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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.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Bulk Composition of Vesta as Constrained by the Dawn Mission and the HED Meteorites

Of the objects in the main asteroid belt, Vesta is of particular interest as it is large enough to have experienced internal differentiation (520 km diameter), and it is known to have a basaltic surface dominated by FeO-bearing pyroxenes. Furthermore, visible-IR spectra of Vesta and associated Vestoids are remarkably similar to laboratory spectra of Howardite-Eucrite-Diogenite (HED) meteorites, leading to the paradigm that the HEDs ultimately came from Vesta. Geochemical and petrological studies of the HEDs confirm the differentiated nature of the near-surface region of their parent body, and imply that crust extraction occurred well within the first 10 Ma of solar system history Vesta is therefore a prime target for studies that aim to constrain the earliest stages of planet building, and it is within this context that the NASA Dawn spacecraft orbited Vesta from July 2011 to September 2012. The results of the Dawn mission so far have significantly reinforced the HED-Vesta connection, confirming a significant degree of internal differentiation, a surface mineralogy compatible with that of the HEDs, and near-surface ratios of Fe/O and Fe/Si consistent with HED lithologies. The combination of data from the HED meteorites and the Dawn mission thus presents an unprecedented opportunity to use Vesta as a natural laboratory of early differentiation processes in the early solar system. However, the bulk composition of Vesta remains a significant unknown parameter, but one that plays a key role on the physical and chemical properties of the internal and surface reservoirs (core, mantle, crust). Several attempts have been made to constrain the bulk composition of the eucrite parent body, early endeavours relying on petrological or cosmochemical constraints. More recently, individual chondrite class compositions, or mixtures thereof, have been considered, constrained by considerations such as O-isotopes, trace-element ratios and siderophile element concentrations of the eucrites. The work presented here builds upon these latter studies, with the primary aims of: i) illustrating the potential diversity of the geochemical and geophysical properties of a fully differentiated Vesta-sized parent body, and ii) assessing which, if any, of the known chondritic bulk compositions are plausible analogues for proto-Vesta.

Toplis, M. J.↗
NASA NTRS2014

Constraining the Structure of the Transition Disk HD 135344B (SAO 206462) by Simultaneous Modeling of Multiwavelength Gas and Dust Observations

Context: Constraining the gas and dust disk structure of transition disks, particularly in the inner dust cavity, is a crucial step toward understanding the link between them and planet formation. HD 135344B is an accreting (pre-)transition disk that displays the CO 4.7 micrometer emission extending tens of AU inside its 30 AU dust cavity. Aims: We constrain HD 135344B's disk structure from multi-instrument gas and dust observations. Methods: We used the dust radiative transfer code MCFOST and the thermochemical code ProDiMo to derive the disk structure from the simultaneous modeling of the spectral energy distribution (SED), VLT/CRIRES CO P(10) 4.75 Micrometers, Herschel/PACS [O(sub I)] 63 Micrometers, Spitzer/IRS, and JCMT CO-12 J = 3−2 spectra, VLTI/PIONIER H-band visibilities, and constraints from (sub-)mm continuum interferometry and near-IR imaging. Results: We found a disk model able to describe the current gas and dust observations simultaneously. This disk has the following structure. (1) To simultaneously reproduce the SED, the near-IR interferometry data, and the CO ro-vibrational emission, refractory grains (we suggest carbon) are present inside the silicate sublimation radius (0.08 is less than R less than 0.2 AU). (2) The dust cavity (R is less than 30 AU) is filled with gas, the surface density of the gas inside the cavity must increase with radius to fit the CO ro-vibrational line profile, a small gap of a few AU in the gas distribution is compatible with current data, and a large gap of tens of AU in the gas does not appear likely. (4) The gas-to-dust ratio inside the cavity is >100 to account for the 870 Micrometers continuum upper limit and the CO P(10) line flux. (5) The gas-to-dust ratio in the outer disk (30 is less than R less than 200 AU) is less than 10 to simultaneously describe the [O(sub I)] 63 Micrometers line flux and the CO P(10) line profile. (6) In the outer disk, most of the gas and dust mass should be located in the midplane, and a significant fraction of the dust should be in large grains. Conclusions: Simultaneous modeling of the gas and dust is required to break the model degeneracies and constrain the disk structure. An increasing gas surface density with radius in the inner cavity echoes the effect of a migrating Jovian planet in the disk structure. The low gas mass (a few Jupiter masses) throughout the HD 135344B disk supports the idea that it is an evolved disk that has already lost a large portion of its mass.

