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NASA NTRS2017

Mars2020 Entry, Descent, and Landing Instrumentation 2 (MEDLI2) Do No Harm Test Series

A total of seventeen instrumented thermal sensor plugs, eight pressure transducers, two heat flux sensors, and one radiometer are planned to be utilized on the Mars 2020 missions thermal protection system (TPS) as part of the Mars Entry, Descent, and Landing Instrumentation II (MEDLI2) project. Of the MEDLI2 instrumentation, eleven instrumented thermal plugs and seven pressure transducers will be installed on the heatshield of the Mars 2020 vehicle while the rest will be installed on the backshell. The goal of the MEDLI2 instrumentation is to directly inform the large performance uncertainties that contribute to the design and validation of a Mars entry system. A better understanding of the entry environment and TPS performance could lead to reduced design margins enabling a greater payload mass-fraction and smaller landing ellipses. To prove that the MEDLI2 system will not degrade the performance of the Mars 2020 TPS, an Aerothermal Do No Harm (DNH) test series was designed and conducted. Like Mars 2020s predecessor, Mars Science Laboratory (MSL), the heatshield material will be Phenolic Impregnated Carbon Ablator (PICA); the Mars 2020 entry conditions are enveloped by the MSL design environments, therefore the development and qualification testing performed during MEDLI is sufficient to show that the similar MEDLI2 heatshield instrumentation will not degrade PICA performance. However, given that MEDLI did not include any backshell instrumentation, the MEDLI2 team was required to design and execute a DNH test series utilizing the backshell TPS material (SLA-561V) with the intended flight sensor suite. To meet the requirements handed down from Mars 2020, the MEDLI2 DNH test series emphasized the interaction between the MEDLI2 sensors and sensing locations with the surrounding backshell TPS and substrucutre. These interactions were characterized by performing environmental testing of four 12 by 12 test panels, which mimicked the construction of the backshell TPS and the integration of the MEDLI2 sensors as seen in Figure 1. The testing included thermal vacuumcycling, random vibration, shock, and arc jet testing. The test panels were fabricated by Lockheed Martin, establishing techniques that will be utilized during the Mars 2020 vehicle installation. Each test panel included one thermal sensor plug (two embedded thermocouples), one heat flux sensor, and multiple pressure port holes for evaluation.This presentation will discuss the planning and execution of the MEDLI2 DNH test series. Selected highlights and results of each environmental test will be presented, and lessons learned will be addressed that will feed forward into the planning for the MEDLI2 flight system certification testing.

backshell↗

NASA NTRS2016

Mars 2020 Entry, Descent and Landing Instrumentation (MEDLI2)

This paper will introduce Mars Entry Descent and Landing Instrumentation (MEDLI2) on NASA's Mars2020 mission. Mars2020 is a flagship NASA mission with science and technology objectives to help answer questions about possibility of life on Mars as well as to demonstrate technologies for future human expedition. Mars2020 is scheduled for launch in 2020. MEDLI2 is a suite of instruments embedded in the heatshield and backshell thermal protection systems of Mars2020 entry vehicle. The objectives of MEDLI2 are to gather critical aerodynamics, aerothermodynamics and TPS performance data during EDL phase of the mission. MEDLI2 builds up the success of MEDLI flight instrumentation on Mars Science Laboratory mission in 2012. MEDLI instrumentation suite measured surface pressure and TPS temperature on the heatshield during MSL entry into Mars. MEDLI data has since been used for unprecedented reconstruction of aerodynamic drag, vehicle attitude, in-situ atmospheric density, aerothermal heating, transition to turbulence, in-depth TPS performance and TPS ablation. [1,2] In addition to validating predictive models, MEDLI data has highlighted extra margin available in the MSL forebody TPS, which can potentially be used to reduce vehicle parasitic mass. MEDLI2 expands the scope of instrumentation by focusing on quantities of interest not addressed in MEDLI suite. The type the sensors are expanded and their layout on the TPS modified to meet these new objectives. The paper will provide key motivation and governing requirements that drive the choice and the implementation of the new sensor suite. The implementation considerations of sensor selection, qualification, and demonstration of minimal risk to the host mission will be described. The additional challenges associated with mechanical accommodation, electrical impact, data storage and retrieval for MEDLI2 system, which extends sensors to backshell will also be described.

