Proper tire inflation branch

This page is a knowledge crystal of subtype method. The page identifier is Op_en7453
Moderator:Jouni (see all)

Upload data {{#opasnet_base_link:Op_en7453}}

Proper tire inflation is a branch of Handprinter. This page is a test case how to manage branches.

Question

How to estimate the life-cycle impacts of keeping tires properly inflated?

Answer

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# This is code Op_en7453/ on page [[Proper tire inflation branch]]
library(OpasnetUtils)

objects.latest("Op_en7453", code_name = "initiate")

if(!is.null(miles)) milesDrivenPerYear <- Ovariable("milesDrivenPerYear", data = data.frame(Result = miles))

if(!is.null(consumption)) gallonsPerMile <- Ovariable("gallonsPerMile", data = data.frame(Result = consumption))

fuelConsumptionReduction <- EvalOutput(fuelConsumptionReduction)

oprint(summary(fuelConsumptionReduction))

Rationale

Data

Tire inflation parameters(-)
Obs	Parameter	Unit	Result
1	Pressure drop per temperature drop	PSI /10°F	0.5:1:2
2	Fuel economy sensitivity	% /PSI	0.4
3	Miles driven per year	mi /a	10000
4	Fuel consumption	gl /mi	0.04
5	Pressure deficit	PSI	2

Calculations

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# This is code Op_en/FuelConsumptionReduction on page [[Proper tire inflation branch]]
library(OpasnetUtils)

fuelConsumptionReduction <- Ovariable("fuelconsuptionReduction",
  dependencies = data.frame(
    Name = c("milesDrivenPerYear", "gallonsPerMile", "pressureDeficit", "fuelEconomySensitivity"),
    Ident = c(
      "Op_en7453/milesDrivenPerYear", 
      "Op_en7453/gallonsPerMile", 
      "Op_en7453/pressureDeficit", 
      "Op_en7453/fuelEconomySensitivity"
    )
  ),
  formula = function(...) {
    fuelConsumptionReduction <- milesDrivenPerYear * gallonsPerMile * pressureDeficit * fuelEconomySensitivity / 100
    return(fuelConsumptionReduction)
  }
)

dat <- opbase.data("Op_en7453", subset = "Tire inflation parameters")
dat$Unit <- as.character(dat$Unit)

milesDrivenPerYear <- Ovariable("mileDrivenPerYear", 
  data = dat[dat$Parameter == "Miles driven per year" , ]["Result"]
)
milesDrivenPerYear@meta <- c(milesDrivenPerYear@meta, Unit = dat$Unit[dat$Parameter == "Miles driven per year"])

gallonsPerMile <- Ovariable("gallonsPerMile", 
  data = dat[dat$Parameter == "Fuel consumption" , ]["Result"]
)
gallonsPerMile@meta <- c(gallonsPerMile@meta, Unit = dat$Unit[dat$Parameter == "Fuel consumption"])

pressureDeficit <- Ovariable("pressureDeficit",
   data = dat[dat$Parameter == "Pressure deficit" , ]["Result"]
)
pressureDeficit@meta <- c(pressureDeficit@meta, Unit = dat$Unit[dat$Parameter == "Pressure deficit"])

temp <- dat[dat$Parameter == "Fuel economy sensitivity" , ]["Result"]
temp$DirectInput <- "Gasoline"
fuelEconomySensitivity <- Ovariable("fuelEconomySensitivity",
  data = temp
)
fuelEconomySensitivity@meta <- c(fuelEconomySensitivity@meta, Unit = dat$Unit[dat$Parameter == "Fuel economy sensitivity"])

objects.store(list=ls())
cat("Objects", ls(), "stored.\n")

Work flow

Modeler decides: Is this an Act / Behavior Change / Choice / Replacement / Influence ? Determining which it is will influence how the action is modeled, what data required. It will also be important information to pass to apps.

Branch Phase 1: Evidence and discussion sections

Gather and summarize key input information and data with citations. This will be done with a series of sections, each of which has the following elements:

A Claim or statement (about cause and effect relationships which are necessary to quantify in order to assess the handprinting action's impacts);
Evidence and details/quantities, and the source(s) for this evidence; and
The introduction of one or more variables or data tables which characterize the cause and effect relationship(s) introduced in the section, and which will be used to calculate the changes in consumption and usage flows that will comprise the handprint.

Claim or statement: Drops in tire pressure reduce fuel economy of vehicles.

