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Acta astronautica, 63 5-6
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www.nature.com/npjmgrav BRIEF COMMUNICATION OPEN Acceleration proﬁles and processing methods for parabolic ﬂight 1,2 1 1 3 2 1 Christopher E. Carr , Noelle C. Bryan , Kendall N. Saboda , Srinivasa A. Bhattaru , Gary Ruvkun and Maria T. Zuber Parabolic ﬂights provide cost-effective, time-limited access to “weightless” or reduced gravity conditions, facilitating research and validation activities that complement infrequent and costly access to space. Although parabolic ﬂights have been conducted for decades, reference acceleration proﬁles and processing methods are not widely available. Here we present a solution for collecting, analyzing, and classifying the altered gravity environments experienced during parabolic ﬂights, which we validated during a Boeing 727-200F ﬂight with 20 parabolas. All data and analysis code are freely available. Our solution can be integrated with diverse experimental designs, does not depend upon accelerometer orientation, and allows unsupervised classiﬁcation of all phases of ﬂight, providing a consistent and open-source approach to quantifying gravito-inertial accelerations (GIA), or g levels. As academic, governmental, and commercial use of space advances, data availability and validated processing methods will enable better planning, execution, and analysis of parabolic ﬂight experiments, and thus facilitate future space activities. npj Microgravity (2018) 4:14 ; doi:10.1038/s41526-018-0050-3 INTRODUCTION research section (Fig. 1b-c). Direct Current (DC) acceleration was recorded at 411 Hz. Additional data, calibration, and calibration Parabolic ﬂights are cost-effective, ground-based analogs that error ( < 2%) assessment are described in Methods. achieve variable g (Earth-relative GIA) level environments that 1,2 In any given experiment, one accelerometer orientation may be recreate conditions experienced during space ﬂight. Specialized more appropriate than another. Thus, our phase of ﬂight aircraft can maintain approximately 20–30 s of a 0 g, freefall identiﬁcation is based on a metric that is independent of environment before an increased GIA recovery phase (Fig. 1a). accelerometer orientation: the Euclidean norm of the acceler- Modiﬁed trajectories can achieve reduced g levels experienced on ometer (x,y,z) axes, which we refer to as g level or g (Methods). the lunar surface or on Mars (0.17 and 0.38 g, respectively). To facilitate our analysis (Fig. 1d, Methods, Supplementary Figs. Parabolic ﬂights serve as valuable proving grounds for experi- 1-5), acceleration data were ﬁltered (Fig. 1e) using a zero-delay, mental efforts to maximize the research potential of the 3,4 low-pass ﬁlter prior to parabola identiﬁcation using change point International Space Station and to accommodate increasing 7,8 5,6 detection. Conceptually, this process ﬁnds the point for which a interest in commercial space ﬂight. statistical property (e.g., mean), has minimum total residual error Here we address the limited availability of open access summed across two groups, e.g., before and after the change acceleration datasets containing parabolic ﬂight proﬁles and point. Here residual error is the difference between an observed enable unsupervised and precise characterization of timing and value and the statistical property for the group. g levels for all ﬂight phases. We demonstrate this approach using a Change point detection was ﬁrst applied to the ﬁltered g level, small (65 g) battery powered commercially available acceler- g , to identify differences in mean g levels in an unsupervised ﬁlt ometer and vibration measurement system. Together, these tools manner (Fig. 1f). To break down the ﬂight into regions of stable g and products reﬂect a comprehensive solution for experiment levels, data within 10 s of each change point was subjected to planning, execution, and analysis of g level and vibrations during secondary change point detection using a linear slope metric, parabolic ﬂight. which segmented the ﬂight into regions of rapid “transition” (indicated by dotted lines, Fig. 1g-i) and more stable regimes. Non- transition periods were subsequently classiﬁed into “parabola,” RESULTS “hypergravity,” and “other” based on their duration and g level Flight operations were conducted on November 17, 2017 onboard (Fig. 