inner dust cavity↗
NASA NTRS2016

An Observationally Constrained Evaluation of the Oxidative Capacity in the Tropical Western Pacific Troposphere

Hydroxyl radical (OH) is the main daytime oxidant in the troposphere and determines the atmospheric lifetimes of many compounds. We use aircraft measurements of O3, H2O, NO, and other species from the Convective Transport of Active Species in the Tropics (CONTRAST) field campaign, which occurred in the tropical western Pacific (TWP) during January-February 2014, to constrain a photochemical box model and estimate concentrations of OH throughout the troposphere. We find that tropospheric column OH (OHCOL) inferred from CONTRAST observations is 12 to 40% higher than found in chemical transport models (CTMs), including CAM-chem-SD run with 2014 meteorology as well as eight models that participated in POLMIP (2008 meteorology). Part of this discrepancy is due to a clear-sky sampling bias that affects CONTRAST observations; accounting for this bias and also for a small difference in chemical mechanism results in our empirically based value of OHCOL being 0 to 20% larger than found within global models. While these global models simulate observed O3 reasonably well, they underestimate NOx (NO +NO2) by a factor of 2, resulting in OHCOL approx.30% lower than box model simulations constrained by observed NO. Underestimations by CTMs of observed CH3CHO throughout the troposphere and of HCHO in the upper troposphere further contribute to differences between our constrained estimates of OH and those calculated by CTMs. Finally, our calculations do not support the prior suggestion of the existence of a tropospheric OH minimum in the TWP, because during January-February 2014 observed levels of O3 and NO were considerably larger than previously reported values in the TWP.

Hydroxyl radical↗
NASA NTRS2017

Constraining the Depth of Polar Ice Deposits and Evolution of Cold Traps on Mercury with Small Craters in Permanently Shadowed Regions

Earth-based radar observations revealed highly reflective deposits at the poles of Mercury [e.g., 1], which collocate with permanently shadowed regions (PSRs) detected from both imagery and altimetry by the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) spacecraft [e.g., 2]. MESSENGER also measured higher hydrogen concentrations at the north polar region, consistent with models for these deposits to be composed primarily of water ice [3]. Enigmatic to the characterization of ice deposits on Mercury is the thickness of these radar-bright features. A current minimum bound of several meters exists from the radar measurements, which show no drop in the radar cross section between 13- and 70-cm wavelength observations [4, 5]. A maximum thickness of 300 m is based on the lack of any statistically significant difference between the height of craters that host radar-bright deposits and those that do not [6]. More recently, this upper limit on the depth of a typical ice deposit has been lowered to approximately 150 m, in a study that found a mean excess thickness of 50 +/- 35 m of radar-bright deposits for 6 craters [7]. Refining such a constraint permits the derivation of a volumetric estimate of the total polar ice on Mercury, thus providing insight into possible sources of water ice on the planet. Here, we take a different approach to constrain the thickness of water-ice deposits. Permanently shadowed surfaces have been resolved in images acquired with the broadband filter on MESSENGER's wide-angle camera (WAC) using low levels of light scattered by crater walls and other topography [8]. These surfaces are not featureless and often host small craters (less than a few km in diameter). Here we utilize the presence of these small simple craters to constrain the thickness of the radar-bright ice deposits on Mercury. Specifically, we compare estimated depths made from depth-to-diameter ratios and depths from individual Mercury Laser Altimeter (MLA) tracks to constrain the fill of material of small craters that lie within the permanently shadowed, radar bright deposits of 7 north polar craters.

