Contemporary solid rocket motor performance prediction techniques.
Solid rocket motor performance prediction including motor pressure, thrust and propellant parameters, discussing mathematical modeling of flow fields
Barrington, D. K.↗
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A review of contemporary solid rocket motor performance prediction techniques
Solid rocket motor performance prediction including motor pressure, thrust and propellant parameters, discussing mathematical modeling of flow fields
Barrington, D. K.↗
HabEx Baseline Telescope: Design & Predicted Performance
Habitable Exoplanet Observatory Mission (HabEx) will image & spectroscopically characterize planetary systems in the habitable zone around nearby sun-like stars. Additionally, HabEx will perform a broad range of general astrophysics science enabled by a 150 to 1700 nm spectral range and 3 x 3 arc-minute FOV. Critical to achieving the HabEx science goals is a large, ultra-stable telescope. The baseline HabEx telescope is a 4-m off-axis unobscured three-mirror-anastigmatic design with diffraction limited performance at 400 nm and wavefront stability of picometers per mK. These specifications are driven by science requirements. STOP (structural thermal optical performance) analysis predicts that the baseline telescope’s opto-mechanical design meets its specified performance tolerances.
Stahl, H. Philip↗
EVA Performance Prediction
Astronaut physical performance capabilities in micro gravity EV A or on planetary surfaces when encumbered by a life support suit and debilitated by a long exposure to micro gravity will be less than unencumbered pre flight capabilities. The big question addressed by human factors engineers is: what can the astronaut be expected to do on EVA or when we arrive at a planetary surface? A second question is: what aids to performance will be needed to enhance the human physical capability? These questions are important for a number of reasons. First it is necessary to carry out accurate planning of human physical demands to ensure that time and energy critical tasks can be carried out with confidence. Second it is important that the crew members (and their ground or planetary base monitors) have a realistic picture of their own capabilities, as excessive fatigue can lead to catastrophic failure. Third it is important to design appropriate equipment to enhance human sensory capabilities, locomotion, materials handling and manipulation. The evidence from physiological research points to musculoskeletal, cardiovascular and neurovestibular degradation during long duration exposure to micro gravity . The evidence from the biomechanics laboratory (and the Neutral Buoyancy Laboratory) points to a reduction in range of motion, strength and stamina when encumbered by a pressurized suit. The evidence from a long history of EVAs is that crewmembers are indeed restricted in their physical capabilities. There is a wealth of evidence in the literature on the causes and effects of degraded human performance in the laboratory, in sports and athletics, in industry and in other physically demanding jobs. One approach to this challenge is through biomechanical and performance modeling. Such models must be based on thorough task analysis, reliable human performance data from controlled studies, and functional extrapolations validated in analog contexts. The task analyses currently carried out for EVA activities are based more on extensive domain experience than any formal analytic structure. Conversely, physical task analysis for industrial and structured evidence from training and EV A contexts. Again on earth there is considerable evidence of human performance degradation due to encumbrance and fatigue. These industrial models generally take the form of a discounting equation. The development of performance estimates for space operations, such as timeline predictions for EVA is generally based on specific input from training activity, for example in the NBL or KC135. uniformed services tasks on earth are much more formalized. Human performance data in the space context has two sources: first there is the micro analysis of performance in structured tasks by the space physiology community and second there is the less structured evidence from training and EV A contexts.
Peacock, Brian↗
A Performance Prediction Model for a Fault-Tolerant Computer During Recovery and Restoration
The modeling and design of a fault-tolerant multiprocessor system is addressed. In particular, the behavior of the system during recovery and restoration after a fault has occurred is investigated. Given that a multicomputer system is designed using the Algorithm to Architecture to Mapping Model (ATAMM), and that a fault (death of a computing resource) occurs during its normal steady-state operation, a model is presented as a viable research tool for predicting the performance bounds of the system during its recovery and restoration phases. Furthermore, the bounds of the performance behavior of the system during this transient mode can be assessed. These bounds include: time to recover from the fault (t(sub rec)), time to restore the system (t(sub rec)) and whether there is a permanent delay in the system's Time Between Input and Output (TBIO) after the system has reached a steady state. An implementation of an ATAMM based computer was developed with the Generic VHSIC Spaceborne Computer (GVSC) as the target system. A simulation of the GVSC was also written based on the code used in ATAMM Multicomputer Operating System (AMOS). The simulation is in turn used to validate the new model in the usefulness and accuracy in tracking the propagation of the delay through the system and predicting the behavior in the transient state of recovery and restoration. The model is validated as an accurate method to predict the transient behavior of an ATAMM based multicomputer during recovery and restoration.
Stoughton, John W.↗
Long life performance predictions of SiGe MHW-RTG.
Recent experimental investigations at JPL, Resalab and GGA are incorporated into an analytical model which predicts generator performance over long time periods. The important aging mechanisms considered are degradation in foil insulation conductivity due to reactions with Si sublimed from the hot shoe and SiO2 from the astroquartz layers; changes in hot shoe dimensions due to Si sublimation; changes in leg dimensions due to SiGe sublimation; baffling factors which are applied to the legs to correct the free sublimation model for various effects such as geometry, reactions with the astroquartz wrapping, and hot shoe Si generation; new bulk property changes due to dopant precipitation; and changes in radiation heat exchange due to gap streaming as leg material sublimes.
