SEARCH · Engineering Papers

Results for “parameter space exploration”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 631 records · Page 35

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problems

We explore using neural operators, or neural network representations of nonlinear maps between function spaces, to accelerate infinite-dimensional Bayesian inverse problems (BIPs) with models governed by nonlinear parametric partial differential equations (PDEs). Neural operators have gained significant attention in recent years for their ability to approximate the parameter-to-solution maps defined by PDEs using as training data solutions of PDEs at a limited number of parameter samples. The computational cost of BIPs can be drastically reduced if the large number of PDE solves required for posterior characterization are replaced with evaluations of trained neural operators. However, reducing error in the resulting BIP solutions via reducing the approximation error of the neural operators in training can be challenging and unreliable. We provide an a priori error bound result that implies certain BIPs can be ill-conditioned to the approximation error of neural operators, thus leading to inaccessible accuracy requirements in training. To reliably deploy neural operators in BIPs, we consider a strategy for enhancing the performance of neural operators: correcting the prediction of a trained neural operator by solving a linear variational problem based on the PDE residual. We show that a trained neural operator with error correction can achieve a quadratic reduction of its approximation error, all while retaining substantial computational speedups of posterior sampling when models are governed by highly nonlinear PDEs. The strategy is applied to two numerical examples of BIPs based on a nonlinear reaction–diffusion problem and deformation of hyperelastic materials. We demonstrate that posterior representations of the two BIPs produced using trained neural operators are greatly and consistently enhanced by error correction.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Numerical Codes for the DESC-LSST Analysis Pipeline: Core Cosmology Library Standard Modules and Beyond wCDM Modules (Final Technical Report)

The overall objective of the project is to investigate and develop specific software modules and analysis components for the software pipeline of LSST Dark Energy Science Collaboration (DESC). Following the key projects of DESC Science Roadmap (SRM), we will write and test computer codes for the Core Cosmology Library (CCL) in order to complete its modules, functionalities, and interface to work with the analysis pipelines from the five science probes of DESC (parts of SRM deliverables CX4.2TJP, CX6.2CS). We will also code modules for CCL to test models beyond w-Cold-Dark-Matter (wCDM) and modification to gravity (MG). Interfaces for MG models will also be developed for the TJPCOSMO software which is the main pipeline of the Theory and Joint Probe (TJP) working group (deliverable TJP2.3). In order to use the full power of LSST data to constrain MG models, we will also work on constraints from nonlinear regime by running and analyzing MG N-Body simulations using a Parameterized-Post-Friedmann framework into the Gadget-2 simulation package (deliverable TJP2.2). Preliminary results for the simulations were obtained in the past. In collaboration with other DESC groups, we plan to make these simulations feedable to cosmic emulators that are practical for likelihood analyzes (deliverable TJP2.2, parts of CX6.2CS). We will also modify and integrate our current codes for consistency tests between data sets and probes into the pipeline (parts of deliverables CX8.2TJP, TJP2.3). We understand that other groups will contribute to some of these objectives but our team will focus and collaborate with others on the particular part of testing MG and models beyond wCDM and refine the DESC pipeline for this purpose. PI has been coordinating his work with the TJP and CS working groups and the DESC management team. PI is a full member of DESC since June 2013. He and his students have been contributing to LSST-DESC activities and work including TJP telecons, collaboration meetings, hack-weeks, and workshops. PI chaired or co-chaired sessions at collaboration meetings and hack-weeks about testing gravity and models beyond wCDM using LSST. He is coordinating the TJP2 projects for testing models beyond wCDM including the writing of DESC-research-note, development of code for pipeline, and N-Body simulations for MG and beyond wCDM models testable with LSST analyses. As stressed in the DESC white paper, SRM, and P5 report, one of the important questions in understanding cosmic acceleration and dark energy is to be able to distinguish whether the acceleration is due to a dark energy component in the universe or a modification to gravity. Answering these questions will have a significant impact on the question of cosmic acceleration and dark energy. The methods that we will use include analytical work, numerical code, and N-Body simulations. A first approach that that we will use consists of using growth rate parameters that enter the perturbed dynamics equations. These parameters take distinctive values for distinct gravity theories and have potential to distinguish between Dark Energy and Modified Gravity. The second method is to look for inconsistencies in Dark Energy parameter spaces using specific combinations of cosmological data sets. Our investigation addresses the Dark Energy problem that is relevant to the mission of the HEP program to understand how our universe works at its most fundamental level. It will allow us to make progress on the HEP mission to explore the nature of Dark Energy and the basic nature of space and time using future surveys such as LSST. The investigation supports the DOE HEP program Cosmic Frontier as it will contribute to the study and understanding of dark energy and fundamental properties of the universe. The investigation contributes directly to LSST-DESC key projects and their deliverables as described in the Science Road-map document to build analysis pipeline and to test dark energy and beyond wCDM models using LSST.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS1976