Thermal Protection System↗

NASA NTRSDate not supplied

MEDLI2 Material Response Model Development and Validation

During the entry of Mars Science Laboratory (MSL) the heatshield was equipped with the instrumentation suite Mars Entry, Descent, and Landing Instruments (MEDLI). In-depth thermocouple (TC) data was used to reconstruct the surface heating and temperatures. Discrepancies between MEDLI’s recorded in-depth heating data and the predicted thermal response and recession led to a campaign to characterize and test Mars Entry, Descent, and Landing Instrument 2 (MEDLI2) flight lot thermal protection materials (TPS) at relevant temperatures and pressures for Martian entry conditions. This paper covers the development and validation of MEDLI2 flight lot material response models for the heatshield material Phenolic Impregnated Carbon Ablator (PICA) and the backshell material Super-Lightweight Ablator (SLA-561V). Virgin and char thermal conductivities were updated as a function of temperature and pressure in the MEDLI2 specific material response models. Other material properties such as virgin and char density, emissivity, absorptivity, and specific heat capacitance was also characterized and compared to the “Heritage” models used during the TPS design phase of Mars 2020. Fully Implicit Ablation and Thermal response (FIAT) simulations were completed using the Heritage and MEDLI2 material response models to provide evidence of the increased accuracy of the MEDLI2 model. FIAT predicted in-depth temperatures were compared to flight lot certification ground-test arc jet test data and MEDLI2 flight thermocouple data.

Material response↗

NASA NTRS2019

MEDLI2 Flight Heat Flux Sensor Environment and Planetary Protection Testing

Mars 2020 will fly the Mars Entry, Descent, and Landing Instrumentation II (MEDLI2) sensor suite consisting of a total of seventeen instrumented thermocouple sensor plugs, eight pressure transducers, two total heat flux sensors, and one radiometer embedded in the thermal protection system (TPS). Of the MEDLI2 instrumentation, eleven instrumented thermocouple plugs and seven pressure transducers will be installed on the heatshield of the Mars 2020 vehicle while the rest will be installed on the backshell. The goal of the MEDLI2 instrumentation is to directly inform the large performance uncertainties that contribute to the design and validation of a Mars entry system. A better understanding of the entry environment and TPS performance could lead to reduced design margins enabling greater payload mass-fraction and smaller landing ellipses. The MEDLI2 total heat flux sensors and radiometer are new instruments that were not flown on the Mars Science Laboratory mission. These sensors directly measure the surface heat flux and radiation at specific backshell locations. The total heat flux sensors use a Schmidt-Boelter sensing element. The radiometer version uses a sapphire window placed over the Schmidt-Boelter sensing element to separate the radiative component of the total heat flux. MEDLI2 recently planned and executed protoflight environmental testing as well planetary protection measures on the flight and flight-spare total heat flux sensors and radiometers. This testing is required to provide confidence in the performance of the flight-lot sensors when exposed to flight-like environments, and to reduce the risk of biological contamination on the planet of Mars with microbes from Earth.

MEDLI2 FLIGHT HEAT FLUX SENSOR ENVIRONMENT AND PLA↗

NASA NTRSDate not supplied

MEDLI2: Ablator Models for Flight

During the entry of Mars Science Laboratory (MSL) the heatshield was equipped with the instrumentation suite Mars Entry, Descent, and Landing Instruments (MEDLI). In-depth thermocouple (TC) data was used to reconstruct the surface heating[1,2]. Discrepancies between MEDLI’s flight heating data and predicted thermal response and recession[3] led to a campaign to characterize and test Mars2020 and MEDLI2 (Mars Entry, Descent, and Landing Instrument 2)[4] flight-lot thermal protection materials (TPS) at relevant ground test conditions. This paper covers the development and validation of flight-lot material response models for the heatshield material Phenolic Impregnated Carbon Ablator (PICA) and the backshell material SLA-561V (Super Lightweight Ablator). Virgin and char properties such as thermal conductivity and emissivity were updated as a function of temperature and pressure in the MEDLI2 specific material response models. Other material properties were characterized and determined to be similar to prior TPS material measurements used for the heritage models. MEDLI2 also investigated the impact of the protective coating that was applied to MSL and Mars2020’s PICA heatshield to reduce the particulate matter shedding. Evidence from arc jet experiments on coated PICA showed that although there were no adverse effects on the performance of the TPS, there were differences in the in-depth temperatures and reduced recession compared to material response predictions of uncoated PICA.[5,6]. Fully Implicit Ablation and Thermal response (FIAT)[7] material response simulations were conducted with the heritage and MEDLI2 flight-lot material models. FIAT-predicted in-depth temperature responses and recession predictions were compared to flight-lot certification ground-test arc jet test data and MEDLI2 thermocouple data.