Caputo, R. S.↗
JPL analytical performance prediction capability
The development of a computer code to simulate and predict the performance of large deployable antenna structures is discussed. The computer code is expected to account for all phases of hardware use, i.e., ground handling, boost, deployment, on orbit station keeping, and transfer from LEO to GEO, if applicable. In addition to accounting for static and dynamic loading of the basic structure, the variation in the precision of the reflector surface and the alignment of the feed support structure as a function orbit are scrutinized in order to accommodate electromagnetic analysis. Specific tasks directed towards the development of the computer code are reported and a number of thermal and structural models are discussed.
Chen, R.↗
Shortcomings in ground testing, environment simulations, and performance predictions for space applications
This paper addresses the issues involved in radiation testing of devices and subsystems to obtain the data that are required to predict the performance and survivability of satellite systems for extended missions in space. The problems associated with space environmental simulations, or the lack thereof, in experiments intended to produce information to describe the degradation and behavior of parts and systems are discussed. Several types of radiation effects in semiconductor components are presented, as for example: ionization dose effects, heavy ion and proton induced Single Event Upsets (SEUs), and Single Event Transient Upsets (SETUs). Examples and illustrations of data relating to these ground testing issues are provided. The primary objective of this presentation is to alert the reader to the shortcomings, pitfalls, variabilities, and uncertainties in acquiring information to logically design electronic subsystems for use in satellites or space stations with long mission lifetimes, and to point out the weaknesses and deficiencies in the methods and procedures by which that information is obtained.
Stassinopoulos, E. G.↗
Centrifugal and Axial Pump Design and Off-Design Performance Prediction
A meanline pump-flow modeling method has been developed to provide a fast capability for modeling pumps of cryogenic rocket engines. Based on this method, a meanline pump-flow code PUMPA was written that can predict the performance of pumps at off-design operating conditions, given the loss of the diffusion system at the design point. The design-point rotor efficiency and slip factors are obtained from empirical correlations to rotor-specific speed and geometry. The pump code can model axial, inducer, mixed-flow, and centrifugal pumps and can model multistage pumps in series. The rapid input setup and computer run time for this meanline pump flow code make it an effective analysis and conceptual design tool. The map-generation capabilities of the code provide the information needed for interfacing with a rocket engine system modeling code. The off-design and multistage modeling capabilities of PUMPA permit the user to do parametric design space exploration of candidate pump configurations and to provide head-flow maps for engine system evaluation.
Veres, Joseph P.↗
Empirical thermophotovoltaic performance predictions and limits
Significant progress has been made in the field of thermophotovoltaics, with efficiency recently rising to over 40% due to improvements in cell design and material quality, higher emitter temperatures, and better spectral management. However, inconsistencies in trends for efficiency with semiconductor bandgap energy across various temperatures pose challenges in predicting optimal bandgaps or expected performance for different applications. To address these issues, here we present realistic performance predictions for various types of single-junction cells over a broad range of emitter temperatures using an empirical model based on past cell measurements. Our model is validated using data from different authors with various bandgaps and emitter temperatures, and an excellent agreement is seen between the model and the experimental data. Using our model, we show that in addition to spectral losses, it is important to consider practical electrical losses associated with series resistance and cell quality to avoid overestimation of system efficiency. Here, we also show the effect of modifying various system parameters such as bandgap, above and below-bandgap reflectance, saturation current, and series resistance on the efficiency and power density of thermophotovoltaics at different temperatures. Finally, we predict the bandgap energies for best performance over a range of emitter temperatures for different cell material qualities.
14 SOLAR ENERGY↗
Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations
Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance. Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks. Funding: AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 ( https://www.nfdi4health.de/task-force-covid-19-2 ) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z).
60 APPLIED LIFE SCIENCES↗
Predictive performance models and multiple task performance
Five models that predict how performance of multiple tasks will interact in complex task scenarios are discussed. The models are shown in terms of the assumptions they make about human operator divided attention. The different assumptions about attention are then empirically validated in a multitask helicopter flight simulation. It is concluded from this simulation that the most important assumption relates to the coding of demand level of different component tasks.
