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DOE OSTI2022

Random field optimization

Herein we present a new modeling paradigm for optimization that we call random field optimization. Random fields are a powerful modeling abstraction that aims to capture the behavior of random variables that live on infinite-dimensional spaces (e.g., space and time) such as stochastic processes (e.g., time series, Gaussian processes, and Markov processes), random matrices, and random spatial fields. This paradigm involves sophisticated mathematical objects (e.g., stochastic differential equations and space-time kernel functions) and has been widely used in neuroscience, geoscience, physics, civil engineering, and computer graphics. Despite of this, however, random fields have seen limited use in optimization; specifically, existing optimization paradigms that involve uncertainty (e.g., stochastic programming and robust optimization) mostly focus on the use of finite random variables. This trend is rapidly changing with the advent of statistical optimization (e.g., Bayesian optimization) and multi-scale optimization (e.g., integration of molecular sciences and process engineering). Our work extends a recently-proposed abstraction for infinite-dimensional optimization problems by capturing more general uncertainty representations. Moreover, we discuss solution paradigms for this new class of problems based on finite transformations and sampling, and identify open questions and challenges.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1989

On optimizing a constellation of altimetric satellites for measuring global oceanic mesoscale

The orbital configuration of the Eos polar-platform constellation is examined to determine if baseline orbital parameters are appropriately specified to meet the mission goal of measuring global oceanic mesoscale variability. Using the Nyquist sampling theorem, spatial and temporal measurement requirements are first established for the constellation. Based upon these requirements, the optimum solution indicates that a total of five spacecraft are required to meet the mesoscale mapping task. Additional analyses are performed on both a two-platform configuration as well as the complete four-platform Eos constellation. The results of these calculations show that an optimized configuration of four properly-phased Eos platforms, placed into identical 835-km sun-synchronous orbits with 13-day repeat periods, can very nearly meet statistically significant sampling criteria for mapping global oceanic mesoscale variability.

Chase, Robert↗

NASA NTRS2017

Lighting Automation - Flying an Earthlike Habit Project

Our proposal will enable the development of automated spacecraft habitats for long duration missions. Majority of spacecraft lighting systems employ lamps or zone specific switches and dimmers. Automation is not in the "picture". If we are to build long duration environments, which provide earth-like habitats, minimize crew time, and optimize spacecraft power reserves, innovation in lighting automation is a must. To transform how spacecraft lighting environments are automated, we will provide performance data on a standard lighting communication protocol. We will investigate utilization and application of an industry accepted lighting control protocol, DMX512. We will demonstrate how lighting automation can conserve power, assist with lighting countermeasures, and utilize spatial body tracking. By using DMX512 we will prove the "wheel" does not need to be reinvented in terms of smart lighting and future spacecraft can use a standard lighting protocol to produce an effective, optimized and potentially earthlike habitat.

Falker, Jay↗

DOE OSTI2023

Proxy quality control of biomass particles using thermogravimetric analysis and Gaussian process regression models

Abstract The temperature experienced by reactants during preparation in a reactor is a key component in determining the yield and homogeneity of usable chemical products such as biomass particles. Thermocouples with sensors can be used to monitor spatial temperature gradients within reactors but these sensors are often too expensive and/or invasive. The present work proposes a strategy to identify optimal machine learning models to infer the maximum effective temperature experienced by particles during oxidative biomass torrefaction using key thermochemical combustion parameters. The maximum rate of weight loss, the corresponding temperature, and fixed carbon content on a dry‐ash‐free basis are used as literature‐based predictor variables obtained from thermogravimetric analysis. The evaluation of 24 machine‐learning models using the standard tenfold cross‐validation method suggests that the exponential Gaussian process regression (GPR) model is the most effective, followed by other GPR models. These high‐performing GPR models were also utilized to predict the effective preparation temperature distribution of reactor‐produced biomass particles under eight conditions of varying residence time and air‐to‐biomass ratio. The effective preparation temperature and residence time of individual biomass particles were then encoded into the torrefaction severity factor and used to estimate the energy yield of the reactor output as a novel quality control method. © 2023 The Authors. Biofuels, Bioproducts and Biorefining published by Society of Industrial Chemistry and John Wiley & Sons Ltd.