2a-b; Methods). These “hypergravity” periods result from a Boeing 727-200F aircraft (G-Force One®, Zero Gravity Corpora- entry into and exit from the “parabola” periods. tion). Four sets of parabolas were performed with 5, 6, 4, and 5 parabolas, respectively. The ﬁrst set targeted, in order, Mars g, DISCUSSION Mars g, Lunar g,0 g, and 0 g. All other parabolas targeted 0 g. Data were collected for 1.77 h during all phases of ﬂight from a Slam Parabolic ﬂights provide the opportunity to perform simulated Stick X™ (Mide Technology Corp.) mounted in the rear of the space research in a cost-effective manner. Recently, Lambot and 1 2 Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA and Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA Correspondence: Christopher E. Carr (firstname.lastname@example.org) Received: 4 January 2018 Revised: 25 June 2018 Accepted: 3 July 2018 Published in cooperation with the Biodesign Institute at Arizona State University, with the support of NASA Acceleration proﬁles and processing methods for parabolic ﬂight CE Carr et al. to aircraft nose a b to aircraft tail c 3 m 45° 20-30 s 1.5-3 km 50.8 cm "Zero G" Baseplate AN6 bolt points 18 m d Raw Data Calibrate Low Pass Filter Segment flight into period s Characterize Classify Other Compute g level (norm) g level, Optimize Compute filtered Change point detection Hypergravity Filter g level (norm) of g level transitions duration Parabola Power Spectral Density 1.5 0.5 0 1000 2000 3000 4000 5000 6000 Time (s) 1.5 gh i 0.5 1000 1500 2000 2500 3000 3500 Time (s) g h i 1.5 1.5 1.5 z z z 1 1 1 0.5 0.5 0.5 y y y 0 0 0 x x x 1250 1260 1270 1280 1290 1330 1340 1350 1360 1370 1410 1420 1430 1440 1450 Time (s) Time (s) Time (s) Fig. 1 Parabolic ﬂight acceleration data and analysis methodology. a Typical ﬂight path during a single parabola. b Research section of aircraft with baseplate location during ﬂight. c Accelerometer orientation on baseplate. d Overview of analysis method. e Measured accelerations after low-pass ﬁltering (g ). f Change points (vertical lines) for mean g levels as measured by g . g–i Second-level linear change points ﬁlt ﬁlt (vertical dotted lines) deﬁne transition regions for a Mars, a lunar, and a 0 g parabola, respectively. Individual parabolas corresponding to g–i are labeled in f. For accelerations during each of the 20 parabolas, see Supplementary Figs. 6–7 Ord (2016) evaluated data from over 400 parabolic ﬂights and addition, the code implementing our methodology to categorize assessed the quality of reduced g datasets. Although consider- all phases of ﬂight and characterize g levels and durations of able effort was dedicated to identifying the highest quality, low g parabolas is publicly available in order to facilitate future parabolic time periods (with variations less than ± 0.01 g) from these ﬂights, ﬂight research. In addition, our methods could be adapted to neither the acquisition hardware, the raw data, nor the code analysis of data from suborbital ﬂights, drop towers, or studies implemented for analysis, are currently available to the public. involving launch and landing accelerations. Indeed, when reviewing the literature, we found no such easily Our hardware solution, the Slam Stick X™, offers a compact, accessible parabolic ﬂight acceleration data nor published analysis ﬂexible, low-power, high resolution solution for acceleration and methodology. vibration monitoring with favorable comparison to alternatives Here, we provide the following: (1) a commercially available (Methods). The small size facilitates integration into experiments hardware solution for data acquisition; (2) raw and calibrated data and measurement of the local GIA environment, which is not for all phases of ﬂight; (3) data analysis methodology that is constant across the aircraft. In addition, mounting with double- independent of accelerometer orientation, and (4) characteriza- sided tape is simple, robust, and does not impact the frequency tion of g levels and durations achieved for 20 parabolas. In response (Methods). npj Microgravity (2018) 14 Published in cooperation with the Biodesign Institute at Arizona State University, with the support of NASA 1234567890():,; g g filt filt filt filt filt Acceleration proﬁles and processing methods for parabolic ﬂight CE Carr et al. Lambot and Ord identiﬁed a “sweet zone” in the middle of the sides, centered on the baseplate. Washers (McMaster Carr