Wickens, Christopher D.↗
Integrated End-to-end Performance Prediction and Diagnosis for Extreme Scientific Workflows
This report details recent progress for the ASCR funded project “Integrated End-to-end Performance Prediction and Diagnosis for Extreme Scientific Workflows”. We refer to the project as IPPD/2, reflecting the 2017 renewal under expanded scope and partners In IPPD/2, we increased our research scope to include data motion. We are focusing on three major aspects: a) observe how data is generated, distributed, and used; b) analyze how data is (repeatedly) consumed with a focus both on repeated patterns and anomalies; and c) explore how to optimize data motion. This new work on data motion will augment and complement IPPD/2’s research that focused on the computational aspects of tasks. We leverage and extend our existing tools and demonstrate our work on the Belle II workflow suite as well as on workflows from NSLS-II. The highlights of our work are as follows: Provenance for Workflows: Provenance is used to provide information enabling quality control, re-run computational workflows, and reproduce results. IPPD/2 has been building a scalable provenance management system that enables the capture of provenance from the high-level workflow through all relevant system levels in one integrated environment. Leveraging this work, our recent efforts have included using provenance as an enabling technique. Workload characterization: Leveraging provenance and analysis, we characterize data movement within network, storage, and memory over a variety of workloads. This characterization enables an understanding by performance analysts and application developers of the range of behaviors that could be expected. Performance Prediction for Workflows: The goal of modeling distributed workflows is to understand performance bottlenecks and enable more intelligent task scheduling to optimize selected metrics of interest (e.g., task throughput or output data rate). IPPD/2 has utilized both analytical and AI/ML modeling methodologies for performance modeling. Advanced Scheduling and Fault Modeling for Workflows: Scheduling of large-scale scientific workflows on geographically distributed resources is a challenging problem. To improve workflow throughput, we combined novel scheduling algorithms with task predictions from performance modeling and fault modeling. Dynamically Alleviating Bottlenecks in Workflows: Exploiting our provenance, analysis, and modeling efforts, we have explored and developed several techniques for dynamically detecting and alleviating bottlenecks in data movement. In particular, we have spent considerable effort demonstrating our techniques on production-like workflow configurations.
97 MATHEMATICS AND COMPUTING↗
Machine Learning-enabled Scalable Performance Prediction of Scientific Codes
Hardware architectures become increasingly complex as the compute capabilities grow to exascale. Here, we present the Analytical Memory Model with Pipelines (AMMP) of the Performance Prediction Toolkit (PPT). PPT-AMMP takes high-level source code and hardware architecture parameters as input and predicts runtime of that code on the target hardware platform, which is defined in the input parameters. PPT-AMMP transforms the code to an (architecture-independent) intermediate representation, then (i) analyzes the basic block structure of the code, (ii) processes architecture-independent virtual memory access patterns that it uses to build memory reuse distance distribution models for each basic block, and (iii) runs detailed basic-block level simulations to determine hardware pipeline usage. PPT-AMMP uses machine learning and regression techniques to build the prediction models based on small instances of the input code, then integrates into a higher-order discrete-event simulation model of PPT running on Simian PDES engine. We validate PPT-AMMP on four standard computational physics benchmarks and present a use case of hardware parameter sensitivity analysis to identify bottleneck hardware resources on different code inputs. We further extend PPT-AMMP to predict the performance of a scientific application code, namely, the radiation transport mini-app SNAP. To this end, we analyze multi-variate regression models that accurately predict the reuse profiles and the basic block counts. We validate predicted SNAP runtimes against actual measured times.
97 MATHEMATICS AND COMPUTING↗
Verification of takeoff performance predictions for the XB-70 airplane
Takeoff performance predictions for XB-70 delta wing supersonic aircraft
Larson, T. J.↗
Design of a Costas loop to operate with the Block 3 receiver and its predicted performance
The system design, hardware construction, and predicted performance of a Costas loop is presented. Its expected performance regarding steady state phase error under assumed Doppler conditions, rms phase jitter, and their respective sensitivities with respect to variations in signal to noise ratios are discussed.
Stevens, G.↗
Overview on Basis and Use of Performance Prediction Methods
The lecture outlines the basic methods of component matching which are central to the prediction of gas turbine performance. Steady state prediction of off-design performance must be done at the beginning of an engine development program, to ensure that the engine can satisfy all the mission requirements. The matching techniques can be extended to predict transient performance, which is essential for controls development and to ensure good engine handling. The large amount of computation required demands the use of computer modelling and the role of modelling in the development program and the basic requirements for performance modelling are described.
H I H Saravanamuttoo↗
Rotor Performance Predictions of a Next Generation Mars Science Helicopter
Since Ingenuity took flight and proved aerial capabilities on Mars, the horizon for rotorcraft on the Red Planet has only expanded. One proposed future Martian rotorcraft is the Mars Science Helicopter (MSH). The MSH is a hexacopter capable of carrying scientific payloads. As a part of its joint development by NASA Ames Research Center (ARC) and the Jet Propulsion Laboratory (JPL), key MSH rotor components will be tested inside the in-development Reduced Atmospheric Pressure Testing Of Rotors (RAPTOR) wind tunnel in the Planetary Aeolian Laboratory (PAL) at NASA ARC. In preparation for the test, the Comprehensive Hierarchical Aeromechanics Rotorcraft Model (CHARM) analysis was utilized to predict the aerodynamic performance for one MSH hexacopter isolated rotor. This paper provides an overview of the planned experiments involving the MSH reference blades, as well as CHARM pretest predictions of rotor performance in hover and forward flight. Additionally, the interference effects of wind tunnel walls on rotor performance will be evaluated to inform the test matrix of the upcoming test in the RAPTOR tunnel.
MHS↗