Dynamic cyclic loading-unloading response of am pmdi compression pads
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Techniques are presented for the solution of structural dynamic systems on an electronic digital computer using FORMA (FORTRAN Matrix Analysis). FORMA is a library of subroutines coded in FORTRAN 4 for the efficient solution of structural dynamics problems. These subroutines are in the form of building blocks that can be put together to solve a large variety of structural dynamics problems. The obvious advantage of the building block approach is that programming and checkout time are limited to that required for putting the blocks together in the proper order.
A listing of the source deck of each dense FORMA subroutine is given to remove the 'black-box' aura of the subroutines so that the analyst may better understand the detail operations of each subroutine. The FORTRAN 4 programming language is used throughout.
A listing of the source deck of each sparse FORMA subroutine is given to remove the 'black-box' aura of the subroutines so that the analyst may better understand the detailed operations of each subroutine. The format of a sparse matrix on a utility tape is also presented. The FORTRAN 4 programming language is used in all sparse FORMA subroutines.
A listing of the source deck of each finite element FORMA subroutine is given to remove the 'black-box' aura of the subroutines so that the analyst may better understand the detailed operations of each subroutine. The FORTRAN 4 programming language is used in all finite element FORMA subroutines.
An explanation is presented of the function of each dense subroutine in the FORMA library. Example problems are given in some cases to clarify the operations performed by a subroutine.
An explanation of the function of each sparse subroutine in the FORMA library is presented. Example problems are given in some cases to clarify the operations performed by a subroutine.
An explanation is presented of the function of each finite element subroutine in the FORMA library. Example problems are given in some cases to clarify the operations performed by a subroutine.
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Abstract not provided.
Systems and methods are described herein that improve grid performance by smoothing demand using thermal reserves. The smoothed demand can reduce peak loads as well as the ramp rate of demand that will otherwise require the use of inefficient, expensive generation sources. These improvements are tied to the selective switching on or off electrical loads that are coupled to thermal reserves, effectively using the thermal reserves as an energy storage mechanism. Historical data of past usage can be used to create load model and ensure that effects on customer comfort are minimized while still accomplishing the beneficial effects for the overall grid, which enables grid owners to both reduce their operational cost by avoiding expensive generation and improve system reliability by achieving more predictable power demand.
Power grid primary frequency response will be significantly impaired by Photovoltaic (PV) penetration increase because of the decrease in inertia and governor response. PV inertia and governor emulation requires reserving PV output and leads to solar energy waste. This paper exploits current grid resources and explores energy storage for primary frequency response under high PV penetration at the interconnection level. Based on the actual models of the U.S. Eastern Interconnection grid and the Texas grid, effects of multiple factors associated with primary frequency response, including the governor ratio, governor deadband, droop rate, fast load response. are assessed under high PV penetration scenarios. In addition, performance of batteries and supercapacitors using different control strategies is studied in the two interconnections. The paper quantifies the potential of various resources to improve interconnection-level primary frequency response under high PV penetration without curtailing solar output.
The Grid Friendly$^\textrm{TM}$ Appliance~(GFA) controller, developed at Pacific Northwest National Laboratory, was designed for the purpose of autonomously switching off appliances by detecting under-frequency events. In this paper, a new frequency responsive load~(FRL) controller is first proposed by extending the functionality of the original GFA controller. The proposed FRL controller can autonomously switch on (or off) end-use loads by detecting over-frequency (or under-frequency) events through local frequency measurement. Then, a hierarchical decentralized control framework is developed for engaging the end-use loads to provide primary frequency response with the proposed FRL controller. The developed framework has several important features that are desirable in terms of providing primary frequency control. It not only exclusively maintains the autonomous operation of the end-use loads, but also effectively overcomes the stability issue associated with high penetration of FRLs. The simulation results illustrate the effectiveness of the developed hierarchical control framework for providing primary frequency response with the proposed FRL controller.