Materials Response↗

NASA NTRS2015

Mars2020 Entry, Descent, and Landing Instrumentation (MEDLI2): Science Objectives and Instrument Requirements

NASAs exploration and technology roadmaps call for capability advancements in Mars entry, descent, and landing (EDL) systems to enable increased landed mass, a higher landing precision, and a wider planetary access. It is also recognized that these ambitious EDL performance goals must be met while maintaining a low mission risk in order to pave the way for future human missions. As NASA is engaged in developing new EDL systems and technologies via testing at Earth, instrumentation of existing Mars missions is providing valuable engineering data for performance improvement, risk reduction, and an improved definition of entry loads and environment. The most notable recent example is the Mars Entry, Descent and Landing Instrument (MEDLI) suite hosted by Mars Science Laboratory for its entry in Aug 2012. The MEDLI suite provided a comprehensive dataset for Mars entry aerodynamics, aerothermodynamics and thermal protection system (TPS) performance. MEDLI data has since been used for unprecedented reconstruction of aerodynamic drag, vehicle attitude, in-situ atmospheric density, aerothermal heating, and transition to turbulence, in-depth TPS performance and TPS ablation. [1,2] In addition to validating predictive models, MEDLI data has demonstrated extra margin available in the MSL forebody TPS, which can potentially be used to reduce vehicle parasitic mass. The presentation will introduce a follow-on MEDLI instrumentation suite (called MEDLI2) that is being developed for Mars-2020 mission. MEDLI2 has an enhanced scope that includes backshell instrumentation, a wider forebody coverage, and instruments that specifically target supersonic aerodynamics. Similar to MEDLI, MEDLI2 uses thermal plugs with embedded thermocouples and ports through the TPS to measure surface pressure. MEDLI2, however, also includes heat flux sensors in the backshell and a low range pressure transducer to measure afterbody pressure.

Aerothermodynamics↗

NASA NTRSDate not supplied

MEDLI2: MISP Inferred Aerothermal Environment and Flow Transition Assessment

The Mars Entry, Descent, and Landing Instrumentation 2 (MEDLI2) sensor suite on the Mars2020 mission contained multiple sensors on the aeroshell to measure the aerothermal environment during entry into the Martian atmosphere. These sensors performed superbly and successfully returned forebody and aftbody heating measurements. Analysis of MEDLI2 data indicated flow transitioning from a laminar to turbulent state on the heatshield. No evidence of flow transition was observed on the backshell. Two methods were used to estimate flow transition times on the heatshield: (1) temperature gradient of near-surface thermocouple data and (2) heat flux gradient from an inverse reconstruction approach using thermocouple data and material response modeling. Both methods produced similar transition times with an estimated accuracy of ±1 s. To assess various transition criteria, transition parameters were evaluated at each sensor location using flow field solutions from computational fluid dynamics (CFD) simulations. The idea was to use conservative values inferred from MEDLI2 data as transition criteria for other Mars missions. To test this hypothesis, MEDLI data from the Mars Science Laboratory (MSL) mission was used to compare predicted vs. actual flow transition times. The comparisons suggest smooth wall transition criteria are not well-suited in modeling the rapid progression of a turbulent transition front. Transition criteria containing a roughness element parameter agreed better with the flight data. In summary, critical transition values derived from MEDLI2 data may be used as a starting point in constructing a flow transition model for future Mars missions.