parabola with low acceleration deviation. Our data are consistent 92503A230) were used for mounting in combination with AN-6 with this conclusion (Supplementary Fig. 8), although Lambot and steel bolts (3/8 inch) provided by ZGC. The Slam Stick X™ was Ord used, after unspeciﬁed low-pass ﬁltering, a much more mounted to a standard baseplate with double-sided sticky tape stringent tolerance (± 0.01 g) that was not met by our ﬁltered data. (3 M 950), which is the preferred mounting method due to its We encourage future parabolic ﬂight experimenters to release raw vibration frequency response (near unity) and robustness: this data as well as to provide data processing details (ideally including method has previously been validated during vibration testing at in executable form) to facilitate validation and improvement of over 75 g at 1 kHz . The Slam Stick X™ was conﬁgured using Slam processing algorithms, as well as reﬁne expected g levels and Stick Lab 1.8. Acquisition was initiated and terminated manually support planning and analysis that can be tailored to speciﬁc using the control pad on the device. Sampling rates were 5 kHz experimenter needs. (piezoelectric vibration sensor), 411 Hz (DC acceleration), and 1 Hz Due to the limited availability and high cost of actual space (pressure and temperature). environments, it is imperative that we continue to utilize parabolic ﬂights as a means to simulate space – and to understand the Data calibration accuracy and limitations of this modality. By making our data and The raw IDE ﬁle generated by the Slam Stick X™ was converted to methods available we hope to enable others to better plan, calibrated MAT (MATLAB, The Mathworks, Natick, MA) ﬁles using execute, and analyze parabolic ﬂight experiments, and thus to the ide2csv.exe command line utility (Mide Technology Corp.) help facilitate future space activities. using the factory calibration. Note that data calibration and export functions can also be performed directly using Slam Stick Lab. Here we focus on data from the DC accelerometer. We note that METHODS temperature varied less than 1 °C and pressure showed a typical Device Selection regulated proﬁle and was highly stable during parabolas The Slam Stick X™ (Mide Technology Corp., www.mide.com) was (Supplementary Fig. 5). selected based on its size (76 mm × 30 mm × 15 mm), low mass (65 g), integrated battery, manual and USB interfaces, and Orientation-independent approach combination DC (Analog Devices ADXL345) and piezoelectric (TE 832M1) accelerometers to enable accuracy at both low (e.g., down In any given experiment, one accelerometer orientation may be to 0 Hz) and high frequencies (up to 20 kHz sample frequency). more appropriate than another. Thus, we based our phase of ﬂight The aluminum body was selected to provide improved high identiﬁcation method (below) on a measure that is independent frequency response. Additional integrated sensors included of the accelerometer orientation: the Euclidean norm of the x, y, temperature and pressure (NXP MPL3115) and control pad and z axes, which we hereafter refer to as the g level or g. As this temperature and pressure (TE MS8607). variable is a positive scalar, it does not capture directional Alternative data acquisition systems include many commercial ﬂuctuations in the gravity vector. Thus, for characterization of off the shelf (COTS) accelerometers, as well as the NASA Suborbital phases of ﬂight, vector-based statistics should be used. For Flight Environment Monitor (SFEM) . There may be potential example, we estimated the mean g level during a 0 g parabola by beneﬁts of using the SFEM, although the Slam Stick X™ offers averaging the x, y, and z components, and then computing the comparable or longer recording time, DC and piezoelectric norm. accelerometers (enabling both g level and high frequency vibration measurements), higher sampling frequencies, a wider Calibration veriﬁcation operating temperature range (− 40 °C to + 80˚C) and much lower The expected value of the g level is unity on Earth under non- ( > 10 × ) mass and volume. Another COTS option is the Lansmont accelerated conditions; as a veriﬁcation of our accelerometer 3X90 , although the Slam Stick X™ speciﬁcations provide beneﬁts calibration, we found the norm under lab bench conditions (14.2 s in several areas (size, mass, sampling rates, and temperature recording) to be 0.9840 (rms) and 0.9840 ± 