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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 NTRSDate not supplied

Data Fusion of In-Flight Aerothermodynamic Heating Measurements Using Kalman Filtering

On February 18th, 2021, the Mars 2020 entry system successfully delivered the Perserverance rover to the surface of Mars at Jezero Crater. The entry capsule carried a set of instrumentation installed on the heat shield and backshell, named the Mars 2020 Entry, Descent, and Landing Instrumentation. The instruments include pressure transducers, thermocouples, heat flux gauges, and radiometers to measure the aerodynamic and aerothermodynamic performance of the entry vehicle. Three of these sensors, a thermocouple plug, heat flux gauge, and a radiometer, are located in close proximity on the backshell. Each sensor is exposed to roughly the same environment, but measured these environments in different ways, each with its own set of modeling and measurement error complications. This paper develops a method for blending each of these measurements together in a single algorithm to produce estimates of the aerothermodynamic environments at that location on the backshell. The approach makes use of the Kalman filter methodology for solving state estimation problems. The filter has a predictor/corrector structure in which simplified process models are used to propagate the aeroheating states forward through time, which are then updated based on the measurement data. The method has been tested on simulated datasets.

Christopher D. Karlgaard↗

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 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

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

Data Fusion of In-Flight Aerothermodynamic Heating Measurements Using Kalman Filtering

On February 18th, 2021, the Mars 2020 entry system successfully delivered the Perseverance rover to the surface of Mars at Jezero Crater. The entry capsule carried instrumentation installed on the heatshield and backshell, named the Mars Entry, Descent, and Landing Instrumentation 2. The instruments included pressure transducers, thermocouples, heat flux gauges, and a radiometer to measure the aerodynamic and aerothermodynamic performance of the entry vehicle. Three of these sensors, a thermocouple plug, heat flux gauge, and a radiometer, were co-located on the backshell. The sensors were exposed to roughly the same aerodynamic heating, but measured these environments in different ways, each with its own set of modeling and measurement error complications. This paper develops a method for blending each of these measurements together in a single algorithm to produce estimates of the aerothermodynamic environments at that backshell location. The approach makes use of the Kalman-Schmidt filter/smoother methodology, where systematic measurement error parameters are modeled as multiplicative states that are estimated by the filter along with the aerothermal states. The results indicate peak convective and radiative heating values of 0.86 and 5.16 W/cm2, respectively, compared to the filter predictive model values of 0.67 and 4.83 W/cm2.

Christopher D Karlgaard↗

NASA NTRSDate not supplied

Data Fusion of In-Flight Aerothermodynamic Heating Measurements Using Kalman Filtering

On February 18th, 2021, the Mars 2020 entry system successfully delivered the Perseverance rover to the surface of Mars at Jezero Crater. The entry capsule carried instrumentation installed on the heatshield and backshell, named the Mars Entry, Descent, and Landing Instrumentation 2. The instruments included pressure transducers, thermocouples, heat flux gauges, and a radiometer to measure the aerodynamic and aerothermodynamic performance of the entry vehicle. Three of these sensors, a thermocouple plug, heat flux gauge, and a radiometer, were co-located on the backshell. The sensors were exposed to roughly the same aerodynamic heating, but measured these environments in different ways, each with its own set of modeling and measurement error complications. This paper develops a method for blending each of these measurements together in a single algorithm to produce estimates of the aerothermodynamic environments at that backshell location. The approach makes use of the Kalman-Schmidt filter/smoother methodology, where systematic measurement error parameters are modeled as multiplicative states that are estimated by the filter along with the aerothermal states. The results indicate peak convective and radiative heating values of 0.86 and 5.16 W/cm$^2$, respectively, compared to the filter predictive model values of 0.67 and 4.83 W/cm$^2$.

Christopher D Karlgaard↗

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↗

NASA NTRSDate not supplied

BOLT II Roughness-Side Flight Results

An initial review of the roughness data from the BOLT II flight is provided. The BOLT II roughness experiment was designed to investigate discrete trips in flight based on the diamond configuration, which has been shown to be an efficient vortex generator and boundary layer trip from extensive ground-based testing. There were three trip locations chosen for the flight test and their size (height and width) were tailored to the local boundary layer thickness, but all three were expected to trip at the same time or flight condition. Details about the flight vehicle design, instrumentation, and best estimated trajectory are discussed. The present paper is limited to only a review and analysis of the thermocouple data. Boundary layer transition onset times are derived from the thermocouple analysis, thus allowing an initial assessment of the performance of these discrete trips. Since the three trips were sized to trip the boundary layer at the same time, based on a previous wind tunnel study, the present analysis indicates that they performed as expected.