Deutsch, Ariel N.↗
NASA NTRS2018

Constraining the Models' Response of Tropical Clouds to SST Forcings Using CALIPSO Observations

Here we present preliminary results from the analysis of the low cloud cover (LCC) and cloud radiative effect (CRE) interannual changes in response to sea surface temperature (SST) forcings in two GISS climate models, and 12 other climate models. We further classify them as a function of their ability to reproduce the vertical structure of the cloud response to SST change against 10 years of CALIPSO observations: “the constrained models, which match the observation constraint, and the unconstrained models”. The constrained models replicate the observed interannual LCC change particularly well (ΔLCC(sub con)=-3.49 ±1.01 %/K vs. ΔLCC(sub obs)=-3.59 ±0.28 %/K) as opposed to the unconstrained models, which largely underestimate it (ΔLCC(sub unc) = -1.32 ± 1.28 %/K). As a result, the amount of short-wave warming simulated by the constrained models (ΔCRE(sub con)=2.60 ±1.13 W/m2/K) is in better agreement with the observations (ΔCRE(sub obs)=3.05 ±0.28 W/m2/K) than the unconstrained models (ΔCRE(sub con)=0.87 ±2.63 W/m2/K). Depending on the type of low cloud, the observed relationship between cloud/radiation and surface temperature varies. Over the stratocumulus regions, increasing SSTs generate higher cloud top height along with a large decrease of the cloud fraction below as opposed to a slight decrease of the cloud fraction at each level over the trade cumulus regions. Our results suggest that the models must generate sustainable stratocumulus decks and moist processes in the planetary boundary layer to reproduce these observed features. Future work will focus on defining a method to objectively discriminate these cloud types that can be applied consistently in both the observations and the models.

Cesana, Gregory↗
NASA NTRS2019

The Ability of GeoCarb to Constrain the Interannual Variability of Carbon Gases over the Amazon

We perform a number of idealized assimilation experiments with the GEOS constituent data assimilation system to test the ability of GeoCarb retrievals of CO, CO2, and CH4 to constrain the interannual variability of these gases over the Amazon. Retrievals for instruments on other satellites which observe in similar channels (e.g. MOPITT, GOSAT, and OCO-2) are limited due to persistent cloud coverage. Given its ability to sample the same location multiple times in one day, the expectation is that GeoCarb retrievals will return more soundings than those from previous missions. The goal of the assimilation experiments is to understand which scanning strategies lead to the best sounding densities and thus have the best chance of constraining interannual variability in the carbon species. The experiments each begin by picking a given year at random from a nature run (i.e., a model simulation meant to represent the truth). The model fields are sampled according to a given strategy and then screened to account for cloud coverage. Next, we pick another year at random and assimilate the synthetic GeoCarb samples into the GEOS model for that year. The output of the assimilation, 6-hourly, 3D fields of each constituent, is then directly comparable to the nature run. This comparison allows us to evaluate the ability of GeoCarb measurements to constrain the interannual variability of each gas.

Weir, B.↗
NASA NTRSDate not supplied

Creating Seasonal Climatologies of Aerosol Lidar Ratios Over Ocean Using MODIS AOD Constrained Retrievals and GOCART Model Simulations

The current CALIPSO algorithms assign one lidar ratio (i.e., extinction-to-backscatter ratio; LR) value globally for each of the seven tropospheric aerosol types. In a future data products release, the CALIPSO project aims to improve these algorithms through the development of regional and seasonal LR climatologies. In this work, aerosol LRs are inferred through CALIOP backscatter profiles constrained by collocated aerosol optical depth (AOD) from Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data. This analysis is subsampled for those profiles that are cloud-free and contain only one CALIOP-classified aerosol type (e.g., marine). The CALIOP profiles are then collocated with aerosol volume fractions obtained through Goddard Chemistry Aerosol Radiation and Transport (GOCART) model simulations. In this poster, we show twelve-year (June 2006-August 2018) mean spatial distributions of inferred aerosol LRs for CALIOP-classified marine aerosols and how they correspond inversely with patterns of GOCART sea salt volume fraction (SSVF). Near land masses, smaller SSVFs and larger LRs are found (due to the influence of over-land aerosols). In the remote ocean regions (likely less impacted by over-land aerosols), larger SSVFs and smaller LRs are found. The developed relationship between the GOCART model SSVFs and MODIS AOD constrained LRs is used to create model-assisted seasonal LR maps. Additionally, we show maps of inferred LRs from constrained retrievals using the CALIPSO Ocean Derived Column Optical Depth (ODCOD) product and comparisons with those from the MODIS analyses. The technique demonstrated here benefits CALIPSO in the near-term, but similar methods can also be applied to the next generation space-based elastic backscatter lidars with collocated passive sensors, such as those of the upcoming NASA Atmosphere Observing System (AOS).

