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At least 181 records · Page 10

All sources NASA NTRS DOE OSTI

NASA NTRS2014

Space Link Extension (SLE) Emulation for High-Throughput Network Communication

As the data rate requirements for space communications increases, significant stress is placed not only on the wireless satellite communication links, but also on the ground networks which forward data from end-users to remote ground stations. These wide area network (WAN) connections add delay and jitter to the end-to-end satellite communication link, effects which can have significant impacts on the wireless communication link. It is imperative that any ground communication protocol can react to these effects such that the ground network does not become a bottleneck in the communication path to the satellite. In this paper, we present our SCENIC Emulation Lab testbed which was developed to test the CCSDS SLE protocol implementations proposed for use on future NASA communication networks. Our results show that in the presence of realistic levels of network delay, high-throughput SLE communication links can experience significant data rate throttling. Based on our observations, we present some insight into why this data throttling happens, and trace the probable issue back to non-optimal blocking communication which is sup-ported by the CCSDS SLE API recommended practices. These issues were presented as well to the SLE implementation developers which, based on our reports, developed a new release for SLE which we show fixes the SLE blocking issue and greatly improves the protocol throughput. In this paper, we also discuss future developments for our end-to-end emulation lab and how these improvements can be used to develop and test future space communication technologies.

Networking↗

NASA NTRS2015

A Burning Rate Emulator (BRE) for Study in Microgravity

A gas-fueled burner, the Burning Rate Emulator (BRE), is used to emulate condensed-phase fuel flames. The design has been validated to easily measure the burning behavior of condensed-phase fuels by igniting a controlled stream of gas fuel and diluent. Four properties, including the heat of combustion, the heat of gasification, the surface temperature, and the laminar smoke point, are assumed to be sufficient to define the steady burning rate of a condensed-phase fuel. The heat of gasification of the fuel is determined by measuring the heat flux and the fuel flow rate. Microgravity BRE tests in the NASA 5.2 s drop facility have examined the burning of pure methane and ethylene (pure and 50 in N2 balance). Fuel flow rates, chamber oxygen concentration and initial pressure have been varied. Two burner sizes, 25 and 50 mm respectively, are chosen to examine the nature of initial microgravity burning. The tests reveal bubble-like flames that increase within the 5.2s drop but the heat flux received from the flame appears to asymptotically approach steady state. Portions of the methane flames appear to locally detach and extinguish at center, while its shape remains fixed, but growing. The effective heat of gasification is computed from the final measured net heat flux and the fuel flow rate under the assumption of an achieved steady burning. Heat flux (or mass flux) and flame position are compared with stagnant layer burning theory. The analysis offers the prospect of more complete findings from future longer duration ISS experiments.

diffusion flames↗

NASA NTRS2017

Precision Projector Laboratory: Detector Characterization with an Astronomical Emulation Testbed

As astronomical observations benefit from improved sensitivity, the effectiveness of scientific programs is becoming limited by systematics that often originate in poorly understood image sensor behavior. Traditional, bottom-up detector characterization methods provide one way to model underlying detector physics, and generate ever more faithful numerical simulations, but this approach is vulnerable to preconceptions and over-simplification. The alternative top-down approach is laboratory emulation, which enables observation, calibration, and analysis scenarios to be tested without relying on a complete understanding of the underlying physics. This complements detector characterization and simulation efforts by testing their validity. We describe a laboratory facility and experimental testbed that supports the emulation of a wide range of mission concepts such as gravitational weak lensing measurements by WFIRST and high precision spectrophotometry of transiting exoplanets by JWST. An Offner relay projects readily customizable "scenes" (e.g. stars, galaxies, spectra) with very low optical aberration over the full area of a typical optical or near infrared image sensor. f/8 and slower focal ratios may be selected, spanning those of most proposed space missions and approximating the point spread function (PSF) size of seeing limited ground based surveys. Diffraction limited PSFs are projected over a wide field of view and wavelength range to deliver highly predictable image patterns down to sub-pixel scales with stable intensity and fine motion control. The testbed enables realistic validation of detector performance on science-like images, which aids mission design and survey strategy, as well as targeted investigations of various detector effects.

Seshadri, Suresh↗

NASA NTRSDate not supplied

Folded Optical Design for High Fidelity Atmospheric Emulation With A Spatial Light Modulator

Atmospheric emulators based on spatial light modulators offer the ability to test atmospheric propagation effects on a laser communication component’s performance in the laboratory setting. To create a high-fidelity atmospheric emulator, details of the optical design are key. This paper discusses the optical design choices and refinements that enabled the creation of a system that was verified to recreate multi-layer turbulence with high fidelity up to D/r 0 =50. Optical design choices that affect the fidelity discussed in this paper include the characteristics of the input laser, the spatial light modulator, the holograms, the image relay optical components, and the spatial filter. Also included in this paper is a comparison of the chosen folded optical layout to an alternative angled layout.