Chun Y Tang↗

NASA NTRSDate not supplied

Overview of NASA's Detailed Investigation into the MEDLI2 Flight Data

- MEDLI2 “Deep Dive” - Why a focused investigation? - Deeper understanding of the MEDLI2 dataset, including TPS in-depth temperatures, backshell pressure, and radiative heat flux - Validate new tools for future Mars missions including Mars Sample Return and Humans-to-Mars - MEDLI2 is considerably more complex than MEDLI - Fully leverage the significant investment in the MEDLI2 instrumentation suite - Core research areas: - PICA-NuSil (PICA-N) testing, model development, and validation - Detailed aeroheating investigations - Sensor fusion of collocated measurements - High-fidelity analyses and uncertainty quantification - Trajectory and aerodynamics investigations

Tom West↗

NASA NTRS2016

Mars 2020 Entry, Descent and Landing Instrumentation 2 (MEDLI2)

This paper introduces the Mars Entry Descent and Landing Instrumentation 2 (MEDLI2) concept for NASAs Mars 2020 mission. Mars 2020 is a flagship-class mission, scheduled for launch in 2020, with science and technology objectives to help answer questions about habitability of Mars as well as to demonstrate technologies for future human expedition. MEDLI2 is a suite of instruments embedded in the heatshield and backshell thermal protection systems (TPS) of the Mars 2020 entry vehicle. The objectives of MEDLI2 are to gather critical aerodynamics, aerothermodynamics and TPS (Thermal Protective System) performance data during the Entry Descent and Landing (EDL) phase of the mission.

Aerothermodynamics↗

NASA NTRS2019

The Entry Descent and Landing Instrumentation (MEDLI2) Suite for the Mars 2020 Mission

This presentation will discuss the current state of each of the main measurements systems for MEDLI2. The first system is a network of pressure transducers, the Mars Entry Atmospheric Data System (MEADS). Improving upon MEDLI, separate MEDLI2 pressure transducers span the measurement range suitable for both hypersonic and supersonic flows. The MEADS will also measure pressure on the backshell of the vehicle to better characterize the contribution of backshell pressure on the overall forces and moments on the entry probe. The second system is the thermal instrumentation, or Mars Instrumented Sensor Plugs. This system includes a network of high-temperature thermocouples embedded in the thermal protection system across the heatshield and backshell. The MISP also includes two types of sensors for directly measuring incident heatflux on the backshell of the vehicle. In addition, a radiometer is included on the backshell to measure radiative heating. The presentation will also cover the current state of the MEDLI2 hardware, expected environments that will be measured, and data analysis techniques being developed to infer vehicle entry performance from both the MISP and MEADS sensor systems.

White, Todd↗

NASA NTRSDate not supplied

Inverse Estimation of Mars 2020 Entry Aeroheating Environments Using MEDLI2 Flight Data

The Mars Entry, Descent, and Landing Instrumentation 2 (MEDLI2) sensor suite collected heating and pressure data during entry into Mars' atmosphere of the Mars 2020 Perseverance rover. MEDLI2 included thermocouples, heat flux sensors, and pressure transducers on both the heatshield and the backshell. This paper covers the inverse estimation of heatshield and backshell surface heating based on the MEDLI2 Instrumented Sensor Plugs (MISPs), a network of thermocouples embedded in thermal protection system plugs across the aeroshell. Monte Carlo analysis was conducted to assess the sensitivity of the surface heat rate and temperature to uncertainties in thermocouple depth and material properties such as density, specific heat capacity, and thermal conductivity. Data from each MISP was also used to estimate the local time of transition from laminar to turbulent flow at each plug location.