09 BIOMASS FUELS↗

DOE OSTI2026

Stable and tunable MeV $$\gamma$$-ray generation via dual-laser inverse Thomson scattering from a laser-plasma accelerator

Abstract Inverse Thomson scattering from laser-plasma accelerators offers a pathway to compact, tunable MeV $$\gamma$$ -ray sources for reduced-dose radiography and enhanced performance in nuclear resonance fluorescence (NRF)-based isotope identification. However, photon yield and spectral quality are often limited by constraints on interaction geometry and scatter-laser tunability. Here we demonstrate a MeV $$\gamma$$ -ray source based on a dual-laser inverse Thomson scattering configuration driven by a 100-TW laser-plasma accelerator. Electron beams tunable from 122 to 204 MeV with $$<5$$ mrad divergence and $$<1$$ mrad pointing stability generate $$\gamma$$ rays with peak energies from 276 keV to 1.2 MeV and yields up to $$2\times 10^{7}$$ photons per shot. By independently controlling the interaction position and the scatter-pulse duration, we experimentally match the scatter pulse to the walk-off-limited interaction length. Extending the scatter pulse to 200 fs increases photon production by approximately $$15\%$$ while maintaining operation in the linear Thomson regime, thereby preserving narrow spectral bandwidth and controlled radiation divergence. Radiographic characterization demonstrates MeV-level penetration and $$\approx 0.1$$ mm spatial resolution, while stable operation is sustained over multi-hour timescales across multiple days. These results show that interaction-length optimization provides a scalable strategy for improving photon yield, spectral control, and operational stability in compact laser-plasma-accelerator-driven $$\gamma$$ -ray sources.

Tsai, Hai-En↗

DOE OSTI2021

Ion temperature and rotation fluctuation measurements with ultra-fast charge exchange recombination spectroscopy (UF-CHERS) in the DIII-D tokamak

An upgraded detector and several optimizations have significantly improved the Ultra-Fast Charge Exchange Recombination Spectroscopy (UF-CHERS) diagnostic sensitivity to ion temperature and parallel velocity fluctuations at turbulence relevant spatio-temporal scales. Normalized broadband ion temperature and parallel velocity fluctuations down to x̃x∼1% (x = Ti, v∥) and up to ∼450 kHz have been measured in a variety of plasmas. The multi-field nature of the CHERS technique also allows measurements of the cross-phase angles of the fluctuating fields. UF-CHERS is optimized to observe emissions from the electron exchange reaction between intrinsic C6+ and hydrogenic neutral beam injected particles near 529 nm. UF-CHERS consists of two chords separated by ∼1 cm radially, less than the turbulence correlation length in DIII-D plasmas, which enables correlated measurements to suppress incoherent electronic and photon noise. The optical components of the spectrometer include a volume-phase-holographic grating with >90% transmission between 528 and 530 nm and f/2 200-mm lenses, selected to maximize the optical efficiency and photon flux. Diffracted light from each chord is collected in eight spectral bins, each with a bandwidth of ∼0.25 nm, and detected and amplified by chilled avalanche photodiodes and custom high-gain, wide bandwidth low-noise preamplifiers to achieve the optimal signal-to-noise ratio. The resulting signals are digitized at 1 MHz, 103–104× faster than the conventional CHERS diagnostics. Spatial coverage is achieved by repositioning a motorized fiber tray between plasmas. UF-CHERS measurements will advance the understanding of turbulent ion transport and contribute to the validation of transport models and simulations.

Truong, D. D. (ORCID:0000000285732539)↗

DOE OSTI2022

Meta-optic accelerators for object classifiers

Rapid advances in deep learning have led to paradigm shifts in a number of fields, from medical image analysis to autonomous systems. These advances, however, have resulted in digital neural networks with large computational requirements, resulting in high energy consumption and limitations in real-time decision-making when computation resources are limited. Here, we demonstrate a meta-optic–based neural network accelerator that can off-load computationally expensive convolution operations into high-speed and low-power optics. In this architecture, metasurfaces enable both spatial multiplexing and additional information channels, such as polarization, in object classification. End-to-end design is used to co-optimize the optical and digital systems, resulting in a robust classifier that achieves 93.1% accurate classification of handwriting digits and 93.8% accuracy in classifying both the digit and its polarization state. This approach could enable compact, high-speed, and low-power image and information processing systems for a wide range of applications in machine vision and artificial intelligence.