Hypersonic↗

NASA NTRSDate not supplied

Computational Icing Analysis on NASA’s SIDRM Geometry to Investigate Collection Efficiency

Computational icing analysis results were compared to experimental icing tunnel data including aerothermal (e.g. dry air) and supercooled water droplet rime-ice conditions from tests conducted in early 2022 at the NASA Icing Research Tunnel (IRT). The Simulated Intercompressor Duct Research Model (SIDRM) test article was used in this study, and its geometry represents the inter-compressor duct region of a turbofan engine. The test article’s purpose is to study the physics of supercooled water icing and ice crystal icing. This study compared three different icing codes: FENSAP-ICE (Eulerian approach), LEWICE3D (Lagrangian approach), and GlennICE (Lagrangian approach). All three icing codes were conducted on SIDRM’s complex body flow-field and compared to different experimental supercooled water rime runs. The test article instrumentation (pressure taps, thermocouples, etc.) and 3D laser scans of final ice shapes were used to compare against the different icing code simulations. The overall objectives are to understand how the icing codes handle capturing collection efficiency on the complex test article’s unheated surfaces. In the aerothermal cases, pressure tap readings matched the CFD results, but dry air CFD underpredicted thermocouple readings. Collection efficiency results from all three icing codes matched well together on the main body leading edge, main body slope, and the strut leading edges. All three icing codes underpredicted the final ice shape using a single-shot constant ice density approach, with more difficulty coming from the strut leading edge ice shape due to the swept wing like flow field. NASA’s overall goal for this effort is to develop computational icing tools to assist in the design and certification of engines for flight in icing conditions.

Supercooled icing, SIDRM, aerodynamics, computatio↗

NASA NTRSDate not supplied

Overview and Performance of the LOFTID Instrumentation Suite

On November 10, 2022, NASA launched the Low-earth Orbit Flight Test of an Inflatable Decelerator (LOFTID) vehicle as a secondary payload mounted inside the launch vehicle adaptor on an Atlas V out of the Vandenburg Space Force Base (VSFB). The primary payload, Joint Polar Satellite System-2 (JPSS-2), was delivered successfully to a sun synchronous trajectory shortly after launch, at which point the Centaur upper stage performed a burn to de-orbit the system. Once on the desired trajectory to enter the atmosphere the payload adaptor canister was ejected by the payload adapter separation system to expose the packed LOFTID vehicle, then the LOFTID aeroshell, a Hypersonic Inflatable Aerodynamic Decelerator (HIAD), was deployed and inflated as planned. The Centaur pointed the LOFTID vehicle to the desired attitude to enter the atmosphere, spun the assembly to roughly three rpm, and separated the reentry vehicle. The LOFTID vehicle entered the atmosphere over Alaska at >8km/sec and decelerated as designed demonstrating stable flight from hypersonic entry through subsonic parachute deployment. On-board visible light cameras captured the reactions of the heatshield through all phases of flight, and co-located infrared light cameras captured the temperature distribution of the aft side of the heat shield anchored to a distribution of thermocouples on the inflatable structure (IS) in the field of view. Thermocouples were also embedded in the forward side of the aeroshell both in the Flexible Thermal Protection System (FTPS) as well as the IS. Heat Flux Gages and Pressure Transducers measured the heating rate and surface pressure experienced by the rigid nose of the reentry vehicle. Loadcells measured the interface loads between the IS and the rigid centerbody. This paper will discuss many of the key instruments flown on the successful LOFTID technology demonstration mission and will provide some high level results, while pointing to more detailed papers on the post-flight analyses.

