X-57 Maxwell Structural Loads Requirements Loads Analysis for Cruise Nacelle Structure
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Wesley Li↗
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Assessment of New Load Schedules for the Machine Calibration of a Force Balance
New load schedules for the machine calibration of a six-component force balance are currently being developed and evaluated at the NASA Ames Balance Calibration Laboratory. One of the proposed load schedules is discussed in the paper. It has a total of 2082 points that are distributed across 16 load series. Several criteria were applied to define the load schedule. It was decided, for example, to specify the calibration load set in force balance format as this approach greatly simplifies the definition of the lower and upper bounds of the load schedule. In addition, all loads are assumed to be applied in a calibration machine by using the one-factor-at-a-time approach. At first, all single-component loads are applied in six load series. Then, three two-component load series are applied. They consist of the load pairs (N1, N2), (S1, S2), and (RM, AF). Afterwards, four three-component load series are applied. They consist of the combinations (N1, N2, AF), (S1, S2, AF), (N1, N2, RM), and (S1, S2, RM). In the next step, one four-component load series is applied. It is the load combination (N1, N2, S1, S2). Finally, two five-component load series are applied. They are the load combination (N1, N2, S1, S2, AF) and (N1, N2, S1, S2, RM). The maximum difference between loads of two subsequent data points of the load schedule is limited to 33 % of capacity. This constraint helps avoid unwanted load "jumps" in the load schedule that can have a negative impact on the performance of a calibration machine. Only loadings of the single- and two-component load series are loaded to 100 % of capacity. This approach was selected because it keeps the total number of calibration points to a reasonable limit while still allowing for the application of some of the more complex load combinations. Data from two of NASA's force balances is used to illustrate important characteristics of the proposed 2082-point calibration load schedule.
wind tunnel testing↗
Crane-Load Contact Sensor
An electronic instrument has been developed as a prototype of a portable crane-load contact sensor. Such a sensor could be helpful in an application in which the load rests on a base in a horizontal position determined by vertical alignment pins (see Figure 1). If the crane is not positioned to lift the load precisely vertically, then the load can be expected to swing once it has been lifted clear of the pins. If the load is especially heavy, large, and/or fragile, it could hurt workers and/or damage itself and nearby objects. By indicating whether the load remains in contact with the pins when it has been lifted a fraction of the length of the pins, the crane-load contact sensor helps the crane operator determine whether it is safe to lift the load clear of the pins: If there is contact, then the load is resting against the sides of the pins and, hence, it may not be safe to lift; if contact is occasionally broken, then the load is probably not resting against the pins, so it should be safe to lift. It is assumed that the load and base, or at least the pins and the surfaces of the alignment holes in the load, are electrically conductive, so the instrument can use electrical contact to indicate mechanical contact. However, DC resistance cannot be used as an indicator of contact for the following reasons: The load and the base are both electrically grounded through cables (the load is grounded through the lifting cable of the crane) to prevent discharge of static electricity. In other words, the DC resistance between the load and the pins is always low, as though they were always in direct contact. Therefore, instead of DC resistance, the instrument utilizes the AC electrical impedance between the pins and the load. The signal frequency used in the measurement is high enough (.1 MHz) that the impedance contributed by the cables and the electrical ground network of the building in which the crane and the base are situated is significantly greater than the contact impedance between the pins and the load. The instrument includes a signal generator and voltage-measuring circuitry, and is connected to the load and the base as shown in Figure 2. The output of the signal generator (typically having amplitude of the order of a volt) is applied to the load via a 50-resistor, and the voltage between the load and the pins is measured. When the load and the pins are not in contact, the impedance between them is relatively high, causing the measured voltage to exceed a threshold value. When the load and the pins are in contact, the impedance between them falls to a much lower value, causing the voltage to fall below the threshold value. The voltage-measuring circuitry turns on a red light-emitting diode (LED) to indicate the lower-voltage/ contact condition. Whenever the contact has been broken and the non-contact/higher-voltage condition has lasted for more than 2 ms, the voltage-measuring circuitry indicates this condition by blinking a green LED.