42 ENGINEERING↗

DOE OSTI2021

Intentional Uranium Tagging for Material Provenance and Pathway Forensics (LA19-Intentional-Forensics-NDD3Bb) (Annual Report for FY21)

This report describes the outcomes of the third and final year of a project to research the feasibility of tagging uranium materials, especially nuclear fuels. The experimental focus remained on metallic uranium forms under prospective surface and bulk tagging scenarios. Overall, the results showed that the tags could be successfully imparted and characterized in both as-built and degraded conditions. This was in line with expectations coming into this project, indicating promise for both surface and bulk tagging of metallic forms of uranium. Multiple surface tagging techniques and detection strategies were explored in FY21, with an emphasis on improving tag quality, readability, and detection in the field. Non-radioactive materials were used as a testbed. Selective deposition via laser beam was determined to be successful in imparting a readable titanium deposit on a stainless steel base plate, and can be read with high resolution characterization techniques (e.g., scanning electron microscopy) and field capable tools (e.g., eddy current testing). Other deposition techniques, such as selective deposition via electron beam and photoluminescent tags, were explored in FY21, and while success for these techniques would be dependent on additional work, these techniques showed potential for surface tagging applications. To survey bulk taggant elements for bulk uranium metal, 16 tagging elements were spread among 18 depleted uranium castings (4 baseline, 3 mix, 1 dilution, and 10 recycle). Most of these were made and characterized in FY21. Taggant acceptability was based upon manufacturability, detectability, and persistence from the standpoint of two detection options: bulk chemical analysis (for “chemical taggants”) and microstructural analysis (for “second phase taggants”). Taggant detection in both up-front manufacturing and in the face of “degradations” such as dilution, mixing, and recycling was generally good. Two independent laboratories carried out chemical analysis on most of the castings, and often at several locations within a casting, and the results are discussed. Scanning electron microscopy+EDS microanalysis revealed the second phases mostly contained the expected tagging elements. The shapes and 2 spatial distributions of these micron-sized carbides, oxides, and intermetallic particles offers opportunities for further science-based investigation and tagging optimization. Overall, V and Co currently appear as the best choices for chemical taggants while Al, Ti, Mn, Co, Pd, and Tb all look good as second phase taggants. The other elements considered here – Sc, Ni, Ge, Nb, Ce, Ta, W, Ir, and Au – while not being ruled out, require more study to become viable options. It is of special note that in the recycling study only one of the 12 elements fell out of detection even after 10 meltings, demonstrating their persistence. An Appendix tabulates all chemical analysis results to enable more quantitative and statistical studies of detection opportunities and limitations, as a part of a related project.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Policy-Driven Sustainable Saline Drainage Disposal and Forage Production in the Western San Joaquin Valley of California