Travis Toth↗
NASA NTRSDate not supplied

Mapping Aerosol Lidar Ratios Over Ocean Using Constrained Retrievals and A Global Aerosol Model

The current NASA Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) algorithms assign one lidar ratio (i.e., extinction-to-backscatter ratio; LR) value globally for each of the seven tropospheric aerosol types. In a future data products release, the CALIPSO project aims to improve these algorithms through the development of regional and seasonal LR climatologies. In this work, aerosol LRs are inferred through Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) backscatter profiles constrained by collocated aerosol optical depth (AOD) from Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data. This analysis is subsampled for those profiles that are cloud-free and contain only one CALIOP-classified aerosol type (e.g., marine). The CALIOP profiles are then collocated with aerosol volume fractions obtained through Goddard Chemistry Aerosol Radiation and Transport (GOCART) model simulations. In this presentation, we show that the twelve-year (June 2006-August 2018) mean spatial distributions of inferred aerosol LRs for CALIOP-classified marine aerosols correspond inversely with patterns of GOCART sea salt volume fraction (SSVF). For example, smaller SSVFs (< 65%) and larger LRs (> 55 sr), are found near land masses. This is indicative of the influence of over-land aerosols (e.g., pollution and biomass burning smoke). In the remote oceans (i.e., regions likely less impacted by non-sea salt aerosols), larger SSVFs (> 95%) and smaller LRs (< 25 sr) are found. The developed relationship between the GOCART SSVFs and MODIS AOD constrained LRs (polynomial fit intersect values of ~56 sr for SSVF of 0% and ~21 sr for SSVF of 100%) is used to produce model-assisted climatological LR maps on seasonal scales. Additionally, we show maps of inferred LRs from constrained retrievals using the CALIPSO Ocean Derived Column Optical Depth (ODCOD) product and comparisons with those from the MODIS analyses. The technique demonstrated in this study not only benefits CALIPSO in the near-term, but similar methods can be applied to future spaceborne elastic backscatter lidars with collocated passive sensors, such as those associated with the upcoming NASA Atmosphere Observing System (AOS).

Travis Toth↗
NASA NTRS2024

Constraining the Thermal and Textural Evolution of Evolved Achondrite DOM 10100: Implications for Volatile Loss

Identification of evolved achrondritic clasts suggests that some early planetesimals experienced extensive differentiation and formed chemically evolved crusts. Many of these samples contain volatile-bearing phases (i.e., apatite, merrillite, nominally anhydrous minerals), which can be used to constrain volatile budgets and abundancies via in-situ measurements. These ancient crustal rocks are subject to high heat, moderate pressures, potentially hot mineral-rich fluids, and degassing that has likely modified many of these volatiles, and their abundancies do not represent the composition of the primary magma from which these achondrites formed. In this work we attempt to deconvolve the thermal evolution of these samples to provide petrologic information that can be used for making inferences regarding their volatile history, and ultimately constraining volatile budgets on planetary bodies in the early Solar System. Here, we combine in-situ major element geochemistry, thermodynamic modeling, and detailed textural and microstructural observations to reconstruct the thermal and textural evolution of howardite DOM 10100, which contains a dacite within its matrix. Previous analyses of several sections of DOM 10100 suggest that the dacitic clast is evidence for partial melting of Vesta’s differentiated basaltic crust, making it an ideal candidate for evolved crustal studies. While its igneous petrogenesis has been well constrained, there is still opportunity to better characterize its post-crystallization thermal history, including possible thermal metamorphism and cooling.

