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Commit c964fbaa authored by davideperani's avatar davideperani
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Added functions to estimate parachute opening force

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RoccarasoFlight/postprocessing/RCS/descentPostProcess.m
+ 5
− 1
View file @ c964fbaa
...@@ -63,7 +63,11 @@ A_dcm = (qs.^2 - pagemtimes(qq, 'transpose', qq, 'none')) .* eye(3) + ... ...@@ -63,7 +63,11 @@ A_dcm = (qs.^2 - pagemtimes(qq, 'transpose', qq, 'none')) .* eye(3) + ...
% A_dcm = repmat(eye(3), [1 1 10]); % A_dcm = repmat(eye(3), [1 1 10]);
animateOrientation(A_dcm, time_qq); % animateOrientation(A_dcm, time_qq);
%% Opening force estimation
function para = descentPost(main, desc, h0, name, S, M) function para = descentPost(main, desc, h0, name, S, M)
......
RoccarasoFlight/postprocessing/commonFunctions/ParachuteOpeningForceModels/computeForceReductionFactorFromBallisticParameter.m 0 → 100644
+ 231
− 0
View file @ c964fbaa
function FRF = computeForceReductionFactorFromBallisticParameter(A, n)
%%%%%%%%%%%%%% INFO %%%%%%%%%%%%%
% Function to compute the Force Reduction Factor from the ballistic
% parameter, equivalent to using the graph on fig. 5.51 of La Bibbia
%
% INPUT:
% A [1] Ballistic Parameter, must be between 0.01 and 1000, otherwise
% the function returns an error
% n [1] exponent of drag area vs time. Can be either 1, 2 or 1/2 (0.5)
% otherwise the function returns an error
%
% OUTPUT:
% FRF [1] = Force Reduction Factor
%
% CONTRIBUTORS:
% Tommaso Mauriello
%
% VERSION:
% 9 nov 2020, Fourth version
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
A_min = 0.01;
A_max = 1000;
errorMessageOutOfBounds = ['The input Ballistic Parameter A (%d) is out of the graph',...
'bounds. It must be between 0.01 and 1000.'];
errorMessageWrongN = 'The requested n index (%d) is not valid. It must be 1, 2 or 0.5.';
if A < A_min || A > A_max
% if the input Ballistic Parameter A is out of the graph bounds
% then return error
error(errorMessageOutOfBounds, A);
else
switch n
case 1
% if evalin('caller', 'exist(''FRF_A_n_1_curve'',''var'')')
% % if the FRF_A_curve has already been created it's saved in
% % the workspace. We use it instead of computing it once again
% savedCurve = evalin('caller', 'FRF_A_n_1_curve');
%
% % we return the value of FRF
% FRF = savedCurve(A);
% else
% it's the first time that the FRF_PCF_curve is needed. We
% get it by interpolating the data obtained from the graph
% and then we save it in the workspace for further usage
% data obtained from La Bibbia fig. 5.51, curve for n = 1
A_data_n_1 = [0.01, 0.011270759, 0.012329285, 0.013357006, 0.014192211, ...
0.015189805, 0.016336641, 0.017274183, 0.018309839, 0.019407636, ...
0.020923608, 0.022285936, 0.023622127, 0.0250384, 0.02719134, ...
0.029601226, 0.033581491, 0.037729032, 0.040973288, 0.045148972, ...
0.047855955, 0.050848421, 0.054290733, 0.058106995, 0.062342461, ...
0.067867424, 0.072111222, 0.077180042, 0.082405255, 0.088626656, ...
0.096247268, 0.101032694, 0.109189228, 0.11714812, 0.123871122, ...
0.13225672, 0.140527185, 0.152981682, 0.162547111, 0.173973269, ...
0.191237662, 0.208692039, 0.225539798, 0.239643573, 0.257735764, ...
0.277194555, 0.298846433, 0.324543738, 0.347356469, 0.366400631, ...
0.387427465, 0.406689369, 0.436334405, 0.467003898, 0.502262865, ...
0.534965691, 0.576753004, 0.615797749, 0.647984231, 0.688504902, ...
