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Load weighing cell used in conjuction with a hoist is isolated during lifting and manipulation of the load. A simple mechanism, attached to a crane hook, provides a screw adjustment for engaging the load cell during weighing of the load and isolating it from lift forces during hoisting of the load.
The LOADS computer program L218 which calculates dynamic load coefficient matrices utilizing the force summation method is described. The load equations are derived for a flight vehicle in straight and level flight and excited by gusts and/or control motions. In addition, sensor equations are calculated for use with an active control system. The load coefficient matrices are calculated for the following types of loads: (1) translational and rotational accelerations, velocities, and displacements; (2) panel aerodynamic forces; (3) net panel forces; and (4) shears, bending moments, and torsions.
The LOADS program L218, a digital computer program that calculates dynamic load coefficient matrices utilizing the force summation method, is described. The load equations are derived for a flight vehicle in straight and level flight and excited by gusts and/or control motions. In addition, sensor equations are calculated for use with an active control system. The load coefficient matrices are calculated for the following types of loads: translational and rotational accelerations, velocities, and displacements; panel aerodynamic forces; net panel forces; shears and moments. Program usage and a brief description of the analysis used are presented. A description of the design and structure of the program to aid those who will maintain and/or modify the program in the future is included.
A comparison of system performance is made for two possible system setups: (1) individual loads powered by separate solar cell sources; and (2) multiple loads powered by a common solar cell source. A proof for resistive loads is given that shows the advantage of a common source over a separate source photovoltaic system for a large range of loads. For identical loads, both systems perform the same.
The fatigue crack opening load is determined as the tangent point on the ascending load-displacement data between the curved portion and the upper linear region. A model for the 'unzipping' behavior of the crack indicates that the curved portion of the load-deflection curve is second order. The opening load is determined by a nonlinear, least squares fit of the data to the model, which optimally locates the tangent point of the two curves. The method provided consistent results for determining opening load P(op) for 7475-T731 aluminum using data from a crack tip opening gage.
The requirement for improving aircraft utilization and responsiveness in airlift operations has been recognized for quite some time by the Canadian Forces. To date, the utilization of scarce airlift resources has been planned mainly through the employment of manpower-intensive manual methods in combination with the expertise of highly qualified personnel. In this paper, we address the problem of facilitating the load planning process for military aircraft cargo planes through the development of a computer-based system. We introduce TALBAS (Transport Aircraft Loading and BAlancing System), a knowledge-based system designed to assist personnel involved in preparing valid load plans for the C130 Hercules aircraft. The main features of this system which are accessible through a convivial graphical user interface, consists of the automatic generation of valid cargo arrangements given a list of items to be transported, the user-definition of load plans and the automatic validation of such load plans.
Despite the use of exercise countermeasures during long-duration space missions, bone mineral density (BMD) and predicted bone strength of astronauts continue to show decreases in the lower extremities and spine. This site-specific bone adaptation is most likely caused by the effects of microgravity on the mechanical loading environment of the crew member. There is, therefore, a need to quantify the mechanical loading experienced on Earth and on-orbit to define the effect of a given "dose" of loading on bone homeostasis. Gene et al. recently proposed an enhanced DLS (EDLS) model that, when used with entire days of in-shoe forces, takes into account recently developed theories on the importance of factors such as saturation, recovery, and standing and their effects on the osteogenic response of bone to daily physical activity. This algorithm can also quantify the tinting and type of activity (sit/unload, stand, walk, run or other loaded activity) performed throughout the day. The purpose of the current study was to use in-shoe force measurements from entire typical work days on Earth and on-orbit in order to quantify the type and amount of loading experienced by crew members. The specific aim was to use these measurements as inputs into the EDLS model to determine activity timing/type and the mechanical "dose" imparted on the musculoskeletal system of crew members and relate this dose to changes in bone homeostasis.