J S Gorce↗
NASA NTRSDate not supplied

Mapping Aerosol Lidar Ratios Over Ocean using MODIS AOD Constrained Retrievals and GOCART Model Simulations

After 17 years, the NASA Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission ceased science operations in August 2023. For the final CALIPSO data products release (Version 5), the CALIPSO project seeks to improve the accuracy of its aerosol extinction by advancing knowledge of aerosol lidar ratios (i.e., extinction-to-backscatter ratios; LRs) for various aerosol types. The current algorithm assigns one LR value globally for each of the seven tropospheric aerosol types. The CALIPSO team aims to improve the retrieval algorithm through the development of regional and seasonal LR climatologies for the same aerosol types. In this study, aerosol LRs are inferred through Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) backscatter profiles constrained by collocated aerosol optical depth (AOD) from Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data over oceans during daytime. This analysis is subsampled for those profiles that are cloud-free and contain only one CALIOP-classified aerosol type. The CALIOP profiles are then collocated with aerosol volume fractions obtained through Goddard Chemistry Aerosol Radiation and Transport (GOCART) model simulations. This presentation will reveal findings that the 12-year (June 2006-August 2018) mean spatial distributions of inferred aerosol LRs for CALIOP-classified marine and dusty marine aerosols correspond inversely with patterns of GOCART sea salt volume fraction (SSVF). For example, smaller SSVFs (< 65%) and larger LRs (> 55 sr), are found near land masses (Fig. 1). This indicates the influence of advected anthropogenic aerosols (e.g., pollution and biomass burning smoke). In the remote oceans (i.e., regions likely less impacted by non-sea salt aerosols), the SSVFs are larger (> 95%) and the LRs are smaller (< 25 sr) (Fig. 1). A polynomial fit of the MODIS AOD constrained LRs to the corresponding GOCART SSVFs (intersect values of ~58 sr for SSVF of 0% and ~21 sr for SSVF of 100%) is further used to produce model-assisted climatological LR maps on seasonal scales. Additionally, we will show results of a LR validation analysis for which we compare the revised CALIPSO AODs obtained by applying the seasonal/regional constrained LRs against CALIPSO Version 4.51 Ocean Derived Column Optical Depth (ODCOD). While the majority of the presentation will focus on LRs for CALIOP-classified marine and dusty marine aerosols, an overview of LR results will show preliminary results for other aerosol types over ocean, such as dust and elevated smoke. The technique demon-strated in this study highlights the benefits not only to the final planned CALIPSO data release in 2025, but similar methods can be applied to future spaceborne elastic backscatter lidars with collocated passive sensors (e.g., such as those associated with NASA’s proposed Atmosphere Observing System).

Travis D Toth↗
NASA NTRS2024

A Multiwavelength Approach to Constraining the Merger Properties of ACT-CL J0034.4+0225

ACT-CL J0034.4+0225 is a previously unrecognized merging galaxy cluster at z = 0.38588 ± 0.00068. Our primary evidence is provided by a 21 ks Chandra image that shows two surface brightness peaks separated by ∼49″ (259 kpc) surrounded by an extended cluster gas distribution. Each gas peak contains a brightest cluster galaxy, offset from the gas peak. We collect new South African Large Telescope optical spectra that, when augmented by archival data, yield redshifts for the two BGCs and 58 other cluster members. Archival Giant Metrewave Radio Telescope and MeerKAT data reveal a radio halo that encompasses the X-ray peaks. We provide and compare three X-ray-based mass estimates (5.0 × 1014Me, 6.4 × 1014Me, and 8.6 × 1014Me). The Planck and ACT Sunyaev–Zel’dovich masses are ≈5.8 × 1014 Me. We constrain the merger state and properties by comparing them to an existing suite of N-body/hydrodynamical models using the measured gas peak separation (259 kpc, projected) and radial velocity difference (0–1000 km s−1 ). This constrains the epoch of the merger to be within ∼100 Myr of first pericenter passage. A strong lensing analysis constrains the mass ratio to be in the range 1:1–1:20, while the cluster morphology prefers values near the equal-mass range.

