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This document includes steps that building owners and operators can implement to achieve smart, healthy, and low-carbon outpatient healthcare buildings within their existing building portfolios. Outpatient healthcare includes diagnostic and treatment facilities for outpatient care, and these buildings often use packaged rooftop units for heating, cooling, and ventilation.
This document includes steps that building owners and operators can implement to achieve smart, healthy, and low-carbon hospitals within their existing building portfolios. Hospitals typically include complex heating and cooling systems and specialty medical equipment.
This document includes steps that building owners and operators can implement to achieve smart, healthy, and low-carbon small hotels within their existing building portfolios. Small hotels are typically four floors or less and less than 50,000 square feet and often use packaged rooftop units for heating, cooling, and ventilation.
This document includes steps that building owners and operators can implement to achieve smart, healthy, and low-carbon retail buildings within their existing building portfolios. Stand-alone retail and strip malls often use packaged rooftop units for heating, cooling, and ventilation.
This fact sheet describes what Automatic Receptacle Controls (ARCs) are, how they work, ARC benefits and savings, and last, how they are being incorporated into energy codes. This fact sheet is meant to be a resource for energy codes adopters and influencers to help encourage them to adopt ARC provisions in their energy code.
The energy use of medical imaging equipment (MIE) in healthcare facilities is estimated to be about 5% of total site energy use. MIE energy use is only expected to increase as technology advances and more patients seek services from MIE, enabling more facilities to install additional MIE. Healthcare organizations need reliable information regarding comprehensive energy use or lifetime energy costs with which to make energy-efficient MIE procurement decisions, and there are currently no MIE energy or efficiency standards in the United States (U.S.). This fact sheet provides background on MIE energy consumption, key terms, and guidance on why and how to meter MIE, helping stakeholders make informed decisions to enhance their facility's performance.
The Laboratory Directed Research and Development (LDRD) program yields foundational scientific research and development (R&D) essential to growing SRNL’s core competencies, in alignment with SRNL’s Strategic Plan to provide long-term benefits to the Department of Energy (DOE), the National Nuclear Security Administration (NNSA), and other customers and stakeholders. Five strategic goals are outlined in SRNL’s strategic plan: 1) Provide applied science and engineering for EM’s active clean-up sites and LM’s post closure management sites 2) Provide science-based solutions for gaps identified in nonproliferation strategic vision and support the government in activities impacting national security 3) Lead Science, Technology & Engineering as the central technical authority for processing tritium loaded reservoirs and support production of plutonium pits 4) Align science and energy security programs by focusing modern modeling, simulation, and data analytics tools on materials engineering and performance applications 5) Build a workforce for the future
The load profile at a feeder-head is usually known to utility engineers while the nodal load profiles are not. However, the nodal load profiles are increasingly important for conducting time-series analysis in distribution systems. Therefore, in this paper, we present a pivot-point based, two-stage feeder load disaggregation algorithm using smart meter data. The two stages are load profile selection (LPS) and load profile allocation (LPA). In the LPS stage, a random load profile selection process is first executed to meet the load diversity requirement. Then, a few pairs of pivot points are selected as the matching targets. After that, a matching algorithm will run repetitively to select one load profile at a time for matching the reference load profile at the pivot points. In the LPA stage, the LPS selected load profiles are allocated to each load node on the feeder considering distribution transformer loading limits, load composition, and square-footage. The proposed method is validated using actual data collected in a North Carolina service area. Finally, simulation results show that the proposed method can generate a unique load shape for each load node while match the shape of their aggregated profile with the actual feeder head load profile.