Dry reforming catalysts, especially those with activity at moderate temperatures, have been intensely investigated to enhance the conversion of biogas. Here, Ru is evaluated as a promoter for Ni-Mg based catalysts. Catalysts based on 1.4 wt%Ni-1.0 wt%Mg-Ce 0.6 Zr 0.4 O 2 with Ru (0.02–0.32 wt%) were prepared using incipient wetness. The reducibility of the catalysts and conversions increased with increasing Ru content. Increases in conversions with increasing Ru loading was attributed to the additional active sites and synergistic effect between Ru and Ni, which weakened Ni-Mg interactions. Samples showed dry reforming activity at low temperatures (450–510 °C). Reaction rates and activation energies of higher loading Ru samples (1.4 wt%Ni-1.0 wt%Mg/Ce 0.6 Zr 0.4 O 2 with 0.16 and 0.32 wt%Ru) decreased when the reduction temperature was raised from 300 to 400 °C. A 20 h TOS study showed stable catalytic activity with minimal coke deposition. Furthermore, the results suggest that Ru is an alternative to Pt in promoting low temperature dry reforming of methane.
Flexibility in inverter-based loads could be used to support the converter-dominated power grid by offering a rapid, autonomous, and adjustable power reserve during system transients to help maintain system stability. Based on technical potential, ancillary service (AS) value, and implementation costs, this study illustrates the cost-benefit analysis of grid-supportive loads (GSLs) for the supply of fast frequency response (FFR). The net benefit for each GSL is demonstrated using a case study and relevant data sources. The findings suggest that implementation costs for enabling GSL features are low compared to the value that grid operators get from the acquisition of responsive reserve services. The authors believe that, given the rising popularity of renewable energy sources, GSLs can be a useful tool for grid stability in low-inertia systems.
FAST.Farm is a mid-fidelity engineering tool developed by the National Renewable Energy Laboratory targeted at accurately and efficiently predicting wind turbine power production and structural loading in wind farm settings, including wake interactions between turbines. FAST.Farm is based on several principles of the dynamic wake meandering (DWM) model, but also addresses limitations of previous DWM implementations. Previous FAST.Farm studies have shown the similarities and differences between FAST.Farm and large eddy simulations for rigid turbine cases. The objective of this work is to quantify the ability of FAST.Farm to accurately predict turbine structural response in a small wind farm. This is done by comparing FAST.Farm structural response to SOWFA-OpenFAST results for three laterally-aligned turbines. The purpose of this study is to characterize the similarities and differences between FAST.Farm and a higher fidelity model for predicting turbine structural response in a wind farm for differing atmospheric inflows. Strong statistical agreement was found between FAST.Farm and SOWFA-OpenFAST structural response for the non-waked upstream turbine, and good agreement was found for the downstream turbines for most structural quantities. Higher differences were seen for downstream turbines with low ambient turbulence intensity or yawed turbines, suggesting areas for FAST.Farm wake dynamics modeling improvements. For all cases and turbines, small statistical differences were seen between blade deflections and bending moments, with larger differences for tower-top and tower-base bending moments. Overall, the results establish confidence for applying FAST.Farm to wind farm power and loads analyses and identify areas where further model validations and model improvements should be targeted.
Low-density aerogel materials currently are being considered for applications in controlling the rate of release for small-molecule pharmaceuticals in vivo. In particular, silica (SiO2) aerogels are an intriguing platform for controlling both the absolute amount of drug loading, but also the crystalline state of small molecule pharmaceuticals has been shown to affect drug release kinetics. To date, loading silica aerogel materials with crystalline pharmaceuticals typically require dissolving small molecules in a supercritical solvent (i.e., CO2) before drying to recover the aerogel/drug composite. Here we demonstrate a simple and low-cost method for controlling the crystallinity of the model drug coenzyme Q10 (CoQ10) within carbon aerogel materials by rationally controlling the chemical microstructure of the aerogel to exhibit ?-conjugated (sp2) chemical bonding. In particular, both pyrolyzed graphene and nanodiamond aerogels exhibit enhanced intrinsic drug loading capacity and CoQ10 crystallinity over a broad range of sp2 surface-curvature. In contrast, amorphous carbon aerogel materials are observed to load CoQ10 pharmaceuticals with strictly amorphous microstructure.
