Struct StateDispersion

pub struct StateDispersion {
    pub param: StateParameter,
    pub mean: Option<f64>,
    pub std_dev: Option<f64>,
}

A dispersions configuration, allows specifying min/max bounds (by default, they are not set)

Fields

param: StateParameter

mean: Option<f64>

std_dev: Option<f64>

Implementations

impl StateDispersion

pub fn builder() -> StateDispersionBuilder<((), (), ())>

Create a builder for building StateDispersion. On the builder, call .param(...), .mean(...)(optional), .std_dev(...)(optional) to set the values of the fields. Finally, call .build() to create the instance of StateDispersion.

impl StateDispersion

pub fn zero_mean(param: StateParameter, std_dev: f64) -> Self

Examples found in repository

examples/03_geo_analysis/stationkeeping.rs (line 92)

fn main() -> Result<(), Box<dyn Error>> {
    pel::init();
    // Set up the dynamics like in the orbit raise.
    let almanac = Arc::new(MetaAlmanac::latest().map_err(Box::new)?);
    let epoch = Epoch::from_gregorian_utc_hms(2024, 2, 29, 12, 13, 14);

    // Define the GEO orbit, and we're just going to maintain it very tightly.
    let earth_j2000 = almanac.frame_from_uid(EARTH_J2000)?;
    let orbit = Orbit::try_keplerian(42164.0, 1e-5, 0., 163.0, 75.0, 0.0, epoch, earth_j2000)?;
    println!("{orbit:x}");

    let sc = Spacecraft::builder()
        .orbit(orbit)
        .mass(Mass::from_dry_and_prop_masses(1000.0, 1000.0)) // 1000 kg of dry mass and prop, totalling 2.0 tons
        .srp(SRPData::from_area(3.0 * 6.0)) // Assuming 1 kW/m^2 or 18 kW, giving a margin of 4.35 kW for on-propulsion consumption
        .thruster(Thruster {
            // "NEXT-STEP" row in Table 2
            isp_s: 4435.0,
            thrust_N: 0.472,
        })
        .mode(GuidanceMode::Thrust) // Start thrusting immediately.
        .build();

    // Set up the spacecraft dynamics like in the orbit raise example.

    let prop_time = 30.0 * Unit::Day;

    // Define the guidance law -- we're just using a Ruggiero controller as demonstrated in AAS-2004-5089.
    let objectives = &[
        Objective::within_tolerance(StateParameter::SMA, 42_164.0, 5.0), // 5 km
        Objective::within_tolerance(StateParameter::Eccentricity, 0.001, 5e-5),
        Objective::within_tolerance(StateParameter::Inclination, 0.05, 1e-2),
    ];

    let ruggiero_ctrl = Ruggiero::from_max_eclipse(objectives, sc, 0.2)?;
    println!("{ruggiero_ctrl}");

    let mut orbital_dyn = OrbitalDynamics::point_masses(vec![MOON, SUN]);

    let mut jgm3_meta = MetaFile {
        uri: "http://public-data.nyxspace.com/nyx/models/JGM3.cof.gz".to_string(),
        crc32: Some(0xF446F027), // Specifying the CRC32 avoids redownloading it if it's cached.
    };
    jgm3_meta.process(true)?;

    let harmonics = Harmonics::from_stor(
        almanac.frame_from_uid(IAU_EARTH_FRAME)?,
        HarmonicsMem::from_cof(&jgm3_meta.uri, 8, 8, true)?,
    );
    orbital_dyn.accel_models.push(harmonics);

    let srp_dyn = SolarPressure::default(EARTH_J2000, almanac.clone())?;
    let sc_dynamics = SpacecraftDynamics::from_model(orbital_dyn, srp_dyn)
        .with_guidance_law(ruggiero_ctrl.clone());

    println!("{sc_dynamics}");

    // Finally, let's use the Monte Carlo framework built into Nyx to propagate spacecraft.

    // Let's start by defining the dispersion.
    // The MultivariateNormal structure allows us to define the dispersions in any of the orbital parameters, but these are applied directly in the Cartesian state space.
    // Note that additional validation on the MVN is in progress -- https://github.com/nyx-space/nyx/issues/339.
    let mc_rv = MvnSpacecraft::new(
        sc,
        vec![StateDispersion::zero_mean(StateParameter::SMA, 3.0)],
    )?;

    let my_mc = MonteCarlo::new(
        sc, // Nominal state
        mc_rv,
        "03_geo_sk".to_string(), // Scenario name
        None, // No specific seed specified, so one will be drawn from the computer's entropy.
    );

    // Build the propagator setup.
    let setup = Propagator::rk89(
        sc_dynamics.clone(),
        IntegratorOptions::builder()
            .min_step(10.0_f64.seconds())
            .error_ctrl(ErrorControl::RSSCartesianStep)
            .build(),
    );

    let num_runs = 25;
    let rslts = my_mc.run_until_epoch(setup, almanac.clone(), sc.epoch() + prop_time, num_runs);

    assert_eq!(rslts.runs.len(), num_runs);

    // For all of the resulting trajectories, we'll want to compute the percentage of penumbra and umbra.

    rslts.to_parquet(
        "03_geo_sk.parquet",
        Some(vec![
            &EclipseLocator::cislunar(almanac.clone()).to_penumbra_event()
        ]),
        ExportCfg::default(),
        almanac,
    )?;

    Ok(())
}

Trait Implementations

impl Clone for StateDispersion

fn clone(&self) -> StateDispersion

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<'de> Deserialize<'de> for StateDispersion

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for StateDispersion

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl Copy for StateDispersion