Atmospheric turbulence↗

NASA NTRSDate not supplied

Spacecraft Operation Emulation Via Six DOF Tendon-Actuated Robot

NASA Johnson Space Center’s (JSC) Simulation, Emulation, Navigation, Sensors and STAR (SENSS) Laboratory has developed a large-scale six degree of freedom tendon-actuated robot (STAR) for emulation of spacecraft proximity operations and Entry, Descent, and Landing approach trajectories to aid in the rapid development and testing of a wide range of guidance, navigation, and control algorithms at low cost.

navigation↗

NASA NTRSDate not supplied

Folded Optical Design for High Fidelity Atmospheric Emulation With A Spatial Light Modulator

Atmospheric emulators based on spatial light modulators offer the ability to test atmospheric propagation effects on a laser communication component’s performance in the laboratory setting. To create a high-fidelity atmospheric emulator, details of the optical design are key. This paper discusses the optical design choices and refinements that enabled the creation of a system that was verified to recreate multi-layer turbulence with high fidelity up to D/r0 =50. Optical design choices that affect the fidelity discussed in this paper include the characteristics of the input laser, the spatial light modulator, the holograms, the image relay optical components, and the spatial filter. Also included in this paper is a comparison of the chosen folded optical layout to an alternative angled layout.

Atmospheric turbulence↗

NASA NTRSDate not supplied

Emulating Nature: The Future of Sustainable Design with BIDARA

BIDARA (Bio-inspired Design and Research Assistant) is a GPT-4o chatbot that was instructed to help scientists and engineers understand, learn from, and emulate the strategies used by living things to create sustainable designs and technologies. BIDARA can guide users through the Biomimicry Institute’s Design Process, a step-by-step method to propose biomimetic solutions to challenges. This process includes defining the problem, biologizing the challenge, discovering natural models, abstracting design strategies, and emulating nature's lessons.

Brandon Ruffridge↗

DOE OSTI2022

Threat emulation framework

A method for emulating threats in virtual network computing environment is provided. The method comprises creating a number of virtual machines in the virtual network computing environment. A number of threat actors are emulated, wherein each threat actor comprises a number of threat artifacts that form a sequence of attack steps against the virtual network computing environment. The threat actors are then deployed against the virtual network computing environment. Behavioral data about actions of the threat actors in the virtual network computing environment is collected, as is performance data about the virtual network computing environment in response to the threat actors. The collected behavioral and performance data is then presented to a user via an interface.

Urias, Vincent↗

DOE OSTI2023

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore’s Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to MLexperts because of lack of training data and relevant, easy-to-use workflows. Wepresent ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator’s macro-scale physical state. The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res2) and code(https://leap-stc.github.io/ClimSim)arereleasedopenlytosupport the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society.

artificial intelligence, machine learning↗

DOE OSTI2020

CEEP (Cyber-Energy Emulation Platform) [SWR-20-102]

NREL's Cyber-Energy Emulation Platform (CEEP) provides the capability to realize cyber-energy security and resilience through automation and orchestration of virtualized systems and software defined networks for the electric grid. CEEP enables testing and validation of grid-security and -control methodologies as the grid evolves to include smart technologies/systems, such as virtualization and containerization of grid components, software defined networking, simulation and co-simulation frameworks, and hardware in the loop. CEEP is a modular system that can be distributed and deployed across different hardware infrastructure sizes and network architectures. For example, CEEP can visualize, emulate, and/or coordinate the Smart-Grid Network Visualization, Intrusion Detection, and Network Healing system. Using CEEP, intrusion-detection and network-self-healing solutions can be deployed at grid control centers, within secure private clouds, and in cyber-energy appliances.

Vaughan, Evan↗

DOE OSTI2022

Cyber Energy Emulation Platform (CEEP) [SWR-20-102]

Rivera, Joshua↗

NASA NTRS1997

High-Performance Software Emulation of 1750A Processor

We describe a software emulator of the MIL-STD-1750A architecture that executes 1750A code at a rate of about 4 MIPS on a Sun 200MHz Ultra2 work-station, and effectively several times faster than that when application-specific optimizations are used.