Hannah S Alpert↗

NASA NTRSDate not supplied

Variance Decomposition of MEDLI2 Reconstructed Heating Using Neural Networks

The Mars Entry, Descent, and Landing Instrumentation (MEDLI2) sensor suite collected data during entry of the Mars 2020 Perseverance rover into Mars’ atmosphere. This suite included a network of MEDLI2 Instrumented Sensor Plugs (MISPs). Each MISP was comprised of a cylinder made of Thermal Protection System (TPS) material with 1-3 embedded thermocouples (TCs), and it was flush mounted into the heatshield or backshell. Data from these in-depth TCs were used to reconstruct the aeroheating environment of the vehicle throughout entry. Surface heating was posed as an inverse problem, with the goal of estimating the surface heating by minimizing an objective function of the difference between MISP temperature measurements during flight and the temperature predictions derived from the Fully Implicit Ablation and Thermal response (FIAT) program. Given an aerothermal environment, FIAT calculates the material response and provides in-depth temperatures throughout the TPS material. To achieve the reverse, an internal tool called FIAT_Opt runs through multiple different environments until the output temperature at the TC depth closely matches the flight data. 95% confidence intervals on the reconstructed surface heating were obtained using Monte Carlo analysis, in which uncertainties in the thermocouple depth and the TPS material properties (e.g., density, thermal conductivity, heat capacity, emissivity) based on flight-lot material testing were included. A variance decomposition method using Sobol indices was employed to assess the sensitivity of the reconstructed peak heating to the TC placement and material property uncertainties. Variance decomposition was found to require tens of thousands of FIAT_Opt runs in order for the Sobol indices to converge. With a single FIAT_Opt run taking on the order of 40 minutes, the required number of computations would take months to complete, even if using multiple CPUs. To mitigate this problem, three machine learning models (ridge regression with cross-validation, random forest regression, and a deep neural network) were trained and tested using the 2000 Monte Carlo runs that were already completed. A subset of 1600 runs were used to train the model (i.e., training set), while the remaining 400 runs were used as the test set. The predictions from the deep neural network (DNN) on the test set showed nearly perfect agreement to the actual values computed with FIAT_Opt (R2 > 0.99). Using the DNN as a surrogate model, the variance decomposition using 50,000 runs was completed within minutes. The resulting Sobol indices showed that the reconstructed peak surface heating was most sensitive to the uncertainties in the thermal conductivity (ST = 0.37) and heat capacity (ST = 0.26). This method can be leveraged to provide requirements for material property measurements needed to improve the accuracy of surface heating prediction and ultimately lead to the reduction of design margins in the future. This presentation will include background on the MEDLI2 suite; the method used for inverse heating estimation; the way that material property uncertainties were accounted for using Monte Carlo analysis; a brief background on variance decomposition; the motivation for using machine learning in this context; how a neural network was trained on the data to enable variance decomposition in a fraction of the time; and the variance decomposition results for one of the MISPs.

Hannah Alpert↗

NASA NTRSDate not supplied

Development of an Optical Model for the MEDLI2 Radiometer

We present an optical model of the MEDLI2 radiometer, which was flown onboard the Mars 2020 vehicle during atmospheric entry on February 18, 2021. The proposed model takes into account the radiometer geometry and optical phenomena such as reflection, refraction, and absorption to estimate the signal loss inherent in the design of the radiometer. The model serves as a filter, which can be used to convert predicted total radiative heat fluxes on the radiometer window into predicted measured heat fluxes, enabling more accurate comparisons of the radiometer flight data with predicted radiometer measurements. Optical properties of the radiometer’s sapphire window are inferred from spectral transmissivity measurements. A modified Lorentz Oscillator Model is used to represent the dielectric function of the window and is fit to the transmission data. The resulting optical model of the radiometer is validated against experimental data used to measure the dependence of the radiometer output voltage on the incident angle of the light impinging on it. Comparison of predicted radiometer measurements with the MEDLI2 flight data show good agreement between the predicted and measured peak heat fluxes, though offset at different times. A linear increase in the signal loss due to radiometer optics alone is also demonstrated, suggesting that the changing shock angle over the course of the trajectory impacts the signal loss.

MEDLI2↗

NASA NTRS2016

Mars 2020 Entry, Descent and Landing Instrumentation 2 (MEDLI2)

The Mars Entry Descent and Landing Instrumentation 2 (MEDLI2) sensor suite will measure aerodynamic, aerothermodynamic, and TPS performance during the atmospheric entry, descent, and landing phases of the Mars 2020 mission. The key objectives are to reduce design margin and prediction uncertainties for the aerothermal environments and aerodynamic database. For MEDLI2, the sensors are installed on both the heatshield and backshell, and include 7 pressure transducers, 17 thermal plugs, and 3 heat flux sensors (including a radiometer). These sensors will expand the set of measurements collected by the highly successful MEDLI suite, collecting supersonic pressure measurements on the forebody, a pressure measurement on the aftbody, direct heat flux measurements on the aftbody, a radiative heating measurement on the aftbody, and multiple near-surface thermal measurements on the thermal protection system (TPS) materials on both the forebody and aftbody. To meet the science objectives, supersonic pressure transducers and heat flux sensors are currently being developed and their qualification and calibration plans are presented. Finally, the reconstruction targets for data accuracy are presented, along with the planned methodologies for achieving the targets.