Probabilistic and deterministic aspects of linear estimation in geodesy

Recent advances in observational techniques related to geodetic work (VLBI, laser ranging) make it imperative that more consideration should be given to modeling problems. Uncertainties in the effect of atmospheric refraction, polar motion and precession-nutation parameters, cannot be dispensed with in the context of centimeter level geodesy. Even physical processes that have generally been previously altogether neglected (station motions) must now be taken into consideration. The problem of modeling functions of time or space, or at least their values at observation points (epochs) is explored. When the nature of the function to be modeled is unknown. The need to include a limited number of terms and to a priori decide upon a specific form may result in a representation which fails to sufficiently approximate the unknown function. An alternative approach of increasing application is the modeling of unknown functions as stochastic processes.

Dermanis, A.↗

NASA NTRS2000

Application of Statistical Methods of Rain Rate Estimation to Data From The TRMM Precipitation Radar

The TRMM Precipitation Radar is well suited to statistical methods in that the measurements over any given region are sparsely sampled in time. Moreover, the instantaneous rain rate estimates are often of limited accuracy at high rain rates because of attenuation effects and at light rain rates because of receiver sensitivity. For the estimation of the time-averaged rain characteristics over an area both errors are relevant. By enlarging the space-time region over which the data are collected, the sampling error can be reduced. However. the bias and distortion of the estimated rain distribution generally will remain if estimates at the high and low rain rates are not corrected. In this paper we use the TRMM PR data to investigate the behavior of 2 statistical methods the purpose of which is to estimate the rain rate over large space-time domains. Examination of large-scale rain characteristics provides a useful starting point. The high correlation between the mean and standard deviation of rain rate implies that the conditional distribution of this quantity can be approximated by a one-parameter distribution. This property is used to explore the behavior of the area-time-integral (ATI) methods where fractional area above a threshold is related to the mean rain rate. In the usual application of the ATI method a correlation is established between these quantities. However, if a particular form of the rain rate distribution is assumed and if the ratio of the mean to standard deviation is known, then not only the mean but the full distribution can be extracted from a measurement of fractional area above a threshold. The second method is an extension of this idea where the distribution is estimated from data over a range of rain rates chosen in an intermediate range where the effects of attenuation and poor sensitivity can be neglected. The advantage of estimating the distribution itself rather than the mean value is that it yields the fraction of rain contributed by the light and heavy rain rates. This is useful in estimating the fraction of rainfall contributed by the rain rates that go undetected by the radar. The results at high rain rates provide a cross-check on the usual attenuation correction methods that are applied at the highest resolution of the instrument.

Meneghini, R.↗

NASA NTRS2014

Persistent Expression Changes of Fibrosis-Related Genes in the Lung Tissues of Rats Exposed to Lunar Dust Particles