Bearing-load response for a pin-loaded hole is studied within the context of two-dimensional finite element analyses. Pin-loaded-hole configurations are representative of mechanically connected structures, such as a stiffener fastened to a rib of an isogrid panel, that are idealized as part of a larger structural component. Within this context, the larger structural component may be idealized as a two-dimensional shell finite element model to identify load paths and high stress regions. Finite element modeling and analysis aspects of a pin-loaded hole are considered in the present paper including the use of linear and nonlinear springs to simulate the pin-bearing contact condition. Simulating pin-connected structures within a two-dimensional finite element analysis model using nonlinear spring or gap elements provides an effective way for accurate prediction of the local effective stress state and peak forces.
A fully reusable Space Shuttle configuration has been analyzed during ascent flight to determine its response to atmospheric turbulence. Propellant sloshing, gust penetration, and automatic control system effects were included. The steady-state aerodynamic method of Woodward was used to derive the generalized aerodynamic forces, using the standard quasi-steady assumption. Aerodynamic interference effects between adjacent wings and bodies were found to be significant, with symmetric responses generally higher than antisymmetric. The stability augmentation system tended to lower booster response while increasing orbiter response. Loads due to 9 m/s quasi-square-wave gusts were considerably higher than the 3 sigma random turbulence loads. The elastic portion of the response accounted for about 15% of the total wing load in the discrete gust analysis, while in the random case the elastic effect was small.
California's energy landscape is undergoing a significant transformation, driven by the increasing integration of renewable energy sources, the increased adoption of distributed energy resources, the electrification of end-use loads, and the growing need for grid efficiency. To address these challenges, recent revisions to the State’s Load Management Standards (LMS) require all of California’s large utilities and community choice aggregators (CCAs) to offer dynamic electricity pricing options to customers by 2027. Dynamic pricing, which involves varying electricity rates based on real-time supply and demand conditions, offers a promising solution for optimizing grid operations, reducing costs, and incentivizing efficient use of grid capacity. Effective implementation of dynamic pricing requires understanding the potential impacts on customer bills, system load, and the cost-effectiveness of automation technologies. This study aims to evaluate the load response of various end-use devices to hourly dynamic prices. The end-uses studied here are space cooling, space heating, water heating, crop irrigation, pool and spa pumps, and electric vehicle (EV) charging, all for both residential and commercial applications, except for crop irrigation. In 2030, these end uses are forecasted to account for 18% of annual electricity demand in the state, but 40% of demand in the peak net load hour. By modeling possible price-responsive load dispatch algorithms and assessing the resulting impacts on both individual bills and the overall grid, we seek to inform policymakers and utilities about the potential benefits and challenges associated with dynamic pricing, and considerations for the design of dynamic pricing tariffs. Additionally, we will explore the cost effectiveness of adopting automation technologies to enable devices to respond more effectively to real-time price signals. This study considers a range of price profiles, accounting for differences across utilities and customer classes, and presents scenarios for dynamic price design via variation in the percentage of total customer electric costs that are allocated dynamically (versus constituting a fixed portion of the hourly volumetric price). We present results focused primarily on 2030, forecasting electricity prices under both low and high-cost scenarios, to inform longer-term tariff design considerations. We design tariffs by starting with 2019 prices that were calculated according to CalFUSE guidance (CPUC, 2022) and that have been used in recent studies; these prices are all-in volumetric rates that vary by utility and are revenue-neutral to each customer class. They are developed by considering six electricity cost components that are allocated hourly based on system load indicators (gross and net load, and wholesale prices). These prices are forecasted to 2030 for low and high cost scenarios, considering recent trends in total electricity costs with and without years of substantial wildfire mitigation investments. These tariffs, which allocate all costs on an hourly basis, are considered our “Full” dynamic tariff design scenario, while two additional scenarios explore allocating a portion of costs as a flat volumetric charge: the “Medium” scenario allocates 50% of revenue dynamically (and keeps 50% flat), while the “Mild” scenario allocates 20% of revenue dynamically. The 20% dynamic allocation on the Mild scenario aims to represent a case where only the marginal operating costs of the grid are included in the dynamic price.