Youngquist, Robert↗
IM3 + EPRI Data Center Load Projections
This dataset contains scenarios of hourly total electricity demand with and without projected loads from data centers over the period 2022-2040. The root projections without data center demands are identical to those documented in Burleyson et al. 2024. In short, those projections encompass hourly electricity demands for 54 Balancing Authorities (BAs) in the United States across a range of eight of weather and socioeconomic scenarios. Refer to the root dataset and accompanying publication, Burleyson et al. 2025, for information about how those projections were generated. For this derivative dataset we used the base loads from the following scenarios: rcp45hotter_ssp3 rcp45hotter_ssp5 rcp85hotter_ssp3 rcp85hotter_ssp5 The root load projections did not reflect the drastic expansion of data centers that has occurred in the last several years to support artificial intelligence and cloud computing. To reflect growth in data center demand, a second set of load projections were created in which we layered in additional data center load projections based on the data center load growth scenarios described in a 2024 report by the Electric Power Research Institute (EPRI): "Powering Intelligence: Analyzing Artificial Intelligence and Data Center Energy Consumption". The EPRI projections from the report are included in this dataset (EPRI_2024_Projections.xlsx). That report contained annual state-level data center load projections for four year-over-year growth rates for data center demands: Low (3.71% annual growth) Moderate (5% annual growth) High (10% annual growth) Higher (15% annual growth) To homogenize the load projections with and without data centers we had to get them to a common scale. The first step was to take the EPRI annual state-level data center energy consumption values and convert them to 8760-hr loads for each year. We did that by assuming a flat (e.g., not weather- or time-sensitive) load profile and distributing the data center loads in each state evenly across all hours in a year. From there the loads were downscaled from the state-level to the county-level using 2019 county-level populations as weights. Finally, the county-level hourly data center loads were summed to the BA-level using the county-to-BA mapping underpinning the root load projections. The net result is 16 (4 weather and socioeconomic scenarios crossed with 4 data center load growth scenarios) unique load projections for the period 2022-2040. The file format follows that of the root dataset with a single additional column "Scaled_TELL_BA_Load_with_DC_MWh" that contains the hourly loads with the added data center loads for a given BA-year-scenario combination. Please refer to the readme file in the root dataset for more information on the file format.
Burleyson, Casey [Pacific Northwest National Labor↗
Strain Gage Load Calibration of the Wing Interface Fittings for the Adaptive Compliant Trailing Edge Flap Flight Test
The safety-of-flight parameters for the Adaptive Compliant Trailing Edge (ACTE) flap experiment require that flap-to-wing interface loads be sensed and monitored in real time to ensure that the structural load limits of the wing are not exceeded. This paper discusses the strain gage load calibration testing and load equation derivation methodology for the ACTE interface fittings. Both the left and right wing flap interfaces were monitored; each contained four uniquely designed and instrumented flap interface fittings. The interface hardware design and instrumentation layout are discussed. Twenty-one applied test load cases were developed using the predicted in-flight loads. Pre-test predictions of strain gage responses were produced using finite element method models of the interface fittings. Predicted and measured test strains are presented. A load testing rig and three hydraulic jacks were used to apply combinations of shear, bending, and axial loads to the interface fittings. Hardware deflections under load were measured using photogrammetry and transducers. Due to deflections in the interface fitting hardware and test rig, finite element model techniques were used to calculate the reaction loads throughout the applied load range, taking into account the elastically-deformed geometry. The primary load equations were selected based on multiple calibration metrics. An independent set of validation cases was used to validate each derived equation. The 2-sigma residual errors for the shear loads were less than eight percent of the full-scale calibration load; the 2-sigma residual errors for the bending moment loads were less than three percent of the full-scale calibration load. The derived load equations for shear, bending, and axial loads are presented, with the calculated errors for both the calibration cases and the independent validation load cases.
