`library(radsafer)`

The radsafer package was developed with these goals:

Provide functions that are commonly used in radiation safety

Provide easy access to data provided in the RadData package

Share some less commonly-used functions that may be of significant value to workers in instrumentation and modeling

Related functions are identified as members of these families:

decay corrections

radionuclides

rad measurements

mcnp tools

A few functions stand alone.

This framework makes it a little easier to find the function you want - the help file for each function lists related functions.

Radsafer includes several functions to manage radioactive decay corrections:

**dk_correct** provides decay corrected activity or
activity-dependent value, such as instrument response rate or dose rate.
The computation is made either based on a single radionuclide, or based
on user-provided half-life, with time unit. The differential time is
either computed based on dates entered or time lapsed based on the time
unit.

Obtain a correction factor for a source to be used today based on a
calibration on `date1`

. Allow `date2`

to be the
default system date.

```
dk_correct(half_life = 10,
time_unit = "y",
date1 = "2010-01-01")
#> half_life RefValue RefDate TargDate dk_value
#> 10 1 2010-01-01 2023-04-27 0.3973055
```

Use this function to correct for the value needed on dates it was
used. Let the function obtain the half-life from the ICRP Pulbication
107 decay data in the `RadData`

R package. In the example, we
have a disk source with an original count rate of 10000 cpm:

```
dk_correct(RN_select = "Sr-90",
date1 = "2005-01-01",
date2 = c("2009-01-01","2009-10-01"),
A1 = 10000)
#> RN half_life units decay_mode
#> Sr-90 28.79 y B-
#>
#> RN RefValue RefDate TargDate dk_value
#> Sr-90 10000 2005-01-01 2009-01-01 9081.895
#> Sr-90 10000 2005-01-01 2009-10-01 8919.954
```

Reverse decay - find out what readings should have been in the past given today’s reading of 3000

```
dk_correct(RN_select = "Cs-137",
date1 = "2019-01-01",
date2 = c("2009-01-01","1999-01-01"),
A1 = 3000)
#> RN half_life units decay_mode
#> Cs-137 30.1671 y B-
#>
#> RN RefValue RefDate TargDate dk_value
#> Cs-137 3000 2019-01-01 2009-01-01 3774.795
#> Cs-137 3000 2019-01-01 1999-01-01 4749.991
```

Other decay functions answer the following questions:

How long does it take to decay something with a given activity, or how old is a sample if it has decayed from?

**dk_time**Given a percentage reduction in activity, how many half-lives have passed.

**dk_pct_to_num_half_life**Given two or more data points, estimate the half-life:

**half_life_2pt**

Search by alpha, beta, photon or use the general screen option.

`RN_search_phot_by_E`

allows screening based on energy,
half-life, and minimum probability. Also available are
`RN_search_alpha_by_E`

, `RN_search_beta_by_E`

, and
`bin_screen_phot`

. `RN_bin_screen_phot`

allows
limiting searches to radionuclides with emissions in an energy bin of
interest with additional filters for not having photons in other
specified energy bins. Results for all these search functions may be
plotted with `RN_plot_spectrum`

.

Here’s a search for photon energy between 0.99 and 1.01 MeV, half-life between 13 and 15 minutes, and probability at least 1e-3

`<- RN_search_phot_by_E(0.99, 1.01, 13 * 60, 15 * 60, 1e-3) search_results `

RN | code_AN | E_MeV | prob | half_life | units | decay_constant |
---|---|---|---|---|---|---|

Pr-136 | G | 0.99100 | 0.0016768 | 13.10 | m | 0.0008819 |

Pr-136 | G | 1.00070 | 0.0503040 | 13.10 | m | 0.0008819 |

Re-178 | G | 1.00440 | 0.0057600 | 13.20 | m | 0.0008752 |

Pr-147 | G | 0.99597 | 0.0083220 | 13.40 | m | 0.0008621 |

Nb-88 | G | 0.99760 | 0.0041000 | 14.50 | m | 0.0007967 |

Mo-101 | G | 1.00740 | 0.0017300 | 14.61 | m | 0.0007907 |

Sm-140 | G | 0.99990 | 0.0012000 | 14.82 | m | 0.0007795 |

```
RN_plot_spectrum(search_results)
#> [1] "No matches"
```

You can also plot a spectrum in one step, skipping the data save:

```
RN_plot_spectrum(
desired_RN = c("Pu-238", "Pu-239", "Am-241"), rad_type = "A",
photon = FALSE, prob_cut = 0.01, log_plot = 0)
```

The `RN_index_screen`

function helps find a radionuclide
of interest based on decay mode, half-life, and total emission
energy.

In this example, we search for radionuclides decaying by spontaneous fission with half-lives between 6 months and 2 years.

`<- RN_index_screen(dk_mode = "SF", min_half_life_seconds = 0.5 * 3.153e7, max_half_life_seconds = 2 * 3.153e7) RNs_selected `

RN | half_life | units |
---|---|---|

Es-254 | 275.7 | d |

Cf-248 | 334.0 | d |

Other radionuclides family functions:

Obtain a specific activity with

**RN_Spec_Act**.Find a potential radionuclide parent with

**RN_find_parent**.

```
RN_find_parent("Th-230")
#> RN
#> 1 Ac-230
#> 2 Pa-230
#> 3 U-234
```

**air_dens_cf** Correct *vented ion chamber
readings* based on difference in air pressure (readings in degrees
Celsius and mm Hg):

```
air_dens_cf(T.actual = 30, P.actual = 760, T.ref = 20, P.ref = 760)
#> [1] 1.034112
```

Let’s try it out combined with the instrument reading:

```
<- 100
rdg <- rdg * air_dens_cf(T.actual = 30, P.actual = 760, T.ref = 20, P.ref = 760))
(rdg_corrected #> [1] 103.4112
```

**neutron_geom_cf**

Correct for *geometry* when reading a close *neutron*
source. Example: neutron rem detector with a radius of 11 cm and source
near surface:

```
neutron_geom_cf(11.1, 11)
#> [1] 0.7236467
```

**disk_to_disk_solid_angle**

Correct for a mismatch between the *source calibration* of a
*counting system* and the item being measured. A significant
factor in the counting efficiency is the solid angle from the source to
the detector. You can also check for the impact of an item not being
centered with the detector.