The purpose of this study was to explore the relationship between carpal tunnel pressure and fingertip force during a simple pressing task. Carpal tunnel pressure was measured in 15 healthy volunteers by means of a saline-filled catheter inserted percutaneously into the carpal tunnel of the nondominant hand. The subjects pressed on a load cell with the tip of the index finger and with 0, 6, 9, and 12 N of force. The task was repeated in 10 wrist postures: neutral; 10 and 20 degrees of ulnar deviation; 10 degrees of radial deviation; and 15, 30, and 45 degrees of both flexion and extension. Fingertip loading significantly increased carpal tunnel pressure for all wrist angles (p = 0.0001). Post hoc analyses identified significant increase (p < 0.05) in carpal tunnel pressure between unloaded (0 N) and all loaded conditions, as well as between the 6 and 12 N load conditions. This study demonstrates that the process whereby fingertip loading elevates carpal tunnel pressure is independent of wrist posture and that relatively small fingertip loads have a large effect on carpal tunnel pressure. It also reveals the response characteristics of carpal tunnel pressure to fingertip loading, which is one step in understanding the relationship between sustained grip and pinch activities and the aggravation or development of median neuropathy at the wrist.
Time-varying buffet forcing functions arise from unsteady aerodynamic pressures and are one of many load environments, which contribute to the overall loading condition of a launch vehicle during ascent through the atmosphere. The buffet environment is typically highest at transonic conditions and can excite the vehicle dynamic modes of vibration. The vehicle response to these buffet forcing functions may cause high structural bending moments and vibratory environments, which can exceed the capabilities of the structure, or of vehicle components such as payloads and avionics. Vehicle configurations, protuberances, payload fairings, and large changes in stage diameter can trigger undesirable buffet environments. The Space Launch System (SLS) multi-body configuration and its structural dynamic characteristics presented challenges to the load cycle design process with respect to buffet-induced loads and responses. An initial wind-tunnel test of a 3-percent scale SLS rigid buffet model was conducted in 2012 and revealed high buffet environments behind the booster forward attachment protuberance, which contributed to reduced vehicle structural margins. Six buffet mitigation options were explored to alleviate the high buffet environments including modified booster nose cones and fences/strakes on the booster and core. These studies led to a second buffet test program that was conducted in 2014 to assess the ability of the buffet mitigation options to reduce buffet environments on the vehicle. This paper will present comparisons of buffet forcing functions from each of the buffet mitigation options tested, with a focus on sectional forcing function rms levels within regions of the vehicle prone to high buffet environments.
The active south polar surface of Enceladus is exposed to strong chemical processing by direct interaction with charged plasma and energetic particles in the local magnetospheric environment of this icy moon. Chemical oxidation activity is suggested by detection of H202 at the surface in this region and less directly by substantial presence of C02, CO, and N2 in the plume gases. Molecular composition of the uppermost surface, including ejecta from plume activity, is radiolytically transformed mostly by penetrating energetic electrons with lesser effects from more depleted populations of energetic protons. The main sources of molecular plasma ions and E-ring dust grains in the magnetospheric environment are the cryovolcanic plume emissions from Enceladus. These molecular ions and the dust grains are chemically processed by magnetospheric interactions that further impact surface chemistry on return to Enceladus. For example, H20 neutrals dominating the emitted plume gas return to the surface mostly as H30+ ions after magnetospheric processing. Surface oxidant loading is further increased by return of radiolytically processed ice grains from the E-ring. Plume frost deposition and micrometeoroid gardening protect some fraction of newly produced molecular species from destruction by further irradiation. The evident horizontal and vertical mobility of surface ices in the south polar region drive mixing of these processed materials into the moon interior with potential impacts on deep ice molecular chemistry and plume gas production. Similarly as suggested previously for Europa, the externally driven source of radiolytic oxidants could affect evolution of life in any subsurface liquid water environments of Enceladus.
Large scale temporal data have flourished in a vast array of applications, and their sophisticated structures, especially the heteroscedasticity among subjects with inter- and intra-temporal dependence, have fueled a great demand for new statistical models. In this paper, with covariate information, we consider a flexible model for large scale temporal data with subject-specific heteroscedasticity. Formally, the model employs latent semiparametric factors to simultaneously account for the subject-specific heteroscedasticity and the contemporaneous and/or serial correlations. The subject-specific heteroscedasticity is modeled as the product of the unobserved factor process and subject’s covariate effect, which is further characterized via additive models. For estimation, we propose a two-step procedure. First, the latent factor process and nonparametric loading are recovered through projection-based methods, and following, we estimate the regression components by approaches motivated from the generalized least squares. By scrupulously examining the non-asymptotic rates for recovering the factor process and its loading, we show the consistency and efficiency of estimated regression coefficients in the absence of prior knowledge of latent factor process and subject’s covariate effect. Here, the statistical guarantees remain valid even for finite time points that makes our method particularly appealing when the subjects significantly outnumber the observation time points. Using comprehensive simulations, we demonstrate the finite sample performance of our method, which corroborates the theoretical findings. Finally, we apply our method to a data set of air quality and energy consumption collected at 129 monitoring sites in the United States in 2015.
