region from the single-phase flow to the nucleate boiling zone. It is found that the CFD approach to detecting the boiling incipience point agrees reasonably with available experimental data. However, its limitations, like inaccuracy for lower flow rates and early transition to boiling flow, are also noticed due to the nature of the considered RPI model.
Multimode engine operation uses two or more combustion modes to maximize engine efficiency across the operational range of a vehicle to achieve higher overall vehicle fuel economy than is possible with a single combustion mode. More specifically for this study, multimode solutions are explored that make use of boosted SI under high load operation and other advanced combustion modes such as advanced compression ignition (ACI) under part-load conditions to enable additional engine efficiency improvements across a broader range of the engine operating map. ACI combustion has well-documented potential to improve efficiency and emissions under part-load operation but poses challenges that limit full engine speed-load range. This study investigates the potential impact of ACI operational range on simulated fuel economy to help focus research on areas with the most opportunity for improving fuel economy. These simulations make use of a vehicle model, discretized engine data, and employ a systematic exploration of ACI operational range to estimate multimode fuel economy for a mid-size passenger vehicle over U.S. Environmental Protection Agency’s Urban Dynamometer Driving Schedule. The results of this study highlight operational ranges with the highest potential fuel economy and correspondingly areas of focus for multimode research and ACI engine operation1.
The Interconnections Seam Study analyzes the grid impacts of increased connections between the Eastern and Western Interconnections in North America.
The goal of this Case Study was to quantify the impacts of variable moisture and ash on hammer mill throughput and energy consumption and on loss of very wet stover that causes failures in the first stage grinder and that are not able to be fed to conversion, as compared to a status quo Base Case system. Also considered was convertible carbohydrate content (minimum total carbohydrate specification), maximum ash content and the delivered feedstock cost impacts of not being able to feed stover that did not meet the total carbohydrate specification to the conversion reactor. Laboratory data on the impacts of moisture content and tissue fraction on throughput and energy consumption in a stage 2 hammer mill were received from FCIC Subtask 5.1: Preprocessing, Corn Stover Preprocessing (Neal Yancey and Sergio Hernandez, INL). Additional air classifier throughput, energy consumption and separation efficiency data were obtained from FCIC Subtask 5.1 (Neal Yancey, INL) for the new air classifier, which has three exit streams (lights, middle and heavies). These data were utilized to develop the necessary response surface equations to perform throughput analysis using discrete event simulation. Because the ash contents and particle sizes had not been analyzed in the laboratory at the time of the model runs, we assumed that the ash distributed proportionally with total mass into the lights and heavies in an air classifier having two exit streams (lights and heavies) and that the lights fraction from the air classifier was not removed.
The performance and durability of proton exchange membrane electrolyzers are governed by the interplay between catalyst inks and integration, the resulting catalyst layer (CL) structure, and the interface between the CL and porous transport layer (PTL). In this study, we examine the impact of these factors on cell performance and degradation using rutile IrO2 as the anode catalyst. The two commercial rutile IrO2 catalysts differ from each other and from the amorphous benchmark in agglomerate size and ink stability. This has implications for CL morphology and electronic connectivity. In the context of long-term pressurized operation for up to 1000 h, the selection of the PTL has a marginal effect on the durability of CLs with good structural and compositional properties, whereas poorly structured CLs demonstrate evident degradation when used with certain PTLs. Poor CL properties amplify the negative impact of non-ideal CL/PTL interfaces, demonstrating the importance of CL/PTL interface engineering.
Awareness of how buildings interact with the occupant experience—especially human performance—is becoming more prevalent, as seen by increasing interest and investment in healthy built environments. However, there is a need to synthesize the wide array of existing indoor environmental assessment and performance research in a way that can translate directly to building design and operation. Existing research in this area typically focuses on a single isolated metric and has not focused on making the results utilizable by building practitioners. The aim of this research is to investigate existing office performance literature through meta‐analyses and produce regression models for four indoor environmental quality (IEQ) metrics to support critical decision‐making for building operation and renovation. To reach this aim, a literature review was conducted to identify studies that measure the impact of changing ventilation rate, temperature, horizontal illuminance, and noise level in offices on occupant task performance. This repository of field and laboratory studies was analyzed to visualize the trends between the selected IEQ metrics and task performance. The temperature, ventilation rate, and horizontal illuminance regression models showed clear improvement potential when modifying indoor conditions toward the defined high‐performance range, while the regression model for noise level was inconclusive. The discussion notes the importance of designing holistically for all components of these IEQ categories to utilize the results, for example, good filtration on outdoor air for quantifying ventilation impact and uniform overhead lighting with low contrast for quantifying horizontal illuminance impact. The novelty of this work is in considering multiple facets of the indoor environment under a single, unified analysis schema and producing IEQ‐based performance gains that can directly inform cost‐benefit analyses of building design and renovation.