Environmental policies to address water quality impairments in the San Joaquin River of California have focused on the reduction of salinity and selenium-contaminated subsurface agricultural drainage loads from westside sources. On 31 December 2019, all of the agricultural drainage from a 44,000 ha subarea on the western side of the San Joaquin River basin was curtailed. This policy requires the on-site disposal of all of the agricultural drainage water in perpetuity, except during flooding events, when emergency drainage to the River is sanctioned. The reuse of this saline agricultural drainage water to irrigate forage crops, such as ‘Jose’ tall wheatgrass and alfalfa, in a 2428 ha reuse facility provides an economic return on this pollutant disposal option. Irrigation with brackish water requires careful management to prevent salt accumulation in the crop root zone, which can impact forage yields. The objective of this study was to optimize the sustainability of this reuse facility by maximizing the evaporation potential while achieving cost recovery. This was achieved by assessing the spatial and temporal distribution of the root zone salinity in selected fields of ‘Jose’ tall wheatgrass and alfalfa in the drainage reuse facility, some of which have been irrigated with brackish subsurface drainage water for over fifteen years. Electromagnetic soil surveys using an EM-38 instrument were used to measure the spatial variability of the salinity in the soil profile. The tall wheatgrass fields were irrigated with higher salinity water (1.2–9.3 dS m -1 ) compared to the fields of alfalfa (0.5–6.5 dS m -1 ). Correspondingly, the soil salinity in the tall wheatgrass fields was higher (12.5 dS m -1 –19.3 dS m -1 ) compared to the alfalfa fields (8.97 dS mm -1 –14.4 dS mm -1 ) for the years 2016 and 2017. Better leaching of salts was observed in the fields with a subsurface drainage system installed (13–1 and 13–2). The depth-averaged root zone salinity data sets are being used for the calibration of the transient hydro-salinity computer model CSUID-ID (a one-dimensional version of the Colorado State University Irrigation Drainage Model). This user-friendly decision support tool currently provides a useful framework for the data collection needed to make credible, field-scale salinity budgets. In time, it will provide guidance for appropriate leaching requirements and potential blending decisions for sustainable forage production. This paper shows the tie between environmental drainage policy and the role of local governance in the development of sustainable irrigation practices, and how well-directed collaborative field research can guide future resource management.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2007

Advanced Image Processing for NASA Applications

The future of space exploration will involve cooperating fleets of spacecraft or sensor webs geared towards coordinated and optimal observation of Earth Science phenomena. The main advantage of such systems is to utilize multiple viewing angles as well as multiple spatial and spectral resolutions of sensors carried on multiple spacecraft but acting collaboratively as a single system. Within this framework, our research focuses on all areas related to sensing in collaborative environments, which means systems utilizing intracommunicating spatially distributed sensor pods or crafts being deployed to monitor or explore different environments. This talk will describe the general concept of sensing in collaborative environments, will give a brief overview of several technologies developed at NASA Goddard Space Flight Center in this area, and then will concentrate on specific image processing research related to that domain, specifically image registration and image fusion.

LeMoign, Jacqueline↗

NASA NTRS2011

Thermal Structure and Dynamics of Saturn's Northern Springtime Disturbance

This article combined several infrared datasets to study the vertical properties of Saturn's northern springtime storm. Spectroscopic observations of Saturn's northern hemisphere at 0.5 and 2.5 / cm spectral resolution were provided by the Cassini Composite Infrared Spectrometer (CIRS, 17). These were supplemented with narrow-band filtered imaging from the ESO Very Large Telescope VISIR instrument (16) to provide a global spatial context for the Cassini spectroscopy. Finally, nightside imaging from the Cassini Visual and Infrared Mapping Spectrometer (VIMS, 22) provided a glimpse of the undulating cloud activity in the eastern branch of the disturbance. Each of these datasets, and the methods used to reduce and analyse them, will be described in detail below. Spatial maps of atmospheric temperatures, aerosol opacity and gaseous distributions are derived from infrared spectroscopy using a suite of radiative transfer and optimal estimation retrieval tools developed at the University of Oxford, known collectively as Nemesis (23). Synthetic spectra created from a reference atmospheric model for Saturn and appropriate sources of spectroscopic line data (6, 24) are convolved with the instrument function for each dataset. Atmospheric properties are then iteratively adjusted until the measurements are accurately reproduced with physically-realistic temperatures, compositions and cloud opacities.

Fletcher, Leigh N.↗

NASA NTRS2015

Multiscale and Multiphysics Modeling of Additive Manufacturing of Advanced Materials