1750A↗

NASA NTRSDate not supplied

NASA’s Prototype Spectral Water Inversion Processor and Emulator (SWIPE): Towards Global Coastal and Inland Water Quality and Algal Biodiversity Monitoring

Degradation of Earth’s inland water resources due to anthropogenic perturbations and climate anomalies at both local and global scales continues to place human health at substantial risk. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This presentation will provide updates on NASA’s prototype open-source aquatic modeling platform, Spectral Water Inversion Processor and Emulator (SWIPE), which is a comprehensive, multi-faceted modeling platform for both forward and inverse modeling of diverse aquatic ecosystems from the benthos to top-of-atmosphere (TOA). SWIPE provides a cohesive application which leverages recent advancements in particle modeling, Big Data analytics, and machine learning to develop a high-fidelity synthetic training ground for sensitivity studies and algorithm development for multispectral or upcoming hyperspectral missions. Some of the prominent features of SWIPE to be discussed include: 1. Advanced hyperspectral modeling of globally diverse algal and non-algal particles using a novel two-layer coated sphere scattering model and radiative transfer modeling, 2. Massive, highly detailed synthetic spectral libraries of Analysis-Ready-Data (ARD) which include spectral libraries of particle microphysics, water biogeophysical and optical properties, as well as surface and TOA reflectances at 1 nm resolution, 3. An ensemble of pre-built analytic, machine learning, and deep learning inversion algorithms for various water quality and biodiversity related retrieval parameters and uncertainty quantification, 4. Sensor-agnostic water quality inversion at wide ranging spatial and spectral resolutions including a codebase for seamless application in the Google Earth Engine and NASA Earth Exchange (NEX) for planetary scale analysis. SWIPE will be a fully open-source platform based in python with comprehensive documentation, tutorials, and options for distributed computing on high performance computing clusters or on single, local machines. Further, we will discuss how we envision SWIPE contributing towards a global analysis of coastal and inland water quality dynamics.

top-of-atmosphere (TOA)↗

DOE OSTI2024

Emulation and detection of physical faults and cyber-attacks on building energy systems through real-time hardware-in-the-loop experiments

The increasing use of remote or mobile access, integrated wearable technologies, data exchange, and cloud-based data analytics in modern smart buildings is steering the building industry towards open communication technologies. The increased connectivity and accessibility could lead to more cyber-attacks in smart buildings. On the other hand, physical faults (e.g., HVAC -heating, ventilation, and air-conditioning faults) may have similar adverse impacts as those from the cyber-attacks on building energy systems, such as occupant discomfort, energy wastage, and equipment downtime. However, current physical behavior-based anomaly detection methods fail to differentiate between cyber-attacks and physical faults in building energy systems. Moreover, the challenge in collecting real-world threat data with ground truth has led researchers to rely on numerical models with user-defined assumptions, which may not accurately reflect real-world conditions due to the lack of in-situ experimental datasets. To address these challenges and gaps, this paper presents a flexible hardware-in-the-loop (HIL) testbed for generating cyber-attack and physical fault datasets and demonstrating threat detection algorithms in a real building automation system (BAS) environment. This testbed combines hardware (i.e., real BAS with local HVAC controllers and a physical network) with software (i.e., high-fidelity models to represent behaviors of building envelope and HVAC energy systems), enabling emulations of realistic threats. Five HIL experiments, including one baseline without any threats, two with physical faults, and two with cyber-attacks, were conducted to generate datasets containing detailed network traffic and system states. A joint classification framework, incorporating a network analyzer and a physical HVAC fault detector, was proposed to automatically detect cyber-physical abnormalities on BAS at both the network and the physical HVAC levels. The network analyzer comprises a conditional random fields (CRF) based command validator and a statistics-based detection strategy. The fault detector employs a weather and schedule-based pattern matching and feature-based principal component analysis (WPM-FPCA) method. Evaluation of the classification using four metrics from the multi-class confusion matrix revealed an average accuracy of 90.2%, recall of 89.7%, precision of 88.5% and F1-score of 89.2%. Finally, these results demonstrate that the proposed joint classification framework can effectively differentiate between specific types of cyber-attacks (e.g., device reinitialization attack, network Denial-of-Service attack) and physical faults (e.g., air handling unit operational fault, cooling coil valve stuck) in real time for improved building energy management.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Emulation of neutron damage with proton irradiation and its effects on microstructure and microchemistry of Zircaloy-4

This work assesses the potential of proton irradiation to simulate the neutron damage to the matrix and laves phase Zr(Fe,Cr) 2 precipitates in Zircaloy-4. Isothermal proton irradiation has been performed on Zircaloy-4 samples at irradiation temperatures ranging from 250 to 350 °C. Two-step proton irradiation was also performed to enhance the amorphization of and iron loss from the laves phase Zr(Fe,Cr) 2 precipitates. The irradiated microstructures, including dislocation loops and rafts near SPPs, were observed in proton irradiated Zircaloy-4, which are consistent with neutron irradiated material at a similar damage level. The amount of irradiation-induced hardening after proton irradiation was similar to post neutron irradiated data. The significant amorphization of the SPPs and concurrent Fe redistribution observed on neutron irradiated materials can be effectively emulated using a two-step proton irradiation on Zircaloy-4. Hence, the neutron irradiation effect on Zircaloy-4 can be mostly captured using the two-step proton irradiation described in this study.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Emulation of neutron-irradiated microstructure of austenitic 21Cr32Ni model alloy using dual-ion irradiation

