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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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Intensifying steam methane reforming and water-gas shift in tandem via rapid pulsed Joule heating
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CO2-selective membrane reactor process for water-gas-shift reaction with CO2 capture in a coal-based IGCC power plant
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The Shifted Boundary Method in Isogeometric Analysis
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Shift in plankton diversity and structure: Influence of runoff composition in the Nagqu River on the Qinghai-Tibet Plateau
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Shifting signals: Correlations among freshwater, marine and climatic indices often investigated in Pacific salmon studies
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Future water resource shifts in the high desert Southwest of Northern New Mexico, USA
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How good are learning-based control v.s. model-based control for load shifting? Investigations on a single zone building energy system
Both model predictive control (MPC) and deep reinforcement learning control (DRL) have been presented as a way to approximate the true optimality of a dynamic programming problem, and these two have shown significant operational cost saving potentials for building energy systems. Furthermore, there is still a lack of in-depth quantitative studies on their approximation levels to the true optimality, especially in the building energy domain. To fill in the gap, this paper provides a numerical framework that enables the evaluation of the optimality levels of different controllers for building energy systems. This framework is then used to comprehensively compare the optimal control performance of both MPC and DRL controllers with given computation budgets for a single zone fan coil unit system. Note the optimality is estimated based on a user-specific selection of trade-off weights among energy costs, thermal comfort and control slew rates. Compared with the best optimality we can find through expensive optimization simulations, the best DRL agent can maximally approximate the optimality by 96.54%, which outperforms the best MPC whose optimality level is 90.11%. However, due to the stochasticity, the DRL agent is only expected to approximate the optimality by 90.42%, which is almost equivalent to the best MPC. Except for Proximal Policy Optimization (PPO), all DRL agents can have a better approximation to the optimality than the best MPC, and are expected to have better approximation than the MPC with a prediction horizon of 32 steps (15 min per step). In terms of reducing energy cost and thermal discomfort, MPC can outperform the rule-based control (RBC) by 18.47%–25.44%. DRL can be expected to outperform RBC by 18.95%–25.65% ,and the best DRL control policy can outperform RBC by 20.29%–29.72%. Although the comparison of the optimality level is performed in a perfect setting, e.g., MPC assumes perfect models, and DRL assumes a perfect offline training process and online deployment process, this can shed insight on their capabilities of approximating to the original dynamic programming problem.
Warming and elevated CO2 induced shifts in carbon partitioning and lipid composition within an ombrotrophic bog plant community
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Fungal endophytes of the invasive grass Eragrostis lehmanniana shift metabolic expression in response to native and invasive grasses
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Differential microbiota shift on whole romaine lettuce subjected to source or forward processing and on fresh-cut products during cold storage
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Modeling analysis of water-gas-shift reaction on catalyst-packed ceramic-carbonate dual-phase membrane reactor for hydrogen production
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Techno-economic analysis and optimization of water-gas shift membrane reactors for low-carbon hydrogen production
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