0.766069417, 0.817932502, 0.866971916, 0.941521382, 1.015064333, ...
1.075924225, 1.154349222, 1.238492263, 1.32233511, 1.432554313, ...
1.590063152, 1.689484446, 1.852635856, 2.021711541, 2.233124572,...
2.401701751, 2.558062553, 2.737852125, 2.951681429, 3.159131874,...
3.405859244, 3.636394917, 3.882530119, 4.145330673, 4.404490468, ...
4.748461814, 5.020913071, 5.334819769, 5.751453094, 6.155661708, ...
6.524659739, 7.017166357, 7.528576382, 8.077226926, 8.58220292, ...
9.388057159, 10, 40, 1000]';
FRF_data_n_1 = [0.044144116, 0.046906657, 0.049002192, 0.05119117, ...
0.052704682, 0.054527041, 0.056824631, 0.058646726, 0.060234291, ...
0.062165769, 0.064785286, 0.066700777, 0.06883961, 0.070874854, ...
0.073861539, 0.077536487, 0.082789711, 0.087970619, 0.092123686, ...
0.097178101, 0.100294224, 0.103510357, 0.106829803, 0.110792127, ...
0.114623062, 0.11945341, 0.12328392, 0.127546683, 0.132598623, ...
0.137516949, 0.143311949, 0.147190898, 0.153765934, 0.159082803, ...
0.164183828, 0.169038355, 0.175307551, 0.184029901, 0.188553697, ...
0.196022204, 0.204283502, 0.21444772, 0.223484223, 0.231772675, ...
0.23978704, 0.249285296, 0.25916001, 0.271394878, 0.280778843, ...
0.289079578, 0.297625962, 0.304201871, 0.316251407, 0.325602491, ...
0.33932231, 0.350203649, 0.364960391, 0.37666421, 0.385921999, ...
0.400234849, 0.423221013, 0.438917934, 0.451894544, 0.473228353, ...
0.491973921, 0.507749605, 0.525306407, 0.544790521, 0.562261264, ...
0.583118056, 0.604753671, 0.619619637, 0.64104956, 0.66321952, ...
0.679531947, 0.694551811, 0.708180878, 0.722078615, 0.738039527, ...
0.750699572, 0.765433677, 0.776676208, 0.786174035, 0.797721197, ...
0.805518179, 0.815371487, 0.823339581, 0.833406626, 0.845650412, ...
0.85599244, 0.864358233, 0.874930543, 0.887782854, 0.894290683, ...
0.903031541, 0.911867156, 0.918549968, 0.98, 1]';
% we interpolate using 'pchipinterp' (Piecewise Cubic Hermite 7
% Interpolating Polynomial) since it looks better than 'smoothingspline'
interpolatedCurve = fit(A_data_n_1, FRF_data_n_1, 'pchipinterp');
% we save the curve object in the workspace for further usage
% assignin('caller', 'FRF_A_n_1_curve', interpolatedCurve);
% we return the value of FRF
FRF = interpolatedCurve(A);
% end
case 2
% if evalin('caller', 'exist(''FRF_A_n_2_curve'',''var'')')
% % if the FRF_A_curve has already been created it's saved in
% % the workspace. We use it instead of computing it once again
% savedCurve = evalin('caller', 'FRF_A_n_2_curve');
%
% % we return the value of FRF
% FRF = savedCurve(A);
% else
% it's the first time that the FRF_PCF_curve is needed. We
% get it by interpolating the data obtained from the graph
% and then we save it in the workspace for further usage
% data obtained from La Bibbia fig. 5.51, curve for n = 2
A_data_n_2 = [0.01, 0.011130674, 0.011898702, 0.012675246, 0.01352295, ...
0.014317013, 0.016825234, 0.017919619, 0.018925181, 0.020431782, ...
0.021758606, 0.023189558, 0.024864972, 0.026661561, 0.028701934, ...
0.030870314, 0.032968083, 0.035890116, 0.038134752, 0.040274559, ...
0.042325435, 0.045523046, 0.048176292, 0.05139804, 0.055043372, ...
0.059379339, 0.063284615, 0.068780331, 0.073871615, 0.080663463, ...