Evidence, details: “According to the US Department of Energy every one psi drop in the pressure of the tires is going to lower gas mileage by 0.4 percent.” Source: http://procarmechanics.com/how-tire-pressure-affects-mpg/

Corresponding equations or data: fuelEconomySensitivity = 0.4 % units: change in fuel consumption per mile, per PSI pressure deficit

Claim or statement: Cold weather brings about tire pressure drops.

Evidence: “It is estimated that the tire pressure will decrease as much as two pounds for every ten degrees temperature drops.” [Other people can come along, add metric units, add other evidence, etc.] Source: http://procarmechanics.com/how-tire-pressure-affects-mpg/

⇤--#: . Although I agree that temperature will obviously affect the pressure, simply using uncertainties would be more plausible than trying to model temperature dependence. There are several confounding factors. At least in Finland people change winter tires when it becomes cold and routinely check the tire pressure at that point, so that the previous temperatures and pressures make no difference. We don't know the temperature of the day when the tires were filled. We don't know how often people check the pressure and whether they know what the right pressure is. All these factors have major impacts on the tire inflation deviation from the optimum.

Instead, we could have just two classes: I carefully maintain tire pressure to optimum, or I check the pressure occasionally, maybe twice a year. The difference between those behaviours is the impact of this action, and it can be described by a distribution (based on expert judgement?). --Jouni (talk) 16:58, 3 March 2016 (UTC) (type: truth; paradigms: science: attack)

Shall we model: pressureDeficit = pressureDropPerTemperatureDrop * temperatureDrop ?

Where pressureDropPerTemperatureDrop = uncertain parameter, using a triangular distribution, with

low of 0.5 PSI per 10 degrees F

mid of 1 PSI per 10 degrees F

high of 2 PSI per 10 degrees F

Claim: temperatureDrop values vary by region, and during the year for most climates.

The modeler(s) find and present links to datasets for average temperatures by month, for states, countries?

Calculation: fuel economy improvement with proper inflation. The benefit depends on the pressure difference between pre-inflation pressure and optimal pressure. Need data and citation regarding typical under-inflation (can update this data with empirical data/results once people start taking the action). How much pressure do tires naturally lose over the course of a year, even without the effects of temperature change?

FuelConsumptionReduction = 
	milesDrivenPerYear * gallonsPerMile *  pressureDeficit * fuelEconomySensitivity

Additional claim, in a new section, because it is about a new impact/benefit of proper tire inflation:

Claim: Tire life is improved by proper pressure.

This claim could be saved as documentation that another cause-effect relationship may be worthy of modeling in the future.

Additional claim, in a new section, because it is about a new impact/benefit of proper tire inflation:

Claim: Vehicle safety is improved by proper pressure.

This claim could be saved as documentation that another cause-effect relationship may be worthy of modeling in the future.

Branch Phase 2: Specifying the direct handprint impacts

This is the section that pulls together the final results of the modeling in Branch Section 1, to provide a table of impact factors, which are generally values for, or (in a few special cases, changes to values for), quantities of consumed flows (e.g., gallons of gasoline) and used flows (e.g., miles driven in a car, or loads of laundry dried in a clothes drier.) Consumed flows are flows that are “gone” after being consumed. For example, an apple is eaten, and is gone, not available to be consumed again. Usage flows may involve wear and tear or partial consumption of durable goods or infrastructure. For example, a bike is ridden, or a microwave is used. Presumably a microwave or a bike have expected lifetimes which depend at least in part upon usage, and so when we use them, we are effectively “consuming a portion of their useful life.”

The table of direct impact factors provides information of the form: Take this action, and it will have these impacts on annual consumption and usage flows.

The rows will correspond to different consumption and usage flows.

The number of columns, and of extra dimensions to the table (e.g., if it has 3, 4, or more dimensions) will depend on how many user input variables are active, and how many values are possible on each user input variable.

As a simple case, imagine no user input variables are needed. Then there is just a single “Global” value for each impact. Then the table will just be a vector, a single column of impact factors, one for each purchase flow and usage flow.

As the next simple case, imagine there is just one user parameter needed to accurately specify the impacts of the action, such as miles traveled per year. Then the table will be a 2-dimensional matrix (rows and columns), the first column will correspond to the “global” default value (based on some global average for miles traveled per year), and the other columns will EITHER correspond to other possible values of the user parameter, OR (probably preferable, no?) the table entries will be expressions which use the user parameter as a variable (e.g., 2.5 * milesTraveledPerYear).

Flows affected:

Tire gage used
Tire pump used (hand pump or pressurized air)
Less fuel consumed amount: FuelConsumptionReduction (calculated above)
Maybe tire life will be extended → tire consumption will go down?
Maybe safety will be improved → vehicle repairs/consumption will go down?