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The upward adoption trend of renewable generation not only means cleaner energy integrated into modern power grids, but also that most new generation sources are based on front-end inverter bridges, used as interfaces to most wind generation and all the solar PV. It is well known that due to their power electronics-based construction rather than rotational shafts, these sources do not provide inertia inherently, nor substantial amounts of short-circuit currents. However, stable energy such as what can be stored in energy storage systems, although interfaced via inverters, can be controlled to respond to system disturbances in a manner that emulates inertial behavior. This paper focuses on the application of such energy storage systems to augment inertia in the island of Puerto Rico. To do so, a user defined inverter model that contains grid forming capabilities and fast frequency response is modeled and integrated into the real transmission system in power flow and dynamics software. Energy storage is then connected to two selected areas so that it not only provides frequency regulation to avoid widespread load shedding events, but also other tangible benefits. The simulated cases suggest that even relatively small energy storage systems can avert load shedding events if adequately placed in the transmission network.
The increasing uncertainties caused by the high-penetration of stochastic renewable generation resources poses a significant threat to the power system voltage stability. To address this issue, this paper proposes a probabilistic deep kernel learning enabled surrogate model to extract the hidden relationship between uncertain sources, i.e., wind power and loads, and load margin for probabilistic load margin assessment (PLMA). Unlike other deep learning approaches, a kernel SHAP provides the sensitivity analysis as well as interpretability of the inputs to outputs influences. This allows identifying the critical factors that affect load margin so that corrective control can be initiated for stability enhancement. Numerical results carried out on the IEEE 118-bus power system demonstrate the accuracy and efficiency of the proposed data-driven PLMA scheme.
This paper presents a modified sequence-to-point (S2P) algorithm for disaggregating the heat, ventilation, and air conditioning (HVAC) load from the total building electricity consumption. The original S2P model is convolutional neural network (CNN) based, which uses load profiles as inputs. We propose three modifications. First, the input convolution layer is changed from 1D to 2D so that normalized temperature profiles are also used inputs to the S2P model. Second, a drop-out layer is added to improve adaptability and generalizability so that the model trained in one area can be transferred to other geographical areas without labelled HVAC data. Third, a fine-tuning process is proposed for areas with a small amount of labelled HVAC data so that the pre-trained S2P model can be fine-tuned to achieve higher disaggregation accuracy (i.e., better transferability) in other areas. The model is first trained and tested using smart meter and sub-metered HVAC data collected in Austin, Texas. Then, the trained model is tested on two other areas: Boulder, Colorado and San Diego, California. Simulation results show that the proposed modified S2P algorithm outperforms the original S2P model and the support-vector machine based approach in accuracy, adaptability, and transferability.
Virtual battery (VB) is an innovative method to model flexibility of building loads and effectively coordinate them with other resources at a system level. Unlike a real battery with a dedicated power conversion system for charging control, methods are required for operating building loads to deviate from the baseline to respond to grid signals. This paper presents a VB control for a commercial heating, ventilation, and air conditioning (HVAC) system to follow the desired power consumption in real-time by adjusting zonal airflow rates. The proposed method consists of two parts. At the system level, a mixed feedforward and feedback control is used to estimate the desired total airflow rate. At the zone level, two priority-based algorithms are then proposed to distribute the total airflow rate to individual zones. In particular, a zonal airflow limit estimation method is proposed using machine-learning techniques, in contrast to physics-based thermal models in existing studies, to more accurately capture zonal thermal dynamics and improve temperature control performance. An office building on the Pacific Northwest National Laboratory campus is implemented in EnergyPlus, and used to illustrate and validate the proposed control.