0.087017618, 0.092041134, 0.098242012, 0.104702883, 0.113294043, ...
0.125982261, 0.134675807, 0.142233154, 0.151587339, 0.161556716, ...
0.17428285, 0.185744833, 0.207804985, 0.223496266, 0.239156198, ...
0.257738975, 0.273027679, 0.289223286, 0.304866904, 0.322592066, ...
0.345899465, 0.368647493, 0.397687202, 0.429014475, 0.457227089, ...
0.487295783, 0.509975841, 0.545163809, 0.581014571, 0.631527836, ...
0.690658392, 0.751872947, 0.818510465, 0.885660179, 0.94964643, ...
1.018250614, 1.095124364, 1.181379986, 1.243880326, 1.308129232, ...
1.395597368, 1.476918875, 1.575666424, 1.66748056, 1.784415453, ...
1.901742972, 2.008430599, 2.146990295, 2.27431396, 2.458716584, ...
2.623107629, 2.881136272, 3.05199236, 3.232985657, 3.456004146, ...
3.660944989, 3.961246992, 4.296621994, 4.540330935, 4.809572293, ...
5.125758482, 5.4627398, 5.839562325, 6.284157152, 6.774908483, ...
7.220309412, 7.671685259, 8.102001474, 9.118993962, 10, 40, 1000]';
FRF_data_n_2 = [0.021215028, 0.022817684, 0.023880278, 0.025083749, ...
0.026332378, 0.027624349, 0.031212646, 0.032840164, 0.034369347, ...
0.036109668, 0.037874089, 0.039637801, 0.041609741, 0.044079027, ...
0.04678896, 0.049517293, 0.052084568, 0.055175765, 0.058096452, ...
0.060433984, 0.062622328, 0.066273927, 0.06957274, 0.072447286, ...
0.076108691, 0.080381293, 0.084636305, 0.090149118, 0.095268682, ...
0.101536986, 0.107237085, 0.112176231, 0.117813746, 0.123300069, ...
0.131014351, 0.141339797, 0.149273876, 0.156224728, 0.163996398, ...
0.172154683, 0.181268524, 0.190286041, 0.20841997, 0.220788667, ...
0.23246786, 0.24552724, 0.255406391, 0.265683044, 0.275554249, ...
0.288366128, 0.303631538, 0.317770981, 0.335610059, 0.354450589, ...
0.368713217, 0.384714792, 0.395366992, 0.412525739, 0.429125234, ...
0.451554776, 0.475767101, 0.504003677, 0.530687246, 0.550372581, ...
0.576003431, 0.597367818, 0.617649168, 0.64055959, 0.65431614, ...
0.666075733, 0.682460257, 0.69924583, 0.710671683, 0.728151136, ...
0.741048948, 0.756966815, 0.770882564, 0.789833968, 0.799490486, ...
0.809041718, 0.822261664, 0.834237756, 0.841879932, 0.852172763, ...
0.862594191, 0.867859994, 0.883821884, 0.891726323, 0.897369788, ...
0.902847883, 0.905610548, 0.91114089, 0.919490488, 0.928672786, ...
0.936424915, 0.942143432, 0.945025333, 0.947914031, 0.959523673, ...
0.968325926, 0.99, 1]';
% we interpolate using 'pchipinterp' (Piecewise Cubic Hermite 7
% Interpolating Polynomial) since it looks better than 'smoothingspline'
interpolatedCurve = fit(A_data_n_2, FRF_data_n_2, 'pchipinterp');
% we save the curve object in the workspace for further usage
% assignin('caller', 'FRF_A_n_2_curve', interpolatedCurve);
% we return the value of FRF
FRF = interpolatedCurve(A);
% end
case 0.5
% if evalin('caller', 'exist(''FRF_A_n_05_curve'',''var'')')
% % if the FRF_A_curve has already been created it's saved in
% % the workspace. We use it instead of computing it once again
% savedCurve = evalin('caller', 'FRF_A_n_05_curve');
%
% % we return the value of FRF
% FRF = savedCurve(A);
% else
% % it's the first time that the FRF_PCF_curve is needed. We
% % get it by interpolating the data obtained from the graph
% % and then we save it in the workspace for further usage
%
% % data obtained from La Bibbia fig. 5.51, curve for n = 0.5
A_data_n_05 = [0.01, 0.010951767, 0.011807143, 0.012667726, 0.013642837, ...
