Free energy via thermostatted dynamic potential-energy changes

1993 ◽  
Vol 47 (6) ◽  
pp. 3852-3861 ◽  
Author(s):  
Brad Lee Holian ◽  
H. A. Posch ◽  
W. G. Hoover
Keyword(s):  
Free Energy ◽  
Potential Energy ◽  
Dynamic Potential

scholarly journals A Generalized Dynamic Potential Energy Model for Multiagent Path Planning

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Liu He ◽  
Haoning Xi ◽  
Tangyi Guo ◽  
Kun Tang
Keyword(s):  
Path Planning ◽  
Potential Energy ◽  
Multiagent System ◽  
Small Time ◽  
Energy Model ◽  
Dijkstra Algorithm ◽  
Energy Field ◽  
Space Potential ◽  
Dynamic Potential ◽  
Planning Decision

Path planning for the multiagent, which is generally based on the artificial potential energy field, reflects the decision-making process of pedestrian walking and has great importance on the field multiagent system. In this paper, after setting the spatial-temporal simulation environment with large cells and small time segments based on the disaggregation decision theory of the multiagent, we establish a generalized dynamic potential energy model (DPEM) for the multiagent through four steps: (1) construct the space energy field with the improved Dijkstra algorithm, and obtain the fitting functions to reflect the relationship between speed decline rate and space occupancy of the agent through empirical cross experiments. (2) Construct the delay potential energy field based on the judgement and psychological changes of the multiagent in the situations where the other pedestrians have occupied the bottleneck cell. (3) Construct the waiting potential energy field based on the characteristics of the multiagent, such as dissipation and enhancement. (4) Obtain the generalized dynamic potential energy field by superposing the space potential energy field, delay potential energy field, and waiting potential energy field all together. Moreover, a case study is conducted to verify the feasibility and effectiveness of the dynamic potential energy model. The results also indicate that each agent’s path planning decision such as forward, waiting, and detour in the multiagent system is related to their individual characters and environmental factors. Overall, this study could help improve the efficiency of pedestrian traffic, optimize the walking space, and improve the performance of pedestrians in the multiagent system.

scholarly journals Simulation of FUS protein condensates with an adapted coarse-grained model

2020 ◽  
Author(s):  
Zakarya Benayad ◽  
Sören von Bülow ◽  
Lukas S. Stelzl ◽  
Gerhard Hummer
Keyword(s):  
Free Energy ◽  
Phase Separation ◽  
Potential Energy ◽  
Energy Function ◽  
Potential Energy Function ◽  
Scaling Factor ◽  
Coarse Grained ◽  
Disordered Proteins ◽  
General Strategy ◽  
Droplet Shape

AbstractDisordered proteins and nucleic acids can condense into droplets that resemble the membraneless organelles observed in living cells. MD simulations offer a unique tool to characterize the molecular interactions governing the formation of these biomolecular condensates, their physico-chemical properties, and the factors controlling their composition and size. However, biopolymer condensation depends sensitively on the balance between different energetic and entropic contributions. Here, we develop a general strategy to fine-tune the potential energy function for molecular dynamics simulations of biopolymer phase separation. We rebalance protein-protein interactions against solvation and entropic contributions to match the excess free energy of transferring proteins between dilute solution and condensate. We illustrate this formalism by simulating liquid droplet formation of the FUS low complexity domain (LCD) with a rebalanced MARTINI model. By scaling the strength of the nonbonded interactions in the coarse-grained MARTINI potential energy function, we map out a phase diagram in the plane of protein concentration and interaction strength. Above a critical scaling factor of αc ≈ 0.6, FUS LCD condensation is observed, where α = 1 and 0 correspond to full and repulsive interactions in the MARTINI model, respectively. For a scaling factor α = 0.65, we recover the experimental densities of the dilute and dense phases, and thus the excess protein transfer free energy into the droplet and the saturation concentration where FUS LCD condenses. In the region of phase separation, we simulate FUS LCD droplets of four different sizes in stable equilibrium with the dilute phase and slabs of condensed FUS LCD for tens of microseconds, and over one millisecond in aggregate. We determine surface tensions in the range of 0.01 to 0.4mN/m from the fluctuations of the droplet shape and from the capillary-wave-like broadening of the interface between the two phases. From the dynamics of the protein end-to-end distance, we estimate shear viscosities from 0.001 to 0.02Pas for the FUS LCD droplets with scaling factors α in the range of 0.625 to 0.75, where we observe liquid droplets. Significant hydration of the interior of the droplets keeps the proteins mobile and the droplets fluid.

Comments on the energetics of the frictional process

1952 ◽  
Vol 212 (1111) ◽  
pp. 477-477
Keyword(s):  
Free Energy ◽  
Potential Energy ◽  
Rolling Friction ◽  
External Friction ◽  
Main Process ◽  
Final Question

I would like to ask what in Dr Bowden’s picture is the mechanism by which the free energy is transformed into heat ? If I understood him rightly, the free energy is needed for the breaking of bonds, but this does not produce heat. Could he tell us a little more about this ? Also I would like to know whether exact measurements have ever been made on the process of dry external friction to show what percentage of the energy is transformed into heat and what turns up in the form of potential energy stored in deformation of the material. A final question which may have some connexion with the first one: if the formation and breaking up of the bonds is the main process consuming energy, how does it come about that rolling friction is so much smaller than gliding friction ?

