A visualization of reversible vs nonreversible processes
“What exactly is the difference between reversible and non-reversible systems?”
Reversible and non-reversible systems are two of the most fundamental and confusing concepts in thermodynamics. But this visualization should help clarify them. Let’s take a ping pong game. If we are playing without score, then after a round is over, everything goes back to normal with no change in the system, making it reversible. However, if we are keeping score, then after every round the number of points change forever, making this process non-reversible!
“How can we maximize or minimize a set of linear equations?”
Often times, when working on problems, we have multiple variables related by multiple equations. For example, let’s start out with this situation. Let’s say we have two machine parts x and y that cost 2 dollars and 5 dollars to make respectively, symbolically p(x,y) = 2x + 5y. And let’s also say that we have to make a total of 100 machine parts respectively, or x + y = 100 (blue). And let’s also say that 202 times the number of part x and 5 times the number of part y must be equal to 1400, or 20x + 5y = 1400 (green). So how can we find the minimum price that meets all of our production needs? Well, let’s plot it on a graph (pictured), check all of the points of intersection (In this case (0,100), (60,40) and (100,0) ), and then see which of these points return the minimum desired quantity (In this case (0,100) –> $200). Linear programming can be applied to all forms of applications, ranging from engineering economic systems to control theory and even to general business!
“What is Industrial Ecology?”
Things are changing on Earth. Climate levels are rising, the human population is expanding, and industrialization is increasing, while our natural resources are going down down down. So how can we create a framework that studies how all of these complex systems interact with one another? Well, after many years of research, an entirely new scientific field has formed, industrial ecology. Industrial ecology is the study of how energy and resources flow through our modern industrial system. Industrial ecology looks at this issue through a multitude of perspectives, such as engineering, economics, natural sciences, and sociology.
Homogeneous and heterogeneous systems
“How do we classify thermodynamic systems?”
Engineering thermodynamics looks at heat, energy, and matter from a macroscopic, or non-atomic perspective. Because of this, objects and materials such as air appear to be uniform in composition. As a result, systems such as these are classified as homogeneous systems. Homogeneous systems stand in direct contract with heterogeneous systems such as a human body (which is composed of many different macroscopic layers). When working out thermodynamics problems, it is extremely important to know if your system is homogeneous or heterogeneous.
Simulation of science 05/20/16
As we have discussed about complex systems before, much of the natural world can not be predicted using simple mechanistic equations, but requires more complex theories instead. Now one may ask, how is it possible for scientists to utilize such complex theories? The answer lies in the fact that many scientists now a days use something called a simulation to control those systems. By recreating the theories as a computer model, scientists can be granted real time control of a particular situation. Simulations permeate every field of science, weather it be solar systems for astrophysics or earth systems in geology.
Complex systems 05/19/16
Anyone who has ever studied and lower division mathematics and physical science is probably familiar with linear systems, But what happens if we branch into more complex Nonlinear systems? Over here arises a problem, how can model a system of such great complexity that are found in fields ranging from Physics to economics to computer science to sociology? Through the introduction of complex systems, we get a paradigm shift in our epistemology. Complex systems take a statistical approach contrary to the mechanistic modeling approaches of renaissance era science, by including variables of all kinds to make one large cohesive system.