Miller, Eric J.↗
Evaluation of the Hinge Moment and Normal Force Aerodynamic Loads from a Seamless Adaptive Compliant Trailing Edge Flap in Flight
A seamless adaptive compliant trailing edge (ACTE) flap was demonstrated in flight on a Gulfstream III aircraft at the NASA Armstrong Flight Research Center. The trailing edge flap was deflected between minus 2 deg up and plus 30 deg down in flight. The safety-of-flight parameters for the ACTE flap experiment require that flap-to-wing interface loads be sensed and monitored in real time to ensure that the structural load limits of the wing are not exceeded. The attachment fittings connecting the flap to the aircraft wing rear spar were instrumented with strain gages and calibrated using known loads for measuring hinge moment and normal force loads in flight. The safety-of-flight parameters for the ACTE flap experiment require that flap-to-wing interface loads be sensed and monitored in real time to ensure that the structural load limits of the wing are not exceeded. The attachment fittings connecting the flap to the aircraft wing rear spar were instrumented with strain gages and calibrated using known loads for measuring hinge moment and normal force loads in flight. The interface hardware instrumentation layout and load calibration are discussed. Twenty-one applied calibration test load cases were developed for each individual fitting. The 2-sigma residual errors for the hinge moment was calculated to be 2.4 percent, and for normal force was calculated to be 7.3 percent. The hinge moment and normal force generated by the ACTE flap with a hinge point located at 26-percent wing chord were measured during steady state and symmetric pitch maneuvers. The loads predicted from analysis were compared to the loads observed in flight. The hinge moment loads showed good agreement with the flight loads while the normal force loads calculated from analysis were over-predicted by approximately 20 percent. Normal force and hinge moment loads calculated from the pressure sensors located on the ACTE showed good agreement with the loads calculated from the installed strain gages.
Hinge Moment Loads↗
Composite load spectra for select space propulsion structural components
The objective of this program is to develop generic load models with multiple levels of progressive sophistication to simulate the composite load spectra that are induced in space propulsion system components, representative of Space Shuttle Main Engines (SSME), such as transfer ducts, turbine blades, and liquid oxygen (LOX) posts and system ducting. These models will be developed using two independent approaches. The first approach consists of using state-of-the-art probabilistic methods to describe the individual loading conditions and combinations of these loading conditions to synthesize the composite load spectra simulation. The methodology required to combine the various individual load simulation models (hot-gas dynamic, vibrations, instantaneous position, centrifugal field, etc.) into composite load spectra simulation models will be developed under this program. A computer code incorporating the various individual and composite load spectra models will be developed to construct the specific load model desired. The second approach, which is covered under the options portion of the contract, will consist of developing coupled models for composite load spectra simulation which combine the (deterministic) models for composite load dynamic, acoustic, high-pressure and high rotational speed, etc., load simulation using statistically varying coefficients. These coefficients will then be determined using advanced probabilistic simulation methods with and without strategically selected experimental data. This report covers the efforts of the third year of the contract. The overall program status is that the turbine blade loads have been completed and implemented. The transfer duct loads are defined and are being implemented. The thermal loads for all components are defined and coding is being developed. A dynamic pressure load model is under development. The parallel work on the probabilistic methodology is essentially completed. The overall effort is being integrated in an expert system code specifically developed for this project.