Reply to Professor Simon’s comments

1952 ◽  
Vol 212 (1111) ◽  
pp. 477-477
Keyword(s):  
Free Energy ◽  
Potential Energy ◽  
Temperature Rise ◽  
Frictional Resistance ◽  
Mechanical Equivalent ◽  
Work Of Deformation ◽  
Work Done

The question raised by Professor Simon on the mechanism by which the work done against the frictional resistance is transformed into heat perhaps requires a more fundamental explanation than can be deduced from frictional experiments alone. I t is true that free energy is required for the formation of a new surface when the intermetallic junctions are ruptured, and this in itself does not produce an appreciable temperature rise. With plastic solids (as distinct from liquids), however, most of the work is required to deform the metal around the junctions. As Taylor & Quinney (1937) have shown at least 90% of the work of deformation is liberated as heat and less than 10 % remains as potential energy in the deformed metal, but if the metal is already heavily deformed the proportion of potential energy retained in the metal is negligibly small. Some of the early determinations of the mechanical equivalent of heat are of course based on the assumption that all the frictional work appears as heat. I would like to ask Dr A. J. W. Moore to comment further on this.

Extracting Substantial Free Energy From Static Ambient Potential Energy Using Distance, Time-Lapse Chaos, and Coriolis Principles

2018 ◽  
Author(s):  
Jim B. Surjaatmadja
Keyword(s):  
Free Energy ◽  
Kinetic Energy ◽  
Potential Energy ◽  
Time Lapse ◽  
Gas Wells ◽  
Direct Path ◽  
Natural Processes ◽  
Perpetual Motion ◽  
Financial Costs ◽  
Save Energy

Extracting free energy has long been a goal of science but is mostly considered impossible to achieve. Natural processes having independent movement, such as rivers and wind, are often used but provide varied effectiveness. However, coordinated instability within a statically pressurized ambience can be used to extract a significant percentage of the ambient potential energy. This method creates two pathways between two adjacent points, one being a chaotic or Coriolis swirling path and the other being a direct path, thereby creating a pressure difference between the two adjacent points, which can be harvested to reduce the kinetic energy input required to perform the process. While some refer to the proposed benefits as “perpetual motion,” it is necessary to understand that 55 to 80% of the required kinetic energy would still be mechanically generated; therefore, they could be better referred to as “coordinated chaos” or a “Coriolis energy extractor” to save energy [1]. This paper studies direct returns—extracting energy directly from a static (not dynamic) ambient energy. While such returns might not be substantial in normal activities, in deepwater or underground applications (e.g., oil or gas wells), they can be significant, often equating to a 20–45% reduction in fuel use or pollutant generation. In operations that use 20,000 horsepower, this could represent a savings of 4,000 horsepower or 10,000,000 Btu/hr with no associated financial costs.

The statistical thermodynamics of multicomponent systems

1951 ◽  
Vol 205 (1081) ◽  
pp. 247-269 ◽  
Keyword(s):  
Free Energy ◽  
Thermodynamic Properties ◽  
Potential Energy ◽  
Multicomponent Systems ◽  
Reference Species ◽  
Two Phase ◽  
Energy Of Mixing ◽  
Volume Of Mixing ◽  
Two Component Systems ◽  
Free Energy Of Mixing

This paper describes a new statistical approach to the theory of multicomponent systems. A ‘conformal solution’ is defined as one satisfying the following conditions: (i) The mutual potential energy of a molecule of species L r and one of species L s at a distance ρ is given by the expression u rs (ρ) = f rs u 00 ( g rs ρ ), where u 00 is the mutual potential energy of two molecules of some reference species L 0 at a distance ρ , and f rs and g rs are constants depending only on the chemical nature of L r and L s . (ii) If L 0 is taken to be one of the components of the solution, then f rs and g rs are close to unity for every pair of components. (iii) The constant g rs equals ½( g rr + g ss ). From these assumptions it is possible to calculate rigorously the thermodynamic properties of a conformal solution in terms of those of the components and their interaction constants. The non-ideal free energy of mixing is given by the equation ∆* G = E 0 ƩƩ rs x r x s d rs , where E 0 equals RT minus the latent heat of vaporization of L 0 , x r is the mole fraction of L r and d rs denotes 2 f rs — f rr — f ss . This equation resembles that defining a regular solution, with the important difference that E 0 is a measurable function of T and p , which makes it possible to relate the free energy, entropy, heat and volume of mixing to the thermodynamic properties of the reference species; and the predicted relationships between these quantities agree well with available data on non-polar solutions. The theory makes no appeal to a lattice model or any other model of the liquid state, and can therefore be applied both to liquids and to imperfect gases, and to two-phase two-component systems near the critical point.

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