Peter Doze↗
DOE OSTI2023

Interpreting Primal-Dual Algorithms for Constrained Multiagent Reinforcement Learning

Constrained multiagent reinforcement learning (C-MARL) is gaining importance as MARL algorithms find new applications in real-world systems ranging from energy systems to drone swarms. Most C-MARL algorithms use a primal-dual approach to enforce constraints through a penalty function added to the reward. In this paper, we study the structural effects of this penalty term on the MARL problem. First, we show that the standard practice of using the constraint function as the penalty leads to a weak notion of safety. However, by making simple modifications to the penalty term, we can enforce meaningful probabilistic (chance and conditional value at risk) constraints. Second, we quantify the effect of the penalty term on the value function, uncovering an improved value estimation procedure. We use these insights to propose a constrained multiagent advantage actor critic (C-MAA2C) algorithm. Simulations in a simple constrained multiagent environment affirm that our reinterpretation of the primal-dual method in terms of probabilistic constraints is effective, and that our proposed value estimate accelerates convergence to a safe joint policy.

chance constraints↗
DOE OSTI2022

Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery

Numerical simulation has become indispensable in advancing cost-effective process optimization and control of flow batteries. We propose an enhanced version of the physics-constrained deep neural network (PCDNN) approach to provide high-accuracy voltage predictions in the vanadium redox flow batteries (VRFBs). The purpose of the PCDNN approach is to enforce the physics-based zero-dimensional (0D) VRFB model in a neural network to assure model generalization for various battery operation conditions. However, limited by the simplifications of the 0D model, the PCDNN cannot capture sharp voltage changes in the extreme SOC regions. To improve the accuracy of voltage prediction at extreme ranges, we introduce a second (enhanced) DNN to mitigate the prediction errors carried from the 0D model itself and call the resulting approach enhanced PCDNN (ePCDNN). By comparing with experimental data, we demonstrate that the ePCDNN approach can accurately capture the voltage response throughout the charge–discharge cycle, including the tail region of the voltage discharge curve. The loss function for training the ePCDNN is designed to be flexible by adjusting the weights of the physics-constrained DNN and the enhanced DNN. In conclusion, this allows the ePCDNN framework to be transferable to battery systems with variable physical model fidelity.

25 ENERGY STORAGE↗
DOE OSTI2020

Constrained Deep Reinforcement Learning for Energy Sustainable Multi-UAV Based Random Access IoT Networks With NOMA

In this paper, we apply the Non-Orthogonal Multiple Access (NOMA) technique to improve the massive channel access of a wireless IoT network where solar-powered Unmanned Aerial Vehicles (UAVs) relay data from IoT devices to remote servers. Specifically, IoT devices contend for accessing the shared wireless channel using an adaptive p-persistent slotted Aloha protocol; and the solar-powered UAVs adopt Successive Interference Cancellation (SIC) to decode multiple received data from IoT devices to improve access efficiency. To enable an energy-sustainable capacity-optimal network, we study the joint problem of dynamic multi-UAV altitude control and multi-cell wireless channel access management of IoT devices as a stochastic control problem with multiple energy constraints. We first formulate this problem as a Constrained Markov Decision Process (CMDP), and propose an online model-free Constrained Deep Reinforcement Learning (CDRL) algorithm based on Lagrangian primal-dual policy optimization to solve the CMDP. Extensive simulations demonstrate that our proposed algorithm learns a cooperative policy in which the altitude of UAVs and channel access probability of IoT devices are dynamically controlled to attain the maximal long-term network capacity while ensuring energy sustainability of UAVs, outperforming baseline schemes. The proposed CDRL agent can be trained on a small network, yet the learned policy can efficiently manage networks with a massive number of IoT devices and varying initial states, which can amortize the cost of training the CDRL agent.