0.014555066, 0.015418268, 0.016502002, 0.017533817, 0.018949181, ...
0.020445024, 0.023860471, 0.026742116, 0.03093212, 0.033673281, ...
0.036607438, 0.042710446, 0.049044166, 0.054834313, 0.060275537, ...
0.067065536, 0.073008511, 0.076438598, 0.081549322, 0.088216597, ...
0.096501096, 0.106077197, 0.117170728, 0.129110057, 0.151161957, ...
0.164857254, 0.197874321, 0.221771738, 0.25098006, 0.271240009, ...
0.293135035, 0.333268233, 0.352687785, 0.390289253, 0.410688345, ...
0.444917743, 0.480833604, 0.502293675, 0.531117042, 0.576780275, ...
0.623338313, 0.66714956, 0.703723996, 0.762376863, 0.806123682, ...
0.877557878, 0.959973789, 1.094717055, 1.176960702, 1.268889132, ...
1.36136906, 1.511047244, 1.689436949, 1.92589631, 2.127292535, ...
2.293439385, 2.498550763, 2.791413604, 3.220934983, 3.653961014, ...
4.236701475, 4.545477448, 5.106789707, 5.599903094, 6.081351691, ...
6.636298543, 7.189373432, 7.826425701, 8.458146888, 9.140834995, ...
10, 20, 1000]';
FRF_data_n_05 = [0.095955515, 0.09927411, 0.101962065, 0.104722621, ...
0.106787857, 0.109414688, 0.111279201, 0.114569561, 0.117385893, ...
0.120564347, 0.12355117, 0.13062536, 0.135472456, 0.142561339, ...
0.147491532, 0.151286924, 0.159800009, 0.167753932, 0.175249612, ...
0.180871495, 0.18895298, 0.19406975, 0.197649803, 0.20087945, ...
0.206219738, 0.212834757, 0.220730881, 0.22947645, 0.235691582, ...
0.249837661, 0.258223827, 0.275995725, 0.286237058, 0.300482752, ...
0.309368634, 0.317745399, 0.329262665, 0.337361104, 0.348469752, ...
0.356169335, 0.365813632, 0.377546421, 0.381234312, 0.389658505, ...
0.399240095, 0.409056595, 0.418097362, 0.42733576, 0.438907091, ...
0.447518543, 0.459637163, 0.477846798, 0.505823651, 0.52023635, ...
0.536921502, 0.547458816, 0.565022831, 0.587412078, 0.61069267, ...
0.627233185, 0.642655024, 0.66085731, 0.682891018, 0.709958701, ...
0.730966068, 0.758098961, 0.771103755, 0.793917673, 0.809504854, ...
0.82740036, 0.843643478, 0.858118668, 0.874964086, 0.889975916, ...
0.900863101, 0.91855075, 0.98, 1]';
% we interpolate using 'pchipinterp' (Piecewise Cubic Hermite 7
% Interpolating Polynomial) since it looks better than 'smoothingspline'
interpolatedCurve = fit(A_data_n_05, FRF_data_n_05, 'pchipinterp');
% we save the curve object in the workspace for further usage
% assignin('caller', 'FRF_A_n_05_curve', interpolatedCurve);
% we return the value of FRF
FRF = interpolatedCurve(A);
% end
otherwise
% if n is different from 1, 2 and 0.5 then return error
error(errorMessageWrongN, n);
end
end
end
RoccarasoFlight/postprocessing/commonFunctions/ParachuteOpeningForceModels/computeForceReductionFactorFromCanopyLoading.m 0 → 100644
+ 77
− 0
View file @ c964fbaa
function FRF = computeForceReductionFactorFromCanopyLoading(PCL)
%%%%%%%%%%%%%% INFO %%%%%%%%%%%%%
% Function to compute the Force Reduction Factor from the Canopy
% Loading, equivalent to using the graph on fig. 5.48 of La Bibbia
% INPUT:
% PCL [1] = Parachute Canopy Loading, must be between 0 and 100
% otherwise the function returns an error
%
% OUTPUT:
% FRF [1] = Force Reduction Factor
%
% CONTRIBUTORS:
% Tommaso Mauriello
%
% VERSION:
% 9 nov 2020, Fourth version
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
PCL_min = 0;
PCL_max = 100;
errorMessageOutOfBounds = ['The input Parachute Canopy Loading PCL (%d) is out of'...