Abstract Climate models are critical tools to study earth system processes and are often further downscaled to provide refined‐resolution data sets for regional water‐energy impact analyses. In this study, the Variable‐Resolution Community Earth System Model (VR‐CESM), a new dynamical downscaling method, is employed to generate 1/8° simulations for China and the western United States over 1970–2006. Precipitation, temperature, snowpack, radiation, and wind speed are compared with two 1° global climate models, regional climate model ensemble, and reference data sets to evaluate their fidelity. Simulated precipitation skill is dependent upon the reference data sets used, a slight (<+10%) and spatially consistent wet bias in China during summer and the western United States during winter. Precipitation bias in VR‐CESM is the smallest among models and demonstrates the added value provided by resolution refinement. However, temperature biases are generally greater than precipitation due to more complicated interactions among processes. VR‐CESM simulates +2°C warm bias in the low‐elevation regions in China but −2°C cold bias in mountains. However, DJF cold bias (−2.23°C to−3.37°C) and JJA warm bias (+1.13°C to +1.87°C) exist in the western United States. The modeling skills of precipitation and temperature at various topographies are similar in the two evaluated regions. The cold bias was likely affected by downward shortwave radiation deficit (−15%) and slow bias of surface wind speeds (−16% to −29%). Despite the wet and cold bias, snow water equivalent and snow cover are underestimated in mountains. We conclude that VR‐CESM outperforms other models evaluated and provides the best estimation of downscaled climate model data for water‐energy studies.
Schedulers are critical for optimal resource utilization in high-performance computing. Traditional methods to evaluate sched- ulers are limited to post-deployment analysis, or simulators, which do not model associated infrastructure. In this work, we present the first-of-its-kind integration of scheduling and digital twins in HPC. This enables what-if studies to understand the impact of parameter configurations and scheduling decisions on the physical assets, even before deployment, or regarching changes not easily realizable in production. We (1) provide the first digital twin framework extended with scheduling capabilities, (2) integrate various top-tier HPC systems given their publicly available datasets, (3) implement extensions to integrate external scheduling simulators. Finally, we show how to (4) implement and evaluate incentive structures, as- well-as (5) evaluate machine learning based scheduling, in such novel digital-twin based meta-framework to prototype scheduling. Our work enables what-if scenarios of HPC systems to evaluate sustainability, and the impact on the simulated system.
Purpose Depression considerably influences the clinical outcomes, treatment compliance, quality of life, and mortality of hemodialysis patients. Exercise plays a beneficial role in depressive patients, but its quantitative effects remain elusive. This study aimed to summarize the effects of exercise training on depression in patients with end-stage renal disease undergoing hemodialysis. Methods The PUBMED, EMBASE, and Cochrane Library databases were systematically searched from inception to April 2023 to identify published articles reporting the effect of exercise training on the depression level of patients with End-Stage Renal Disease undergoing hemodialysis. Data were extracted from the included studies using predefined data fields by two independent researchers. The Cochrane Handbook for Systematic Reviews of Interventions and Joanna Briggs Institute Critical Appraisal Checklist for Quasi-Experimental Studies were employed for quality evaluation. Results A total of 22 studies enrolling 1,059 patients who participated in exercise interventions were included. Hemodialysis patients exhibited superior outcomes with intradialytic exercise (SMD = −0.80, 95% CI: −1.10 to −0.49) and lower levels of depression following aerobic exercise (SMD = −0.93, 95%CI: −1.32 to −0.55) compared to combined exercise (c − 0.85, 95% CI: −1.29 to −0.41) and resistance exercise (SMD = −0.40, 95%CI: −0.96 to 0.17). Regarding exercise duration, patients manifested lower depression levels when engaging in exercise activities for a duration exceeding 6 months (SMD = −0.92, 95% CI: −1.67 to −0.17). Concerning the duration of a single exercise session, the most significant improvement was noted when the exercise duration exceeded 60 min (SMD = −1.47, 95% CI: −1.87 to −1.06). Conclusion Our study determined that exercise can alleviate depression symptoms in hemodialysis patients. This study established the varying impacts of different exercise parameters on the reduction of depression levels in hemodialysis patients and is anticipated to lay a theoretical reference for clinicians and nurses to devise tailored exercise strategies for interventions in patients with depression. Systematic review registration https://www.crd.york.ac.uk/prospero/ , This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO) database, with registration number CRD42023434181.