Here, in this study, the capability of heavy-ion irradiation to emulate neutron irradiation was demonstrated on an austenitic 21Cr32Ni type ternary model alloy. The model alloy used in this study is chemically analogous but compositionally simpler than of alloy 800H, which is a candidate austenitic Ni alloy which has been proposed for use in Generation IV reactors. The microstructure of the 21Cr32Ni model alloy irradiated in the BOR-60 fast reactor to 17.1 dpa and 35 dpa at ~380°C was characterized using transmission electron microscopy (TEM). The 17.1 dpa BOR-60 irradiated microstructure was then compared with the microstructure of the same material developed under dual-ion (DI) irradiation using various He/dpa ratios between 0.1 and 16.6 appm He/dpa in the temperature range of 430°C-500°C. The results showed that both neutron and DI irradiation of 21Cr32Ni model alloy produced dislocations in the form of a dislocation network as well as {111}-type faulted dislocation loops, cavities, and radiation-induced Ni enrichment at radiation-induced sinks. When the dose and the He/dpa ratio were kept similar to those in neutron irradiation, DI irradiation of the 21Cr32Ni model alloy at 460°C resulted in over-nucleation of small cavities and in a high density of faulted dislocation loops compared to those observed in the fast-neutron irradiated alloy of the same heat irradiated at ~380°C. The optimal condition for reproducing the neutron-irradiated microstructure was DI irradiation at 460°C and 0.1 appm He/dpa. In that case, the faulted loop and cavity size distributions in the BOR-60 irradiated 21Cr32Ni model alloy samples closely matched with those measured in the DI irradiated 21Cr32Ni model alloy sample. The fact that the He/dpa is an order of magnitude smaller than the helium generation rate for fast neutron irradiation, stops over nucleation and allows for the development of a similar microstructure as for neutron irradiation.

21Cr32Ni model alloy↗

DOE OSTI2021

Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation

Non-intrusive reduced-order models (ROMs) have recently generated considerable interest for constructing computationally efficient counterparts of nonlinear dynamical systems emerging from various domain sciences. They provide a low-dimensional emulation framework for systems that may be intrinsically high-dimensional. This is accomplished by utilizing a construction algorithm that is purely data-driven. It is no surprise, therefore, that the algorithmic advances of machine learning have led to non-intrusive ROMs with greater accuracy and computational gains. However, in bypassing the utilization of an equation-based evolution, it is often seen that the interpretability of the ROM framework suffers. This becomes more problematic when black-box deep learning methods are used which are notorious for lacking robustness outside the physical regime of the observed data. In this article, we propose the use of a novel latent-space interpolation algorithm based on Gaussian process regression. Notably, this reduced-order evolution of the system is parameterized by control parameters to allow for interpolation in space. The use of this procedure also allows for a continuous interpretation of time which allows for temporal interpolation. The latter aspect provides information, with quantified uncertainty, about full-state evolution at a finer resolution than that utilized for training the ROMs. This research assesses the viability of this algorithm for an advection-dominated system given by the inviscid shallow water equations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Emulator-based Bayesian calibration of a subglacial drainage model

Subglacial drainage models, often motivated by the relationship between hydrology and ice flow, sensitively depend on numerous unconstrained parameters. We explore using borehole water-pressure time series to calibrate the uncertain parameters of a popular subglacial drainage model, taking a Bayesian perspective to quantify the uncertainty in parameter estimates and in the calibrated model predictions. To reduce the computation time associated with Markov Chain Monte Carlo sampling, we construct a fast Gaussian process emulator to stand in for the subglacial drainage model. We first carry out a calibration experiment using synthetic observations consisting of model simulations with hidden parameter values as a demonstration of the method. Using real borehole water pressures measured in western Greenland, we find meaningful constraints on four of the eight model parameters and a factor-of-three reduction in uncertainty of the calibrated model predictions. These experiments illustrate Gaussian process-based Bayesian inference as a useful tool for calibration and uncertainty quantification of complex glaciological models using field data. However, significant differences between the calibrated model and the borehole data suggest that structural limitations of the model, rather than poorly constrained parameters or computational cost, remain the most important constraint on subglacial drainage modelling.

58 GEOSCIENCES↗