The invention is an improved process for using surface strain data to obtain real-time, operational loads data for complex structures that significantly reduces the time and cost versus current methods.
Tool distributes clamping load stably and fairly evenly among five clamping feet. Designed to stabilize and even out pressure applied to foam pads during bonding and also used in other situations where necessary to maintain fairly even clamping loads during fabrication processes. Five clamping feet, four of which adjustable, distribute clamping load and maintain clamping configuration during adhesive bonding or similar fabrication process. Used to clamp nonflat as well as flat surfaces.
This thesis provides design and analysis of techniques for global load balancing on ensemble architectures running soft-real-time object-oriented applications with statistically periodic loads. It focuses on estimating the instantaneous average load over all the processing elements. The major contribution is the use of explicit stochastic process models for both the loading and the averaging itself. These models are exploited via statistical time-series analysis and Bayesian inference to provide improved average load estimates, and thus to facilitate global load balancing. This thesis explains the distributed algorithms used and provides some optimality results. It also describes the algorithms' implementation and gives performance results from simulation. These results show that the authors' techniques allow more accurate estimation of the global system loading, resulting in fewer object migrations than local methods. The authors' method is shown to provide superior performance, relative not only to static load-balancing schemes but also to many adaptive load-balancing methods. Results from a preliminary analysis of another system and from simulation with a synthetic load provide some evidence of more general applicability.
This thesis provides design and analysis of techniques for global load balancing on ensemble architectures running soft-real-time object-oriented applications with statistically periodic loads. It focuses on estimating the instantaneous average load over all the processing elements. The major contribution is the use of explicit stochastic process models for both the loading and the averaging itself. These models are exploited via statistical time-series analysis and Bayesian inference to provide improved average load estimates, and thus to facilitate global load balancing. This thesis explains the distributed algorithms used and provides some optimality results. It also describes the algorithms' implementation and gives performance results from simulation. These results show that our techniques allow more accurate estimation of the global system load ing, resulting in fewer object migration than local methods. Our method is shown to provide superior performance, relative not only to static load-balancing schemes but also to many adaptive methods.
A limited evaluation of the F/A-18 baseline loads model was performed on the Systems Research Aircraft at NASA Dryden Flight Research Center (Edwards, California). Boeing developed the F/A-18 loads model using a linear aeroelastic analysis in conjunction with a flight simulator to determine loads at discrete locations on the aircraft. This experiment was designed so that analysis of doublets could be used to establish aircraft aerodynamic and loads response at 20 flight conditions. Instrumentation on the right outboard leading edge flap, left aileron, and left stabilator measured the hinge moment so that comparisons could be made between in-flight-measured hinge moments and loads model-predicted values at these locations. Comparisons showed that the difference between the loads model-predicted and in-flight-measured hinge moments was up to 130 percent of the flight limit load. A stepwise regression technique was used to determine new loads derivatives. These derivatives were placed in the loads model, which reduced the error to within 10 percent of the flight limit load. This paper discusses the flight test methodology, a process for determining loads coefficients, and the direct comparisons of predicted and measured hinge moments and loads coefficients.
This paper describes and illustrates two ways of performing time-correlated gust-load calculations. The first is based on Matched Filter Theory; the second on Random Process Theory. Both approaches yield theoretically identical results and represent novel applications of the theories, are computationally fast, and may be applied to other dynamic-response problems. A theoretical development and example calculations using both Matched Filter Theory and Random Process Theory approaches are presented.