Example: You are counting an air sample with an active collection diameter of 45 mm, your detector has a radius of 25 mm and there is a gap between the two of 5 mm. (The function is based on radius, not diameter so be sure to divide the diameter by two.) The relative solid angle is:

```
<- as.numeric(disk_to_disk_solid_angle(r.source = 45/2, gap = 20, r.detector = 12.5, runs = 1e4, plot.opt = "n")))
(as_rel_solid_angle #> [1] 0.045377650 0.002076333
```

An optional plot is available in 2D or 3D:

```
library(ggplot2)
theme_update(# axis labels
axis.title = element_text(size = 7),
# tick labels
axis.text = element_text(size = 5),
# title
title = element_text(size = 5))
<- as.numeric(disk_to_disk_solid_angle(r.source = 45/2, gap = 20, r.detector = 12.5, runs = 1e4, plot.opt = "3d"))) (as_rel_solid_angle
```

`#> [1] 0.050134690 0.002179832`

Continuing the example: the only calibration source you had available with the appropriate isotope has an active diameter of 20 mm. Is this a big deal? Let’s estimate the relative solid angle of the calibration, then take a ratio of the two.

```
<- disk_to_disk_solid_angle(r.source = 20, gap = 20, r.detector = 12.5, runs = 1e4, plot.opt = "n"))
(cal_rel_solid_angle #> mean_eff SEM
#> 0.05307093 0.002255094
```

Correct for the mismatch:

```
<- cal_rel_solid_angle / as_rel_solid_angle)
(cf #> mean_eff SEM
#> 1.058567 1.034527
```

This makes sense - the air sample has particles originating outside the source radius, so more of them will be lost, thus an adjustment is needed for the activity measurement.

**scaler_sim**

*Scaler counts*: obtain quick distributions for parameters of
interest:

`scaler_sim(true_bkg = 50, true_samp = 10, ct_time = 20, trials = 1e5)`

**rate_meter_sim**

*Rate meters*: In the ratemeter simulation, readings are
plotted once per second for a default time of 600 seconds. The meter
starts with a reading of zero and builds up based on the time constant.
Resolution uncertainty is established to express the uncertainty from
reading an analog scale, including the instability of its readings. Many
standard references identify the precision or resolution uncertainty of
analog readings as half of the smallest increment. This should be
considered the single coverage uncertainty for a very stable reading.
When a reading is not very stable, evaluation of the reading fluctuation
is evaluated in terms of numbers of scale increments covered by meter
indication over a reasonable evaluation period. Example with default
time constant:

`rate_meter_sim(cpm_equilibrium = 270, meter_scale_increments = seq(100, 1000, 20))`

To estimate *time constant*, use `tau.estimate`

Given a dose rate, dose allowed, and a safety margin (default = 20%),
calculate stay time with: `stay_time`

```
stay_time(dose_rate = 120, dose_allowed = 100, margin = 20)
#> [1] "Time allowed is 40 minutes"
#> [1] 40
```

Given a set of radiation levels through varying thicknesses of material, compute the half-values, tenth-values, and homogeneity coefficients.

```
hvl(x = mm_Al, y = mR_h)
#> thickness response mu hvl tvl hc
#> 1 0 7.428 NA NA NA NA
#> 2 1 6.272 0.1691614 4.097550 13.61177 0.9676121
#> 3 2 5.325 0.1636826 4.234704 14.06738 0.9810918
#> 4 3 4.535 0.1605876 4.316317 14.33850 0.9745802
#> 5 4 3.878 0.1565055 4.428899 14.71248 0.9984235
#> 6 5 3.317 0.1562588 4.435892 14.73571 NA
```

If you create MCNP inputs, these functions may be helpful:

**mcnp_sdef_erg_line** Obtain emission data from the
`RadData`

package for use with the radiation transport code,
MCNP, when you want a “line” source from individual radionuclides. Use
**mcnp_sdef_erg_hist** when you have histogram data and
want to format it nicely for MCNP.

**mcnp_matrix_rotations**

- Determine the entries needed for MCNP
*coordinate transformation rotation*

```
mcnp_matrix_rotations("z", 90)
#> [1] 0 1 0 -1 0 0 0 0 1
```

**mcnp_cone_angle** This is just the square of the
tangent of the angle in radians, but the argument used here is
degree.

```
mcnp_cone_angle(30)
#> [1] 0.3333333
```

**mcnp_plot_out_spec**

For *MCNP outputs*, plot the results of a tally with
*energy bins*. The fastest way to do this is with
`mcnp_scan2plot`

.

Alternatively, if you want to get your data into R, first save it in
a text file and import it to R. (Base R provides methods with
`read.table`

or you might prefer options from the
`readr`

or `readxl`

packages.) Or you can copy and
paste your data using `mcnp_scan_save`

. You can then plot
with `mcnp_plot_out_spec`

(below) or design your own
plot.

`mcnp_plot_out_spec(photons_cs137_hist, 'example Cs-137 well irradiator')`