The Moon's surface is covered by a layer of fine, potential reactive dust. Lunar dust contain about 1‐2% of very fine respirable dust (less than 3 micrometers). The habitable area of any lunar landing vehicle and outpost would inevitably be contaminated with lunar dust that could pose a health risk. The purpose of the study is to evaluate the toxicity of Apollo moon dust in rodents to assess the health risk of dust exposures to humans. One of the particular interests in the study is to evaluate dust‐induced changes of the expression of fibrosis‐related genes, and to identify specific signaling pathways involved in lunar dustinduced toxicity. F344 rats were exposed for 4 weeks (6h/d; 5d/wk) in nose‐only inhalation chambers to concentrations of 0 (control air), 2.1, 6.8, 21, and 61 milligrams per cubic meters of lunar dust. Five rats per group were euthanized at 1 day, 1 week, 1 month, and 3 months after the last inhalation exposure. The bronchoalveolar lavage fluid (BALF) was collected by lavaging with phosphate‐buffered saline (PBS). A zymosan‐induced luminolbased chemiluminescence assay was used to assess the activity of BAL cells. The lavaged lung tissue was snap frozen in LN2 and total RNA was isolated using the Qigen RNeasy kit. The expression of 84 fibrosisrelated genes were analyzed using the RT2 Profiler PCR Array technique. The expression of 18 genes of interest were further measured using real‐time PCR technique in all the samples. 10 out of 18 genes of interest showed persistently significant expression changes in the local lung tissue exposed to lunar dust, indicating a prolonged proinflammatory response. The expressions of several of these genes were dose‐ and time‐dependent and were significantly correlated with other pathological parameters. The potential signaling pathways and upstream regulators were further analyzed using IPA pathway analysis tool based on the gene expression data. The data presented in this study, for the first time, explore the molecular mechanisms of lunar dust induced toxicity, contributing not only the risk assessment for future space exploration, but also understandings of the dust‐induced toxicity in humans on earth.

Zhang, Ye↗

DOE OSTI2026

Pre-training Vision Models for the Classification of Alerts from Wide-field Time-domain Surveys

Modern wide-field time-domain surveys facilitate the study of transient, variable and moving phenomena by conducting image differencing and relaying alerts to their communities. Machine learning tools have been used on data from these surveys and their precursors for more than a decade, and convolutional neural networks (CNNs), which make predictions directly from input images, saw particularly broad adoption through the 2010s. Since then, continually rapid advances in computer vision have transformed the standard practices around using such models. It is now commonplace to use standardized architectures pre-trained on large corpora of everyday images (e.g., ImageNet). In contrast, time-domain astronomy studies still typically design custom CNN architectures and train them from scratch. Here, we explore the effects of adopting various pre-training regimens and standardized model architectures on the performance of alert classification. We find that the resulting models match or outperform a custom, specialized CNN like what is typically used for filtering alerts. Moreover, our results show that pre-training on galaxy images from Galaxy Zoo tends to yield better performance than pre-training on ImageNet or training from scratch. We observe that the design of standardized architectures are much better optimized than the custom CNN baseline, requiring significantly less time and memory for inference despite having more trainable parameters. On the eve of the Legacy Survey of Space and Time and other image-differencing surveys, these findings advocate for a paradigm shift in the creation of vision models for alerts, demonstrating that greater performance and efficiency, in time and in data, can be achieved by adopting the latest practices from the computer vision field.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Superlative mechanical energy absorbing efficiency discovered through self-driving lab-human partnership

Energy absorbing efficiency is a key determinant of a structure’s ability to provide mechanical protection and is defined by the amount of energy that can be absorbed prior to stresses increasing to a level that damages the system to be protected. Here, we explore the energy absorbing efficiency of additively manufactured polymer structures by using a self-driving lab (SDL) to perform >25,000 physical experiments on generalized cylindrical shells. We use a human-SDL collaborative approach where experiments are selected from over trillions of candidates in an 11-dimensional parameter space using Bayesian optimization and then automatically performed while the human team monitors progress to periodically modify aspects of the system. The result of this human-SDL campaign is the discovery of a structure with a 75.2% energy absorbing efficiency and a library of experimental data that reveals transferable principles for designing tough structures.