The objective of this proposed project is to research and develop a prediction tool for advanced additive manufacturing (AAM) processes for advanced materials and develop experimental methods to provide fundamental properties and establish validation data. Aircraft structures and engines demand materials that are stronger, useable at much higher temperatures, provide less acoustic transmission, and enable more aeroelastic tailoring than those currently used. Significant improvements in properties can only be achieved by processing the materials under nonequilibrium conditions, such as AAM processes. AAM processes encompass a class of processes that use a focused heat source to create a melt pool on a substrate. Examples include Electron Beam Freeform Fabrication and Direct Metal Deposition. These types of additive processes enable fabrication of parts directly from CAD drawings. To achieve the desired material properties and geometries of the final structure, assessing the impact of process parameters and predicting optimized conditions with numerical modeling as an effective prediction tool is necessary. The targets for the processing are multiple and at different spatial scales, and the physical phenomena associated occur in multiphysics and multiscale. In this project, the research work has been developed to model AAM processes in a multiscale and multiphysics approach. A macroscale model was developed to investigate the residual stresses and distortion in AAM processes. A sequentially coupled, thermomechanical, finite element model was developed and validated experimentally. The results showed the temperature distribution, residual stress, and deformation within the formed deposits and substrates. A mesoscale model was developed to include heat transfer, phase change with mushy zone, incompressible free surface flow, solute redistribution, and surface tension. Because of excessive computing time needed, a parallel computing approach was also tested. In addition, after investigating various methods, a Smoothed Particle Hydrodynamics Model (SPH Model) was developed to model wire feeding process. Its computational efficiency and simple architecture makes it more robust and flexible than other models. More research on material properties may be needed to realistically model the AAM processes. A microscale model was developed to investigate heterogeneous nucleation, dendritic grain growth, epitaxial growth of columnar grains, columnar-to-equiaxed transition, grain transport in melt, and other properties. The orientations of the columnar grains were almost perpendicular to the laser motion's direction. Compared to the similar studies in the literature, the multiple grain morphology modeling result is in the same order of magnitude as optical morphologies in the experiment. Experimental work was conducted to validate different models. An infrared camera was incorporated as a process monitoring and validating tool to identify the solidus and mushy zones during deposition. The images were successfully processed to identify these regions. This research project has investigated multiscale and multiphysics of the complex AAM processes thus leading to advanced understanding of these processes. The project has also developed several modeling tools and experimental validation tools that will be very critical in the future of AAM process qualification and certification.

Liou, Frank↗

NASA NTRS2019

Extreme Case of Spectral Band Difference Correction Between the OSIRIS-REX-NAVCAM2 and DSCOVR-EPIC Imagers

Earth-viewed images acquired during a recent asteroid intercept mission present a unique opportunity for radiometric calibration of visible imagers onboard a space exploration probe. Measurements from the CERES consistent DSCOVR-EPIC imager act as a reference in providing spatially, temporally, and angularly matched radiance values for deriving OSIRIS-REx-NavCam sensor calibration gains. The calibration is accomplished using an optimized all-sky tropical ocean ray-matching technique, which employs complex pixel remapping, navigation correction, and angular geometry consideration. Of critical consideration in this specific inter-calibration event is the extreme difference in spectral response function (SRF) width between the NavCam and EPIC imagers, which could cause a rather large bias. The NASA-LaRC SCIAMACHY based online spectral band adjustment factor (SBAF) calculation tool provides an empirical solution to such potential spectral-difference-induced biases through a high spectral- resolution hyper spectral convolution approach. The adjustments produced from this tool can effectively reduce the calibration gain bias of NavCam2 by nearly 6%, thereby adjusting the NavCam2 sensor to within 3.2% of its prelaunch calibration. These results highlight the capability of the SBAF tool to account for exceptionally disparate SRFs.

Scarino, Benjamin↗

NASA NTRSDate not supplied

Development and Assessment of a New Particle Image Velocimetry System in the NASA GRC 225 cm2 Wind Tunnel

As part of an ongoing campaign to provide detailed and thorough measurements in a two-dimensional impinging shock-wave/boundary-layer interaction (SWBLI), a custom implementation of the Particle Image Velocimetry (PIV) measurement technique in the NASA Glenn 225 cm2 Wind Tunnel was constructed. The flow seeding apparatus was devised to locally seed the region of interest in the overall flowfield to avoid coating the viewing window with seed material. The ability of the PIV system to provide small particles which faithfully track the underlying gas-phase flowfield was of key importance for the success of this system to make quality measurements in the SWBLI flowfield. In order to optimize and assess the performance of the particle seeding system, PIV measurements were made across a planar oblique shock wave and particle response was analyzed to quantify the particle lag effects due to the finite inertia of the seed droplets. Video recordings from a camera placed in the wind tunnel plenum tank were also used to discern the near-field behavior of the seeded plume and guide the design evolution of the seeding apparatus. The resulting optimized seeding configuration was applied to make PIV measurements in the wind tunnel test section boundary layer to assess the seed particle spatial coverage and concentration. This sample boundary layer dataset provides an analog to the eventual boundary layer measurements to be made in the axisymmetric test section under much more challenged viewing conditions.