Newell, J. F.↗
Using Maximal Isometric Force to Determine the Optimal Load for Measuring Dynamic Muscle Power
Maximal power output occurs when subjects perform ballistic exercises using loads of ~30-50% of one-repetition maximum (1-RM). However, performing 1-RM testing prior to power measurement requires considerable time, especially when testing involves multiple exercises. Maximal isometric force (MIF), which requires substantially less time to measure than 1-RM, might be an acceptable alternative for determining the optimal load for power testing. PURPOSE: To determine the optimal load based on MIF for maximizing dynamic power output during leg press and bench press exercises. METHODS: Twenty healthy volunteers (12 men and 8 women; mean +/- SD age: 31+/-6 y; body mass: 72 +/- 15 kg) performed isometric leg press and bench press movements, during which MIF was measured using force plates. Subsequently, subjects performed ballistic leg press and bench press exercises using loads corresponding to 20%, 30%, 40%, 50%, and 60% of MIF presented in randomized order. Maximal instantaneous power was calculated during the ballistic exercise tests using force plates and position transducers. Repeated-measures ANOVA and Fisher LSD post hoc tests were used to determine the load(s) that elicited maximal power output. RESULTS: For the leg press power test, six subjects were unable to be tested at 20% and 30% MIF because these loads were less than the lightest possible load (i.e., the weight of the unloaded leg press sled assembly [31.4 kg]). For the bench press power test, five subjects were unable to be tested at 20% MIF because these loads were less than the weight of the unloaded aluminum bar (i.e., 11.4 kg). Therefore, these loads were excluded from analysis. A trend (p = 0.07) for a main effect of load existed for the leg press exercise, indicating that the 40% MIF load tended to elicit greater power output than the 60% MIF load (effect size = 0.38). A significant (p . 0.05) main effect of load existed for the bench press exercise; post hoc analysis indicated that the effect of load on power output was: 30% > 40% > 50% = 60%. CONCLUSION: Loads of 40% and 30% of MIF elicit maximal power output during dynamic leg presses and bench presses, respectively. These findings are similar to those obtained when loading is based on 1-RM.
Spiering, Barry A.↗
Maximizing TDRS Command Load Lifetime
The GNC software onboard ISS utilizes TORS command loads, and a simplistic model of TORS orbital motion to generate onboard TORS state vectors. Each TORS command load contains five "invariant" orbital elements which serve as inputs to the onboard propagation algorithm. These elements include semi-major axis, inclination, time of last ascending node crossing, right ascension of ascending node, and mean motion. Running parallel to the onboard software is the TORS Command Builder Tool application, located in the JSC Mission Control Center. The TORS Command Builder Tool is responsible for building the TORS command loads using a ground TORS state vector, mirroring the onboard propagation algorithm, and assessing the fidelity of current TORS command loads onboard ISS. The tool works by extracting a ground state vector at a given time from a current TORS ephemeris, and then calculating the corresponding "onboard" TORS state vector at the same time using the current onboard TORS command load. The tool then performs a comparison between these two vectors and displays the relative differences in the command builder tool GUI. If the RSS position difference between these two vectors exceeds the tolerable lim its, a new command load is built using the ground state vector and uplinked to ISS. A command load's lifetime is therefore defined as the time from when a command load is built to the time the RSS position difference exceeds the tolerable limit. From the outset of TORS command load operations (STS-98), command load lifetime was limited to approximately one week due to the simplicity of both the onboard propagation algorithm, and the algorithm used by the command builder tool to generate the invariant orbital elements. It was soon desired to extend command load lifetime in order to minimize potential risk due to frequent ISS commanding. Initial studies indicated that command load lifetime was most sensitive to changes in mean motion. Finding a suitable value for mean motion was therefore the key to achieving this goal. This goal was eventually realized through development of an Excel spreadsheet tool called EMMIE (Excel Mean Motion Interactive Estimation). EMMIE utilizes ground ephemeris nodal data to perform a least-squares fit to inferred mean anomaly as a function of time, thus generating an initial estimate for mean motion. This mean motion in turn drives a plot of estimated downtrack position difference versus time. The user can then manually iterate the mean motion, and determine an optimal value that will maximize command load lifetime. Once this optimal value is determined, the mean motion initially calculated by the command builder tool is overwritten with the new optimal value, and the command load is built for uplink to ISS. EMMIE also provides the capability for command load lifetime to be tracked through multiple TORS ephemeris updates. Using EMMIE, TORS command load lifetimes of approximately 30 days have been achieved.