42 ENGINEERING↗

DOE OSTI2024

Learning thermodynamically constrained equations of state with uncertainty

Numerical simulations of high energy-density experiments require equation of state (EOS) models that relate a material’s thermodynamic state variables—specifically pressure, volume/density, energy, and temperature. EOS models are typically constructed using a semi-empirical parametric methodology, which assumes a physics-informed functional form with many tunable parameters calibrated using experimental/simulation data. Since there are inherent uncertainties in the calibration data (parametric uncertainty) and the assumed functional EOS form (model uncertainty), it is essential to perform uncertainty quantification (UQ) to improve confidence in EOS predictions. Model uncertainty is challenging for UQ studies since it requires exploring the space of all possible physically consistent functional forms. Thus, it is often neglected in favor of parametric uncertainty, which is easier to quantify without violating thermodynamic laws. This work presents a data-driven machine learning approach to constructing EOS models that naturally captures model uncertainty while satisfying the necessary thermodynamic consistency and stability constraints. We propose a novel framework based on physics-informed Gaussian process regression (GPR) that automatically captures total uncertainty in the EOS and can be jointly trained on both simulation and experimental data sources. A GPR model for the shock Hugoniot is derived, and its uncertainties are quantified using the proposed framework. We apply the proposed model to learn the EOS for the diamond solid state of carbon using both density functional theory data and experimental shock Hugoniot data to train the model and show that the prediction uncertainty is reduced by considering thermodynamic constraints.

Sharma, Himanshu (ORCID:000900050235718X)↗

DOE OSTI2024

Natural anomaly mediation from the landscape with implications for LHC SUSY searches

Supersymmetric models with the anomaly-mediated supersymmetry (SUSY) breaking (AMSB) form for soft SUSY breaking terms arise in two different settings: (1) extra-dimensional models where SUSY breaking occurs in a sequestered sector, and (2) 4 − d models with dynamical SUSY breaking in a hidden sector where scalars gain masses of order the gravitino mass m 3 / 2 , but gaugino masses and trilinear soft terms are assumed to be of the AMSB form. Both models run into serious conflicts with (1) LHC sparticle and Higgs mass constraints, (2) constraints from winolike weakly interacting massive particle dark matter searches, and (3) bounds from naturalness. These conflicts may be avoided by introducing minor changes to the underlying phenomenological models consisting of nonuniversal bulk scalar Higgs masses and A terms, providing a setting for natural anomaly mediation (nAMSB). In nAMSB, the wino is still expected to be the lightest of the gauginos, but the Higgsinos are expected to be the lightest electroweakinos (EWinos) in accord with naturalness. We examine what sorts of spectra are expected to emerge when nAMSB arises from a string landscape setting: While model 2 can only be natural for a Higgs mass m h ≲ 123 GeV , model 1 can accommodate naturalness along with m h ∼ 125 GeV while still respecting LHC bounds on sparticle masses. We explore the LHC phenomenology of nAMSB models where we find that for Higgsino pair production, typically larger dilepton mass gaps arise from the soft dilepton-plus-jets signature than in models with gaugino mass unification. For wino-pair production, the higher m 3 / 2 portion of nAMSB parameter space is excluded by recent LHC bounds from gaugino pair production searches. We characterize the dominant LHC signatures arising from the remaining lower m 3 / 2 ∼ 90 – 200 TeV range of parameter space, which should be fully testable at high-luminosity LHC via EWino pair production searches. Published by the American Physical Society 2024

Astronomy & Astrophysics↗

NASA NTRS1995

Validation of space/ground antenna control algorithms using a computer-aided design tool

The validation of the algorithms for controlling the space-to-ground antenna subsystem for Space Station Alpha is an important step in assuring reliable communications. These algorithms have been developed and tested using a simulation environment based on a computer-aided design tool that can provide a time-based execution framework with variable environmental parameters. Our work this summer has involved the exploration of this environment and the documentation of the procedures used to validate these algorithms. We have installed a variety of tools in a laboratory of the Tracking and Communications division for reproducing the simulation experiments carried out on these algorithms to verify that they do meet their requirements for controlling the antenna systems. In this report, we describe the processes used in these simulations and our work in validating the tests used.