PIV↗

NASA NTRSDate not supplied

Development and Assessment of a New Particle Image Velocimetry System in the NASA GRC 225 cm2 Wind Tunnel

PIV↗

DOE OSTI2023

Optimal white-noise stochastic forcing for linear models of turbulent channel flow

In the present study an optimisation problem is formulated to determine the forcing of an eddy-viscosity-based linearised Navier–Stokes model in channel flow at $Re_\tau \approx 5200$ ( $Re_\tau$ is the friction Reynolds number), where the forcing is white-in-time and spatially decorrelated. The objective functional is prescribed such that the forcing drives a response to best match a set of velocity spectra from direct numerical simulation (DNS), as well as remaining sufficiently smooth. Strong quantitative agreement is obtained between the velocity spectra from the linear model with optimal forcing and from DNS, but only qualitative agreement between the Reynolds shear stress co-spectra from the model and DNS. The forcing spectra exhibit a level of self-similarity, associated with the primary peak in the velocity spectra, but they also reveal a non-negligible amount of energy spent in phenomenologically mimicking the non-self-similar part of the velocity spectra associated with energy cascade. By exploiting linearity, the effect of the individual forcing components is assessed and the contributions from the Orr mechanism and the lift-up effect are also identified. Finally, the effect of the strength of the eddy viscosity on the optimisation performance is investigated. The inclusion of the eddy viscosity diffusion operator is shown to be essential in modelling of the near-wall features, while still allowing the forcing of the self-similar primary peak. In particular, reducing the strength of the eddy viscosity results in a considerable increase in the near-wall forcing of wall-parallel components.

Mechanics↗

NASA NTRS1980

Automatic flare transition penetrating wind shears

In this paper, the development of a digital three-dimensional automatic control law designated to achieve an optimal transition of a large commercial aircraft between the glideslope conditions and the desired final touchdown condition is presented. The linearized equations representing the perturbed motion of the aircraft from the nominal glideslope trajectory are presented. A method of incorporating the spatial, low-level wind shears into these perturbed equations of motion is indicated. It is shown that the system equations then assume the familiar form of the linear regulator problem, acted upon by a constant disturbance. A design procedure is presented to compute a digital, time-invariant, optimal control law for the discrete regulator problem acted upon by a constant disturbance. The response trajectories presented demonstrate the capability of this controller to perform optimal flare maneuvers in the presence of various wind shear conditions.

Nadkarni, A. A.↗

NASA NTRS2018

Instrument Modeling Concepts for Tradespace Analysis of Satellite Constellations

Constellations are gaining popularity in government and commercial space-based missions for Earth Observation (EO) due to their risk tolerance and ability to improve observation sampling in space and time. NASA Goddard Space Flight Center (GSFC) is developing a pre-Phase A tool called Tradespace Analysis Tool for Constellations (TAT-C) to initiate constellation mission design. The tool will allow users to explore the tradespace between various performance, cost and risk metrics (as a function of their science mission) and select Pareto optimal architectures that meet their requirements. This paper will describe the concept of modeling the primary science instruments within TAT-C, using a radar as an example, but extendable to imagers, occulters and lidars. The modularity of TAT-C's software architecture allows for crisply defining the interface between TAT-C's user defined or internal variables and the payload variables. The described module will inform TAT-C users of payload-dependent performance differences among thousands of constellation architectures (e.g. revisit time of the sensor swath, differential signal to noise ratio (SNR), spatial resolution of measurements) and allow them to pick an appropriate constellation architecture for detailed development. The module may also inform operational decisions of satellite modes, based on ground optimization or onboard autonomy.

Nag, Sreeja↗