Temperature Simulations

To facilitate conformational transitions in the before-mentioned adenylate kinase, Elamrani and co-workers scaled all atomic masses by a large factor thus allowing the use of a high effective simulation temperature of 2000K ([Elamrani et al. 1996]). To prevent protein unfolding, elements of secondary structure had to be constrained. 

To generate characteristic velocities and bring a molecular system toequillbrium at th e sim illation temperature, atom s are allowed to in teract W ith each other through the equation s of motion. For isothermal simulations, a temperature bath" scales velocities to drive the system towards the simulation temperature,. Scaling occurs at each step of a simulation, according to equation 2S. 

Th e sim u lation or run tim e m eludes time for the system lo equilibrate at Ibe simulation temperature plus tbe time for data collection, while the trajectory evolves. Simulation timesdepend on the time scale of tbc property you are investigating. 

In the heating phase (assuming i j is non-zero), the velocities are periodically rescaled to change the system temperature from the initial tern perature T to the simulation temperature T2 in incrc-m eiiis of th e temperature step AT. Th e h eaiin g period for rescaling the velocities, P, is defined by ... 

As comparison of the simulated temperature fields shows, fluid slippage results in temperature peak shifting from a location furthest away from the... 

The direct evaluation of velocities gives a useful handle for controlling the simulation temperature (via velocity scaling). 

Figure 8.1.4r Simulated temperature profiles with true kinetics and coolant temperature as parameters. ...

Usually a linear interpolation of density, viscosity (for Newtonian fluids) and heat capacity will provide suitable fluid properties, if the simulated temperatures fall within that range of data. 

Run the dynamics for 1000 iterations or until you are satisfied that the run is producing a relatively constant set of attributes. Repeat the run 10 times and compute the average values for each attribute. Convert each value to a fraction of the total number of water molecules, 2100 in this case. The sum of the fx values then equals 1.0. These fractions become a structural profile of water at any simulated temperature. 

Using the parameter setups for Example 3.1, change the temperature of the water by changing the / b(WW) and J(WW) values according to the relationships shown in Table 3.2. For example, use Tb(WW) = 0.50 and J(WW) = 0.71. Run several of these temperatures from hot to cold water and collect the fx attributes. Convert the fx values to a fraction of 1.00 and then plot the set of fx values versus the temperature. This set of relational values is a set of structures of water at different simulated temperatures (see Figure 3.3). They can be used as independent variables to explore the relationships of water versus various physical properties at different temperatures as shown in Table 3.2. 

Repeat Example 3.2 using a simulated temperature of 80°C using Eb(AA) = 0.8 and J(AA) = 0.25. Change the water concentration according to Table 3.1. Collect the fx values and convert them to a fraction of 1.0. Compare these values with those from Study 3.1. 

Water molecules are placed in the lower half of the grid, leaving the upper half empty. A temperature is selected using the Fb and J parameters and the CA is allowed to run for a specified time. The number of water molecules in each row of the upper half of the grid is counted. The grid is defined as a cylinder with the upper and lower boundaries stationary. This prevents water movement past the bottom boundary. A profile of evaporation versus temperature can be obtained by varying the simulated temperature. Use Example 3.5 in the Program CASim. 

Fig. 9.5. Plot of ln[ 2Q.(s)/ 0)(e)]. The region between —2.5kcalmol 1 and - 0.5 kcal moP1 is satisfactorily linear, and (9.17) yields T w 302 K, in good agreement with the simulation temperature of 298 K. The intercept gives //ex =2.5 kcal mol-1 in agreement with a value of 2.5 kcalmoP1 for the SPC water model [63]...

By tuning /. to obtain a bimodal particle number distribution, it was possible to locate the point of coexistence between saturated thin and thick films on the adsorbing surface. The prewetting coexistence curve was then obtained through the application of this procedure at each simulation temperature. 

Sarathchandra SU, Perrott KW, Littler RA (1989) Soil microbial biomass influence of simulated temperature changes on size, activity and nutrient content. Soil Biol Biochem 21 987-993... 

Fig. 16. Simulated temperature field in the liquid and solid phases.

No crystalline order is visible for the bead-spring model upon cooling to the frozen-in phase at T = 0.3. The break in the volume-temperature curve (described in the section on thermodynamic information) occurring between T = 0.4 and T = 0.45 leads us to expect that the two-step decay described by MCT should be observable at simulation temperatures above (and close to) this region. This expectation is borne out in Figure 10, which shows the... 

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