Gregory T Swanson↗

NASA NTRSDate not supplied

Overview and Performance of the LOFTID Instrumentation Suite

spacecraft design↗

NASA NTRSDate not supplied

Assessment of Pioneer Venus Entry Heating with Coupled Radiation and Ablation

The objective of this work is to reconstruct thermocouple temperature measurements taken at the stagnation point of the Pioneer Venus large probe using uncoupled and coupled ablation calculations. The time history of the large probe forebody entry heating was obtained from a trajectory-based analysis that employed a material response solver to model equilibrium and nonequilibrium ablation chemistry at the surface. In the uncoupled approach, equilibrium ablation chemistry was assumed in the material response calculations, but the material response outputs were not returned to the flowfield solver. Therefore, the effects of char and pyrolysis gas injection into the boundary layer due to ablation (e.g., convective blockage and radiative energy absorption) did not influence the flowfield solutions. In the coupled approach, nonequilibrium ablation chemistry was performed and the material response outputs were returned to the flowfield solver. Therefore, the effects of char and pyrolysis gas injection into the boundary layer were accounted for in the flowfield solutions. Both uncoupled and coupled approaches produced reconstructed temperatures that were within 10% of the flight measurements through the entry heat pulse, with the coupled results comparing more favorably with the measurements. Overall, the reconstructed thermocouple temperatures in this work significantly improved on the results of previous reconstructions for the large probe.

Jannuel V V Cabrera↗

DOE OSTI2014

A Validation Study of Pin Heat Transfer for UO2 Fuel Based on the IFA-432 Experiments

The IFA-432 (Integrated Fuel Assessment) experiments from the International Fuel Performance Experiments (IFPE) database were designed to study the effects of gap size, fuel density, and fuel densification on fuel centerline temperature in light-water-reactor fuel. An evaluation of nuclear fuel pin heat transfer in the FRAPCON-3.4 and Exnihilo codes for uranium dioxide (UO$_2$) fuel systems was performed, with a focus on the densification stage (2.2 \unitfrac{GWd}{MT(UO$_{2}$)}). In addition, sensitivity studies were performed to evaluate the effect of the radial power shape and approximations to the geometry to account for the thermocouple hole. The analysis demonstrated excellent agreement for rods 1, 2, 3, and 5 (varying gap thicknesses and density with traditional fuel), demonstrating the accuracy of the codes and their underlying material models for traditional fuel. For rod 6, which contained unstable fuel that densified an order of magnitude more than traditional, stable fuel, the magnitude of densification was over-predicted and the temperatures were outside of the experimental uncertainty. The radial power shape within the fuel was shown to significantly impact the predicted centerline temperatures, whereas modeling the fuel at the thermocouple location as either annular or solid was relatively negligible. This has provided an initial benchmarking of the pin heat transfer capability of Exnihilo for UO$_2$ fuel with respect to a well-validated nuclear fuel performance code.

Phillippe, Aaron M↗

DOE OSTI2014

Experimental investigation of piston heat transfer under conventional diesel and reactivity-controlled compression ignition combustion regimes

The piston of a heavy-duty single-cylinder research engine was instrumented with 11 fast-response surface thermocouples, and a commercial wireless telemetry system was used to transmit the signals from the moving piston. The raw thermocouple data were processed using an inverse heat conduction method that included Tikhonov regularization to recover transient heat flux. By applying symmetry, the data were compiled to provide time-resolved spatial maps of the piston heat flux and surface temperature. A detailed comparison was made between conventional diesel combustion and reactivity-controlled compression ignition combustion operations at matched conditions of load, speed, boost pressure, and combustion phasing. The integrated piston heat transfer was found to be 24% lower, and the mean surface temperature was 25 C lower for reactivity-controlled compression ignition operation as compared to conventional diesel combustion, in spite of the higher peak heat release rate. Lower integrated piston heat transfer for reactivity-controlled compression ignition was found over all the operating conditions tested. The results showed that increasing speed decreased the integrated heat transfer for conventional diesel combustion and reactivity-controlled compression ignition. The effect of the start of injection timing was found to strongly influence conventional diesel combustion heat flux, but had a negligible effect on reactivity-controlled compression ignition heat flux, even in the limit of near top dead center high-reactivity fuel injection timings. These results suggest that the role of the high-reactivity fuel injection does not significantly affect the thermal environment even though it is important for controlling the ignition timing and heat release rate shape. The integrated heat transfer and the dynamic surface heat flux were found to be insensitive to changes in boost pressure for both conventional diesel combustion and reactivity-controlled compression ignition. However, for reactivity-controlled compression ignition, the mean surface temperature increased with changes in boost suggesting that equivalence ratio affects steady-state heat transfer.

