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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.
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In this paper, we investigate the trade-offs among the subsystems of a hybrid electric vehicle (HEV), e.g., the engine, motor, and the battery, and discuss the related im- plications for fuel consumption and battery capacity and lifetime. Addressing this problem can provide insights on how to prioritize these objectives based on consumers needs and preferences.
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Energy Storage Systems (ESSs) are gaining great attention, as they can help operate grid power grids more reliably. The ESS acquisition and operation is motivated by the economics revolving around its provided services and revenue streams. These economics are greatly impacted by uncertainties in predicting the parameters which influence the ESS operation. The main goal of this paper is in addressing these uncertainties. We organize sources of uncertainties which directly impact the value-driven operation of the ESS and propose a framework which combines the uncertain requirements from processes both internal and external to ESS.
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This research proposes a sensitivity-based framework for selecting the optimal prescribed loading path for a biaxial cruciform specimen. Optimality here is determined by the direction and magnitude of the prescribed displacement that minimizes the influence of random noise on the material model parameter identification. Using simulated experimental data based on finite element simulation, in this work, we identify the material model parameters of a Ludwik hardening model and plane stress implementation of the Hill-48 yield criterion using finite element model updating (FEMU). Our analysis reveals that the identification (or estimator) uncertainty of model parameters depends on the displacement boundary conditions (i.e., loading sequence) and the ground-truth value of the individual parameters. Optimal experimental design (OED) criteria based on the Fisher information matrix were investigated to mitigate indecision in the choice of optimal load path when the identification uncertainty of different material model parameters optimized at different load paths. The determinant of the Fisher information matrix was chosen here as the more useful metric due to its ability to capture uncertainty of the most influential material model parameters. The proposed framework demonstrates potential for real-time automated load step selection using scalar criteria derived prior to mechanical loading. The framework can be generalized to other geometries, boundary conditions and material models, allowing this procedure to be utilized for different experimental configurations and materials.
Manufacturers consume about 27% of the total electricity in the U.S. and are among the main contributors in the rising electricity demand. End-user electricity demand response is an effective demand side management tool that can help energy suppliers reduce electricity generation expenditures while providing opportunities for manufacturers to decrease operating costs. Several studies on demand response for manufacturers have been conducted. However, there lacks a unified production model that balances production capability degradation, maintenance requirements, and time-of-use (TOU) electricity prices simultaneously such that the interaction between production, maintenance, and electricity costs is considered. In this paper, a cost-effective production and maintenance scheduling model considering TOU electricity demand response is presented. Additionally, an aggregate cost model is formulated, which considers production, maintenance, and demand response parameters in the same function. The proposed models provide manufacturers with tools for implementing feasible and cost-effective demand response while meeting production targets and efficiently allocating maintenance resources. A case study is performed and illustrates that 19% in cost savings can be achieved when using the proposed model compared to solely minimizing the electricity billing cost. In addition, 14% in cost savings can be achieved when using the proposed model compared to a strategy where only the maintenance cost is minimized. Finally, the benefits of demand response driven production and maintenance scheduling under different cost and parameter settings are investigated; where the rated power, production rate, and initial machine production capability show to have the largest impact on the cost effectiveness of implementing demand response.
It is shown that the multiplicities and angular and energy distributions of neutrons and photons evaporated from thermalized fragments originating from the spontaneous and low-energy induced fission of nuclei, the relative yields of ground and isomeric states of final fragments, and the features of delayed neutrons emitted upon the beta decay of the above fragments can successfully be described by employing nonequilibrium distributions of spins and relative orbital angular momenta of fission fragments formed in the vicinity of the scission point for the fissile nucleus being studied. It is also shown that these distributions, which are characterized by large mean values of the spins and orbital angular momenta directed orthogonally to the symmetry axis of the fissioning nucleus are successfully constructed upon simultaneously taking into account zero-mode transverse wriggling and bending vibrations of a fissile compound nucleus in the vicinity of its scission point, the wriggling vibrations being dominant. It is confirmed that the zero-mode wriggling vibrations considered immediately above are directly involved in the formation of the angular distributions of fragments originating from the spontaneous and low-energy fission of nuclei. This makes it possible to describe successfully such distributions for photofission fragments.
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METS-R ADDSEVS simulator is a high fidelity, parallel, agent-based evacuation simulator for multi-modal energy-optimal trip scheduling in real-time (METS-R) at transportation hubs. It consists of two modules. The first one is the traffic simulator module; the second one is the high-performance computing (HPC) module. More details can be found at https://umnilab.github.io/METS-R_doc/.
Designed for building energy managers, utilities and firms providing energy services, FEDS provides a comprehensive and user-friendly method for users to quickly and accurately identifying energy conversion and end-use technology improvements that offer maximum savings in energy usage and cost. FEDS enables the user to assess energy efficiency and consumption, identify retrofit opportunities, select minimum life cycle costs, determine payback, and prioritize investments in energy efficiency retrofit projects for multiple or single buildings across a particular site. As part of the life-cycle cost optimization it also considers several options for partial or complete abandonment of central energy plants including: individual technologies dropping off a thermal loop, a loop and all technologies dropping off a central plant, and all technologies and loops dropping off a central plant resulting in its complete abandonment. Improvements in release 7.0 include: FEDS 7.0 is now a full hourly simulation model (runs every hour of the year as opposed to the previous simplified hourly approach which ran 9 24-hour periods to represent each month). Enhanced building operation and occupancy modeling; New HVAC technology options (VAV, Economizers, Demand Controlled Ventilation, Terminal Reheat, Subcool Dehumidification); Additional chiller types; New lighting technologies (LED, Halogen, Induction, additional fluorescent options); Completely updated retrofit cost and performance data; Regional cost data tied to zip code (previously applied state level averages); Updated and enhanced reporting capabilities; and New pier floor model
Incorporation of real-time component information using equipment condition assessment (ECA) through the development of enhanced risk monitors (ERM) for active components in advanced reactor (AR) and advanced small modular reactor (SMR) designs. We incorporate time-dependent failure probabilities from prognostic health management (PHM) systems to dynamically update the risk metric of interest. This information is used to augment data used for supervisory control and plant-wide coordination of multiple modules by providing the incremental risk incurred due to aging and demands placed on components that support mission requirements.