Browse Topic: Energy consumption
Innovators at NASA Johnson Space Center, in collaboration with innovators at American Oxygen, have developed a solid-state system and process that separates oxygen from ambient air and compresses the resulting purified oxygen — with a significant reduction in power consumption compared to prior state-of-the-art. It is based upon a proven solid oxide electrochemical oxygen separation and compression technique that derives purified oxygen from ambient air and compresses it using an electrochemical pumping method.
This paper solves the problem of resource and energy constraints on orbit computing for LEO satellites. By combining MADDPG reinforcement learning and Lyapunov optimization, the paper proposes a computing framework and implements an adaptive task offloading model for space flight using a multi-agent deep actor critic algorithm, MADDPG. The joint optimization mechanism is implemented by multi-agent dynamic task offloading. Through the transformation from the state with long-term constraints into optimization of the status of queue stability, the load scheduling under threshold energy in accordance with the characteristics of energy constraints was realized by introducing Lyapunov virtual queues into the process of policy evaluation of deep reinforcement learning. The experimental results show that the proposed framework enables a lightweight preliminary calculation, balanced energy consumption to reduce resource allocation, and realizes the stable queues through adaptability of tasks under energy balance conditions, which can provide high-efficiency computing assistance and support for space orbit tasks such as monitoring remote sensing of Earth.
The global trend towards green and low-carbon development is that hydrogen fuel cells, as a new type of green power device, have the characteristics of zero emissions and no pollution. Its basic principle is that hydrogen fuel directly converts chemical energy into electrical energy through electrochemical reactions, achieving energy conversion between fuel cells and internal combustion engines, thereby providing sustained and stable power. The PEMFC has attracted significant attention due to advantages such as fast start-up times and long lifespans. However, excessive temperature during the reaction process of solid-state hydrogen proton fuel cells can lead to a decrease in efficiency. This article studies the temperature control device of solid-state hydrogen fuel cells and finds that active temperature control technology can achieve precise temperature regulation, but it consumes more energy; the passive temperature control scheme can reduce energy consumption, but the response speed to low-temperature start-up is limited; The application of intelligent algorithm fuzzy PID significantly improves the temperature control accuracy under dynamic loads and effectively enhances the hydrogen release rate.
In the field of measuring carbon emissions from road traffic, the carbon emission factor method has remarkable advantages in terms of standardization, operational simplicity, and adaptability. Backed by the IPCC international standard framework, this method offers convenient access to a dynamic factor database and incorporates an adaptive adjustment mechanism for real-world scenarios, such as technological advancements and regional disparities. Against this backdrop, this study employs the carbon emission factor method to establish refined measurement models based on load capacity and fuel consumption, respectively. These models are then applied to quantify carbon emissions from trucks on specific sections of the G30 highway in Xinjiang. The load-based model calculates emissions by integrating truck axle weight and driving distance, while the fuel-based model analyzes fuel consumption data in conjunction with driving mileage. A comparison of the two models in terms of measurement differences is also carried out in the research. Furthermore, it provides a granular breakdown of energy consumption data for fully loaded trucks exceeding 31 tons, as specified by national standards. This introduces a novel approach to precise carbon emission measurement in heavy-duty transportation in northwestern China. It also provides a method for establishing an emission mitigation policy that is region-specific on a scientific basis.
Battery energy awareness is an important aspect of tasking Unmanned Aerial Systems (UAS) safely and efficiently. By considering energy expenditure during mission planning, flight plans are assigned to the UAS only if there is sufficient energy onboard to complete the mission. In this work, several methods are developed for predicting the energy consumed during a flight, and their accuracy is assessed. Three simulation-based models derived from momentum-theory, blade-element theory, and computational fluid dynamics (CFD) are considered in addition to two data-driven models derived from flight test data (linear regression and Kriging), and four multi-fidelity models (Optimized Kirchstein, Hover-corrected, Additive Bridge, and Predictor-Corrector). Each model is used to predict the energy consumption of a representative mission and their predictions are compared to the measured energy consumption. From this analysis, it is found that a linear regression model trained on flight test data is able to deliver predictions within 3% of the measured value and outperforms other simulation-based and multi-fidelity models despite a simple architecture. The high level of accuracy and low computational requirements make this linear regression model desirable for energy-aware mission planning and energy-leash computations.
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