42 ENGINEERING↗
DOE OSTI2023

Online Model-Free Chance-Constrained Distribution System Voltage Control Using DERs

This paper proposes an online data-driven distributed energy resource management system (DERMS) optimization method using chance-constrained formulation to address distribution system voltage regulation. This is achieved via the local sensitivity factor (LSF)-enabled reformulation of the DER control into a linear programming (LP) problem, which is easy and computationally efficient to solve. The LSF is estimated using online measurements and does not need the assumption of node load information. The latter is usually required for existing optimization-based methods but is difficult to obtain in practice. To mitigate measurement uncertainties, a scenario-based chance-constrained formulation is constructed. Compared with other control methods, the results carried out in a realistic distribution system show that the proposed method can effectively eliminate voltage violation issues.

chance-constrained optimization↗
DOE OSTI2024

TNet: A Model-Constrained Tikhonov Network Approach for Inverse Problems

Deep learning (DL), in particular deep neural networks, by default is purely data-driven and in general does not require physics. This is the strength of DL but also one of its key limitations when applied to science and engineering problems in which underlying physical properties—such as stability, conservation, and positivity—and accuracy are required. DL methods in their original forms are often not capable of respecting the underlying mathematical models or achieving desired accuracy even in big-data regimes. On the other hand, many data-driven science and engineering problems, such as inverse problems, typically have limited experimental or observational data, and DL would overfit the data in this case. Leveraging information encoded in the underlying mathematical models, we argue, not only compensates for missing information in low data regimes but also provides opportunities to equip DL methods with the underlying physics, hence promoting better generalization. This paper develops a model-constrained DL approach and its variant TNet—a Tikhonov neural network—which are capable of learning not only information hidden in the training data but also in the underlying mathematical models to solve inverse problems governed by partial differential equations in low data regimes. We provide the constructions and some theoretical results for the proposed approaches for both linear and nonlinear inverse problems. Since TNet is designed to learn inverse solutions with Tikhonov regularization, it is interpretable: in fact it recovers Tikhonov solutions for linear cases while potentially approximating Tikhonov solutions for nonlinear inverse problems. We also prove that data randomization can enhance not only the smoothness of the networks but also their generalizations. Comprehensive numerical results confirm the theoretical findings and show that with even as little as 1 training data sample for one-dimensional (1D) deconvolution, 5 for an inverse 2D heat conductivity problem, 100 for inverse initial conditions for a time-dependent 2D Burgers’s equation, and 50 for inverse initial conditions for 2D Navier–Stokes equations, TNet solutions can be as accurate as Tikhonov solutions while being several orders of magnitude faster. Furthermore, this is possible owing to the model-constrained term, replications, and randomization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

On the Solution of ℓ 0 -Constrained Sparse Inverse Covariance Estimation Problems

The sparse inverse covariance matrix is used to model conditional dependencies between variables in a graphical model to fit a multivariate Gaussian distribution. Estimating the matrix from data are well known to be computationally expensive for large-scale problems. Sparsity is employed to handle noise in the data and to promote interpretability of a learning model. Although the use of a convex ℓ 1 regularizer to encourage sparsity is common practice, the combinatorial ℓ 0 penalty often has more favorable statistical properties. In this paper, we directly constrain sparsity by specifying a maximally allowable number of nonzeros, in other words, by imposing an ℓ 0 constraint. Here, we introduce an efficient approximate Newton algorithm using warm starts for solving the nonconvex ℓ 0 -constrained inverse covariance learning problem. Numerical experiments on standard data sets show that the performance of the proposed algorithm is competitive with state-of-the-art methods.

$\ell_0$-Constrained↗
NASA NTRS2004

On the Constrained Attitude Control Problem

In this paper, we consider various classes of constrained attitude control (CAC) problem in single and multiple spacecraft settings. After categorizing attitude constraints into four distinct types, we provide an overview of the existing approaches to this problem. We then proceed to further expand on a recent algorithmic approach to the CAC problem. The paper concludes with an example demonstrating the viability of the proposed algorithm for a multiple spacecraft constrained attitude reconfiguration scenario.

semidefinite programming↗