0.0055 (mean ± SD), range). consistent with < 2% error. This is a lower bound when vibration One consideration for parabolic ﬂight experiments is that the or speciﬁc force other than that caused by gravity is present, GIA environment is not constant across the aircraft. In some cases, consistent with the rms value (1.07) observed during ﬂight. it may be adequate to have a single reference ﬂight proﬁle to be Speciﬁc force was concentrated in the z axis as measured by root used by multiple experiment teams. However, some applications mean square (rms) values (0.0466, 0.0775, 1.0662 for x, y, and z may be better served through measurement of the local GIA axes, respectively), consistent with the accelerometer orientation environment of a given experimental apparatus. Here, the small (Fig. 1c). Calibration accuracy was also assessed after ﬁltering (see size of our solution facilitates direct incorporation into a payload, below). as well as placement in the desired location or orientation. When selecting a data acquisition solution, it is also important to consider how the mounting of the accelerometer itself may Phase of ﬂight characterization impact the frequency response; in our case, use of double-sided For parabola identiﬁcation, we ﬁrst ﬁltered the raw data using a sticky tape represents both an extremely practical and low bias zero-phase 12th order Butterworth ﬁlter using the designﬁlt() option, enabled by the low device mass. Because no additional function using a half power frequency (HPF) as described below. materials separate the accelerometer from the aircraft, there is no 7,8 Next, we utilized change point detection as implemented by the need to correct for the frequency response of the mounting MATLAB FindChangePts() function. interface. Change point detection was ﬁrst applied to the ﬁltered g level g to identify differences in mean g levels. A known number of ﬁlt Device mounting and data acquisition change points was speciﬁed based on the parabola number within Zero Gravity Corporation (ZGC) utilizes a standard system of each set, e.g., two times the number of parabolas, plus two mounting hardware to the aircraft structure consisting of a additional transitions (ﬁrst pre-parabola pull up; last post-parabola baseplate (61 cm × 61 cm × 1.27 cm aluminum plate, e.g., McMas- pull up) for each set of parabolas. In our case sets of 5, 6, 4, and 5 ter Carr 86825K25) bolted to the aircraft structure using four (20 total) parabolas become 12, 14, 10, and 12 change points. This clearance holes at the corners of a square with 50.8 cm (20 in) total number of change points (48) was speciﬁed and Published in cooperation with the Biodesign Institute at Arizona State University, with the support of NASA npj Microgravity (2018) 14 Acceleration proﬁles and processing methods for parabolic ﬂight CE Carr et al. however, based on the wide separation between “parabola,” a 4 10 Straight & “hypergravity,” and “other” classes, this is not expected to present level flight Transition a signiﬁcant challenge to standard unsupervised classiﬁcation Parabola Turns & approaches (e.g., k-means). Hypergravity setup for 10 Other last set of parabolas Filter optimization To optimize the ﬁlter, we selected a HPF based on the g level power spectral density (PSD, Supplementary Fig. 2a). The PSD was computed via the MATLAB pwelch() function with default parameters. To select the HPF, we examined the cumulative sum 1 of the PSD (Supplementary Fig. 2b), which revealed a sharp increase in power above 0.01 Hz. We chose this value (HPF = 0.01 Hz) to maximize the low frequency content of the ﬁltered data while rejecting as much spectral power from higher frequencies as possible. As an example, ﬁltering at HPF = 0.01 Hz 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 preserves parabola dynamics, whereas ﬁltering at HPF = 0.001 Hz Mean g level does not (Supplementary Fig. 2c). Our selected value provides the smoothest data for identifying parabolas, while still accurately representing g level transitions. A manual procedure identiﬁed 0 g Lunar Mars similar values, e.g., adjusting the HPF toward DC until the rapid (N=17) (N=1) (N=2) transitions between g levels showed systematic bias, then setting the HPF to 10 × this value, also gave HPF = 0.01 Hz. Filtering reduced the root mean square speciﬁc force in the 15 lateral (x) direction but little in other directions (0.0161, 0.0681, 