Brown, Aaron J.↗
Multiplexer/Demultiplexer Loading Tool (MDMLT)
The purpose of the MDMLT is to improve the reliability and speed of loading multiplexers/demultiplexers (MDMs) in the Software Development and Integration Laboratory (SDIL) by automating the configuration management (CM) of the loads in the MDMs, automating the loading procedure, and providing the capability to load multiple or all MDMs concurrently. This loading may be accomplished in parallel, or single MDMs (remote). The MDMLT is a Web-based tool that is capable of loading the entire International Space Station (ISS) MDM configuration in parallel. It is able to load Flight Equivalent Units (FEUs), enhanced, standard, and prototype MDMs as well as both EEPROM (Electrically Erasable Programmable Read-Only Memory) and SSMMU (Solid State Mass Memory Unit) (MASS Memory). This software has extensive configuration management to track loading history, and the performance improvement means of loading the entire ISS MDM configuration of 49 MDMs in approximately 30 minutes, as opposed to 36 hours, which is what it took previously utilizing the flight method of S-Band uplink. The laptop version recently added to the MDMLT suite allows remote lab loading with the CM of information entered into a common database when it is reconnected to the network. This allows the program to reconfigure the test rigs quickly between shifts, allowing the lab to support a variety of onboard configurations during a single day, based on upcoming or current missions. The MDMLT Computer Software Configuration Item (CSCI) supports a Web-based command and control interface to the user. An interface to the SDIL File Transfer Protocol (FTP) server is supported to import Integrated Flight Loads (IFLs) and Internal Product Release Notes (IPRNs) into the database. An interface to the Monitor and Control System (MCS) is supported to control the power state, and to enable or disable the debug port of the MDMs to be loaded. Two direct interfaces to the MDM are supported: a serial interface (debug port) to receive MDM memory dump data and the calculated checksum, and the Small Computer System Interface (SCSI) to transfer load files to MDMs with hard disks. File transfer from the MDM Loading Tool to EEPROM within the MDM is performed via the MILSTD- 1553 bus, making use of the Real- Time Input/Output Processors (RTIOP) when using the rig-based MDMLT, and via a bus box when using the laptop MDMLT. The bus box is a cost-effective alternative to PC-1553 cards for the laptop. It is noted that this system can be modified and adapted to any avionic laboratory for spacecraft computer loading, ship avionics, or aircraft avionics where multiple configurations and strong configuration management of software/firmware loads are required.
Brewer, Lenox Allen↗
Optimal load attachment of a deeply embedded ring anchor in clay
A Deeply Embedded Ring Anchor (DERA) system has been developed as a cost-effective solution for mooring arrays of floating offshore wind turbines (FOWTs) to the seabed. The DERA boasts several key features, including its versatility in various soil types, compact size, compatibility with diverse mooring systems, multi-line potential, and robust performance even under unintentional loading conditions. While prior preliminary studies have provided valuable insights into how the DERA can enhance cost-effectiveness by offering a high load capacity, these studies have predominantly focused on optimizing anchor performance under translational horizontal and vertical loading. However, to design the DERA optimally, we must also consider its ability to handle inclined loading conditions in addition to lateral and axial loadings. Due to its shorter length compared to a conventional caisson, the DERA has less resistance to moments, making it more sensitive to horizontal load capacity and the optimal load attachment depth concerning load angle. For this reason, our study introduces an analytical approach to evaluate the effects of inclined loading on anchor performance, utilizing the previously validated upper bound plastic limit analysis (PLA) method. In investigating the optimal load attachment of the DERA, this paper conducts a parametric study to analyze how factors such as load attachment depth, anchor aspect ratio, and load inclination affect the DERA’s load capacity. Our findings indicate that PLA can serve as a valuable analytical tool for assessing the ultimate load capacity of the DERA, particularly under inclined loading conditions.
42 ENGINEERING↗
The composite load spectra project
Probabilistic methods and generic load models capable of simulating the load spectra that are induced in space propulsion system components are being developed. Four engine component types (the transfer ducts, the turbine blades, the liquid oxygen posts and the turbopump oxidizer discharge duct) were selected as representative hardware examples. The composite load spectra that simulate the probabilistic loads for these components are typically used as the input loads for a probabilistic structural analysis. The knowledge-based system approach used for the composite load spectra project provides an ideal environment for incremental development. The intelligent database paradigm employed in developing the expert system provides a smooth coupling between the numerical processing and the symbolic (information) processing. Large volumes of engine load information and engineering data are stored in database format and managed by a database management system. Numerical procedures for probabilistic load simulation and database management functions are controlled by rule modules. Rules were hard-wired as decision trees into rule modules to perform process control tasks. There are modules to retrieve load information and models. There are modules to select loads and models to carry out quick load calculations or make an input file for full duty-cycle time dependent load simulation. The composite load spectra load expert system implemented today is capable of performing intelligent rocket engine load spectra simulation. Further development of the expert system will provide tutorial capability for users to learn from it.