Gantenbein, Rex E.↗

NASA NTRS2014

Improving FermiI Orbit Determination and Prediction in an Uncertain Atmospheric Drag Environment

Orbit determination and prediction of the Fermi Gamma-ray Space Telescope trajectory is strongly impacted by the unpredictability and variability of atmospheric density and the spacecrafts ballistic coefficient. Operationally, Global Positioning System point solutions are processed with an extended Kalman filter for orbit determination, and predictions are generated for conjunction assessment with secondary objects. When these predictions are compared to Joint Space Operations Center radar-based solutions, the close approach distance between the two predictions can greatly differ ahead of the conjunction. This work explores strategies for improving prediction accuracy and helps to explain the prediction disparities. Namely, a tuning analysis is performed to determine atmospheric drag modeling and filter parameters that can improve orbit determination as well as prediction accuracy. A 45 improvement in three-day prediction accuracy is realized by tuning the ballistic coefficient and atmospheric density stochastic models, measurement frequency, and other modeling and filter parameters.

orbit prediction↗

NASA NTRS2014

Improving Fermi Orbit Determination and Prediction in an Uncertain Atmospheric Drag Environment

Orbit determination and prediction of the Fermi Gamma-ray Space Telescope trajectory is strongly impacted by the unpredictability and variability of atmospheric density and the spacecraft's ballistic coefficient. Operationally, Global Positioning System point solutions are processed with an extended Kalman filter for orbit determination, and predictions are generated for conjunction assessment with secondary objects. When these predictions are compared to Joint Space Operations Center radar-based solutions, the close approach distance between the two predictions can greatly differ ahead of the conjunction. This work explores strategies for improving prediction accuracy and helps to explain the prediction disparities. Namely, a tuning analysis is performed to determine atmospheric drag modeling and filter parameters that can improve orbit determination as well as prediction accuracy. A 45% improvement in three-day prediction accuracy is realized by tuning the ballistic coefficient and atmospheric density stochastic models, measurement frequency, and other modeling and filter parameters.

orbit prediction↗

NASA NTRS1990

Figure of merit studies of beam power concepts for advanced space exploration

Surface to surface, millimeter wavelength beam power systems for power transmission on the lunar base were investigated. Qualitative/quantitative analyses and technology assessment of 35, 110 and 140 GHz beam power systems were conducted. System characteristics including mass, stowage volume, cost and efficiency as a function of range and power level were calculated. A simple figure of merit analysis indicates that the 35 GHz system would be the preferred choice for lunar base applications, followed closely by the 110 GHz system. System parameters of a 35 GHz beam power system appropriate for power transmission on a recent lunar base concept studied by NASA-Johnson and the necessary deployment sequence are suggested.

Miller, Gabriel↗

NASA NTRSDate not supplied

Creating the Environment for Exploration Habitats

The Crew and Thermal Systems Division’s (CTSD) 20 Foot Chamber has undergone significant modifications to support NASA’s future human exploration missions to the Moon, Mars, and beyond. Existing chamber systems such as Vacuum/Repress, Heating Ventilation and Air Conditioning (HVAC), and Fire Suppression System (FSS), were upgraded, while new systems such as Air Revitalization and Gas Distribution were added. In addition, the chamber was outfitted with crew quarters, hygiene areas, storage, and workspaces. Teams across the Johnson Space Center (JSC) worked together to overcome the logistical and operational challenges of the planned test parameters. The ultimate goal was to create an analog testbed in which a maximum of 8 human test subjects could safely live and work for at least 11 days in the same conditions (reduced pressures, Oxygen concentrations, etc.) as those expected inside a base spacecraft. This would allow researchers to study the effects of such environments on humans and to validate proposed Prebreathe Protocols to enable the safe performance of surface extravehicular activities (EVAs). Over the last eighteen months, the CTSD Systems Test Branch has performed six tests of varying lengths and atmospheres for both NASA and commercial partners. To date, researchers now have collected data on 48 test subjects in the course of 42 days and over 130 simulated EVAs. This paper will discuss the history of the 20 Foot Chamber, its long road to becoming an analog testbed for human exploration, and the capabilities that make it a unique, world class facility for NASA and commercial human exploration missions. It will provide an overview of past and future testing, and the lessons learned along the way.