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 1.0623 for x, y, z, respectively), consistent with low frequency Mean g level aircraft accelerations mainly due to pitch maneuvers. The g level was near unity during periods of relative calm (Supplemental Fig. Fig. 2 Phases of ﬂight classiﬁcation and characterization. a After identifying transitions, non-transition events were classiﬁed as 3a-b), including the ﬁrst 1000 s of data collected during largely duration < 100 s (horizontal line) and by mean g level (vertical straight and level ﬂight (rms 0.9919 and 0.9856, raw and ﬁltered, dotted lines) into “parabola,”“hypergravity,” and “other.” The subset respectively). This unﬁltered estimate is 0.8% higher than under of “hypergravity” periods with lower duration were identiﬁed as lab bench conditions, and both are consistent with accurate those at the start or end of a set of parabolas. All phases of ﬂight sensor calibration at DC to lower than 2% error, based on the were unambiguously classiﬁed. b Parabola characteristics (see also: factory calibration. Supplementary Information). Error bars: mean ± SD Regression analysis FindChangePts() identiﬁed all g level change points in an Regression of parabola g level on duration was performed using unsupervised manner (Fig. 1f). the MATLAB ﬁtlm() function. Conﬁdence intervals were deter- mined using the MATLAB coefCI() function. We desire to break down the ﬂight into regions of stable g levels. Thus, for each change point, we used a secondary change point detection to identify differences in the slope of the g level Code availability vs. time curves. Data within 10 s of each change point was The MATLAB scripts implementing our analysis are available at: subjected to this secondary change point detection using a linear https://github.com/CarrCE/zerog. slope metric. This step successfully segmented the ﬂight into regions of rapid “transition” (indicated by dotted lines, Fig. 1g-i) Data availability and more stable regimes. This resulted in 97 ﬂight periods (2 × the Raw and calibrated data are available via the Open Science number of change points + 1). Framework at: https://osf.io/nk2w4/. Classiﬁcation of non-“transition” ﬂight periods into “parabola,” “hypergravity,” and “other” (which includes straight and level ﬂight as well as standard rate turns) was then performed, ﬁrst by ACKNOWLEDGEMENTS categorizing any periods with duration > 100 s as “other”, then by We thank the MIT Media Lab Space Exploration Initiative for providing the parabolic segmenting data according to g level (“parabola” ≤ 0.9 g, ﬂight. This work was supported by NASA award NNX15AF85G. N.C.B. was supported 0.9 < “other” ≤ 1.1 g, “hypergravity > 1.1 g). Despite its simplicity, by NASA Postdoctoral Fellowship award 80NSSC17K0688. this classiﬁer achieved good separation between classes (Fig. 2a). Parabola durations (mean ± s.d.) were 19.5 ± 1.4 s (0 g, N = 17, AUTHOR CONTRIBUTIONS range 17 to 24 s), 23.7 s (Lunar g; N = 1), and 28.9 ± 0.7 s (Mars g; N C.E.C. designed the experiment. C.E.C., K.S., S.A.B., and N.C.B. built and tested the = 2). The g levels achieved were 0.041 ± 0.005 g (0 g) and 0.159 g hardware. N.C.B. and M.T.Z. collected the data. C.E.C. and N.C.B. processed the data. G. (lunar g). Both Mars parabolas achieved 0.356 g, indicating high R. advised on the experiment design. C.E.C. and N.C.B. wrote, and all authors edited consistency between parabolas targeting similar g levels. Higher g and approved, the paper. C.E.C. is the guarantor. levels were signiﬁcantly associated with longer-duration parabolas (Supplementary Fig. 4a), although not when lunar and Mars data were excluded (Supplemental Fig. 4b). ADDITIONAL INFORMATION Some limitations are inherent in our study, which focused solely Supplementary information accompanies the paper on the npj Microgravity website on one ﬂight and 20 parabolas. If analyzing multiple ﬂights, with (https://doi.org/10.1038/s41526-018-0050-3). parabolas performed under more varied conditions, it is possible a Competing interests: The authors declare no competing interest. slightly more complex classiﬁcation strategy might be required; npj Microgravity (2018) 14 Published in cooperation with the Biodesign Institute at Arizona State University, with the support of NASA Duration (s) Duration (s) Acceleration proﬁles and processing methods for parabolic ﬂight CE Carr et al. 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