'the graph bounds. It must be between 0 and 100.'];
if PCL < PCL_min || PCL > PCL_max
%if the input Canopy Loading PCF is out of the graph bounds then returns error
%formattedError = sprintf(errorMessageOutOfBounds, PCL);
error(errorMessageOutOfBounds, PCL);
else
if evalin('caller', 'exist(''FRF_PCL_curve'', ''var'')')
% if the FRF_PCF_curve has already been created it's saved in
% the workspace. We use it instead of computing it once again
savedCurve = evalin('caller', 'FRF_PCL_curve');
FRF = savedCurve(PCL);
else
% it's the first time that the FRF_PCF_curve is needed. We
% get it by interpolating the data obtained from the graph
% and then we save it in the workspace for further usage
%data obtained from La Bibbia fig. 5.48
PCL_data = [0, 0.2, 0.315238664, 0.353618869, 0.396643869, 0.469187078, 0.518862451, ...
0.55955745, 0.618800824, 0.667294804, 0.719589146, 0.775981673, 0.858088507, ...
0.927411952, 0.99163793, 1.075987155, 1.164534389, 1.251414299, 1.349138417, ...
1.454781569, 1.568697018, 1.711207738, 1.90463473, 2.01757444, 2.240172577, ...
2.518493085, 2.728899831, 3.01197155, 3.258187307, 3.504498865, 3.87060744, ...
4.358457656, 4.675440475, 4.965487707, 5.38024354, 6.731032391, 7.428725575, ...
8.295048363, 9.370986014, 10.84382219, 12.10145674, 13.25453732, 14.18324569, ...
15.39337664, 17.13135833, 18.7805021, 20.86587709, 23.60741993, 26.81084133, ...
31.11332399, 33.70945518, 38.14698112, 44.21401955, 56.83565422, 64.70349943, ...
69.52742428, 74.70418146, 80.68133451, 87.13535882, 93.48211331, 100]';
FRF_data = [0.020092574, 0.020092574, 0.020902911, 0.021456589, 0.026215821, ...
0.032543213, 0.039641814, 0.043210055, 0.050308656, 0.057388317, 0.064467978, ...
0.071547639, 0.082157661, 0.089471295, 0.097465569, 0.106908065, 0.115810715, ...
0.126145192, 0.13516989, 0.145760971, 0.156352053, 0.170305721, 0.185806806, ...
0.19533046, 0.212097039, 0.230409394, 0.246824866, 0.262786929, 0.278266572, ...
0.290392115, 0.309704495, 0.332690949, 0.34722972, 0.358957219, 0.37630726, ...
0.422270079, 0.442437695, 0.466352411, 0.495416078, 0.526509595, 0.550748537, ...
0.572309583, 0.587316492, 0.606069645, 0.628579987, 0.649209843, 0.671718923, ...
0.699156344, 0.726113031, 0.759058834, 0.775350621, 0.802546413, 0.833063827, ...
0.878901694, 0.902476415, 0.915614595, 0.927300202, 0.939512313, 0.952659353, ...
0.96206163, 0.968656595]';
% we interpolate using 'pchipinterp' (Piecewise Cubic Hermite 7
% Interpolating Polynomial) since it looks better than 'smoothingspline'
interpolatedCurve = fit(PCL_data, FRF_data, 'pchipinterp');
% we save the curve object in the workspace for further usage
assignin('caller', 'FRF_PCL_curve', interpolatedCurve);
FRF = interpolatedCurve(PCL);
end
end
end
\ No newline at end of file
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