Cristina Aurora Anchondo↗

NASA NTRSDate not supplied

Creating the Environment for Exploration Habitats

Cristina A Anchondo↗

NASA NTRS2024

Creating the Test Environment for Exploration Habitats

The Crew and Thermal Systems Division’s (CTSD) 20 Foot Chamber has undergone significant modifications to support NASA’s future human exploration missions to the Moon, Mars, and beyond. Existing chamber systems such as Vacuum/Repress, Heating Ventilation and Air Conditioning (HVAC), and Fire Suppression System (FSS), were upgraded, while new systems such as Air Revitalization and Gas Distribution were added. In addition, the chamber was outfitted with crew quarters, hygiene areas, storage, and workspaces. Teams across the Johnson Space Center (JSC) worked together to overcome the logistical and operational challenges of the planned test parameters. The ultimate goal was to create an analog testbed in which a maximum of eight human test subjects could safely live and work for at least 11 days in the same conditions (e.g., reduced pressures, oxygen concentrations, and so on) as those expected inside a base spacecraft. This would allow researchers to study the effects of such environments on humans and to validate proposed Prebreathe Protocols to enable the safe performance of surface extravehicular activities (EVAs). Over the last 18 months, the CTSD Systems Test Branch has performed six tests of varying lengths and atmospheres for both NASA and commercial partners. To date, researchers now have collected data on 48 test subjects in the course of 42 days and over 130 simulated EVAs. This paper will discuss the history of the 20 Foot Chamber, its long road to becoming an analog testbed for human exploration, and the capabilities that make it a unique, world class facility for NASA and commercial human exploration missions. It will provide an overview of past and future testing and the lessons learned along the way.

Cristina A Anchondo↗

NASA NTRSDate not supplied

Creating the Test Environment for Exploration Habitats

The Crew and Thermal Systems Division’s (CTSD) 20 Foot Chamber has undergone significant modifications to support NASA’s future human exploration missions to the Moon, Mars, and beyond. Existing chamber systems such as Vacuum/Repress, Heating Ventilation and Air Conditioning (HVAC), and Fire Suppression System (FSS), were upgraded, while new systems such as Air Revitalization and Gas Distribution were added. In addition, the chamber was outfitted with crew quarters, hygiene areas, storage, and workspaces. Teams across the Johnson Space Center (JSC) worked together to overcome the logistical and operational challenges of the planned test parameters. The ultimate goal was to create an analog testbed in which a maximum of eight human test subjects could safely live and work for at least 11 days in the same conditions (e.g., reduced pressures, oxygen concentrations, and so on) as those expected inside a base spacecraft. This would allow researchers to study the effects of such environments on humans and to validate proposed Prebreathe Protocols to enable the safe performance of surface extravehicular activities (EVAs). Over the last 18 months, the CTSD Systems Test Branch has performed six tests of varying lengths and atmospheres for both NASA and commercial partners. To date, researchers now have collected data on 48 test subjects in the course of 42 days and over 130 simulated EVAs. This paper will discuss the history of the 20 Foot Chamber, its long road to becoming an analog testbed for human exploration, and the capabilities that make it a unique, world class facility for NASA and commercial human exploration missions. It will provide an overview of past and future testing and the lessons learned along the way.

Chris Briggs↗

NASA NTRS2001

Human Exploration Missions Study: Space Surveillance Telescope Transfer to and Station at a Halo Orbit at the Earth-Sun Libration Point L2

This study was undertaken to determine mission profile and delta velocity requirements to place a telescope at the Earth-Sun libration point L2. The program, Integrated Mission Program (IMP), was selected to be used in the investigation. A description of IMP and its capabilities may be found in the Addenda. The Addenda also contains the libration halo equations, constants and other parameters. Comments regarding the chaotic nature of numerical integration near the libration points are also attached in the Addenda. A basic two stage S/C with a simple mission profile was selected. This profile is shown.

Dauro, Vincent A., Sr.↗