Newell, J. F.↗
Characterizing the uncertainty in holddown post load measurements
In order to understand unexpectedly erratic load measurements in the launch-pad supports for the space shuttle, the sensitivities of the load cells in the supports were analyzed using simple probabilistic techniques. NASA engineers use the loads in the shuttle's supports to calculate critical stresses in the shuttle vehicle just before lift-off. The support loads are measured with 'load cells' which are actually structural components of the mobile launch platform which have been instrumented with strain gauges. Although these load cells adequately measure vertical loads, the horizontal load measurements have been erratic. The load measurements were simulated in this study using Monte Carlo simulation procedures. The simulation studies showed that the support loads are sensitive to small deviations in strain and calibration. In their current configuration, the load cells will not measure loads with sufficient accuracy to reliably calculate stresses in the shuttle vehicle. A simplified model of the holddown post (HDP) load measurement system was used to study the effect on load measurement accuracy for several factors, including load point deviations, gauge heights, and HDP geometry.
Richardson, J. A.↗
Strain-Gage Loads Calibration Parametric Study
This paper documents a parametric study of various aircraft wing-load test features that affect the quality of the resultant derived shear, bending-moment, and torque strain-gage load equations. The effect of the following on derived strain-gage equation accuracy are compared: single-point loading compared with distributed loading, variation in applied test load magnitude, number of applied load cases, and wing-box-only compared with control-surface loading. The subject of this study is an extensive wing-load calibration test of the Active Aeroelastic Wing F/A-18 airplane. Selected subsets of the available test data were used to derive load equations using the linear regression method. Results show the benefit of distributed loading and the diminishing-return benefits of test load magnitudes and number of load cases. The use of independent check cases as a quality metric for the derived load equations is shown to overcome blind extrapolating beyond the load data used to derive the load equations.
Lokos, William A.↗
Application of a Temperature-Dependent Load Prediction Method to a RUAG Six-Component Block-Type Balance
Temperature-dependent data of a RUAG six-component block-type balance was analyzed to assess the accuracy of two load prediction methods for temperature-dependent balance data. The supplied data was prepared for the analysis by splitting it into calibration and check load data subsets. The first calibration data subset was obtained at a temperature of 294 Kelvin. The second calibration data subset was obtained at a temperature of 315 Kelvin. A subset of 38 points was extracted from the second data set and used as check loads so that the accuracy of the two load prediction methods could be tested. First, the Iterative Method in combination with an extended independent and dependent variable set was used for the balance load prediction. This approach fits electrical outputs as a function of loads and the temperature and, afterwards, constructs a load iteration scheme from the regression coefficients so that loads can be predicted from outputs and the temperature during a wind tunnel test. The Non-Iterative Method was also used for the load prediction. This alternate method can more easily be implemented in a data system as loads are directly fitted as a function of electrical outputs and the temperature. Analysis results for the axial force are only discussed in the paper as similar results were obtained for the other five load components. Results for both methods clearly show that the cross-product term constructed from either a primary gage load or a primary gage output and the temperature explains the majority of the temperature-dependent part of the predicted balance load. This term models the temperature dependent nature of the gage sensitivity. Therefore, it is recommended to apply primary gage loadings at different temperatures during a balance calibration whenever temperature effects need to be described. These loadings will contain information about the temperature-dependent nature of the gage sensitivities that can be quantified by related cross-product terms in regression models of the data.
