Last time we looked at a rule-of-thumb for energy used in running. Today we’ll look at
another rule-of -thumb for the effect of runner weight on pace. There is a fair amount of wild speculation, rigorous study, anecdotal experience, and intuitive assertion about this topic, but a lot of conclusions seem to center around a time penalty of 1-4 seconds per mile per pound of body weight. We’ll look at that range in terms of energy use.
In the last two posts, here and here, we looked at the relationships between grade, power
expenditure, and pace for running. In those posts, we had to make an assumption about a “base” power use which we defined as the power required to run at a steady pace on flat ground. Since we were interested mostly in the additional power associated with running up a grade, and since that “base” power is so complicated to estimate for any particular individual, we simply invoked a common rule-of-thumb and used 100 Cal per mile as our base power. Today, we’ll explore that rule-of-thumb in a little more depth.
Last post we looked at the energy cost of running uphill at a certain pace. This time we’ll flip that around and look at the uphill pace that could be maintained for a fixed amount of power. We’ll also look at the total power required as a function of pace, grade, and weight.
Earlier, we talked about the power required for bicycling uphill. Today we’ll talk about the same effects for running uphill.
In keeping with the theme of material properties from the last post, today we’ll talk about thermal conductivity which is a material property that relates to the conduction of heat.
Earlier, we looked at the specific heat ratio, and we also looked at the variation in specific heat with temperature here and here. Today we’ll look in a little more detail at the properties constant volume and constant pressure specific heat.
We’ve been talking here and here about the effects of weight, speed, and grade on climbing hills on a bicycle. Now, let’s reverse direction and think about coasting down a hill.
Last time we looked at how weight and speed and grade affect the power requirement for a bicyclist climbing a hill. Today, we’ll reverse the question and assume that the cyclist has a fixed amount of power to deliver. How will that affect climbing speed?
Not long ago I was huffing and puffing on my bicycle up a very steep hill and another cyclist blew by me with apparently little effort. Now, in addition to looking younger, stronger, and having a better bicycle, he appeared to be carrying significantly less excess, ahem, baggage. So I got to wondering how much difference one’s weight (or to be charitable, a loaded backpack) makes when ascending a hill on a bicycle.
In the last two posts, we considered the units of food energy, and some of the issues associated with calculating human energy use. Today we’ll look at different views of calculating energy use from exercise.
Last post we talked about the units used for describing the energy content of food. For the purposes of an energy balance on the body, we also have to consider the efficiency of converting food energy into activity.
Sometimes it seems like most of the people you meet are on a diet of some kind—either to lose weight, gain weight, or stay the same. In any case, it seems reasonable to assume that diets ultimately have to be about the amount of energy that you take in as food or drink, and the amount of energy that your body burns through exercise or just existing. Today we’ll talk about the units used to measure energy in food.
We’ve talked before about the contact temperature and the effect that different materials have on the temperature that you feel when you first touch them. Today we’ll look at that effect in a more applied way.
If you live in an area with snow, you’ve probably experienced a snowfall followed by several cold clear days. If it lasted long enough, you may have noticed that the snow gradually starts to disappear (sublimate) even when the air temperature never gets above the melting point. In this post, we’ll look at the speed of that effect.
Last post we looked at another extension of lumped capacitance beyond our original exploration here and here. In the last post, we looked at a case where the heat transfer coefficient increased linearly with time, which might be a good approximation for the initial moments of the heat transfer for an object in a duct with the fan just starting up. Today we’ll look at a case where the heat transfer coefficient is decreasing.
Lumped capacitance is a very useful heat transfer approximation that we mentioned originally here, and explored in more depth with extensions here and here. Today, we’ll look at another extension of the basic lumped capacitance approach.
News flash! A major spill of refrigerant R718 occurred on the Interstate last night! The refrigerant soaked the highway and surrounding ground. Luckily, in a few hours it dried up with no effect other than helping nearby plants and small animals. Because, of course, refrigerant R718 is the designation for pure water. Today we’ll consider advantages and disadvantages of using water as a refrigerant.
Last post we talked about using the ideal gas law on the right side of the steam dome and found that it could be used as a decent approximation for very low pressures, even when the vapor is about to start condensing into liquid. Today we’ll look at the accuracy of the ideal gas approximation out in the superheated region.
The ideal gas law describes the approximate behavior of gases at temperatures that are high relative to the critical temperature and at pressures that are low relative to the critical pressure. This figure shows the approximate region where the ideal gas law applies best.
Normally, one would use steam tables to determine properties close to the saturation region, and only use the ideal gas approximation far away, as shown in the figure. Today we’ll look at the error introduced by the ideal gas approximation as we get close to the saturation region.
Last post we demonstrated a parabolic model for the pressure-flow relationship in a pipe, and showed a more robust way to represent the flow-pressure relationship. In this post, we’ll discuss the application of both models.