Splitter, Derek A↗

DOE OSTI2020

As-run thermal hydraulics analysis of the EPRI-1 Experiment

The EPRI-1 experiment was designed to irradiate various types of reactor pressure vessel steels at a temperature of 288°C (PLN-3934). The specimens were irradiated in an instrumented test train inside a pressurized water loop in the center lobe of the ATR during cycle 157C. Temperature was monitored using thermocouples placed at the top of the test train. Additional temperature indications were obtained by post-irradiation examination of melt wires placed within the test train and spanning the temperature range 239°C to 327°C. The purpose of this analysis is to calculate specimen temperature using measured data on reactor power and as-run calculations of heating rates of the test train. The accuracy of the model is assessed by comparing the measured and calculated in-pile tube inlet to outlet temperature difference, comparing the measured and calculated thermocouple temperatures, and comparing the calculated specimen temperature to the temperature range indicated by the melt wires.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

Impact to Cooling Airflow from Truck Platooning: Preprint

We investigate tradeoffs between the airflow strategies related to engine cooling and the aerodynamic-enabled fuel savings created by platooning. By analyzing air temperatures, engine temperatures and cooling air flow at different platoon distances, we show the thermal impact to the engine from truck platooning. Previously, we collected wind and thermal data for numerous heavy-duty truck platoon configurations (gaps ranging from 4 to 87 meters) and reported the significant fuel savings enabled by these configurations. The fuel consumption for all trucks in the platoon were measured using the SAE J1321 gravimetric procedure as well as calibrated J1939 instantaneous fuel rate while travelling at 65 mph and loaded to a gross weight of 65,000 lb. Using thermocouples mounted 1 m ahead of each truck, anemometers at the grill and a grid of under-hood thermocouples as well as J1939 reported engine temperatures, we analyze the impact to critical operating temperatures from different platoon configurations. Results show significant changes in the engine and under-hood air temperatures that correlate with vehicle gap distance and platoon position.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Uncertainty Quantification of Calculated Temperatures for the AGR 5/6/7 Experiment

This report documents the quantification of uncertainty of the calculated temperature data for the Advanced Gas Reactor (AGR) 5/6/7 fuel irradiation experiment conducted in the Advanced Test Reactor at Idaho National Laboratory in support of the Advanced Reactor Technologies? research and development program. Recognizing uncertainties inherent in physics and thermal simulations of the AGR 5/6/7 capsules, the results of the numerical simulations are used in combination with statistical analysis methods to improve qualification of measured data. The calculated fuel temperatures for AGR tests are also used for validation of the fission product transport and fuel performance simulation models. These crucial roles of the calculated fuel temperatures in ensuring achievement of the AGR experimental program objectives require accurate determination of the model temperature uncertainties. This report covers temperature uncertainty results for each of the five AGR 5/6/7 capsules. To quantify the uncertainty of calculated temperatures determined using the ABAQUS finite element heat transfer code, this study identifies and analyzes model parameters of potential importance to the calculated temperatures of fuel compacts and thermocouples. The selection of input parameters for uncertainty quantification is based on the ranking of their influences upon temperature predictions. Thus, selected input parameters include those with high sensitivity and those with the largest uncertainty. Propagation of model parameter uncertainty and sensitivity is then used to quantify the overall uncertainty of calculated temperatures. Measurement uncertainty, analysis of modeling assumptions, and expert judgment are used as the basis to quantify the uncertainty range for selected input parameters. The input uncertainties are dynamic, accounting for the effect of unplanned events and changes in thermal properties of capsule components over extended exposure to high temperatures and fast neutron irradiation. The sensitivity analysis performed in this work went beyond the traditional local sensitivity. Using experimental design, analysis of pairwise interactions of model parameters was performed to establish sufficiency of the time dependent first order (linear) expansion terms in constructing the temperature response surface. To achieve completeness, uncertainty propagation made use of pairwise noise correlations of model parameters. Furthermore, using an interpolation scheme over the input parameter domain, the analysis obtains time dependent sensitivity over the test campaign duration. This allows computation of uncertainty for the calculated fuel temperatures and the calculated graphite temperatures at thermocouple locations during the entire irradiation period.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