Aerodynamics↗

NASA NTRS2017

Mars 2020 Entry, Descent, and Landing Instrumentation 2 (MEDLI2) Sensor Suite

The Mars 2020 Entry, Descent, and Landing Instrumentation 2 (MEDLI2) sensor suite seeks to address the aerodynamic, aerothermodynamic, and thermal protection system (TPS) performance issues during atmospheric entry, descent, and landing of the Mars 2020 mission. Based on the highly successful instrumentation suite that flew on Mars Science Laboratory (MEDLI), the new sensor suite expands on the types of measurements and also seeks to answer questions not fully addressed by the previous mission. Sensor Package: MEDLI2 consists of 7 pressure transducers, 17 thermal plugs, 2 heat flux sensors, and one radiometer. The sensors are distributed across both the heatshield and backshell, unlike MEDLI (the first sensor suite), which was located solely on the heat-shield. The sensors will measure supersonic pressure on the forebody, a pressure measurement on the aftbody, near-surface and in-depth temperatures in the heatshield and backshell TPS materials, direct total heat flux on the aftbody, and direct radiative heating on the aftbody. Instrument Development: The supersonic pressure transducers, the direct heat flux sensors, and the radiometer all were tested during the development phase. The status of these sensors, including the piezo-resistive pressure sensors, will be presented. The current plans for qualification and calibration for all of the sensors will also be discussed. Post-Flight Data Analysis: Similar to MEDLI, the estimated flight trajectory will be reconstructed from the data. The aerodynamic parameters that will be reconstructed will be the axial force coefficient, freestream Mach number, base pressure, atmospheric density, and winds. The aerothermal quantities that will be determined are the heatshield and backshell aero-heating, turbulence transition across the heatshield, and TPS in-depth performance of PICA. By directly measuring the radiative and total heat fluxes on the back-shell, the convective portion of the heat flux will be estimated. The status of the current tools to perform the post-flight data analysis will be presented, along with plans for model improvements.

Hwang, Helen H.↗

NASA NTRS2017

Mars 2020 Entry, Descent, and Landing Instrumentation 2 (MEDLI2) Sensor Suite

Hwang, Helen↗

NASA NTRS2017

Independent Assessment of the Backshell Pressure Field for Mars Entry, Descent, and Landing Instrumentation 2 (MEDLI2)

The Mars Entry, Descent, and Landing Instrumentation 2 (MEDLI2) project requested that the NASA Engineering and Safety Center (NESC) support a ballistic range test to measure backshell pressures on scale models of the Mars 2020 entry capsule. The MEDLI2 project needed the test to provide important dynamic pressure data to help select a backshell pressure port, quantify drag coefficient reconstruction uncertainties, and design the data acquisition hardware. This document contains the outcome of the NESC assessment.

Prince, Jill L.↗

NASA NTRSDate not supplied

Variance Decomposition of MEDLI2 Reconstructed Heating Using Neural Networks

The Mars Entry, Descent, and Landing Instrumentation (MEDLI2) sensor suite collected data during entry of the Mars 2020 Perseverance rover into Mars’ atmosphere. An inverse estimation of the backshell and heatshield surface aeroheating was performed, using the data from the MEDLI2 Instrumented Sensor Plugs, a network of thermocouples embedded within the thermal protection system across the aeroshell. Monte Carlo analysis was conducted to assess the sensitivity of the surface heat rate, temperature, and heat load to uncertainties in thermocouple depth and material properties. In this paper, a variance decomposition method using Sobol indices was employed to understand the relative contributions of each uncertainty parameter. Performing this analysis using results from the inverse analysis tool FIAT_Opt was found to require incredibly high computation time, and thus machine learning models were trained and evaluated as a surrogate model for FIAT_Opt. This paper demonstrates that machine learning models can be an efficient, accurate alternative to state-of-the-art inverse analysis tools like FIAT_Opt, especially for computationally-expensive processes. Using these models, the sensitivity analysis showed that uncertainties in heat capacity and thermal conductivity were the main drivers for the overall uncertainty in peak reconstructed heating and heat load.

H S Alpert↗