Several recent theories address the efficiency of a macroscopic thermodynamic motor at maximum power and question the so-called Curzon-Ahlborn (CA) efficiency. Considering the entropy exchanges and productions in an n-sources motor, we study the maximization of its power and show that the controversies are partly due to some imprecision in the maximization variables. When power is maximized with respect to the system temperatures, these temperatures are proportional to the square root of the corresponding source temperatures, which leads to the CA formula for a bithermal motor. On the other hand, when power is maximized with respect to the transition durations, the Carnot efficiency of a bithermal motor admits the CA efficiency as a lower bound, which is attained if the duration of the adiabatic transitions can be neglected. Additionally, we compute the energetic efficiency, or "sustainable efficiency," which can be defined for n sources, and we show that it has no other universal upper bound than 1, but that in certain situations, which are favorable for power production, it does not exceed ½.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1103/PhysRevE.85.021129 | DOI Listing |
Nat Commun
January 2025
Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, WI, 53706, USA.
Identifying transitional states is crucial for understanding protein conformational changes that underlie numerous biological processes. Markov state models (MSMs), built from Molecular Dynamics (MD) simulations, capture these dynamics through transitions among metastable conformational states, and have demonstrated success in studying protein conformational changes. However, MSMs face challenges in identifying transition states, as they partition MD conformations into discrete metastable states (or free energy minima), lacking description of transition states located at the free energy barriers.
View Article and Find Full Text PDFSci Rep
December 2024
Business Segment Networks, Stadtwerke Flensburg GmbH, 24939, Flensburg, Germany.
In response to climate change mitigation efforts, improving the efficiency of heat networks is becoming increasingly important. An efficient operation of energy systems depends on faultless performance. Following the need for effective fault detection and elimination methods, this study suggests a three-step workflow for increasing automation in managing defective substations on the user level within heat networks.
View Article and Find Full Text PDFEur J Neurol
January 2025
Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland.
Background: Magnetic resonance imaging may suggest spinal cord compression and structural lesions in degenerative cervical myelopathy (DCM) but cannot reveal functional impairments in spinal pathways. We aimed to assess the value of contact heat evoked potentials (CHEPs) in addition to MRI and hypothesized that abnormal CHEPs may be evident in DCM independent of MR-lesions and are related to dynamic mechanical cord stress.
Methods: Individuals with DCM underwent neurologic examination including segmental sensory (pinprick, light touch) and motor testing.
Phys Rev E
November 2024
West Los Angeles College, Science Division, 9000 Overland Ave, Culver City, California 90230, USA.
The thermodynamic relations for a Brownian particle moving in a discrete ratchet potential coupled with quadratically decreasing temperature are explored as a function of time. We show that this thermal arrangement leads to a higher velocity (lower efficiency) compared to a Brownian particle operating between hot and cold baths, and a heat bath where the temperature linearly decreases along with the reaction coordinate. The results obtained in this study indicate that if the goal is to design a fast-moving motor, the quadratic thermal arrangement is more advantageous than the other two thermal arrangements.
View Article and Find Full Text PDFPeerJ
December 2024
College of Sports Science, Zhuhai College of Science and Technology, Zhuhai, Guangdong, China.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!