strain-gage balance↗
Methodology and Development of SLS Ascent Loads
The methodology and development of the Space Launch System (SLS) Block 1 Boost and Core phase Ascent loads will be presented in this paper. As is common practice, a series of fixed time/mass Finite Element Models of the vehicle were developed for the purpose of evaluating the structural loads and accelerations for particular Mach or time ranges associated with the Boost and Core phases of flight. Load contributors, which vary between bins, include the flight mechanic induced portion of the loads (e.g., Static-Elastic (STEL) or Vehicle Load Indicator (VLI) tool results), gust loads, buffet loads, Solid Rocket Booster thrust oscillations, Core Stage Engine thrust oscillations, maneuvering loads, thrust vector-related dispersions, and Programmed Test Input induced loads. Each of these load contributors will be examined in this paper, as well as the Loads Combination Equations used to generate the final loads. In addition to the typical coupled loads analysis, an Ascent loads evaluation is included in the SLS Artemis 1 Day of Launch process; a summary of this evaluation will also be presented in this paper.
SLS↗
Heat load measurements for the PIP-II pHB650 cryomodule
This study presents a brief overview of the 1st and 2nd phases and an in-depth analysis of the 3rd phase heat load testing performed on the pHB650 (prototype High Beta 650 MHz) cryomodule at PIP2IT (PIP-II Injector Test Facility), with a focus on both the results and the methodological advancements that have improved testing efficiency and accuracy. A key challenge identified in the testing campaign is the higher-than-expected heat loads observed in the first PIP-II (Proton Improvement Plan II) prototype cryomodules (pSSR1 and pHB650) tested at PIP2IT. Elevated heat loads are concerning given the fixed capacity of the PIP-II cryoplant that is currently being installed at Fermilab. However, understanding the sources of these elevated heat loads offers a critical opportunity to implement effective heat load mitigations on upcoming PIP-II cryomodules to stay within the available capacity of the PIP-II cryoplant. The study includes a summary of test results, descriptions of measurement procedures, and key observations on parameters directly and indirectly related to heat load measurements. Direct observations include measured heat loads and the effectiveness of JT heat exchanger under varying conditions, while indirect observation analyze factors such as the temperature distribution on the two-phase pipe and relief piping under varying conditions. Thermal acoustic oscillations (TAO) were identified during testing, which was mitigated by replacing the original G10 stem with a stainless steel stem equipped with wipers for the cryomodule cooldown valve. A major innovation during pHB650 Phase 3 testing was the development of an automated Python script to streamline data acquisition, analysis, and reporting of heat load results. This script automatically retrieved data from ACNET (Accelerator Control Network), performed heat load calculations, and generated detailed reports featuring plots and tables. This advancement significantly reduced manual labor and enhanced the thoroughness of data analysis compared to earlier campaigns. The heat load test reports were promptly uploaded to the electronic logbook shortly after each test, enabling rapid feedback and collaboration between the SRF and cryogenic teams. The heat load measurements included various components: HTTS (high-temperature thermal shield), LTTS (low-temperature thermal shield), 2K isothermal and non-isothermal heat loads. Results were recorded both within the cryomodule and between the bayonet can supply and return. Measurements were conducted under different operating conditions such as "standard", "linac", and "simulated dynamic". Additionally, HTTS and LTTS heat loads were calculated in real time, allowing for the tracking of thermal stability and identification of changes during testing, both in steady-state and transient conditions. The results of this testing campaign not only provide valuable insights into the performance of the pHB650 cryomodule but also highlight best practices and lessons learned that will inform future cryomodule testing at PIP2IT. These include adopting automated tools for data analysis, refining real-time measurement capabilities, and emphasizing detailed pre-test planning. The framework established in this campaign aims to set an improved standard for cryomodule testing and heat load reporting in future cryomodule test campaigns.
Porwisiak, D. [Fermilab; Wroclaw Tech. U.]↗
Composite load spectra for select space propulsion structural components
The objective of the Composite Load Spectra (CLS) project is to build a knowledge based system to synthesize probabilistic loads for selected space propulsion engine components. The knowledge based system has a load expert system module and a load calculation module. The load expert system provides load information and the load calculation module generates the probabilistic load distributions. The engine loads are divided into 4 broad classes: the engine independent loads, the engine system dependent load, the component local independent loads and the component loads. These classes are defined and illustrated.
Ho, H.↗